From a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Tue, 13 Feb 2024 16:06:57 +0100 Subject: [PATCH 01/59] commiting docs functionality for testing deployment --- docs/3dgan_doc.rst | 44 ++++++++++ docs/Makefile | 20 +++++ docs/conf.py | 36 ++++++++ docs/index.rst | 23 ++++++ docs/itwinai.cli.rst | 7 ++ docs/itwinai.cluster.rst | 7 ++ docs/itwinai.components.rst | 8 ++ docs/itwinai.loggers.rst | 8 ++ docs/itwinai.parser.rst | 8 ++ docs/itwinai.pipeline.rst | 8 ++ docs/itwinai.serialization.rst | 8 ++ docs/itwinai.types.rst | 8 ++ docs/itwinai.utils.rst | 8 ++ docs/make.bat | 35 ++++++++ docs/mnist_doc.rst | 78 ++++++++++++++++++ docs/modules.rst | 16 ++++ docs/use_cases.rst | 20 +++++ {docs => docs_old}/.gitignore | 0 {docs => docs_old}/404.html | 0 {docs => docs_old}/Gemfile | 0 {docs => docs_old}/Gemfile.lock | 0 {docs => docs_old}/LICENSE | 0 {docs => docs_old}/README.md | 0 {docs => docs_old}/_config.yml | 0 {docs => docs_old}/docs/CLI.md | 0 {docs => docs_old}/docs/Concepts.md | 0 .../docs/How-to-use-this-software.md | 0 .../docs/img/Workflow DAG concept.png | Bin {docs => docs_old}/docs/img/cwl-workflow.png | Bin .../img/user-platform interaction full.png | Bin .../docs/img/user-platform interaction.png | Bin {docs => docs_old}/docs/use-cases/index.md | 0 {docs => docs_old}/docs/use-cases/mnist.md | 0 {docs => docs_old}/favicon.ico | Bin {docs => docs_old}/index.md | 0 use-cases/3dgan/__init__.py | 1 + 36 files changed, 343 insertions(+) create mode 100644 docs/3dgan_doc.rst create mode 100644 docs/Makefile create mode 100644 docs/conf.py create mode 100644 docs/index.rst create mode 100644 docs/itwinai.cli.rst create mode 100644 docs/itwinai.cluster.rst create mode 100644 docs/itwinai.components.rst create mode 100644 docs/itwinai.loggers.rst create mode 100644 docs/itwinai.parser.rst create mode 100644 docs/itwinai.pipeline.rst create mode 100644 docs/itwinai.serialization.rst create mode 100644 docs/itwinai.types.rst create mode 100644 docs/itwinai.utils.rst create mode 100644 docs/make.bat create mode 100644 docs/mnist_doc.rst create mode 100644 docs/modules.rst create mode 100644 docs/use_cases.rst rename {docs => docs_old}/.gitignore (100%) rename {docs => docs_old}/404.html (100%) rename {docs => docs_old}/Gemfile (100%) rename {docs => docs_old}/Gemfile.lock (100%) rename {docs => docs_old}/LICENSE (100%) rename {docs => docs_old}/README.md (100%) rename {docs => docs_old}/_config.yml (100%) rename {docs => docs_old}/docs/CLI.md (100%) rename {docs => docs_old}/docs/Concepts.md (100%) rename {docs => docs_old}/docs/How-to-use-this-software.md (100%) rename {docs => docs_old}/docs/img/Workflow DAG concept.png (100%) rename {docs => docs_old}/docs/img/cwl-workflow.png (100%) rename {docs => docs_old}/docs/img/user-platform interaction full.png (100%) rename {docs => docs_old}/docs/img/user-platform interaction.png (100%) rename {docs => docs_old}/docs/use-cases/index.md (100%) rename {docs => docs_old}/docs/use-cases/mnist.md (100%) rename {docs => docs_old}/favicon.ico (100%) rename {docs => docs_old}/index.md (100%) create mode 100644 use-cases/3dgan/__init__.py diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst new file mode 100644 index 00000000..1ddd592e --- /dev/null +++ b/docs/3dgan_doc.rst @@ -0,0 +1,44 @@ +3DGAN Documentation +==================== + +The 3DGAN use case is a part of the use cases which... + +.. literalinclude:: ../use-cases/3dgan/model.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/train.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/trainer.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/saver.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/dataloader.py + :language: python + +.. .. automodule:: 3dgan.model +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.train +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.saver +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.dataloader +.. :members: +.. :undoc-members: +.. :show-inheritance: diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d4bb2cbb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..6bab8aec --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,36 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys +sys.path.insert(0, os.path.abspath('../use-cases/')) +sys.path.insert(0, os.path.abspath('../use-cases/3dgan/')) +sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch-lightning/')) +sys.path.insert(0, os.path.abspath('../src/itwinai')) +sys.path.insert(0, os.path.abspath('../')) + +project = 'itwinai' +copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' +author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' +release = '0.0.1' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode'] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'sphinx_rtd_theme' #'alabaster' +html_static_path = ['_static'] diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..9bf6cf33 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,23 @@ +.. itwinai documentation master file, created by + sphinx-quickstart on Fri Feb 9 13:58:30 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to itwinai's documentation! +=================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + modules + use_cases + 3dgan_doc + mnist_doc + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/itwinai.cli.rst b/docs/itwinai.cli.rst new file mode 100644 index 00000000..18e0d0ef --- /dev/null +++ b/docs/itwinai.cli.rst @@ -0,0 +1,7 @@ +itwinai.cli +=========== + +.. automodule:: itwinai.cli + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/itwinai.cluster.rst b/docs/itwinai.cluster.rst new file mode 100644 index 00000000..7360c981 --- /dev/null +++ b/docs/itwinai.cluster.rst @@ -0,0 +1,7 @@ +itwinai.cluster +=============== + +.. automodule:: itwinai.cluster + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/itwinai.components.rst b/docs/itwinai.components.rst new file mode 100644 index 00000000..db3b0956 --- /dev/null +++ b/docs/itwinai.components.rst @@ -0,0 +1,8 @@ +itwinai.components +================== + +.. automodule:: itwinai.components + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.loggers.rst b/docs/itwinai.loggers.rst new file mode 100644 index 00000000..513e3942 --- /dev/null +++ b/docs/itwinai.loggers.rst @@ -0,0 +1,8 @@ +itwinai.loggers +=============== + +.. automodule:: itwinai.loggers + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.parser.rst b/docs/itwinai.parser.rst new file mode 100644 index 00000000..f9c7d930 --- /dev/null +++ b/docs/itwinai.parser.rst @@ -0,0 +1,8 @@ +itwinai.parser +============== + +.. automodule:: itwinai.parser + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.pipeline.rst b/docs/itwinai.pipeline.rst new file mode 100644 index 00000000..b849240e --- /dev/null +++ b/docs/itwinai.pipeline.rst @@ -0,0 +1,8 @@ +itwinai.pipeline +================ + +.. automodule:: itwinai.pipeline + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.serialization.rst b/docs/itwinai.serialization.rst new file mode 100644 index 00000000..691a3721 --- /dev/null +++ b/docs/itwinai.serialization.rst @@ -0,0 +1,8 @@ +itwinai.serialization +===================== + +.. automodule:: itwinai.serialization + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.types.rst b/docs/itwinai.types.rst new file mode 100644 index 00000000..20367596 --- /dev/null +++ b/docs/itwinai.types.rst @@ -0,0 +1,8 @@ +itwinai.types +============= + +.. automodule:: itwinai.types + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.utils.rst b/docs/itwinai.utils.rst new file mode 100644 index 00000000..b487da7f --- /dev/null +++ b/docs/itwinai.utils.rst @@ -0,0 +1,8 @@ +itwinai.utils +============= + +.. automodule:: itwinai.utils + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..32bb2452 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/mnist_doc.rst b/docs/mnist_doc.rst new file mode 100644 index 00000000..56dfa654 --- /dev/null +++ b/docs/mnist_doc.rst @@ -0,0 +1,78 @@ +MNIST Use Case +============== + +This section covers the MNIST use case, which utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: + +torch-lightning +--------------- + +.. toctree:: + :maxdepth: 5 + +dataloader.py ++++++++++++++ + +The `dataloader.py` script is responsible for loading the MNIST dataset and preparing it for training. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py + :language: python + +.. .. automodule:: torch-lightning.dataloader +.. :members: +.. :undoc-members: +.. :show-inheritance: + +pipeline.yaml ++++++++++++++ + +This YAML file defines the pipeline configuration for the MNIST use case. It includes settings for the model, training, and evaluation. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/pipeline.yaml + :language: yaml + +startscript ++++++++++++ + +The `startscript` is a shell script to initiate the training process. It sets up the environment and starts the training using the `train.py` script. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/startscript + :language: bash + +train.py +++++++++ + +This script contains the training loop and is where the model is trained using the data prepared by `dataloader.py`. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/train.py + :language: python + +.. .. automodule:: torch-lightning.train +.. :members: +.. :undoc-members: +.. :show-inheritance: + +trainer.py +++++++++++ + +The `trainer.py` file defines the `Trainer` class which sets up the training parameters and the training process. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/trainer.py + :language: python + +.. .. automodule:: torch-lightning.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +utils.py +++++++++ + +The `utils.py` script includes utility functions and classes that are used across the MNIST use case. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/utils.py + :language: python + +.. .. automodule:: torch-lightning.utils +.. :members: +.. :undoc-members: +.. :show-inheritance: diff --git a/docs/modules.rst b/docs/modules.rst new file mode 100644 index 00000000..b220e0ff --- /dev/null +++ b/docs/modules.rst @@ -0,0 +1,16 @@ +itwinai +======= + +.. toctree:: + :maxdepth: 4 + + itwinai.cli + itwinai.cluster + itwinai.components + itwinai.loggers + itwinai.parser + itwinai.pipeline + itwinai.serialization + itwinai.types + itwinai.utils + diff --git a/docs/use_cases.rst b/docs/use_cases.rst new file mode 100644 index 00000000..de870cf6 --- /dev/null +++ b/docs/use_cases.rst @@ -0,0 +1,20 @@ +.. _use-cases: + +Integrated Use Cases +==================== + +Here you can find a collection of use cases including various projects. + +3DGAN CERN use case +------------------- + +Description of the use case etc... + +.. toctree:: + :maxdepth: 2 + :caption: 3DGAN use case Documentation Contents: + + 3dgan_doc + mnist_doc + + diff --git a/docs/.gitignore b/docs_old/.gitignore similarity index 100% rename from docs/.gitignore rename to docs_old/.gitignore diff --git a/docs/404.html b/docs_old/404.html similarity index 100% rename from docs/404.html rename to docs_old/404.html diff --git a/docs/Gemfile b/docs_old/Gemfile similarity index 100% rename from docs/Gemfile rename to docs_old/Gemfile diff --git a/docs/Gemfile.lock b/docs_old/Gemfile.lock similarity index 100% rename from docs/Gemfile.lock rename to docs_old/Gemfile.lock diff --git a/docs/LICENSE b/docs_old/LICENSE similarity index 100% rename from docs/LICENSE rename to docs_old/LICENSE diff --git a/docs/README.md b/docs_old/README.md similarity index 100% rename from docs/README.md rename to docs_old/README.md diff --git a/docs/_config.yml b/docs_old/_config.yml similarity index 100% rename from docs/_config.yml rename to docs_old/_config.yml diff --git a/docs/docs/CLI.md b/docs_old/docs/CLI.md similarity index 100% rename from docs/docs/CLI.md rename to docs_old/docs/CLI.md diff --git a/docs/docs/Concepts.md b/docs_old/docs/Concepts.md similarity index 100% rename from docs/docs/Concepts.md rename to docs_old/docs/Concepts.md diff --git a/docs/docs/How-to-use-this-software.md b/docs_old/docs/How-to-use-this-software.md similarity index 100% rename from docs/docs/How-to-use-this-software.md rename to docs_old/docs/How-to-use-this-software.md diff --git a/docs/docs/img/Workflow DAG concept.png b/docs_old/docs/img/Workflow DAG concept.png similarity index 100% rename from docs/docs/img/Workflow DAG concept.png rename to docs_old/docs/img/Workflow DAG concept.png diff --git a/docs/docs/img/cwl-workflow.png b/docs_old/docs/img/cwl-workflow.png similarity index 100% rename from docs/docs/img/cwl-workflow.png rename to docs_old/docs/img/cwl-workflow.png diff --git a/docs/docs/img/user-platform interaction full.png b/docs_old/docs/img/user-platform interaction full.png similarity index 100% rename from docs/docs/img/user-platform interaction full.png rename to docs_old/docs/img/user-platform interaction full.png diff --git a/docs/docs/img/user-platform interaction.png b/docs_old/docs/img/user-platform interaction.png similarity index 100% rename from docs/docs/img/user-platform interaction.png rename to docs_old/docs/img/user-platform interaction.png diff --git a/docs/docs/use-cases/index.md b/docs_old/docs/use-cases/index.md similarity index 100% rename from docs/docs/use-cases/index.md rename to docs_old/docs/use-cases/index.md diff --git a/docs/docs/use-cases/mnist.md b/docs_old/docs/use-cases/mnist.md similarity index 100% rename from docs/docs/use-cases/mnist.md rename to docs_old/docs/use-cases/mnist.md diff --git a/docs/favicon.ico b/docs_old/favicon.ico similarity index 100% rename from docs/favicon.ico rename to docs_old/favicon.ico diff --git a/docs/index.md b/docs_old/index.md similarity index 100% rename from docs/index.md rename to docs_old/index.md diff --git a/use-cases/3dgan/__init__.py b/use-cases/3dgan/__init__.py new file mode 100644 index 00000000..079a8283 --- /dev/null +++ b/use-cases/3dgan/__init__.py @@ -0,0 +1 @@ +## This file can be empty but must be present \ No newline at end of file From 3f6b6d4064c6007327f2d02c31bdab76cf549473 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Tue, 13 Feb 2024 16:38:58 +0100 Subject: [PATCH 02/59] adding documentation deployment relevant files --- .readthedocs.yaml | 32 ++++++++ requirements.txt | 185 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 217 insertions(+) create mode 100644 .readthedocs.yaml create mode 100644 requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 00000000..39111812 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,32 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.9.12" + # You can also specify other tool versions: + # nodejs: "19" + # rust: "1.64" + # golang: "1.19" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..55918109 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,185 @@ +absl-py==1.2.0 +alabaster==0.7.16 +alembic==1.8.1 +antlr4-python3-runtime==4.9.3 +anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1693488585952/work +appdirs==1.4.4 +arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1696128962909/work +attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1683424013410/work +autopage==0.5.1 +Babel==2.14.0 +backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work +beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1680888073205/work +blessed @ file:///home/conda/feedstock_root/build_artifacts/blessed_1666523113356/work +blinker==1.6.3 +Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work +build @ file:///home/conda/feedstock_root/build_artifacts/python-build_1682868691601/work +CacheControl @ file:///home/conda/feedstock_root/build_artifacts/cachecontrol-split_1690307937243/work +certifi==2023.7.22 +cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1696001742886/work +charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1698005359935/work +cleo @ file:///home/conda/feedstock_root/build_artifacts/cleo_1669193745932/work +click @ file:///home/conda/feedstock_root/build_artifacts/click_1692311806742/work +cliff==4.0.0 +cloudpickle==2.2.1 +cmaes==0.8.2 +cmd2==2.4.2 +colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work +colorlog==6.7.0 +crashtest @ file:///home/conda/feedstock_root/build_artifacts/crashtest_1667467006729/work +croniter @ file:///home/conda/feedstock_root/build_artifacts/croniter_1685103477055/work +cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1695163786734/work +cycler==0.12.1 +databricks-cli==0.18.0 +dateutils @ file:///home/conda/feedstock_root/build_artifacts/dateutils_1603450192521/work +deepdiff @ file:///home/conda/feedstock_root/build_artifacts/deepdiff_1697589976995/work +distlib @ file:///home/conda/feedstock_root/build_artifacts/distlib_1689598491484/work +docker==6.1.3 +docker-pycreds==0.4.0 +docstring-parser==0.15 +docutils==0.20.1 +dulwich @ file:///home/conda/feedstock_root/build_artifacts/dulwich_1696599729805/work +einops==0.4.1 +entrypoints==0.4 +exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1692026125334/work +fastapi @ file:///home/conda/feedstock_root/build_artifacts/fastapi_1669571890356/work +filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1694629184119/work +Flask==2.3.3 +fonttools==4.37.4 +fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1697919321618/work +gast==0.4.0 +gitdb==4.0.9 +GitPython==3.1.27 +google==3.0.0 +greenlet==1.1.3 +gunicorn==21.2.0 +h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1664132893548/work +h5py==3.7.0 +idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work +imagesize==1.4.1 +importlib-metadata==5.0.0 +importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1695414790617/work +inquirer @ file:///home/conda/feedstock_root/build_artifacts/inquirer_1678135536462/work +installer @ file:///home/conda/feedstock_root/build_artifacts/python-installer_1679300325491/work +itsdangerous @ file:///home/conda/feedstock_root/build_artifacts/itsdangerous_1648147185463/work +-e git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai +jaraco.classes @ file:///home/conda/feedstock_root/build_artifacts/jaraco.classes_1689112411129/work +jeepney @ file:///home/conda/feedstock_root/build_artifacts/jeepney_1649085214306/work +Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work +joblib==1.3.2 +jsonargparse==4.26.1 +jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1669810440410/work +keyring @ file:///home/conda/feedstock_root/build_artifacts/keyring_1696001558711/work +kiwisolver==1.4.5 +libclang==14.0.6 +lightning @ file:///home/conda/feedstock_root/build_artifacts/lightning_1680807776127/work +lightning-cloud @ file:///home/conda/feedstock_root/build_artifacts/lightning-cloud_1695991149929/work +lightning-utilities @ file:///home/conda/feedstock_root/build_artifacts/lightning-utilities_1688117204588/work +Mako==1.2.3 +Markdown==3.5 +markdown-it-py @ file:///home/conda/feedstock_root/build_artifacts/markdown-it-py_1686175045316/work +MarkupSafe==2.1.1 +matplotlib==3.5.2 +mdurl @ file:///home/conda/feedstock_root/build_artifacts/mdurl_1639515908913/work +mlflow==2.7.1 +more-itertools @ file:///home/conda/feedstock_root/build_artifacts/more-itertools_1691086935839/work +msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1695464112187/work +mysqlclient==2.1.1 +numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1695290879892/work/dist/numpy-1.26.0-cp39-cp39-linux_x86_64.whl#sha256=2145d30cdd6d3c9c4a4be216bb158edeac64e1c9aba388e733918d65b9481735 +oauthlib==3.2.2 +omegaconf==2.3.0 +optuna==2.10.1 +ordered-set @ file:///home/conda/feedstock_root/build_artifacts/ordered-set_1643221357603/work +orjson @ file:///home/conda/feedstock_root/build_artifacts/orjson_1696973386080/work/target/wheels/orjson-3.9.8-cp39-cp39-linux_x86_64.whl#sha256=fb8cc3ab1dcff1e49ae7ab69473d2ddce4fc439c8e520c83f7c3bd12c2a0ee50 +packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1696202382185/work +pandas==2.1.1 +pathtools==0.1.2 +pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work +Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1675487166627/work +pkginfo @ file:///home/conda/feedstock_root/build_artifacts/pkginfo_1673281726124/work +pkgutil_resolve_name @ file:///home/conda/feedstock_root/build_artifacts/pkgutil-resolve-name_1694617248815/work +platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1696272223550/work +plotly==5.10.0 +poetry @ file:///home/conda/feedstock_root/build_artifacts/poetry_1692649045332/work +poetry-core @ file:///home/conda/feedstock_root/build_artifacts/poetry-core_1692602937170/work +poetry-plugin-export @ file:///home/conda/feedstock_root/build_artifacts/poetry-plugin-export_1692602977187/work +promise==2.3 +protobuf==4.24.4 +psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1695367186825/work +ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl +pyarrow==13.0.0 +pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work +pydantic @ file:///home/conda/feedstock_root/build_artifacts/pydantic_1697791018681/work +Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1691408637400/work +PyJWT @ file:///home/conda/feedstock_root/build_artifacts/pyjwt_1689721553971/work +PyMySQL==1.0.2 +pyparsing==3.1.1 +pyperclip==1.8.2 +pyproject_hooks @ file:///home/conda/feedstock_root/build_artifacts/pyproject_hooks_1670268672584/work +pyrsistent @ file:///home/conda/feedstock_root/build_artifacts/pyrsistent_1695501249085/work +PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work +python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work +python-editor==1.0.4 +python-multipart @ file:///home/conda/feedstock_root/build_artifacts/python-multipart_1679167423335/work +pytorch-lightning @ file:///home/conda/feedstock_root/build_artifacts/pytorch-lightning_1697150960795/work +pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1693930252784/work +PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1695373447169/work +querystring-parser==1.2.4 +rapidfuzz @ file:///home/conda/feedstock_root/build_artifacts/rapidfuzz_1696683887041/work +readchar @ file:///home/conda/feedstock_root/build_artifacts/readchar_1679045163536/work +requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work +requests-toolbelt @ file:///home/conda/feedstock_root/build_artifacts/requests-toolbelt_1682953341151/work +rich @ file:///home/conda/feedstock_root/build_artifacts/rich-split_1696096668362/work/dist +scikit-learn==1.3.2 +scipy==1.12.0 +SecretStorage @ file:///home/conda/feedstock_root/build_artifacts/secretstorage_1695551752261/work +sentry-sdk==1.9.10 +setproctitle==1.3.2 +shellingham @ file:///home/conda/feedstock_root/build_artifacts/shellingham_1698144360966/work +shortuuid==1.0.9 +six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work +smmap==5.0.0 +sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work +snowballstemmer==2.2.0 +soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1693929250441/work +Sphinx==7.2.6 +sphinx-rtd-theme==2.0.0 +sphinxcontrib-applehelp==1.0.8 +sphinxcontrib-devhelp==1.0.6 +sphinxcontrib-htmlhelp==2.0.5 +sphinxcontrib-jquery==4.1 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.7 +sphinxcontrib-serializinghtml==1.1.10 +SQLAlchemy==1.4.41 +sqlparse==0.4.4 +starlette @ file:///home/conda/feedstock_root/build_artifacts/starlette-recipe_1668726462865/work +starsessions @ file:///home/conda/feedstock_root/build_artifacts/starsessions_1659923701614/work +submitit==1.5.0 +tabulate==0.9.0 +tenacity==8.1.0 +tensorflow-io-gcs-filesystem==0.27.0 +threadpoolctl==3.2.0 +tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1644342247877/work +tomlkit @ file:///home/conda/feedstock_root/build_artifacts/tomlkit_1690472772788/work +torch==1.13.1 +torchaudio==0.13.1 +torchmetrics @ file:///home/conda/feedstock_root/build_artifacts/torchmetrics_1695676540578/work +torchvision==0.14.1 +tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work +traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1696377679271/work +trove-classifiers @ file:///home/conda/feedstock_root/build_artifacts/trove-classifiers_1697647699431/work +typer==0.9.0 +types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1689882883784/work +typeshed-client==2.4.0 +typing_extensions==4.5.0 +tzdata==2023.3 +urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1697720414277/work +uvicorn @ file:///home/conda/feedstock_root/build_artifacts/uvicorn-split_1695566113922/work +virtualenv @ file:///home/conda/feedstock_root/build_artifacts/virtualenv_1698092630749/work +wandb==0.15.12 +wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1696255154857/work +websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1696770128353/work +websockets @ file:///home/conda/feedstock_root/build_artifacts/websockets_1695409988029/work +Werkzeug==3.0.0 +zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work From 4e37c1dbfb9a4df1dfc31fe905d80851adeaa551 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 15 Feb 2024 16:12:38 +0100 Subject: [PATCH 03/59] updating readthedocs.yaml --- .readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 39111812..a1a43723 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,7 +9,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.9.12" + python: "3.9" # You can also specify other tool versions: # nodejs: "19" # rust: "1.64" From 65c9b60c9e3e90e840e4c28aa494098c5f99b23b Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 15 Feb 2024 16:19:06 +0100 Subject: [PATCH 04/59] changing directory of requirements.txt --- requirements.txt => docs/requirements.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename requirements.txt => docs/requirements.txt (100%) diff --git a/requirements.txt b/docs/requirements.txt similarity index 100% rename from requirements.txt rename to docs/requirements.txt From 7c0300180e0bbe86459b570891805d3f67a67317 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 15 Feb 2024 16:37:05 +0100 Subject: [PATCH 05/59] updating reqs file --- docs/requirements.txt | 190 +++++++++++++++++++++--------------------- 1 file changed, 95 insertions(+), 95 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 55918109..23528ceb 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -2,146 +2,146 @@ absl-py==1.2.0 alabaster==0.7.16 alembic==1.8.1 antlr4-python3-runtime==4.9.3 -anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1693488585952/work +anyio appdirs==1.4.4 -arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1696128962909/work -attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1683424013410/work +arrow +attrs autopage==0.5.1 Babel==2.14.0 -backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work -beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1680888073205/work -blessed @ file:///home/conda/feedstock_root/build_artifacts/blessed_1666523113356/work +backports.functools-lru-cache +beautifulsoup4 +blessed blinker==1.6.3 -Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work -build @ file:///home/conda/feedstock_root/build_artifacts/python-build_1682868691601/work -CacheControl @ file:///home/conda/feedstock_root/build_artifacts/cachecontrol-split_1690307937243/work +Brotli +build +CacheControl certifi==2023.7.22 -cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1696001742886/work -charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1698005359935/work -cleo @ file:///home/conda/feedstock_root/build_artifacts/cleo_1669193745932/work -click @ file:///home/conda/feedstock_root/build_artifacts/click_1692311806742/work +cffi +charset-normalizer +cleo +click cliff==4.0.0 cloudpickle==2.2.1 cmaes==0.8.2 cmd2==2.4.2 -colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work +colorama colorlog==6.7.0 -crashtest @ file:///home/conda/feedstock_root/build_artifacts/crashtest_1667467006729/work -croniter @ file:///home/conda/feedstock_root/build_artifacts/croniter_1685103477055/work -cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1695163786734/work +crashtest +croniter +cryptography cycler==0.12.1 databricks-cli==0.18.0 -dateutils @ file:///home/conda/feedstock_root/build_artifacts/dateutils_1603450192521/work -deepdiff @ file:///home/conda/feedstock_root/build_artifacts/deepdiff_1697589976995/work -distlib @ file:///home/conda/feedstock_root/build_artifacts/distlib_1689598491484/work +dateutils +deepdiff +distlib docker==6.1.3 docker-pycreds==0.4.0 docstring-parser==0.15 docutils==0.20.1 -dulwich @ file:///home/conda/feedstock_root/build_artifacts/dulwich_1696599729805/work +dulwich einops==0.4.1 entrypoints==0.4 -exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1692026125334/work -fastapi @ file:///home/conda/feedstock_root/build_artifacts/fastapi_1669571890356/work -filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1694629184119/work +exceptiongroup +fastapi +filelock Flask==2.3.3 fonttools==4.37.4 -fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1697919321618/work +fsspec gast==0.4.0 gitdb==4.0.9 GitPython==3.1.27 google==3.0.0 greenlet==1.1.3 gunicorn==21.2.0 -h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1664132893548/work +h11 h5py==3.7.0 -idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work +idna imagesize==1.4.1 importlib-metadata==5.0.0 -importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1695414790617/work -inquirer @ file:///home/conda/feedstock_root/build_artifacts/inquirer_1678135536462/work -installer @ file:///home/conda/feedstock_root/build_artifacts/python-installer_1679300325491/work -itsdangerous @ file:///home/conda/feedstock_root/build_artifacts/itsdangerous_1648147185463/work +importlib-resources +inquirer +installer +itsdangerous -e git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai -jaraco.classes @ file:///home/conda/feedstock_root/build_artifacts/jaraco.classes_1689112411129/work -jeepney @ file:///home/conda/feedstock_root/build_artifacts/jeepney_1649085214306/work -Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work +jaraco.classes +jeepney +Jinja2 joblib==1.3.2 jsonargparse==4.26.1 -jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1669810440410/work -keyring @ file:///home/conda/feedstock_root/build_artifacts/keyring_1696001558711/work +jsonschema +keyring kiwisolver==1.4.5 libclang==14.0.6 -lightning @ file:///home/conda/feedstock_root/build_artifacts/lightning_1680807776127/work -lightning-cloud @ file:///home/conda/feedstock_root/build_artifacts/lightning-cloud_1695991149929/work -lightning-utilities @ file:///home/conda/feedstock_root/build_artifacts/lightning-utilities_1688117204588/work +lightning +lightning-cloud +lightning-utilities Mako==1.2.3 Markdown==3.5 -markdown-it-py @ file:///home/conda/feedstock_root/build_artifacts/markdown-it-py_1686175045316/work +markdown-it-py MarkupSafe==2.1.1 matplotlib==3.5.2 -mdurl @ file:///home/conda/feedstock_root/build_artifacts/mdurl_1639515908913/work +mdurl mlflow==2.7.1 -more-itertools @ file:///home/conda/feedstock_root/build_artifacts/more-itertools_1691086935839/work -msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1695464112187/work +more-itertools +msgpack mysqlclient==2.1.1 -numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1695290879892/work/dist/numpy-1.26.0-cp39-cp39-linux_x86_64.whl#sha256=2145d30cdd6d3c9c4a4be216bb158edeac64e1c9aba388e733918d65b9481735 +numpy oauthlib==3.2.2 omegaconf==2.3.0 optuna==2.10.1 -ordered-set @ file:///home/conda/feedstock_root/build_artifacts/ordered-set_1643221357603/work -orjson @ file:///home/conda/feedstock_root/build_artifacts/orjson_1696973386080/work/target/wheels/orjson-3.9.8-cp39-cp39-linux_x86_64.whl#sha256=fb8cc3ab1dcff1e49ae7ab69473d2ddce4fc439c8e520c83f7c3bd12c2a0ee50 -packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1696202382185/work +ordered-set +orjson +packaging pandas==2.1.1 pathtools==0.1.2 -pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work -Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1675487166627/work -pkginfo @ file:///home/conda/feedstock_root/build_artifacts/pkginfo_1673281726124/work -pkgutil_resolve_name @ file:///home/conda/feedstock_root/build_artifacts/pkgutil-resolve-name_1694617248815/work -platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1696272223550/work +pexpect +Pillow +pkginfo +pkgutil_resolve_name +platformdirs plotly==5.10.0 -poetry @ file:///home/conda/feedstock_root/build_artifacts/poetry_1692649045332/work -poetry-core @ file:///home/conda/feedstock_root/build_artifacts/poetry-core_1692602937170/work -poetry-plugin-export @ file:///home/conda/feedstock_root/build_artifacts/poetry-plugin-export_1692602977187/work +poetry +poetry-core +poetry-plugin-export promise==2.3 protobuf==4.24.4 -psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1695367186825/work -ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl +psutil +ptyprocess pyarrow==13.0.0 -pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work -pydantic @ file:///home/conda/feedstock_root/build_artifacts/pydantic_1697791018681/work -Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1691408637400/work -PyJWT @ file:///home/conda/feedstock_root/build_artifacts/pyjwt_1689721553971/work +pycparser +pydantic +Pygments +PyJWT PyMySQL==1.0.2 pyparsing==3.1.1 pyperclip==1.8.2 -pyproject_hooks @ file:///home/conda/feedstock_root/build_artifacts/pyproject_hooks_1670268672584/work -pyrsistent @ file:///home/conda/feedstock_root/build_artifacts/pyrsistent_1695501249085/work -PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work -python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work +pyproject_hooks +pyrsistent +PySocks +python-dateutil python-editor==1.0.4 -python-multipart @ file:///home/conda/feedstock_root/build_artifacts/python-multipart_1679167423335/work -pytorch-lightning @ file:///home/conda/feedstock_root/build_artifacts/pytorch-lightning_1697150960795/work -pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1693930252784/work -PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1695373447169/work +python-multipart +pytorch-lightning +pytz +PyYAML querystring-parser==1.2.4 -rapidfuzz @ file:///home/conda/feedstock_root/build_artifacts/rapidfuzz_1696683887041/work -readchar @ file:///home/conda/feedstock_root/build_artifacts/readchar_1679045163536/work -requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work -requests-toolbelt @ file:///home/conda/feedstock_root/build_artifacts/requests-toolbelt_1682953341151/work -rich @ file:///home/conda/feedstock_root/build_artifacts/rich-split_1696096668362/work/dist +rapidfuzz +readchar +requests +requests-toolbelt +rich scikit-learn==1.3.2 scipy==1.12.0 -SecretStorage @ file:///home/conda/feedstock_root/build_artifacts/secretstorage_1695551752261/work +SecretStorage sentry-sdk==1.9.10 setproctitle==1.3.2 -shellingham @ file:///home/conda/feedstock_root/build_artifacts/shellingham_1698144360966/work +shellingham shortuuid==1.0.9 -six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work +six smmap==5.0.0 -sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work +sniffio snowballstemmer==2.2.0 -soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1693929250441/work +soupsieve Sphinx==7.2.6 sphinx-rtd-theme==2.0.0 sphinxcontrib-applehelp==1.0.8 @@ -153,33 +153,33 @@ sphinxcontrib-qthelp==1.0.7 sphinxcontrib-serializinghtml==1.1.10 SQLAlchemy==1.4.41 sqlparse==0.4.4 -starlette @ file:///home/conda/feedstock_root/build_artifacts/starlette-recipe_1668726462865/work -starsessions @ file:///home/conda/feedstock_root/build_artifacts/starsessions_1659923701614/work +starlette +starsessions submitit==1.5.0 tabulate==0.9.0 tenacity==8.1.0 tensorflow-io-gcs-filesystem==0.27.0 threadpoolctl==3.2.0 -tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1644342247877/work -tomlkit @ file:///home/conda/feedstock_root/build_artifacts/tomlkit_1690472772788/work +tomli +tomlkit torch==1.13.1 torchaudio==0.13.1 -torchmetrics @ file:///home/conda/feedstock_root/build_artifacts/torchmetrics_1695676540578/work +torchmetrics torchvision==0.14.1 -tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work -traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1696377679271/work -trove-classifiers @ file:///home/conda/feedstock_root/build_artifacts/trove-classifiers_1697647699431/work +tqdm +traitlets +trove-classifiers typer==0.9.0 -types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1689882883784/work +types-python-dateutil typeshed-client==2.4.0 typing_extensions==4.5.0 tzdata==2023.3 -urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1697720414277/work -uvicorn @ file:///home/conda/feedstock_root/build_artifacts/uvicorn-split_1695566113922/work -virtualenv @ file:///home/conda/feedstock_root/build_artifacts/virtualenv_1698092630749/work +urllib3 +uvicorn +virtualenv wandb==0.15.12 -wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1696255154857/work -websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1696770128353/work -websockets @ file:///home/conda/feedstock_root/build_artifacts/websockets_1695409988029/work +wcwidth +websocket-client +websockets Werkzeug==3.0.0 -zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work +zipp From 8e9f041269f88a445d737fc8eb71731a4d45431b Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Fri, 16 Feb 2024 16:01:23 +0100 Subject: [PATCH 06/59] commiting changes and adding pages for tutorials --- docs/advanced_workflow.rst | 19 + docs/basic_comp.rst | 19 + docs/basic_workflow.rst | 19 + docs/conf.py | 2 + docs/index.rst | 25 +- docs/intermediate_workflow.rst | 19 + docs/mnist_doc.rst | 73 +++ docs/tutorials.rst | 22 + docs/use_cases.rst | 13 +- docs_old/.gitignore | 10 - docs_old/404.html | 12 - docs_old/Gemfile | 7 - docs_old/Gemfile.lock | 79 --- docs_old/LICENSE | 21 - docs_old/README.md | 181 ------ docs_old/_config.yml | 26 - docs_old/docs/CLI.md | 53 -- docs_old/docs/Concepts.md | 35 -- docs_old/docs/How-to-use-this-software.md | 540 ------------------ docs_old/docs/img/Workflow DAG concept.png | Bin 224511 -> 0 bytes docs_old/docs/img/cwl-workflow.png | Bin 375527 -> 0 bytes .../img/user-platform interaction full.png | Bin 211421 -> 0 bytes .../docs/img/user-platform interaction.png | Bin 30005 -> 0 bytes docs_old/docs/use-cases/index.md | 12 - docs_old/docs/use-cases/mnist.md | 185 ------ docs_old/favicon.ico | Bin 15406 -> 0 bytes docs_old/index.md | 64 --- 27 files changed, 207 insertions(+), 1229 deletions(-) create mode 100644 docs/advanced_workflow.rst create mode 100644 docs/basic_comp.rst create mode 100644 docs/basic_workflow.rst create mode 100644 docs/intermediate_workflow.rst create mode 100644 docs/tutorials.rst delete mode 100644 docs_old/.gitignore delete mode 100644 docs_old/404.html delete mode 100644 docs_old/Gemfile delete mode 100644 docs_old/Gemfile.lock delete mode 100644 docs_old/LICENSE delete mode 100644 docs_old/README.md delete mode 100644 docs_old/_config.yml delete mode 100644 docs_old/docs/CLI.md delete mode 100644 docs_old/docs/Concepts.md delete mode 100644 docs_old/docs/How-to-use-this-software.md delete mode 100644 docs_old/docs/img/Workflow DAG concept.png delete mode 100644 docs_old/docs/img/cwl-workflow.png delete mode 100644 docs_old/docs/img/user-platform interaction full.png delete mode 100644 docs_old/docs/img/user-platform interaction.png delete mode 100644 docs_old/docs/use-cases/index.md delete mode 100644 docs_old/docs/use-cases/mnist.md delete mode 100644 docs_old/favicon.ico delete mode 100644 docs_old/index.md diff --git a/docs/advanced_workflow.rst b/docs/advanced_workflow.rst new file mode 100644 index 00000000..140d8ee0 --- /dev/null +++ b/docs/advanced_workflow.rst @@ -0,0 +1,19 @@ +Advanced workflow +================= + +Description... + + +tutorial_2_advanced_workflow.py ++++++++++++++++++++++++++++++++ + +The `tutorial_2_advanced_workflow.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: tutorial_2_advanced_workflow + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/basic_comp.rst b/docs/basic_comp.rst new file mode 100644 index 00000000..d4fb3a18 --- /dev/null +++ b/docs/basic_comp.rst @@ -0,0 +1,19 @@ +Basic components +================ + +Description... + + +basic_components.py ++++++++++++++++++++ + +The `basic_components.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: basic_components + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/basic_workflow.rst b/docs/basic_workflow.rst new file mode 100644 index 00000000..7b6fca2d --- /dev/null +++ b/docs/basic_workflow.rst @@ -0,0 +1,19 @@ +Basic workflow +============== + +Description... + + +tutorial_0_basic_workflow.py +++++++++++++++++++++++++++++ + +The `tutorial_0_basic_workflow.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: tutorial_0_basic_workflow + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/conf.py b/docs/conf.py index 6bab8aec..e21e2464 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,6 +11,8 @@ sys.path.insert(0, os.path.abspath('../use-cases/')) sys.path.insert(0, os.path.abspath('../use-cases/3dgan/')) sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch-lightning/')) +sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch/')) +sys.path.insert(0, os.path.abspath('../tutorials/ml-workflows/')) sys.path.insert(0, os.path.abspath('../src/itwinai')) sys.path.insert(0, os.path.abspath('../')) diff --git a/docs/index.rst b/docs/index.rst index 9bf6cf33..29e47aa2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -6,18 +6,37 @@ Welcome to itwinai's documentation! =================================== +``itwinai`` is a framework for advanced AI/ML workflows in digital twins. + +This platform is intended to support general-purpose MLOps for digital twin use cases in `interTwin `_ project. + +Platform for machine learning workflows in digital twins +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +The goal of this platform is to provide ML researchers with an easy-to-use endpoint to manage general-purpose ML workflows, +with limited engineering overhead, while providing state-of-the-art MLOps best practices. + +The user is going to provide as input a set of configuration files, to fully describe ML workflows, in the context of digital twin applications. +``itwinai`` platform instantiates ML workflows with the configurations provided by the DT developer. +The execution of ML workflows produces as output a set of ML metrics, which are visualized by ``itwinai`` via `MLFlow `_. +As a result of ML training, the best model (on validation dataset) is saved to the Models Registry for future predictions. + +In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversimplifying all the rest (e.g., pre-processing, authentication, workflow execution). + + .. toctree:: :maxdepth: 2 :caption: Contents: modules use_cases - 3dgan_doc - mnist_doc + tutorials + Indices and tables ================== * :ref:`genindex` * :ref:`modindex` -* :ref:`search` +.. * :ref:`search` + diff --git a/docs/intermediate_workflow.rst b/docs/intermediate_workflow.rst new file mode 100644 index 00000000..79c0472b --- /dev/null +++ b/docs/intermediate_workflow.rst @@ -0,0 +1,19 @@ +Intermediate workflow +===================== + +Description... + + +tutorial_1_intermediate_workflow.py ++++++++++++++++++++++++++++++++++++ + +The `tutorial_1_intermediate_workflow.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: tutorial_1_intermediate_workflow + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/mnist_doc.rst b/docs/mnist_doc.rst index 56dfa654..afedbb29 100644 --- a/docs/mnist_doc.rst +++ b/docs/mnist_doc.rst @@ -76,3 +76,76 @@ The `utils.py` script includes utility functions and classes that are used acros .. :members: .. :undoc-members: .. :show-inheritance: + + +This section covers the MNIST use case, which utilizes the `torch` framework for training and evaluation. The following files are integral to this use case: + +torch +----- + +.. toctree:: + :maxdepth: 5 + +dataloader.py ++++++++++++++ + +The `dataloader.py` script is responsible for loading the MNIST dataset and preparing it for training. + +.. literalinclude:: ../use-cases/mnist/torch/dataloader.py + :language: python + + +Dockerfile +++++++++++ + +.. literalinclude:: ../use-cases/mnist/torch/Dockerfile + :language: bash + + +inference-pipeline.yaml ++++++++++++++++++++++++ + +This YAML file defines the pipeline configuration for the MNIST use case inference. + +.. literalinclude:: ../use-cases/mnist/torch/inference-pipeline.yaml + :language: yaml + +model.py +++++++++ + +The `model.py` script is responsible for loading a simple model. + +.. literalinclude:: ../use-cases/mnist/torch/model.py + :language: python + +pipeline.yaml ++++++++++++++ + +This YAML file defines the pipeline configuration for the MNIST use case. It includes settings for the model, training, and evaluation. + +.. literalinclude:: ../use-cases/mnist/torch/pipeline.yaml + :language: yaml + +startscript ++++++++++++ + +The `startscript` is a shell script to initiate the training process. It sets up the environment and starts the training using the `train.py` script. + +.. literalinclude:: ../use-cases/mnist/torch/startscript + :language: bash + +train.py +++++++++ + +This script contains the training loop and is where the model is trained using the data prepared by `dataloader.py`. + +.. literalinclude:: ../use-cases/mnist/torch/train.py + :language: python + +saver.py +++++++++ +... + +.. literalinclude:: ../use-cases/mnist/torch/saver.py + :language: python + diff --git a/docs/tutorials.rst b/docs/tutorials.rst new file mode 100644 index 00000000..5a5772d5 --- /dev/null +++ b/docs/tutorials.rst @@ -0,0 +1,22 @@ +.. _tutorials: + +ML workflow tutorials +===================== + +Here you can find a collection of tutorials for various complexity ML workflows. + +Tutorials +--------- + +Description... + +.. toctree:: + :maxdepth: 2 + :caption: Examples + + basic_comp + basic_workflow + intermediate_workflow + advanced_workflow + + \ No newline at end of file diff --git a/docs/use_cases.rst b/docs/use_cases.rst index de870cf6..dd59b82b 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -12,9 +12,20 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - :caption: 3DGAN use case Documentation Contents: + :caption: 3DGAN use case Documentation 3dgan_doc + + +MNIST use case +-------------- + +Description of the use case etc... + +.. toctree:: + :maxdepth: 2 + :caption: MNIST Documentation + mnist_doc diff --git a/docs_old/.gitignore b/docs_old/.gitignore deleted file mode 100644 index f55f6395..00000000 --- a/docs_old/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -# Copied from https://github.com/github/gitignore/blob/main/Jekyll.gitignore -# Ignore metadata generated by Jekyll -_site/ -.sass-cache/ -.jekyll-cache/ -.jekyll-metadata - -# Ignore folders generated by Bundler -.bundle/ -vendor/ diff --git a/docs_old/404.html b/docs_old/404.html deleted file mode 100644 index b8547546..00000000 --- a/docs_old/404.html +++ /dev/null @@ -1,12 +0,0 @@ ---- -layout: default -title: 404 -permalink: /404 -nav_exclude: true -search_exclude: true ---- - -

Page not found

- -

The page you requested could not be found. Try using the navigation {% if site.search_enabled != false %}or search {% - endif %}to find what you're looking for or go to this site's home page.

\ No newline at end of file diff --git a/docs_old/Gemfile b/docs_old/Gemfile deleted file mode 100644 index 387154f8..00000000 --- a/docs_old/Gemfile +++ /dev/null @@ -1,7 +0,0 @@ -source 'https://rubygems.org' - -gem "jekyll", "~> 4.3" # installed by `gem jekyll` -# gem "webrick" # required when using Ruby >= 3 and Jekyll <= 4.2.2 - -gem "just-the-docs", "0.5.0" # pinned to the current release -# gem "just-the-docs" # always download the latest release diff --git a/docs_old/Gemfile.lock b/docs_old/Gemfile.lock deleted file mode 100644 index 81efc419..00000000 --- a/docs_old/Gemfile.lock +++ /dev/null @@ -1,79 +0,0 @@ -GEM - remote: https://rubygems.org/ - specs: - addressable (2.8.1) - public_suffix (>= 2.0.2, < 6.0) - colorator (1.1.0) - concurrent-ruby (1.1.10) - em-websocket (0.5.3) - eventmachine (>= 0.12.9) - http_parser.rb (~> 0) - eventmachine (1.2.7) - ffi (1.15.5) - forwardable-extended (2.6.0) - http_parser.rb (0.8.0) - i18n (1.12.0) - concurrent-ruby (~> 1.0) - jekyll (4.3.0) - addressable (~> 2.4) - colorator (~> 1.0) - em-websocket (~> 0.5) - i18n (~> 1.0) - jekyll-sass-converter (>= 2.0, < 4.0) - jekyll-watch (~> 2.0) - kramdown (~> 2.3, >= 2.3.1) - kramdown-parser-gfm (~> 1.0) - liquid (~> 4.0) - mercenary (>= 0.3.6, < 0.5) - pathutil (~> 0.9) - rouge (>= 3.0, < 5.0) - safe_yaml (~> 1.0) - terminal-table (>= 1.8, < 4.0) - webrick (~> 1.7) - jekyll-sass-converter (2.2.0) - sassc (> 2.0.1, < 3.0) - jekyll-seo-tag (2.8.0) - jekyll (>= 3.8, < 5.0) - jekyll-watch (2.2.1) - listen (~> 3.0) - just-the-docs (0.5.0) - jekyll (>= 3.8.5) - jekyll-seo-tag (>= 2.0) - rake (>= 12.3.1) - kramdown (2.4.0) - rexml - kramdown-parser-gfm (1.1.0) - kramdown (~> 2.0) - liquid (4.0.3) - listen (3.7.1) - rb-fsevent (~> 0.10, >= 0.10.3) - rb-inotify (~> 0.9, >= 0.9.10) - mercenary (0.4.0) - pathutil (0.16.2) - forwardable-extended (~> 2.6) - public_suffix (5.0.0) - rake (13.0.6) - rb-fsevent (0.11.2) - rb-inotify (0.10.1) - ffi (~> 1.0) - rexml (3.2.5) - rouge (4.0.0) - safe_yaml (1.0.5) - sassc (2.4.0) - ffi (~> 1.9) - terminal-table (3.0.2) - unicode-display_width (>= 1.1.1, < 3) - unicode-display_width (2.3.0) - webrick (1.7.0) - -PLATFORMS - arm64-darwin-21 - x86_64-darwin-19 - x86_64-linux - -DEPENDENCIES - jekyll (~> 4.3) - just-the-docs (= 0.5.0) - -BUNDLED WITH - 2.3.9 diff --git a/docs_old/LICENSE b/docs_old/LICENSE deleted file mode 100644 index 7d510d02..00000000 --- a/docs_old/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 just-the-docs - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/docs_old/README.md b/docs_old/README.md deleted file mode 100644 index 4a405acd..00000000 --- a/docs_old/README.md +++ /dev/null @@ -1,181 +0,0 @@ -# just-the-docs-template - -This is a *bare-minimum* template to create a [Jekyll] site that: - -- uses the [Just the Docs] theme; -- can be built and published on [GitHub Pages]; -- can be built and previewed locally, and published on other platforms. - -More specifically, the created site: - -- uses a gem-based approach, i.e. uses a `Gemfile` and loads the `just-the-docs` gem; -- uses the [GitHub Pages / Actions workflow] to build and publish the site on GitHub Pages. - -To get started with creating a site, just click "[use this template]"! - -If you want to maintain your docs in the `docs` directory of an existing project repository, see -[Hosting your docs from an existing project repository](#hosting-your-docs-from-an-existing-project-repository). - -After completing the creation of your new site on GitHub, update it as needed: - -## Replace the content of the template pages - -Update the following files to your own content: - -- `index.md` (your new home page) -- `README.md` (information for those who access your site repository on GitHub) - -## Changing the version of the theme and/or Jekyll - -Simply edit the relevant line(s) in the `Gemfile`. - -## Adding a plugin - -The Just the Docs theme automatically includes the [`jekyll-seo-tag`] plugin. - -To add an extra plugin, you need to add it in the `Gemfile` *and* in `_config.yml`. For example, to add [`jekyll-default-layout`]: - -- Add the following to your site's `Gemfile`: - - ```ruby - gem "jekyll-default-layout" - ``` - -- And add the following to your site's `_config.yml`: - - ```yaml - plugins: - - jekyll-default-layout - ``` - -Note: If you are using a Jekyll version less than 3.5.0, use the `gems` key instead of `plugins`. - -## Publishing your site on GitHub Pages - -1. If your created site is `YOUR-USERNAME/YOUR-SITE-NAME`, update `_config.yml` to: - - ```yaml - title: YOUR TITLE - description: YOUR DESCRIPTION - theme: just-the-docs - - url: https://YOUR-USERNAME.github.io/YOUR-SITE-NAME - - aux_links: # remove if you don't want this link to appear on your pages - Template Repository: https://github.com/YOUR-USERNAME/YOUR-SITE-NAME - ``` - -2. Push your updated `_config.yml` to your site on GitHub. - -3. In your newly created repository on GitHub: - - go to the `Settings` tab -> `Pages` -> `Build and deployment`, then select `Source`: `GitHub Actions`. - - if there were any failed Actions, go to the `Actions` tab and click on `Re-run jobs`. - -## Building and previewing your site locally - -Assuming [Jekyll] and [Bundler] are installed on your computer: - -1. Change your working directory to the root directory of your site. - -2. Run `bundle install`. - -3. Run `bundle exec jekyll serve` to build your site and preview it at `localhost:4000`. - - The built site is stored in the directory `_site`. - -## Publishing your built site on a different platform - -Just upload all the files in the directory `_site`. - -## Customization - -You're free to customize sites that you create with this template, however you like! - -[Browse our documentation][Just the Docs] to learn more about how to use this theme. - -## Hosting your docs from an existing project repository - -You might want to maintain your docs in an existing project repository. Instead of creating a new repository using -the [just-the-docs template](https://github.com/just-the-docs/just-the-docs-template), you can copy the template -files into your existing repository and configure the template's GitHub Actions workflow to -build from a `docs` directory. You can clone the template to your local machine or download the `.zip` file -to access the files. - -### Copy the template files - -1. Create a `.github/workflows` directory at your project root if your repository doesn't already have one. -Copy the `pages.yml` file into this directory. GitHub Actions searches this directory for workflow files. - -2. Create a `docs` directory at your project root and copy all remaining template files into this directory. - -### Modify the GitHub Actions workflow - -The GitHub Actions workflow that builds and deploys your site to GitHub Pages is defined by the `pages.yml` file. -You'll need to edit this file to that so that your build and deploy steps look to your `docs` directory, -rather than the project root. - -1. Set the default `working-directory` param for the build job. - - ```yaml - build: - runs-on: ubuntu-latest - defaults: - run: - working-directory: docs - ``` - -2. Set the `working-directory` param for the Setup Ruby step. - - ```yaml - - name: Setup Ruby - uses: ruby/setup-ruby@v1 - with: - ruby-version: '3.1' - bundler-cache: true - cache-version: 0 - working-directory: '${{ github.workspace }}/docs' - ``` - -3. Set the path param for the Upload artifact step: - - ```yaml - - name: Upload artifact - uses: actions/upload-pages-artifact@v1 - with: - path: "docs/_site/" - ``` - -4. Modify the trigger so that only changes within the `docs` directory start the workflow. -Otherwise, every change to your project (even those that don't affect the docs) would trigger a new site build and deploy. - - ```yaml - on: - push: - branches: - - "main" - paths: - - "docs/**" - ``` - -## Licensing and Attribution - -This repository is licensed under the [MIT License]. You are generally free to reuse or extend upon this code as you -see fit; just include the original copy of the license (which is preserved when you "make a template"). -While it's not necessary, we'd love to hear from you if you do use this template, and how we can improve it for future use! - -The deployment GitHub Actions workflow is heavily based on GitHub's mixed-party [starter workflows]. -A copy of their MIT License is available in [actions/starter-workflows]. - ----- - -[Jekyll]: https://jekyllrb.com -[Just the Docs]: https://just-the-docs.github.io/just-the-docs/ -[GitHub Pages]: https://docs.github.com/en/pages -[GitHub Pages / Actions workflow]: https://github.blog/changelog/2022-07-27-github-pages-custom-github-actions-workflows-beta/ -[Bundler]: https://bundler.io -[use this template]: https://github.com/just-the-docs/just-the-docs-template -[`jekyll-default-layout`]: https://github.com/benbalter/jekyll-default-layout -[`jekyll-seo-tag`]: https://jekyll.github.io/jekyll-seo-tag -[MIT License]: https://en.wikipedia.org/wiki/MIT_License -[starter workflows]: https://github.com/actions/starter-workflows/blob/main/pages/jekyll.yml -[actions/starter-workflows]: https://github.com/actions/starter-workflows/blob/main/LICENSE diff --git a/docs_old/_config.yml b/docs_old/_config.yml deleted file mode 100644 index ba7498ac..00000000 --- a/docs_old/_config.yml +++ /dev/null @@ -1,26 +0,0 @@ -title: itwinai -description: Docs for task T6.5 prototype of interTwin project -theme: just-the-docs - -url: https://interTwin-eu.github.io/T6.5-AI-and-ML - -aux_links: - Template Repository: https://github.com/interTwin-eu/T6.5-AI-and-ML - -favicon_ico: "favicon.ico" - -nav_external_links: - - title: itwinai on GitHub - url: https://github.com/interTwin-eu/T6.5-AI-and-ML - hide_icon: false # set to true to hide the external link icon - defaults to false - - title: interTwin on GitHub - url: https://github.com/interTwin-eu/ - hide_icon: false # set to true to hide the external link icon - defaults to false - - title: interTwin project - url: https://www.intertwin.eu/ - hide_icon: false # set to true to hide the external link icon - defaults to false - -mermaid: - # Version of mermaid library - # Pick an available version from https://cdn.jsdelivr.net/npm/mermaid/ - version: "10.1.0" diff --git a/docs_old/docs/CLI.md b/docs_old/docs/CLI.md deleted file mode 100644 index a2383b46..00000000 --- a/docs_old/docs/CLI.md +++ /dev/null @@ -1,53 +0,0 @@ ---- -layout: default -title: CLI -nav_order: 3 ---- - -# Command-line interface (CLI) - - -The `itwinai` package provides a custom CLI, which can be accessed, for instance -from the development environment: - -```bash -# Activate development environment -micromamba activate ./.venv-dev - -# Access itwinai CLI -itwinai --help -``` - -## Visualization - -Some visualization functionalities offered by `itwinai` CLI. - -```bash -# Datasets registry -itwinai datasets --help - -# Workflows (any file '*-workflow.yml') -itwinai workflows --help -``` - -## Machine learning - -```bash -# Training -itwinai train --help - -# Launch MLFlow UI to visualize ML logs -itwinai mlflow-ui --help - -# Inference -itwinai predict --help -``` diff --git a/docs_old/docs/Concepts.md b/docs_old/docs/Concepts.md deleted file mode 100644 index 5830fcfe..00000000 --- a/docs_old/docs/Concepts.md +++ /dev/null @@ -1,35 +0,0 @@ ---- -layout: default -title: Concepts -nav_order: 4 ---- - -# Concepts - - -Here we presents the key concepts on which `itwinai` is based. - -## Workflow - -We define a workflow as a directed acyclic graph (DAG) of data processing -operations, in which each step can have multiple inputs and outputs, and -each input or output is a dataset. - -![image](img/Workflow%20DAG%20concept.png) - -In the picture above, the yellow boxes with numbers represent the steps -in the example workflow, whereas the blue cylinders represent data -(e.g., dataset, configuration file). - -Each step runs in *isolation* from the others, and data is the *interface*. -Isolation can be guaranteed by executing each step in a Docker container or -in a separate Python virtual environment. diff --git a/docs_old/docs/How-to-use-this-software.md b/docs_old/docs/How-to-use-this-software.md deleted file mode 100644 index 15a36b44..00000000 --- a/docs_old/docs/How-to-use-this-software.md +++ /dev/null @@ -1,540 +0,0 @@ ---- -layout: default -title: How to use this software -nav_order: 2 ---- - -# How to use this software -{: .no_toc } - -## Table of contents -{: .no_toc .text-delta } - -1. TOC -{:toc} - ---- - -This guide provides a detailed explanation on how to use the AI/ML workflow -tool, developed in the context of [interTwin](https://github.com/interTwin-eu/). - -**Target audience**: anyone aiming to simplify MLOps for their digital twin (DT) -use case/project. Use cases from interTwin project. - -## Clone this repo - -```bash -git clone git@github.com:interTwin-eu/T6.5-AI-and-ML.git -``` - -A new use case/project can be added under `./use-cases` folder. - -Build the workflow runner environment and development environment -following the instructions on the README file. - -## Define a DT workflow - -Before delving into workflow definition rules, make sure to have -understood *what is* a [workflow](./Concepts#workflow) in this context. - -You can define one or more workflows for your DT use case (e.g., ML training, -ML inference, other). A workflow is -defined through configuration files in the use case subfolder. -For the same use case, a DT developer can define multiple workflows, -in which multiple datasets are involved. - -Currently, each step is executed in an isolated Python virtual environment, -built according to [conda](https://docs.conda.io/en/latest/) standards. -In practice, it is built with -[Micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html), -which is faster. - -To begin with, you can start by looking at an example of the -[MNIST toy use case](use-cases/mnist), located at `use-cases/mnist` -in the code repository. - -### Use case metadata - -The main configuration file of an use case is `meta.yml`, which stores -the metadata of it. When creating a new use case, you need to update the -`root` field with the path to the use case folder, with respect to the -repo root. - -The datasets registry is a field in this configuration file, -which stores the metadata -for all datasets involved in a use case. This configuration provides a -unified place where datasets can be maintained, making it easy to access -them from other configuration files. - -The dataset registry has the format: - -```yaml -datasets: - some-dataset-name: - doc: Documentation string for this dataset - location: path/to/dataset/disk/location -``` - -Example of `meta.yml` from [MNIST use case](use-cases/mnist): - -```yaml -# Use case root location. End without path '/' char! -root: ./use-cases/mnist - -# AI folder location. End without path '/' char! -ai-root: ./ai - -# Datasets registry -datasets: - preproc-images: - doc: Preprocessed MNIST images - location: ${root}/data/preproc-images - ml-logs: - doc: MLflow tracking URI for local logging - location: ${root}/data/ml-logs - ml-predictions: - doc: predictions on unseen data - location: ${root}/data/ml-predictions -``` - -Datasets are imported from the datasets registry to other files by means -of [OmegaConf](https://omegaconf.readthedocs.io/)'s -[variable interpolation](https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#variable-interpolation). -This way, you can easily import datasets metadata (e.g., location on -file system) from datasets registry. - -Dataset registry of an use case can be visualized using [itwinai CLI](./CLI#visualization): - -```bash -USE_CASE_ROOT='use-cases/mnist/' -micromamba activate ./.venv-dev && \ - itwinai datasets --use-case $USE_CASE_ROOT -``` - -### Workflow configuration - -Use case workflows are defined with `*-workflow.yml` files in the use case root, -and there are two ways to define a workflow: - -- "Custom" format for workflow definition is an intuitive standard we created -for this prototype, for easy prototyping. -- [Common Workflow Language](https://www.commonwl.org/) (CWL), which is -currently under development, and not ready to be used. - -Which of the two is used is defined by setting the `--cwl` flag (explained -[below](#run-the-workflow)). - -#### Custom workflow definition - -To define a workflow with the custom format, the DT developer must follow -the structure provided below. - -The `steps` section defines the steps of the workflow, in the order in which -they have to be executed: - -```yaml -steps: - - some-step-name: - doc: Documentation string for this step - env: # micromamba environment metadata - file: some-conda-env.yml - prefix: path/to/conda/env/ - command: Command to execute inside micromamba env - args: # Command arguments. - # Note interpolation with datasets registry here! - some-arg: ${datasets.my-dataset.location} - some-other-arg: 42 - - next-step-name: - ... -``` - -Example workflow from [MNIST use case](use-cases/mnist), defined in -`training-workflow.yml`: - -```yaml -steps: - - preprocessing: - doc: Download and split MNIST dataset into train and test sets - command: python ${root}/mnist-preproc.py - env: - file: ${root}/env-files/preproc-env.yml - prefix: ${root}/.venv-preproc - args: - output: ${datasets.preproc-images.location} - stage: train - - ml-training: - doc: Train a neural network to classify MNIST images - command: itwinai train - env: - file: ${ai-root}/env-files/pytorch-lock.yml - prefix: ${ai-root}/.venv-pytorch - source: ${ai-root} - args: - train-dataset: ${datasets.preproc-images.location} - ml-logs: ${datasets.ml-logs.location} - config: ${root}/mnist-ai-train.yml -``` - -Step 1 is named `preprocessing` and uses the `mnist-preproc.py` script to pre-process the MNIST dataset. It takes no -input, generates an output dataset named `preproc-images`, and uses an environment defined in a YAML file named -`preproc-env.yml` located in the `./use-cases/mnist` directory. -Step 2 is named `ml-training` and trains a machine learning model using the preprocessed image data generated in -the first step. The training is performed using the train command from the `itwinai` tool. The input dataset is -`preproc-images`, and the output is `ml-logs`. The step uses an environment defined in a YAML file named -`pytorch-env.yml` located in the `./ai` directory. The machine learning model is configured using the `mnist-ai-train.yml` -file located in the `./use-cases/mnist` directory. - -#### CWL: under development and not ready to be used yet - -**NOTE**. At the moment, support for CWL is under development, -and is not available. - - - -## Implement workflow steps - -Implement use case-specific steps. -Note that the implementation of steps involving AI/ML are addressed in the next -step, and they can be implemented a bit more easily. - -Each step of a workflow is characterized by its python virtual environment -and a command to be executed in that -environment. A command can be implemented by providing, for instance, a python script to be executed in some environment. - -To execute a step, the workflow engine will run something like: - -```bash -micromamba run -p PATH_TO_STEP_ENV CMD --arg1 ARG_1_VAL ... --argN ARG_N_VAL -``` - -Where: - -- `PATH_TO_STEP_ENV` is the path to the micromamba environment for this step. -- `CMD` is the command to execute in that environment. -- The developer can use additional parameters which are automatically appended -to the command. - -*Example*: in the [MNIST use case](use-cases/mnist), -the preprocessing step is implemented by a python script, which downloads and -splits the MNIST dataset in a specific location. Using a command similar to: - -```bash -micromamba run -p ./use-cases/mnist/.venv-preproc \ - python ./use-cases/mnist/mnist-preproc.py \ - --output ./data/mnist/preproc-images-inference \ - --stage train -``` - -## Define AI/ML workflow - -AI/ML workflows are implemented by the `itwinai` module. -The DT developer, who wants to include a new use case, needs to provide -only a reduced amount of code to describe a neural network, plus some -configuration files. - -The developer must implement the neural network to train and include it inside -`itwinai` python package, under `ai/src/itwinai`. For instance, under -`ai/src/itwinai/plmodels` when using PyTorch Lightning. - -For instance, `LitMNIST` neural network used in [MNIST use case](use-cases/mnist) -has been added under `ai/src/itwinai/plmodels/mnist.py` - -Once a model has been included inside the `itwinai` python module, it can be imported during training. -In the future, `itwinai` will support also neural networks not provided out-of-the-box by `itwinai`. - -The developer must define two configuration files to access `itwinai` -functionalities. -First, ML training configuration, associated with `$ itwinai train` [CLI](./CLI) command. -Second, ML inference configuration, associated with `$ itwinai predict` [CLI](./CLI) command. - -MLOps heavily relies on commands provided by [itwinai CLI](./CLI). -Therefore, before continuing, make sure to have understood how -[itwinai CLI](./CLI) works. - -### ML training configuration - -ML training configuration is provided in a with naming convention `*-ai-train.yml` -under the use case root directory. - -An example configuration file is provided below, where the fields have been replaced with their respective description: - -```yaml -# Configuration file of AI workflows for X use case - -# Training configuration -train: - type: > - can be 'lightning' or 'tf', depending whether the neural network is defined - using PyTorch Lightning or TensorFlow. - At the moment, only 'lightning' is supported. - - # Configuration format defined by PyTorch Lightning CLI - # https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html - conf: - # See discussion below - ... - -# MLFlow logger configuration -logger: - experiment_name: > - Unique name for an experiment, to group all similar - runs under the same experiment - description: Description for this specific run. - log_every_n_epoch: how often to log (epochs) - log_every_n_steps: how often to log (steps, i.e., batches) - registered_model_name: > - Unique name used in Models Registry to identify an ML model. - If given, it is automatically registered in the Models Registry. -``` - -When using PyTorch Lightning (PL) ML framework, the training configuration is easy to define, as it follows the schema -pre-defined by lightning authors for the PL CLI. See its documentation -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#trainer-callbacks-and-arguments-with-class-type), -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#trainer-callbacks-and-arguments-with-class-type), -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#multiple-models-and-or-datasets), and -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#optimizers-and-learning-rate-schedulers). - -An example taken from -[MNIST use case](use-cases/mnist) located at `use-cases/mnist/mnist-ai-training.yml`: - -```yaml -# Pytorch lightning config for training -train: - type: lightning - # Follows lightning config file format: - # https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#multiple-models-and-or-datasets - conf: - seed_everything: 4231162351 - - # Lightning Trainer configuration - trainer: - accelerator: auto - strategy: auto - devices: auto - num_nodes: 1 - precision: 32-true - - # MLFlow logger (initial) configuration. - # To be completed with run details, later on - logger: - class_path: lightning.pytorch.loggers.MLFlowLogger - init_args: - experiment_name: ${logger.experiment_name} - save_dir: ./mlruns - - # Callbacks - callbacks: - - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping - init_args: - monitor: val_loss - patience: 2 - - class_path: lightning.pytorch.callbacks.lr_monitor.LearningRateMonitor - init_args: - logging_interval: step - - class_path: lightning.pytorch.callbacks.ModelCheckpoint - init_args: - dirpath: checkpoints - filename: best-checkpoint - save_top_k: 1 - verbose: true - monitor: val_loss - mode: min - - max_epochs: 1 - - # Lightning Model configuration - model: - class_path: itwinai.plmodels.mnist.LitMNIST - init_args: - hidden_size: 64 - - # Lightning data module configuration - data: - class_path: itwinai.plmodels.mnist.MNISTDataModule - init_args: - data_dir: ${cli.train_dataset} - batch_size: 32 - - # Torch Optimizer configuration - optimizer: - class_path: torch.optim.AdamW - init_args: - lr: 0.001 - - # Torch LR scheduler configuration - lr_scheduler: - class_path: torch.optim.lr_scheduler.ExponentialLR - init_args: - gamma: 0.1 - -# Mlflow -logger: - experiment_name: MNIST classification lite - description: A MLP classifier for MNIST dataset. - log_every_n_epoch: 1 - log_every_n_steps: 1 - # Name used in Models Registry. If given, it is automatically - # registered in the Models Registry. - registered_model_name: MNIST-clf-lite -``` - -Note the field `data_dir: ${cli.train_dataset}` in the above configuration. -More on this later. - -### ML inference configuration - -ML training configuration is provided in a with naming convention -`*-ai-inference.yml` under the use case root directory. - -An example configuration file is provided below, where the fields have been replaced with their respective description: - -```yaml -inference: - experiment_name: > - Unique name for an experiment, to group all similar - runs under the same experiment - run_id: Run ID in MLFlow server of pre-trained model - ckpt_path: model/checkpoints/best-checkpoint/best-checkpoint.ckpt - train_config_artifact_path: name of training config saved to MLFlow artifacts folder - type: > - can be 'lightning' or 'tf', depending whether the neural network is defined - using PyTorch Lightning or TensorFlow. - At the moment, only 'lightning' is supported. - - # Configuration format defined by PyTorch Lightning CLI - # https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html - conf: - # See discussion below - ... -``` - -Regarding the `inference.conf` field, same considerations hold as for `train.conf` field of ML training configuration. - -An example taken from -[MNIST use case](use-cases/mnist) located at `use-cases/mnist/mnist-ai-training.yml`: - -```yaml -inference: - type: lightning - experiment_name: MNIST classification lite - # Run ID in MLFlow server: pre-trained model - run_id: 54f790100be646e0a7ccbb1235729d00 - ckpt_path: model/checkpoints/best-checkpoint/best-checkpoint.ckpt - train_config_artifact_path: pl-training.yml - conf: - # Lightning data module configuration - data: - class_path: itwinai.plmodels.mnist.MNISTDataModule - init_args: - data_dir: ${cli.input_dataset} - batch_size: 32 -``` - -### Accessing CLI args from config file - -As explained above, train and predict commands in itwinai CLI receive as input -specific configuration files: - -- The `train` command receives `*-ai-train.yml` as configuration. -- The `predict` command receives `*-ai-inference.yml` as configuration. - -With [OmegaConf](https://omegaconf.readthedocs.io/)'s -[variable interpolation](https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#variable-interpolation) -you can access the args from the itwinai CLI command from the configuration file -associated with this command. - -Example: the field `data_dir: ${cli.input_dataset}` in the above configuration -accesses the value of `--input-dataset` argument of `itwinai predict` command. - -### ML framework: PyTorch vs. TensorFlow - -At the moment, only PyTorch are supported. TensorFlow support is planned for -future releases. - -## Run the workflow - -Once a workflow has been configured, it can be run by executing `run-workflow.py` in the root of this repo: - -```bash -micromamba run -p ./.venv python run-workflow.py -f WORKFLOW_DEFINITION_FILE -``` - -This script performs two main actions: - -1. Deploy ste steps of a workflow as python environments, managed with Conda. -2. Run a workflow step-by-step, following the directives given in the config file. - -See some examples of workflow executions in `examples.sh`, for instance: - -```bash -# Run workflow for MNIST toy use case -micromamba run -p ./.venv python run-workflow.py -f ./use-cases/mnist/training-workflow.yml -``` - - - -## Write tests cases - -Integrating an new use case means defining new workflows for it. -It is strongly suggested to define "integration" test cases for -those workflows. This way, every time `itwinai` -framework is updated, integration tests automatically verify that -the use case integrates well with the new changes introduced in the -main framework. -Moreover, integration tests verify that an use case case is stable, -and is not hiding some "bug". - -Add test for your use case under the `test/` folder. 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    \ No newline at end of file diff --git a/docs_old/Gemfile b/docs_old/Gemfile deleted file mode 100644 index 387154f8..00000000 --- a/docs_old/Gemfile +++ /dev/null @@ -1,7 +0,0 @@ -source 'https://rubygems.org' - -gem "jekyll", "~> 4.3" # installed by `gem jekyll` -# gem "webrick" # required when using Ruby >= 3 and Jekyll <= 4.2.2 - -gem "just-the-docs", "0.5.0" # pinned to the current release -# gem "just-the-docs" # always download the latest release diff --git a/docs_old/Gemfile.lock b/docs_old/Gemfile.lock deleted file mode 100644 index 81efc419..00000000 --- a/docs_old/Gemfile.lock +++ /dev/null @@ -1,79 +0,0 @@ -GEM - remote: https://rubygems.org/ - specs: - addressable (2.8.1) - public_suffix (>= 2.0.2, < 6.0) - colorator (1.1.0) - concurrent-ruby (1.1.10) - em-websocket (0.5.3) - eventmachine (>= 0.12.9) - http_parser.rb (~> 0) - eventmachine (1.2.7) - ffi (1.15.5) - forwardable-extended (2.6.0) - http_parser.rb (0.8.0) - i18n (1.12.0) - concurrent-ruby (~> 1.0) - jekyll (4.3.0) - addressable (~> 2.4) - colorator (~> 1.0) - em-websocket (~> 0.5) - i18n (~> 1.0) - jekyll-sass-converter (>= 2.0, < 4.0) - jekyll-watch (~> 2.0) - kramdown (~> 2.3, >= 2.3.1) - kramdown-parser-gfm (~> 1.0) - liquid (~> 4.0) - mercenary (>= 0.3.6, < 0.5) - pathutil (~> 0.9) - rouge (>= 3.0, < 5.0) - safe_yaml (~> 1.0) - terminal-table (>= 1.8, < 4.0) - webrick (~> 1.7) - jekyll-sass-converter (2.2.0) - sassc (> 2.0.1, < 3.0) - jekyll-seo-tag (2.8.0) - jekyll (>= 3.8, < 5.0) - jekyll-watch (2.2.1) - listen (~> 3.0) - just-the-docs (0.5.0) - jekyll (>= 3.8.5) - jekyll-seo-tag (>= 2.0) - rake (>= 12.3.1) - kramdown (2.4.0) - rexml - kramdown-parser-gfm (1.1.0) - kramdown (~> 2.0) - liquid (4.0.3) - listen (3.7.1) - rb-fsevent (~> 0.10, >= 0.10.3) - rb-inotify (~> 0.9, >= 0.9.10) - mercenary (0.4.0) - pathutil (0.16.2) - forwardable-extended (~> 2.6) - public_suffix (5.0.0) - rake (13.0.6) - rb-fsevent (0.11.2) - rb-inotify (0.10.1) - ffi (~> 1.0) - rexml (3.2.5) - rouge (4.0.0) - safe_yaml (1.0.5) - sassc (2.4.0) - ffi (~> 1.9) - terminal-table (3.0.2) - unicode-display_width (>= 1.1.1, < 3) - unicode-display_width (2.3.0) - webrick (1.7.0) - -PLATFORMS - arm64-darwin-21 - x86_64-darwin-19 - x86_64-linux - -DEPENDENCIES - jekyll (~> 4.3) - just-the-docs (= 0.5.0) - -BUNDLED WITH - 2.3.9 diff --git a/docs_old/LICENSE b/docs_old/LICENSE deleted file mode 100644 index 7d510d02..00000000 --- a/docs_old/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 just-the-docs - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/docs_old/README.md b/docs_old/README.md deleted file mode 100644 index 4a405acd..00000000 --- a/docs_old/README.md +++ /dev/null @@ -1,181 +0,0 @@ -# just-the-docs-template - -This is a *bare-minimum* template to create a [Jekyll] site that: - -- uses the [Just the Docs] theme; -- can be built and published on [GitHub Pages]; -- can be built and previewed locally, and published on other platforms. - -More specifically, the created site: - -- uses a gem-based approach, i.e. uses a `Gemfile` and loads the `just-the-docs` gem; -- uses the [GitHub Pages / Actions workflow] to build and publish the site on GitHub Pages. - -To get started with creating a site, just click "[use this template]"! - -If you want to maintain your docs in the `docs` directory of an existing project repository, see -[Hosting your docs from an existing project repository](#hosting-your-docs-from-an-existing-project-repository). - -After completing the creation of your new site on GitHub, update it as needed: - -## Replace the content of the template pages - -Update the following files to your own content: - -- `index.md` (your new home page) -- `README.md` (information for those who access your site repository on GitHub) - -## Changing the version of the theme and/or Jekyll - -Simply edit the relevant line(s) in the `Gemfile`. - -## Adding a plugin - -The Just the Docs theme automatically includes the [`jekyll-seo-tag`] plugin. - -To add an extra plugin, you need to add it in the `Gemfile` *and* in `_config.yml`. For example, to add [`jekyll-default-layout`]: - -- Add the following to your site's `Gemfile`: - - ```ruby - gem "jekyll-default-layout" - ``` - -- And add the following to your site's `_config.yml`: - - ```yaml - plugins: - - jekyll-default-layout - ``` - -Note: If you are using a Jekyll version less than 3.5.0, use the `gems` key instead of `plugins`. - -## Publishing your site on GitHub Pages - -1. If your created site is `YOUR-USERNAME/YOUR-SITE-NAME`, update `_config.yml` to: - - ```yaml - title: YOUR TITLE - description: YOUR DESCRIPTION - theme: just-the-docs - - url: https://YOUR-USERNAME.github.io/YOUR-SITE-NAME - - aux_links: # remove if you don't want this link to appear on your pages - Template Repository: https://github.com/YOUR-USERNAME/YOUR-SITE-NAME - ``` - -2. Push your updated `_config.yml` to your site on GitHub. - -3. In your newly created repository on GitHub: - - go to the `Settings` tab -> `Pages` -> `Build and deployment`, then select `Source`: `GitHub Actions`. - - if there were any failed Actions, go to the `Actions` tab and click on `Re-run jobs`. - -## Building and previewing your site locally - -Assuming [Jekyll] and [Bundler] are installed on your computer: - -1. Change your working directory to the root directory of your site. - -2. Run `bundle install`. - -3. Run `bundle exec jekyll serve` to build your site and preview it at `localhost:4000`. - - The built site is stored in the directory `_site`. - -## Publishing your built site on a different platform - -Just upload all the files in the directory `_site`. - -## Customization - -You're free to customize sites that you create with this template, however you like! - -[Browse our documentation][Just the Docs] to learn more about how to use this theme. - -## Hosting your docs from an existing project repository - -You might want to maintain your docs in an existing project repository. Instead of creating a new repository using -the [just-the-docs template](https://github.com/just-the-docs/just-the-docs-template), you can copy the template -files into your existing repository and configure the template's GitHub Actions workflow to -build from a `docs` directory. You can clone the template to your local machine or download the `.zip` file -to access the files. - -### Copy the template files - -1. Create a `.github/workflows` directory at your project root if your repository doesn't already have one. -Copy the `pages.yml` file into this directory. GitHub Actions searches this directory for workflow files. - -2. Create a `docs` directory at your project root and copy all remaining template files into this directory. - -### Modify the GitHub Actions workflow - -The GitHub Actions workflow that builds and deploys your site to GitHub Pages is defined by the `pages.yml` file. -You'll need to edit this file to that so that your build and deploy steps look to your `docs` directory, -rather than the project root. - -1. Set the default `working-directory` param for the build job. - - ```yaml - build: - runs-on: ubuntu-latest - defaults: - run: - working-directory: docs - ``` - -2. Set the `working-directory` param for the Setup Ruby step. - - ```yaml - - name: Setup Ruby - uses: ruby/setup-ruby@v1 - with: - ruby-version: '3.1' - bundler-cache: true - cache-version: 0 - working-directory: '${{ github.workspace }}/docs' - ``` - -3. Set the path param for the Upload artifact step: - - ```yaml - - name: Upload artifact - uses: actions/upload-pages-artifact@v1 - with: - path: "docs/_site/" - ``` - -4. Modify the trigger so that only changes within the `docs` directory start the workflow. -Otherwise, every change to your project (even those that don't affect the docs) would trigger a new site build and deploy. - - ```yaml - on: - push: - branches: - - "main" - paths: - - "docs/**" - ``` - -## Licensing and Attribution - -This repository is licensed under the [MIT License]. You are generally free to reuse or extend upon this code as you -see fit; just include the original copy of the license (which is preserved when you "make a template"). -While it's not necessary, we'd love to hear from you if you do use this template, and how we can improve it for future use! - -The deployment GitHub Actions workflow is heavily based on GitHub's mixed-party [starter workflows]. -A copy of their MIT License is available in [actions/starter-workflows]. - ----- - -[Jekyll]: https://jekyllrb.com -[Just the Docs]: https://just-the-docs.github.io/just-the-docs/ -[GitHub Pages]: https://docs.github.com/en/pages -[GitHub Pages / Actions workflow]: https://github.blog/changelog/2022-07-27-github-pages-custom-github-actions-workflows-beta/ -[Bundler]: https://bundler.io -[use this template]: https://github.com/just-the-docs/just-the-docs-template -[`jekyll-default-layout`]: https://github.com/benbalter/jekyll-default-layout -[`jekyll-seo-tag`]: https://jekyll.github.io/jekyll-seo-tag -[MIT License]: https://en.wikipedia.org/wiki/MIT_License -[starter workflows]: https://github.com/actions/starter-workflows/blob/main/pages/jekyll.yml -[actions/starter-workflows]: https://github.com/actions/starter-workflows/blob/main/LICENSE diff --git a/docs_old/_config.yml b/docs_old/_config.yml deleted file mode 100644 index ba7498ac..00000000 --- a/docs_old/_config.yml +++ /dev/null @@ -1,26 +0,0 @@ -title: itwinai -description: Docs for task T6.5 prototype of interTwin project -theme: just-the-docs - -url: https://interTwin-eu.github.io/T6.5-AI-and-ML - -aux_links: - Template Repository: https://github.com/interTwin-eu/T6.5-AI-and-ML - -favicon_ico: "favicon.ico" - -nav_external_links: - - title: itwinai on GitHub - url: https://github.com/interTwin-eu/T6.5-AI-and-ML - hide_icon: false # set to true to hide the external link icon - defaults to false - - title: interTwin on GitHub - url: https://github.com/interTwin-eu/ - hide_icon: false # set to true to hide the external link icon - defaults to false - - title: interTwin project - url: https://www.intertwin.eu/ - hide_icon: false # set to true to hide the external link icon - defaults to false - -mermaid: - # Version of mermaid library - # Pick an available version from https://cdn.jsdelivr.net/npm/mermaid/ - version: "10.1.0" diff --git a/docs_old/docs/CLI.md b/docs_old/docs/CLI.md deleted file mode 100644 index a2383b46..00000000 --- a/docs_old/docs/CLI.md +++ /dev/null @@ -1,53 +0,0 @@ ---- -layout: default -title: CLI -nav_order: 3 ---- - -# Command-line interface (CLI) - - -The `itwinai` package provides a custom CLI, which can be accessed, for instance -from the development environment: - -```bash -# Activate development environment -micromamba activate ./.venv-dev - -# Access itwinai CLI -itwinai --help -``` - -## Visualization - -Some visualization functionalities offered by `itwinai` CLI. - -```bash -# Datasets registry -itwinai datasets --help - -# Workflows (any file '*-workflow.yml') -itwinai workflows --help -``` - -## Machine learning - -```bash -# Training -itwinai train --help - -# Launch MLFlow UI to visualize ML logs -itwinai mlflow-ui --help - -# Inference -itwinai predict --help -``` diff --git a/docs_old/docs/Concepts.md b/docs_old/docs/Concepts.md deleted file mode 100644 index 5830fcfe..00000000 --- a/docs_old/docs/Concepts.md +++ /dev/null @@ -1,35 +0,0 @@ ---- -layout: default -title: Concepts -nav_order: 4 ---- - -# Concepts - - -Here we presents the key concepts on which `itwinai` is based. - -## Workflow - -We define a workflow as a directed acyclic graph (DAG) of data processing -operations, in which each step can have multiple inputs and outputs, and -each input or output is a dataset. - -![image](img/Workflow%20DAG%20concept.png) - -In the picture above, the yellow boxes with numbers represent the steps -in the example workflow, whereas the blue cylinders represent data -(e.g., dataset, configuration file). - -Each step runs in *isolation* from the others, and data is the *interface*. -Isolation can be guaranteed by executing each step in a Docker container or -in a separate Python virtual environment. diff --git a/docs_old/docs/How-to-use-this-software.md b/docs_old/docs/How-to-use-this-software.md deleted file mode 100644 index 15a36b44..00000000 --- a/docs_old/docs/How-to-use-this-software.md +++ /dev/null @@ -1,540 +0,0 @@ ---- -layout: default -title: How to use this software -nav_order: 2 ---- - -# How to use this software -{: .no_toc } - -## Table of contents -{: .no_toc .text-delta } - -1. TOC -{:toc} - ---- - -This guide provides a detailed explanation on how to use the AI/ML workflow -tool, developed in the context of [interTwin](https://github.com/interTwin-eu/). - -**Target audience**: anyone aiming to simplify MLOps for their digital twin (DT) -use case/project. Use cases from interTwin project. - -## Clone this repo - -```bash -git clone git@github.com:interTwin-eu/T6.5-AI-and-ML.git -``` - -A new use case/project can be added under `./use-cases` folder. - -Build the workflow runner environment and development environment -following the instructions on the README file. - -## Define a DT workflow - -Before delving into workflow definition rules, make sure to have -understood *what is* a [workflow](./Concepts#workflow) in this context. - -You can define one or more workflows for your DT use case (e.g., ML training, -ML inference, other). A workflow is -defined through configuration files in the use case subfolder. -For the same use case, a DT developer can define multiple workflows, -in which multiple datasets are involved. - -Currently, each step is executed in an isolated Python virtual environment, -built according to [conda](https://docs.conda.io/en/latest/) standards. -In practice, it is built with -[Micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html), -which is faster. - -To begin with, you can start by looking at an example of the -[MNIST toy use case](use-cases/mnist), located at `use-cases/mnist` -in the code repository. - -### Use case metadata - -The main configuration file of an use case is `meta.yml`, which stores -the metadata of it. When creating a new use case, you need to update the -`root` field with the path to the use case folder, with respect to the -repo root. - -The datasets registry is a field in this configuration file, -which stores the metadata -for all datasets involved in a use case. This configuration provides a -unified place where datasets can be maintained, making it easy to access -them from other configuration files. - -The dataset registry has the format: - -```yaml -datasets: - some-dataset-name: - doc: Documentation string for this dataset - location: path/to/dataset/disk/location -``` - -Example of `meta.yml` from [MNIST use case](use-cases/mnist): - -```yaml -# Use case root location. End without path '/' char! -root: ./use-cases/mnist - -# AI folder location. End without path '/' char! -ai-root: ./ai - -# Datasets registry -datasets: - preproc-images: - doc: Preprocessed MNIST images - location: ${root}/data/preproc-images - ml-logs: - doc: MLflow tracking URI for local logging - location: ${root}/data/ml-logs - ml-predictions: - doc: predictions on unseen data - location: ${root}/data/ml-predictions -``` - -Datasets are imported from the datasets registry to other files by means -of [OmegaConf](https://omegaconf.readthedocs.io/)'s -[variable interpolation](https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#variable-interpolation). -This way, you can easily import datasets metadata (e.g., location on -file system) from datasets registry. - -Dataset registry of an use case can be visualized using [itwinai CLI](./CLI#visualization): - -```bash -USE_CASE_ROOT='use-cases/mnist/' -micromamba activate ./.venv-dev && \ - itwinai datasets --use-case $USE_CASE_ROOT -``` - -### Workflow configuration - -Use case workflows are defined with `*-workflow.yml` files in the use case root, -and there are two ways to define a workflow: - -- "Custom" format for workflow definition is an intuitive standard we created -for this prototype, for easy prototyping. -- [Common Workflow Language](https://www.commonwl.org/) (CWL), which is -currently under development, and not ready to be used. - -Which of the two is used is defined by setting the `--cwl` flag (explained -[below](#run-the-workflow)). - -#### Custom workflow definition - -To define a workflow with the custom format, the DT developer must follow -the structure provided below. - -The `steps` section defines the steps of the workflow, in the order in which -they have to be executed: - -```yaml -steps: - - some-step-name: - doc: Documentation string for this step - env: # micromamba environment metadata - file: some-conda-env.yml - prefix: path/to/conda/env/ - command: Command to execute inside micromamba env - args: # Command arguments. - # Note interpolation with datasets registry here! - some-arg: ${datasets.my-dataset.location} - some-other-arg: 42 - - next-step-name: - ... -``` - -Example workflow from [MNIST use case](use-cases/mnist), defined in -`training-workflow.yml`: - -```yaml -steps: - - preprocessing: - doc: Download and split MNIST dataset into train and test sets - command: python ${root}/mnist-preproc.py - env: - file: ${root}/env-files/preproc-env.yml - prefix: ${root}/.venv-preproc - args: - output: ${datasets.preproc-images.location} - stage: train - - ml-training: - doc: Train a neural network to classify MNIST images - command: itwinai train - env: - file: ${ai-root}/env-files/pytorch-lock.yml - prefix: ${ai-root}/.venv-pytorch - source: ${ai-root} - args: - train-dataset: ${datasets.preproc-images.location} - ml-logs: ${datasets.ml-logs.location} - config: ${root}/mnist-ai-train.yml -``` - -Step 1 is named `preprocessing` and uses the `mnist-preproc.py` script to pre-process the MNIST dataset. It takes no -input, generates an output dataset named `preproc-images`, and uses an environment defined in a YAML file named -`preproc-env.yml` located in the `./use-cases/mnist` directory. -Step 2 is named `ml-training` and trains a machine learning model using the preprocessed image data generated in -the first step. The training is performed using the train command from the `itwinai` tool. The input dataset is -`preproc-images`, and the output is `ml-logs`. The step uses an environment defined in a YAML file named -`pytorch-env.yml` located in the `./ai` directory. The machine learning model is configured using the `mnist-ai-train.yml` -file located in the `./use-cases/mnist` directory. - -#### CWL: under development and not ready to be used yet - -**NOTE**. At the moment, support for CWL is under development, -and is not available. - - - -## Implement workflow steps - -Implement use case-specific steps. -Note that the implementation of steps involving AI/ML are addressed in the next -step, and they can be implemented a bit more easily. - -Each step of a workflow is characterized by its python virtual environment -and a command to be executed in that -environment. A command can be implemented by providing, for instance, a python script to be executed in some environment. - -To execute a step, the workflow engine will run something like: - -```bash -micromamba run -p PATH_TO_STEP_ENV CMD --arg1 ARG_1_VAL ... --argN ARG_N_VAL -``` - -Where: - -- `PATH_TO_STEP_ENV` is the path to the micromamba environment for this step. -- `CMD` is the command to execute in that environment. -- The developer can use additional parameters which are automatically appended -to the command. - -*Example*: in the [MNIST use case](use-cases/mnist), -the preprocessing step is implemented by a python script, which downloads and -splits the MNIST dataset in a specific location. Using a command similar to: - -```bash -micromamba run -p ./use-cases/mnist/.venv-preproc \ - python ./use-cases/mnist/mnist-preproc.py \ - --output ./data/mnist/preproc-images-inference \ - --stage train -``` - -## Define AI/ML workflow - -AI/ML workflows are implemented by the `itwinai` module. -The DT developer, who wants to include a new use case, needs to provide -only a reduced amount of code to describe a neural network, plus some -configuration files. - -The developer must implement the neural network to train and include it inside -`itwinai` python package, under `ai/src/itwinai`. For instance, under -`ai/src/itwinai/plmodels` when using PyTorch Lightning. - -For instance, `LitMNIST` neural network used in [MNIST use case](use-cases/mnist) -has been added under `ai/src/itwinai/plmodels/mnist.py` - -Once a model has been included inside the `itwinai` python module, it can be imported during training. -In the future, `itwinai` will support also neural networks not provided out-of-the-box by `itwinai`. - -The developer must define two configuration files to access `itwinai` -functionalities. -First, ML training configuration, associated with `$ itwinai train` [CLI](./CLI) command. -Second, ML inference configuration, associated with `$ itwinai predict` [CLI](./CLI) command. - -MLOps heavily relies on commands provided by [itwinai CLI](./CLI). -Therefore, before continuing, make sure to have understood how -[itwinai CLI](./CLI) works. - -### ML training configuration - -ML training configuration is provided in a with naming convention `*-ai-train.yml` -under the use case root directory. - -An example configuration file is provided below, where the fields have been replaced with their respective description: - -```yaml -# Configuration file of AI workflows for X use case - -# Training configuration -train: - type: > - can be 'lightning' or 'tf', depending whether the neural network is defined - using PyTorch Lightning or TensorFlow. - At the moment, only 'lightning' is supported. - - # Configuration format defined by PyTorch Lightning CLI - # https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html - conf: - # See discussion below - ... - -# MLFlow logger configuration -logger: - experiment_name: > - Unique name for an experiment, to group all similar - runs under the same experiment - description: Description for this specific run. - log_every_n_epoch: how often to log (epochs) - log_every_n_steps: how often to log (steps, i.e., batches) - registered_model_name: > - Unique name used in Models Registry to identify an ML model. - If given, it is automatically registered in the Models Registry. -``` - -When using PyTorch Lightning (PL) ML framework, the training configuration is easy to define, as it follows the schema -pre-defined by lightning authors for the PL CLI. See its documentation -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#trainer-callbacks-and-arguments-with-class-type), -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#trainer-callbacks-and-arguments-with-class-type), -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#multiple-models-and-or-datasets), and -[here](https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#optimizers-and-learning-rate-schedulers). - -An example taken from -[MNIST use case](use-cases/mnist) located at `use-cases/mnist/mnist-ai-training.yml`: - -```yaml -# Pytorch lightning config for training -train: - type: lightning - # Follows lightning config file format: - # https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html#multiple-models-and-or-datasets - conf: - seed_everything: 4231162351 - - # Lightning Trainer configuration - trainer: - accelerator: auto - strategy: auto - devices: auto - num_nodes: 1 - precision: 32-true - - # MLFlow logger (initial) configuration. - # To be completed with run details, later on - logger: - class_path: lightning.pytorch.loggers.MLFlowLogger - init_args: - experiment_name: ${logger.experiment_name} - save_dir: ./mlruns - - # Callbacks - callbacks: - - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping - init_args: - monitor: val_loss - patience: 2 - - class_path: lightning.pytorch.callbacks.lr_monitor.LearningRateMonitor - init_args: - logging_interval: step - - class_path: lightning.pytorch.callbacks.ModelCheckpoint - init_args: - dirpath: checkpoints - filename: best-checkpoint - save_top_k: 1 - verbose: true - monitor: val_loss - mode: min - - max_epochs: 1 - - # Lightning Model configuration - model: - class_path: itwinai.plmodels.mnist.LitMNIST - init_args: - hidden_size: 64 - - # Lightning data module configuration - data: - class_path: itwinai.plmodels.mnist.MNISTDataModule - init_args: - data_dir: ${cli.train_dataset} - batch_size: 32 - - # Torch Optimizer configuration - optimizer: - class_path: torch.optim.AdamW - init_args: - lr: 0.001 - - # Torch LR scheduler configuration - lr_scheduler: - class_path: torch.optim.lr_scheduler.ExponentialLR - init_args: - gamma: 0.1 - -# Mlflow -logger: - experiment_name: MNIST classification lite - description: A MLP classifier for MNIST dataset. - log_every_n_epoch: 1 - log_every_n_steps: 1 - # Name used in Models Registry. If given, it is automatically - # registered in the Models Registry. - registered_model_name: MNIST-clf-lite -``` - -Note the field `data_dir: ${cli.train_dataset}` in the above configuration. -More on this later. - -### ML inference configuration - -ML training configuration is provided in a with naming convention -`*-ai-inference.yml` under the use case root directory. - -An example configuration file is provided below, where the fields have been replaced with their respective description: - -```yaml -inference: - experiment_name: > - Unique name for an experiment, to group all similar - runs under the same experiment - run_id: Run ID in MLFlow server of pre-trained model - ckpt_path: model/checkpoints/best-checkpoint/best-checkpoint.ckpt - train_config_artifact_path: name of training config saved to MLFlow artifacts folder - type: > - can be 'lightning' or 'tf', depending whether the neural network is defined - using PyTorch Lightning or TensorFlow. - At the moment, only 'lightning' is supported. - - # Configuration format defined by PyTorch Lightning CLI - # https://pytorch-lightning.readthedocs.io/en/1.6.5/common/lightning_cli.html - conf: - # See discussion below - ... -``` - -Regarding the `inference.conf` field, same considerations hold as for `train.conf` field of ML training configuration. - -An example taken from -[MNIST use case](use-cases/mnist) located at `use-cases/mnist/mnist-ai-training.yml`: - -```yaml -inference: - type: lightning - experiment_name: MNIST classification lite - # Run ID in MLFlow server: pre-trained model - run_id: 54f790100be646e0a7ccbb1235729d00 - ckpt_path: model/checkpoints/best-checkpoint/best-checkpoint.ckpt - train_config_artifact_path: pl-training.yml - conf: - # Lightning data module configuration - data: - class_path: itwinai.plmodels.mnist.MNISTDataModule - init_args: - data_dir: ${cli.input_dataset} - batch_size: 32 -``` - -### Accessing CLI args from config file - -As explained above, train and predict commands in itwinai CLI receive as input -specific configuration files: - -- The `train` command receives `*-ai-train.yml` as configuration. -- The `predict` command receives `*-ai-inference.yml` as configuration. - -With [OmegaConf](https://omegaconf.readthedocs.io/)'s -[variable interpolation](https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#variable-interpolation) -you can access the args from the itwinai CLI command from the configuration file -associated with this command. - -Example: the field `data_dir: ${cli.input_dataset}` in the above configuration -accesses the value of `--input-dataset` argument of `itwinai predict` command. - -### ML framework: PyTorch vs. TensorFlow - -At the moment, only PyTorch are supported. TensorFlow support is planned for -future releases. - -## Run the workflow - -Once a workflow has been configured, it can be run by executing `run-workflow.py` in the root of this repo: - -```bash -micromamba run -p ./.venv python run-workflow.py -f WORKFLOW_DEFINITION_FILE -``` - -This script performs two main actions: - -1. Deploy ste steps of a workflow as python environments, managed with Conda. -2. Run a workflow step-by-step, following the directives given in the config file. - -See some examples of workflow executions in `examples.sh`, for instance: - -```bash -# Run workflow for MNIST toy use case -micromamba run -p ./.venv python run-workflow.py -f ./use-cases/mnist/training-workflow.yml -``` - - - -## Write tests cases - -Integrating an new use case means defining new workflows for it. -It is strongly suggested to define "integration" test cases for -those workflows. This way, every time `itwinai` -framework is updated, integration tests automatically verify that -the use case integrates well with the new changes introduced in the -main framework. -Moreover, integration tests verify that an use case case is stable, -and is not hiding some "bug". - -Add test for your use case under the `test/` folder. 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zb;2lA+h%5G@d-B&L7;J$f%00qe+1HDsxrOE7(1UgG!%90$;DRldu_Aqo9`^bYXl_Y zGn4wWB+f~SeZhe^Igzib)DPgb0PDJczicb7!^bmHA_ z_Zl%G`AU3=9C%BE8H}@BZsO@GUd5y@&?X4~DV~^Z#_Ka3F;$G0*>2=&@lw@cHzT$^ hCD>8?-@6E{seF`-B-0hx*FgOGX-)kT`RbQ~{y!RhR7L;* diff --git a/docs_old/docs/use-cases/index.md b/docs_old/docs/use-cases/index.md deleted file mode 100644 index 681caeb6..00000000 --- a/docs_old/docs/use-cases/index.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -layout: default -title: Use cases -nav_order: 4 -has_children: true ---- - -# Use cases - -Digital twin use cases employing machine learning workflows, currently integrated -in this prototype. -To integrate a new use case, please refer to [this page](../How-to-use-this-software). diff --git a/docs_old/docs/use-cases/mnist.md b/docs_old/docs/use-cases/mnist.md deleted file mode 100644 index e22f7ab1..00000000 --- a/docs_old/docs/use-cases/mnist.md +++ /dev/null @@ -1,185 +0,0 @@ ---- -layout: default -title: MNIST -parent: Use cases -nav_order: 1 ---- - -# MNIST: toy example for DT workflows -{: .no_toc } - -## Table of contents -{: .no_toc .text-delta } - -1. TOC -{:toc} - ---- - -Of course MNIST images classification is not a digital twin. Still, it is useful to -provide an example on how to define an end-to-end digital twin workflow with the -software provided in this repository. - -The MNIST use case implements two workflows: - -1. Training workflow: train a neural network to classify MNIST images, and save the trained -neural network to the Models Registry. - - ```mermaid - flowchart LR - %% Nodes - preproc(Pre-processing) - ai(ML training) - reg[(Models Registry:\npre-trained ML models)] - - %% Workflow - preproc --> ai - - %% Connections - ai -.-> |Saves to| reg - ``` - - This workflow is executed by running the command: - - ```bash - micromamba run -p ./.venv python run-workflow.py -f ./use-cases/mnist/training-workflow.yml - ``` - -1. Inference workflow: use the pre-trained neural network to classify unseen images (the test set, in this case). - - ```mermaid - flowchart LR - %% Nodes - preproc(Pre-processing) - ai_depl(ML inference) - pred[(Predictions)] - - %% Workflow - preproc --> ai_depl - - %% Connections - ai_depl -.-> |Saves to| pred - ``` - - This workflow is executed by running the command: - - ```bash - micromamba run -p ./.venv python run-workflow.py -f ./use-cases/mnist/inference-workflow.yml - ``` - -The interactions among workflows and their steps can be described in more details as the following, where conceptual ordering -among different workflow steps is represented by solid arrows: - -```mermaid -graph TD - %% Nodes - remote_repo[(Remote repo)] - preproc(Pre-processing) - ai(ML training) - ai_depl(ML inference) - train_set[(Train dataset)] - test_set[(Test dataset)] - ml_logs[(ML logs)] - reg[(Models Registry:\npre-trained ML models)] - pred[(Predictions)] - - %% Workflow - preproc --> ai ---> ai_depl - - %% Connections - preproc -.-> |Fetches| remote_repo - preproc -.-> |Stores| train_set - preproc -.-> |Stores| test_set - ai -.-> |Trains/validates model| train_set - ai -.-> |Tests model| test_set - ai -.-> |Stores model to| reg - ai -.-> |Logs| ml_logs - ai_depl -.-> |Loads from| reg - ai_depl -.-> |Predict from| test_set - ai_depl -.-> |Stores| pred -``` - -## Workflow steps - -Here we explain in more details how the workflow steps have been configured. -Configuration files and Python scripts are organized under `use-cases/mnist/` -folder, in the core repository. - -### Pre-processing - -This step is implemented by executing `mnist-preproc.py` script in its dedicated micromamba environment, defined by -`preproc-env.yml`. This solution gives full freedom to the DT developer to implement any preprocessing logic, adaptable -to any custom dataset format. - -### ML training - -Is the step in which a neural network is trained on the training dataset, and -validated on the validation dataset. -The mentioned datasets are a result from a split of the pre-processed training -dataset, produced by the pre-processing step. -This step completes the **training workflow**, and results into ML logs and a -trained neural network, which is saved to -the Models Registry. The training workflow can be re-run multiple times with different (hyper)parameters, with the goal -of optimizing some ML validation metric. The neural network with the best validation performances is used to make -predictions on unseen data, in the inference step. - -ML training logic is implemented by the `itwinai` library, requiring the DT developer to produce only a set of YAML -configuration files. For the moment, we assume that the neural network and the training code is already present in -`itwinai` library. - -Both ML training and inference are implemented by commands executed in the same virtual environment. At the moment, -only PyTorch is supported. The corresponding virtual environment definition, used by the `itwinai` library, -is located at `ai/pytorch-env.yml`. - -The ML training logic provided by `itwinai` library is accessed via the -[itwinai CLI](../CLI). - -The DT developer must provide a training configuration file, following some -rules explained in [this section](../How-to-use-this-software#ml-training-configuration). For MNIST use case, the -training configuration is provided by `mnist-ai-train.yml` file. - -Training command is automatically managed by the workflow runner, but it can also -be triggered from withing the ai environment running the following command: - -```bash -micromamba activate ./ai/.venv-pytorch && \ - itwinai train --train-dataset $TRAINING_DATASET_PATH \ - --ml-logs $MLFLOW_TRACKING_URI \ - --config ./use-cases/mnist/mnist-ai-train.yml -``` - -While training is running, the produced ML logs can be inspected in real-time from MLFlow UI by running the command in -the training virtual environment (Conda): - -```bash -micromamba activate ./ai/.venv-pytorch && \ - itwinai mlflow-ui --path $PATH_TO_ML_LOGS -``` - -### ML inference - -A pre-trained neural network is applied to a set of data which was not used to train it. In fact, this is defined as -"unseen" data, from the neural network perspective. An example of ML inference is the application of a trained neural -network to make predictions on new data, to support decision making. *Example*: forecast fire risk maps in Sicily in -August 2023, starting from newly-collected satellite images, to alert local authorities in case of elevated fire risk. - -To select a pre-trained ML model, the DT developer must retrieve the `RUN ID` of -the training run created by MLFLow for some specific training. - -The, the DT developer can update the inference configuration file -`mnist-ai-inference.yml` and run inference workflow. - -Inference/prediction command is automatically managed by the workflow runner, but it can also be triggered from within -the ai environment running the following command: - -```bash -micromamba activate ./ai/.venv-pytorch && \ - itwinai predict --input-dataset $UNSEEN_EXAMPLES_DATASET_PATH \ - --predictions-location $PREDICTIONS_LOCATION \ - --ml-logs $PATH_TO_ML_LOGS \ - --config ./use-cases/mnist/mnist-ai-inference.yml -``` - -## References - -To learn more on how to use this software, e.g., to deploy a new use case, please refer to [this guide](../How-to-use-this-software). diff --git a/docs_old/favicon.ico b/docs_old/favicon.ico deleted file mode 100644 index 1665919207a78e8012a58e4425fd3fb90f51c3f4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 15406 zcmeHNU1(fI6rR~MB$33l52nTjS+R)~MN*bZkd`ipL`oD96-sF-rKz?rqDAaO!TL}e z4Q*Al6#q6=@Q)&GL{P+neUQFbL8`GTt)eqv`yT1xmezvd+#-eTPzCU8mF%w1EgD z(&b2k(o!_s5B?E6nPEP&jCEerfQr|_(_ryn*0BxST8a6iU@tjmIon!wP^YtvU!F1~ z_Gt#Q)}rR$AgZ<@E8o!2AZ=}JqFFa%hcafY(&gP>zk6U$^aTe9oBI!!N{BWEk>Rgiowq@C}#YMoj;gQ`J@?>psDd6$`|XuMhA7y_@Wt?nbhn3 zp}ZmElg2kV$9(pCe~o-zDc6}OI1c9eR03*-jy2#nz{kNOVD_8LXO^*!ZP>O{bPE(E zz!Tu0KESq=p=_aa4Fvaqr@%q&I@?i}I;blj!fv5XfSvjZ%~Kb32B8=lod*|c%S(T z?csXIccPbX6a|}+NJKVo-Yln2pU&>%d>9=amHz&IX=-Zn!a z_#ySY@m=}g;72yQ&dkuzkStoXC|7@a4AuYx*KB)zq(=1CBeUDi#6eFUz3McXlRtLs znAFtN*lnb3n!UtUr(+KXvduA=_sW?3a^>$Fo6dsOt5@4>Wcqv@Dfcd0o$h@vuzh0K zef&?Sxt#o=Lx*fOGB(W~YOmY=O?BDjf%G$5?_|pe${jae9 zRr~$p8er>xDqp$vr`qc;x|@m>bH~7pOL_Mn+OPKhOAOVFKWM+&@fR^vGybG~QZ?i6 zG=lUvlrk31=ljE8pg+(ji-}7qOC4_UHi`og-^~QrJw`NZggq_A_oow_i4{%EWMYSM ze}6l1{HyXjC&3Y|S`RMKISDl7Py{6lz&C??zF)(#jf}$`o06qieSzA!NUhSxZx(2{ea5+drDQ*eW zQ{W`n3rn%()J+?-MVqCfI23&bF2#8J^^rDcD-JG}I0lb`&w;s~`q?g3J#En@Z5KKZ z^^ko6Tuy)S>o08+1F_^o>@(#qg8giiyPg<`C7*fVyj64Dont~Q#NnX8X<0ZyVj6nk9*7etlzV&X=cdZw_qE{lf-M)~Y&V0ESc#d$_?0oBzu(WR6Mf@S(Q9ABef=M~XQ6vWB(mv4(YOy#b&Xj} zjg5`6a^*@H930Hv?OP9G$H&Lzz<~o2i^Zg_t}ZJFOkU5#O3cK*0L(bnt8Kcx`Fhd! z?-1QLoQpeiW^T^$b^kfhkL*qTuYvz#*tc(=jE#+1A-g`0jEu;xUAv^UwRLtZX{^Ld z?EH>6J1hPM_{y67qV`_&#cukbUc4mg@y}%N^Dkv;YRVO_TjAv7q$CoFAU;LR=DW$e zzd9oNo~@!@x)L0_Wj-GIWI~emzjV04wv|m!Ps_G#+fsKxvySV`H=if|$W6;c_n$2Y zJLjTle^TE0VM+igFQUJ7b#-~-%Ji!bDZfeG+ul+rx(s z`^|y*)^^38x>J1i`=ar;zL}JtNB!@joD(!WJS-g@9j@3iW9~dBu(j2J+3aUSSP=fIV`6DY;~owdJlWVkN*pOF^7&(Q{L#lfW_ z&I8w0?hobkC+emR+M>;JP!viBz>JqlF(1@HT?61KxDv$JBL)uR|2M4TW6s~rIWWIP Y&6%F{T!YVZ>Hjm Beware! As the code is frequently changed, the docs are unstable and may not reflect the actual state of the code base. -> Therefore, if you are looking for a more stable version, check out our -> [releases](https://github.com/interTwin-eu/T6.5-AI-and-ML/releases). - -Additional resources include: - -- Detailed instructions on [How to use this software](docs/How-to-use-this-software). -- Roadmap towards a prototype for T6.5 AI workflows for -digital twins here: [Prototype for T6.5](https://github.com/interTwin-eu/T6.5-AI-and-ML/wiki/Prototype-for-T6.5). - -## Platform for machine learning workflows in digital twins - -The goal of this platform is to provide ML researchers with an easy-to-use endpoint -to manage general-purpose machine learning (ML) workflows, with limited engineering overhead, -while providing state-of-the-art MLOps best practices. - -We call this platform `itwinai`. - -The user is going to provide as input a set of configuration files, to fully -describe ML workflows, in the context of digital twin (DT) applications. -`itwinai` platform instantiates ML workflows with the configurations -provided by the DT developer. The execution of ML workflows produces as output a -set of ML metrics, which are visualized by `itwinai` via -[MLFlow](https://mlflow.org/). -As a result of ML training, the best model (on validation dataset) -is saved to the *Models Registry* for future predictions. - -![image](docs/img/user-platform%20interaction%20full.png) - -### Simulating a whole DT workflow - -A DT workflow is more than ML. Generally speaking, MLOps -(i.e., ML model lifecycle management), -can be considered just as a step of a larger DT workflow. - -![image](docs/img/cwl-workflow.png) - -In `itwinai` platform, we focus mainly on the MLOps step, simulating or oversimplifying all the rest -(e.g., pre-processing, authentication, workflow execution). - -For further details on how to define a DT workflow in `itwinai`, follow [this guide](docs/How-to-use-this-software#2-define-a-dt-workflow). - -### How to integrate a new use case - -To integrate an existing use case in this platform, the ML engineer rewrites -the ML experiments according to a format supported by `itwinai`. -Some examples can be found by looking at the use cases -already integrated [here](docs/use-cases/index), located under `use-cases/` -in the code repository. - -A detailed guide on how to integrate a new use case in `itwinai` can be found [here](docs/How-to-use-this-software). From 8dd0a91c4a4e2ec709a401638e81bca908e59d62 Mon Sep 17 00:00:00 2001 From: zoechbauer1 Date: Fri, 16 Feb 2024 16:51:15 +0100 Subject: [PATCH 07/59] fixed distributed trainer in cyclones use case --- use-cases/cyclones/cyclones_vgg.py | 176 ++++++++++++++--------------- use-cases/cyclones/trainer.py | 11 +- 2 files changed, 94 insertions(+), 93 deletions(-) diff --git a/use-cases/cyclones/cyclones_vgg.py b/use-cases/cyclones/cyclones_vgg.py index 79e4136d..a2272505 100644 --- a/use-cases/cyclones/cyclones_vgg.py +++ b/use-cases/cyclones/cyclones_vgg.py @@ -582,94 +582,94 @@ def VGG_V4(patch_size, label_no_cyclone, channels, activation, regularizer): """ -# def ModelV5(patch_size, channels, last_activation, kernel_size=3): -# # kernel initializer -# initializer = tf.random_normal_initializer(0.0, 0.02) - -# # input layer -# inputs = tf.keras.layers.Input(shape=(patch_size, patch_size, -# channels[0])) - -# conv_blocks = [ -# ConvBlock( -# filters=32, -# initializer=initializer, -# kernel_size=kernel_size, -# strides=2, -# apply_batchnorm=True, -# apply_dropout=False, -# apply_gaussian_noise=True, -# ), -# ConvBlock( -# filters=64, -# initializer=initializer, -# kernel_size=kernel_size, -# strides=2, -# apply_batchnorm=False, -# apply_dropout=False, -# apply_gaussian_noise=False, -# ), -# ConvBlock( -# filters=128, -# initializer=initializer, -# kernel_size=3, -# strides=2, -# apply_batchnorm=False, -# apply_dropout=True, -# apply_gaussian_noise=False, -# ), -# ConvBlock( -# filters=256, -# initializer=initializer, -# kernel_size=3, -# strides=2, -# apply_batchnorm=False, -# apply_dropout=False, -# apply_gaussian_noise=True, -# ), -# ConvBlock( -# filters=512, -# initializer=initializer, -# kernel_size=3, -# strides=2, -# apply_batchnorm=False, -# apply_dropout=False, -# apply_gaussian_noise=False, -# ), -# ConvBlock( -# filters=1024, -# initializer=initializer, -# kernel_size=3, -# strides=2, -# apply_batchnorm=True, -# apply_dropout=True, -# apply_gaussian_noise=False, -# ), -# ] -# x = inputs -# for block in conv_blocks: -# x = block(x) - -# x = tf.keras.layers.Flatten()(x) -# x = tf.keras.layers.Dense( -# units=1024, activation="relu", kernel_initializer=initializer -# )(x) -# x = tf.keras.layers.Dense( -# units=512, activation="relu", kernel_initializer=initializer -# )(x) -# x = tf.keras.layers.Dense( -# units=256, activation="relu", kernel_initializer=initializer -# )(x) -# x = tf.keras.layers.Dense( -# units=128, activation="relu", kernel_initializer=initializer -# )(x) - -# outputs = tf.keras.layers.Dense( -# channels[1], activation=last_activation, -# kernel_initializer=initializer -# )(x) - -# return tf.keras.Model(inputs=inputs, outputs=outputs, name="model_V5") +def ModelV5(patch_size, channels, last_activation, kernel_size=3): + # kernel initializer + initializer = tf.random_normal_initializer(0.0, 0.02) + + # input layer + inputs = tf.keras.layers.Input(shape=(patch_size, patch_size, +channels[0])) + + conv_blocks = [ + ConvBlock( + filters=32, + initializer=initializer, + kernel_size=kernel_size, + strides=2, + apply_batchnorm=True, + apply_dropout=False, + apply_gaussian_noise=True, + ), + ConvBlock( + filters=64, + initializer=initializer, + kernel_size=kernel_size, + strides=2, + apply_batchnorm=False, + apply_dropout=False, + apply_gaussian_noise=False, + ), + ConvBlock( + filters=128, + initializer=initializer, + kernel_size=3, + strides=2, + apply_batchnorm=False, + apply_dropout=True, + apply_gaussian_noise=False, + ), + ConvBlock( + filters=256, + initializer=initializer, + kernel_size=3, + strides=2, + apply_batchnorm=False, + apply_dropout=False, + apply_gaussian_noise=True, + ), + ConvBlock( + filters=512, + initializer=initializer, + kernel_size=3, + strides=2, + apply_batchnorm=False, + apply_dropout=False, + apply_gaussian_noise=False, + ), + ConvBlock( + filters=1024, + initializer=initializer, + kernel_size=3, + strides=2, + apply_batchnorm=True, + apply_dropout=True, + apply_gaussian_noise=False, + ), + ] + x = inputs + for block in conv_blocks: + x = block(x) + + x = tf.keras.layers.Flatten()(x) + x = tf.keras.layers.Dense( + units=1024, activation="relu", kernel_initializer=initializer + )(x) + x = tf.keras.layers.Dense( + units=512, activation="relu", kernel_initializer=initializer + )(x) + x = tf.keras.layers.Dense( + units=256, activation="relu", kernel_initializer=initializer + )(x) + x = tf.keras.layers.Dense( + units=128, activation="relu", kernel_initializer=initializer + )(x) + + outputs = tf.keras.layers.Dense( + channels[1], activation=last_activation, +kernel_initializer=initializer + )(x) + + return tf.keras.Model(inputs=inputs, outputs=outputs, name="model_V5") """ diff --git a/use-cases/cyclones/trainer.py b/use-cases/cyclones/trainer.py index 2fb3c1bc..1c47819b 100644 --- a/use-cases/cyclones/trainer.py +++ b/use-cases/cyclones/trainer.py @@ -44,10 +44,10 @@ def __init__( self.regularization_strength, self.regularizer = ( regularization_strength.value ) - self.loss_name, self.loss = loss.value - # Optimizers, Losses - self.optimizer = keras.optimizers.Adam(learning_rate=learning_rate) + # Loss name and learning rate + self.loss_name = loss.value + self.learning_rate = learning_rate # Parse global config self.setup_config(self.global_config) @@ -86,9 +86,10 @@ def execute(self, train_data, validation_data, channels) -> None: logging.debug( f"Model loaded from backup at {self.best_model_name}") + optimizer = keras.optimizers.Adam(learning_rate=self.learning_rate) metrics = [keras.metrics.MeanAbsoluteError(name="mae")] - model.compile(loss=self.loss, - optimizer=self.optimizer, metrics=metrics) + model.compile(loss=self.loss_name, + optimizer=optimizer, metrics=metrics) logging.debug("Model compiled") # print model summary to check if model's architecture is correct From 98acbfc4baa3906a61b067c687635c86eaeca60e Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Mon, 26 Feb 2024 15:01:53 +0100 Subject: [PATCH 08/59] adding installation instructions in docs --- docs/getting_started_with_itwinai.rst | 138 ++++++++++++++++++++++++++ docs/index.rst | 23 ++++- 2 files changed, 160 insertions(+), 1 deletion(-) create mode 100644 docs/getting_started_with_itwinai.rst diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst new file mode 100644 index 00000000..4ba53df3 --- /dev/null +++ b/docs/getting_started_with_itwinai.rst @@ -0,0 +1,138 @@ +Getting started with itwinai +============================ + +Installation +------------ + +Requirements: ++++++++++++++ + +* Linux environment. +* Windows and macOS were never tested. + + +Micromamba installation ++++++++++++++++++++++++ + +To manage Conda environments we use micromamba, a light weight version of conda. + +It is suggested to refer to the `Manual installation guide `_. + +Consider that Micromamba can eat a lot of space when building environments because packages are cached on +the local filesystem after being downloaded. To clear cache you can use `micromamba clean -a`. +Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage +space and it would be cleverer to install Micromamba in another location with more storage space. +Thus by changing the `$MAMBA_ROOT_PREFIX` variable. See a complete installation example for Linux below, where the +default `$MAMBA_ROOT_PREFIX` is overridden: + + +.. code-block:: bash + + cd $HOME + + # Download micromamba (This command is for Linux Intel (x86_64) systems. Find the right one for your system!) + curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba + + # Install micromamba in a custom directory + MAMBA_ROOT_PREFIX='my-mamba-root' + ./bin/micromamba shell init $MAMBA_ROOT_PREFIX + + # To invoke micromamba from Makefile, you need to add explicitly to $PATH + echo 'PATH="$(dirname $MAMBA_EXE):$PATH"' >> ~/.bashrc + +**Reference**: `Micromamba installation guide `_. + + +Workflow orchestrator ++++++++++++++++++++++ + +Install the (custom) orchestrator virtual environment. + +.. code-block:: bash + + source ~/.bashrc + # Create local env + make + + # Activate env + micromamba activate ./.venv + +To run tests on workflows use: + +.. code-block:: bash + + # Activate env + micromamba activate ./.venv + + pytest tests/ + + +Development env setup +--------------------- + +Requirements: + +* Linux, macOS environment. Windows was never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +Installation: + +.. code-block:: bash + + make dev-env + + # Activate env + micromamba activate ./.venv-dev + +To run tests on itwinai package: + +.. code-block:: bash + + # Activate env + micromamba activate ./.venv-dev + + pytest tests/ai/ + + +AI environment setup +-------------------- + +Requirements: + +* Linux, macOS environment. Windows was never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +**NOTE**: this environment gets automatically setup when a workflow is executed! + +However, you can also set it up explicitly with: + +.. code-block:: bash + + make ai-env + + # Activate env + micromamba activate ./ai/.venv-pytorch + +Updating the environment files +++++++++++++++++++++++++++++++ + +The files under `ai/env-files/` are of two categories: + +* Simple environment definition, such as `pytorch-env.yml` +and `pytorch-env-gpu.yml` +* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, +generated by [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/). + +**When you install the ai environment, install it from the lock file!** + +When the "simple" environment file (e.g., `pytorch-env.yml`) changes, +lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): + +.. code-block:: bash + + micromamba activate ./.venv + + make lock-ai + diff --git a/docs/index.rst b/docs/index.rst index 29e47aa2..35529c1f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -26,13 +26,34 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi .. toctree:: :maxdepth: 2 - :caption: Contents: + :hidden: + :caption: 💡 Installation + + getting_started_with_itwinai + +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: 🪄 itwinai Modules modules + +.. toctree:: + :maxdepth: 2 + :caption: 📚 Integrated Use-cases + use_cases + +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: 🚀 Tutorials + tutorials +`interTwin Demo: itwinai integration with other DTE modules `_ + Indices and tables ================== From 9e48fb00685c0c185eccd1c71d9cb7b3bbb3dd8a Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Mon, 26 Feb 2024 15:16:49 +0100 Subject: [PATCH 09/59] adding latest changes to docs --- docs/getting_started_with_itwinai.rst | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 4ba53df3..2e0388b7 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -120,15 +120,12 @@ Updating the environment files The files under `ai/env-files/` are of two categories: -* Simple environment definition, such as `pytorch-env.yml` -and `pytorch-env-gpu.yml` -* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, -generated by [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/). +* Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` +* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by ```conda-lock ```_. **When you install the ai environment, install it from the lock file!** -When the "simple" environment file (e.g., `pytorch-env.yml`) changes, -lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): +When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): .. code-block:: bash From 372608d5cd9012fcfec74a1cedafd08f0d9fead6 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 29 Feb 2024 15:42:21 +0100 Subject: [PATCH 10/59] adding new pages for itwinai modules and other modifications --- docs/conf.py | 6 +- docs/getting_started_with_itwinai.rst | 167 +++++++++++++++++++------- docs/index.rst | 3 +- docs/itwinai.tf.modules.rst | 20 +++ docs/itwinai.torch.modules.rst | 34 ++++++ docs/modules.rst | 7 ++ 6 files changed, 188 insertions(+), 49 deletions(-) create mode 100644 docs/itwinai.tf.modules.rst create mode 100644 docs/itwinai.torch.modules.rst diff --git a/docs/conf.py b/docs/conf.py index e21e2464..64a10acb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -14,10 +14,12 @@ sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch/')) sys.path.insert(0, os.path.abspath('../tutorials/ml-workflows/')) sys.path.insert(0, os.path.abspath('../src/itwinai')) +sys.path.insert(0, os.path.abspath('../src/itwinai/tensorflow')) +sys.path.insert(0, os.path.abspath('../src/itwinai/ptorch')) sys.path.insert(0, os.path.abspath('../')) project = 'itwinai' -copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' +copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki on behalf of CERN' author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' release = '0.0.1' @@ -29,7 +31,7 @@ templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - +autodoc_mock_imports = ["mlflow"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 2e0388b7..9956a89e 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -43,93 +43,168 @@ default `$MAMBA_ROOT_PREFIX` is overridden: **Reference**: `Micromamba installation guide `_. -Workflow orchestrator -+++++++++++++++++++++ +Environment setup +----------------- + +**Requirements:** + +* Linux environment. Windows and macOS were never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +Tensorflow +++++++++++ -Install the (custom) orchestrator virtual environment. +Installation: .. code-block:: bash - source ~/.bashrc - # Create local env - make + # Install TensorFlow 2.13 + make tf-2.13 # Activate env - micromamba activate ./.venv + micromamba activate ./.venv-tf -To run tests on workflows use: +Other TF versions are available, using the following targets `tf-2.10`, and `tf-2.11`. -.. code-block:: bash - # Activate env - micromamba activate ./.venv +PyTorch (+ Lightning) ++++++++++++++++++++++ - pytest tests/ +Installation: +.. code-block:: bash -Development env setup ---------------------- + # Install PyTorch + lightning + make torch-gpu -Requirements: + # Activate env + micromamba activate ./.venv-pytorch -* Linux, macOS environment. Windows was never tested. -* Micromamba: see the installation instructions above. -* VS Code, for development. +Other also CPU-only version is available at the target `torch-cpu`. -Installation: + +Development environment ++++++++++++++++++++++++ + +This is for developers only. To have it, update the installed `itwinai` package adding the `dev` extra: .. code-block:: bash - make dev-env + pip install -e .[dev] - # Activate env - micromamba activate ./.venv-dev +**Test with `pytest`** To run tests on itwinai package: .. code-block:: bash # Activate env - micromamba activate ./.venv-dev + micromamba activate ./.venv-pytorch # or ./.venv-tf - pytest tests/ai/ + pytest -v -m "not slurm" tests/ -AI environment setup --------------------- +However, some tests are intended to be executed only on an HPC system, where SLURM is available. They are marked with "slurm" tag. To run also those tests, use the dedicated job script: -Requirements: +.. code-block:: bash -* Linux, macOS environment. Windows was never tested. -* Micromamba: see the installation instructions above. -* VS Code, for development. + sbatch tests/slurm_tests_startscript -**NOTE**: this environment gets automatically setup when a workflow is executed! + # Upon completion, check the output: + cat job.err + cat job.out -However, you can also set it up explicitly with: -.. code-block:: bash - make ai-env - # Activate env - micromamba activate ./ai/.venv-pytorch +.. Workflow orchestrator +.. +++++++++++++++++++++ -Updating the environment files -++++++++++++++++++++++++++++++ +.. Install the (custom) orchestrator virtual environment. -The files under `ai/env-files/` are of two categories: +.. .. code-block:: bash -* Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` -* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by ```conda-lock ```_. +.. source ~/.bashrc +.. # Create local env +.. make -**When you install the ai environment, install it from the lock file!** +.. # Activate env +.. micromamba activate ./.venv -When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): +.. To run tests on workflows use: -.. code-block:: bash +.. .. code-block:: bash + +.. # Activate env +.. micromamba activate ./.venv + +.. pytest tests/ + + +.. Development env setup +.. --------------------- + +.. Requirements: + +.. * Linux, macOS environment. Windows was never tested. +.. * Micromamba: see the installation instructions above. +.. * VS Code, for development. + +.. Installation: + +.. .. code-block:: bash + +.. make dev-env + +.. # Activate env +.. micromamba activate ./.venv-dev + +.. To run tests on itwinai package: + +.. .. code-block:: bash + +.. # Activate env +.. micromamba activate ./.venv-dev + +.. pytest tests/ai/ + + +.. AI environment setup +.. -------------------- + +.. Requirements: + +.. * Linux, macOS environment. Windows was never tested. +.. * Micromamba: see the installation instructions above. +.. * VS Code, for development. + +.. **NOTE**: this environment gets automatically setup when a workflow is executed! + +.. However, you can also set it up explicitly with: + +.. .. code-block:: bash + +.. make ai-env + +.. # Activate env +.. micromamba activate ./ai/.venv-pytorch + +.. Updating the environment files +.. ++++++++++++++++++++++++++++++ + +.. The files under `ai/env-files/` are of two categories: + +.. * Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` +.. * Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by `conda-lock `_. + +.. **When you install the ai environment, install it from the lock file!** + +.. When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with `conda-lock `_: + +.. .. code-block:: bash - micromamba activate ./.venv +.. micromamba activate ./.venv - make lock-ai +.. make lock-ai diff --git a/docs/index.rst b/docs/index.rst index 35529c1f..d56cb069 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -52,7 +52,8 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi tutorials -`interTwin Demo: itwinai integration with other DTE modules `_ +**`interTwin Demo: itwinai integration with other DTE modules `_** ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Indices and tables ================== diff --git a/docs/itwinai.tf.modules.rst b/docs/itwinai.tf.modules.rst new file mode 100644 index 00000000..aad490d8 --- /dev/null +++ b/docs/itwinai.tf.modules.rst @@ -0,0 +1,20 @@ +itwinai.tensorflow.modules +========================== + +.. .. automodule:: itwinai.tensorflow.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.tensorflow.utils +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. literalinclude:: ../src/itwinai/tensorflow/trainer.py + :language: python + +.. literalinclude:: ../src/itwinai/tensorflow/utils.py + :language: python + + diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst new file mode 100644 index 00000000..883fd4e8 --- /dev/null +++ b/docs/itwinai.torch.modules.rst @@ -0,0 +1,34 @@ +itwinai.torch.modules +===================== + +.. automodule:: itwinai.ptorch.cluster + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.inference + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.mlflow + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.trainer + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.types + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.utils + :members: + :undoc-members: + :show-inheritance: + + diff --git a/docs/modules.rst b/docs/modules.rst index b220e0ff..a9e96c97 100644 --- a/docs/modules.rst +++ b/docs/modules.rst @@ -14,3 +14,10 @@ itwinai itwinai.types itwinai.utils + +.. toctree:: + :maxdepth: 4 + + itwinai.tf.modules + itwinai.torch.modules + From f14ed3cd68cde7fa945fca7585d5423c98ead99e Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 29 Feb 2024 15:49:41 +0100 Subject: [PATCH 11/59] modified src/itwinai/torch directory name to solve namespace conflict --- src/itwinai/ptorch/__init__.py | 0 src/itwinai/ptorch/cluster.py | 225 ++++++ src/itwinai/ptorch/data/__init__.py | 0 src/itwinai/ptorch/inference.py | 219 ++++++ src/itwinai/ptorch/mlflow.py | 77 +++ src/itwinai/ptorch/models/__init__.py | 0 src/itwinai/ptorch/models/mnist.py | 75 ++ src/itwinai/ptorch/trainer.py | 941 ++++++++++++++++++++++++++ src/itwinai/ptorch/types.py | 64 ++ src/itwinai/ptorch/utils.py | 84 +++ 10 files changed, 1685 insertions(+) create mode 100644 src/itwinai/ptorch/__init__.py create mode 100644 src/itwinai/ptorch/cluster.py create mode 100644 src/itwinai/ptorch/data/__init__.py create mode 100644 src/itwinai/ptorch/inference.py create mode 100644 src/itwinai/ptorch/mlflow.py create mode 100644 src/itwinai/ptorch/models/__init__.py create mode 100644 src/itwinai/ptorch/models/mnist.py create mode 100644 src/itwinai/ptorch/trainer.py create mode 100644 src/itwinai/ptorch/types.py create mode 100644 src/itwinai/ptorch/utils.py diff --git a/src/itwinai/ptorch/__init__.py b/src/itwinai/ptorch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/itwinai/ptorch/cluster.py b/src/itwinai/ptorch/cluster.py new file mode 100644 index 00000000..aece16e2 --- /dev/null +++ b/src/itwinai/ptorch/cluster.py @@ -0,0 +1,225 @@ +"""Cluster environments where to run AI workflows. Partially adapted from: +https://github.com/facebookresearch/detr/blob/master/util/misc.py and +https://github.com/ramyamounir/Template/blob/main/lib/utils/distributed.py +""" + +from __future__ import annotations +from typing import Optional +import os +import signal +import subprocess +from pathlib import Path +from contextlib import contextmanager + +import numpy as np + +import torch +import torch.distributed as dist +import torch.backends.cudnn as cudnn + +from ..cluster import ( + ClusterEnvironment, + setup_for_distributed, + handle_sigusr1, + handle_sigterm +) +from .types import TorchDistributedBackend as BackendT + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + + +class TorchCluster(ClusterEnvironment): + def __init__(self) -> None: + super().__init__() + + def _set_backend(self, backend_name: str) -> None: + if backend_name not in BackendT: + raise ValueError( + "Unrecognized 'backend' field. Allowed values " + f"are: {BackendT.list()}. Received '{backend_name}'") + self._backend = backend_name + + def is_cuda_available(self) -> bool: + return self.use_cuda and torch.cuda.is_available() + + def is_main_worker(self) -> bool: + """Checks if the current process is the main/master process + in the whole job. + """ + return self.global_rank == 0 + + def cleanup_resources(self): + dist.barrier() + dist.destroy_process_group() + + +class LocalCluster(TorchCluster): + """Simple single node cluster with optional access to multiple GPUs.""" + + def __init__( + self, + backend: Optional[str] = None, + gpus: Optional[str] = '', + port: int = 49153, + rnd_seed: Optional[int] = 42 + ) -> None: + """Initialize local cluster for multi-GPU access. + + Args: + backend (Optional[str], optional): supported PyTorch backends. + If None, workload is not distributed. Defaults to None. + gpus (Optional[str], optional): list of visible GPU devices + (e.g., '1,2,3'). If empty string uses all available GPUs. + If None, CPU is used. Defaults to ''. + port (int, optional): TCP port used by the master process. + Defaults to 49153. + rnd_seed (Optional[int], optional): random seed to be setup after + all processes are setup. Defaults to 42. + """ + super().__init__() + self.backend = backend + self.gpus = gpus + self.port = port + self.dist_url = f'tcp://127.0.0.1:{self.port}' + self.rnd_seed = rnd_seed + + if self.gpus != '' and self.gpus is not None: + # Restrict the number of GPUs visible according to user needs + os.environ['CUDA_VISIBLE_DEVICES'] = self.gpus + + self.ngpus_per_node = torch.cuda.device_count() + self.global_rank = 0 + self.global_world_size = self.ngpus_per_node + + print(f"{self.ngpus_per_node} GPUs are available.") + self.distributed = True + # This flag tells whether the user wants to use the GPU(s) + self.use_cuda = ( + self.gpus is not None # GPU is not manually disabled + and torch.cuda.device_count() >= 1 # At least one GPU is selected + ) + if self.backend is None or self.ngpus_per_node <= 1: + print("Distributed has been disabled.") + self.distributed = False + self.dist_url = None + self.global_world_size = 1 + self.global_rank = 0 + if not self.is_cuda_available(): + print("CUDA disabled... Running on single CPU.") + self.use_cuda = False + self.distributed = False + self.dist_url = None + self.global_world_size = 1 + self.global_rank = 0 + + # Since single node case + self.local_world_size = self.global_world_size + + @contextmanager + def init_dist_gpu(self, worker_id) -> torch.device: + if self.distributed: + torch.cuda.set_device(worker_id) + self.global_rank += worker_id + # print(f'GLOBAL RANK: {self.global_rank}') + # Since single node case + self.local_rank = self.global_rank + # Simplification: worker ID mapped to GPU ID + self.gpu_id = worker_id + + try: + dist.init_process_group( + backend=self.backend, + init_method=self.dist_url, + world_size=self.global_world_size, + rank=self.global_rank + ) + fix_random_seeds(self.rnd_seed) + torch.cuda.set_device(self.gpu_id) + cudnn.benchmark = True + dist.barrier() + + setup_for_distributed(self.is_main_worker()) + print("SETUP DISTRIBUTED COMPLETE") + yield torch.device('cuda', worker_id) + finally: + self.cleanup_resources() + else: + # Distributed is disabled + # Since single node case + self.global_rank = 0 + self.local_rank = self.global_rank + if self.use_cuda: + torch.cuda.set_device(worker_id) + yield torch.device('cuda', worker_id) + else: + yield torch.device('cpu') + + +class SLURMCluster(TorchCluster): + """SLURM cluster with access to multi-node multi-GPU.""" + + def __init__( + self, + port: int = 49153, + backend: str = 'gloo', + rnd_seed: Optional[int] = 42 + ) -> None: + super().__init__() + self.port = port + self.backend = backend + self.rnd_seed = rnd_seed + if 'SLURM_JOB_ID' not in os.environ: + raise RuntimeError( + "'SLURM_JOB_ID' environment variable is not set. " + "Perhaps you are not running in a slurm cluster?" + ) + + self.ngpus_per_node = torch.cuda.device_count() + + # requeue job on SLURM preemption + signal.signal(signal.SIGUSR1, handle_sigusr1) + signal.signal(signal.SIGTERM, handle_sigterm) + + # find a common host name on all nodes + cmd = 'scontrol show hostnames ' + os.getenv('SLURM_JOB_NODELIST') + stdout = subprocess.check_output(cmd.split()) + host_name = stdout.decode().splitlines()[0] + self.dist_url = f'tcp://{host_name}:{self.port}' + + # distributed parameters + self.global_rank = int(os.getenv('SLURM_NODEID')) * self.ngpus_per_node + self.global_world_size = int( + os.getenv('SLURM_NNODES')) * self.ngpus_per_node + + @contextmanager + def init_dist_gpu(self): + import submitit + try: + job_env = submitit.JobEnvironment() + self.output_dir = Path( + str(self.output_dir).replace("%j", str(job_env.job_id))) + self.gpu = job_env.local_rank + self.global_rank = job_env.global_rank + + dist.init_process_group( + backend=self.backend, + init_method=self.dist_url, + world_size=self.global_world_size, + rank=self.global_rank + ) + fix_random_seeds(self.rnd_seed) + torch.cuda.set_device(self.gpu) + cudnn.benchmark = True + dist.barrier() + + setup_for_distributed(self.is_main_worker()) + yield + finally: + self.cleanup_resources() diff --git a/src/itwinai/ptorch/data/__init__.py b/src/itwinai/ptorch/data/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/itwinai/ptorch/inference.py b/src/itwinai/ptorch/inference.py new file mode 100644 index 00000000..02882f06 --- /dev/null +++ b/src/itwinai/ptorch/inference.py @@ -0,0 +1,219 @@ +from typing import Optional, Dict, Any, Union +import os +import abc + +import torch +from torch import nn +from torch.utils.data import DataLoader, Dataset + +from ..utils import dynamically_import_class +from .utils import clear_key +from ..components import Predictor, monitor_exec +from .types import TorchDistributedStrategy as StrategyT +from .types import Metric, Batch +from ..serialization import ModelLoader + + +class TorchModelLoader(ModelLoader): + """Loads a torch model from somewhere. + + Args: + model_uri (str): Can be a path on local filesystem + or an mlflow 'locator' in the form: + 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' + """ + + def __call__(self) -> nn.Module: + """"Loads model from model URI. + + Raises: + ValueError: if the model URI is not recognized + or the model is not found. + + Returns: + nn.Module: torch neural network. + """ + if os.path.exists(self.model_uri): + # Model is on local filesystem. + model = torch.load(self.model_uri) + return model.eval() + + if self.model_uri.startswith('mlflow+'): + # Model is on an MLFLow server + # Form is 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' + import mlflow + from mlflow import MlflowException + _, tracking_uri, run_id, artifact_path = self.model_uri.split('+') + mlflow.set_tracking_uri(tracking_uri) + + # Check that run exists + try: + mlflow.get_run(run_id) + except MlflowException: + raise ValueError(f"Run ID '{run_id}' was not found!") + + # Download model weights + ckpt_path = mlflow.artifacts.download_artifacts( + run_id=run_id, + artifact_path=artifact_path, + dst_path='tmp/', + tracking_uri=mlflow.get_tracking_uri() + ) + model = torch.load(ckpt_path) + return model.eval() + + raise ValueError( + 'Unrecognized model URI: model may not be there!' + ) + + +class TorchPredictor(Predictor): + """Applies a pre-trained torch model to unseen data.""" + + model: nn.Module = None + test_dataset: Dataset + test_dataloader: DataLoader = None + _strategy: StrategyT = StrategyT.NONE.value + epoch_idx: int = 0 + train_glob_step: int = 0 + validation_glob_step: int = 0 + train_metrics: Dict[str, Metric] + validation_metrics: Dict[str, Metric] + + def __init__( + self, + model: Union[nn.Module, ModelLoader], + test_dataloader_class: str = 'torch.utils.data.DataLoader', + test_dataloader_kwargs: Optional[Dict] = None, + # strategy: str = StrategyT.NONE.value, + # seed: Optional[int] = None, + # logger: Optional[List[Logger]] = None, + # cluster: Optional[ClusterEnvironment] = None, + # test_metrics: Optional[Dict[str, Metric]] = None, + name: str = None + ) -> None: + super().__init__(model=model, name=name) + self.save_parameters(**self.locals2params(locals())) + self.model = self.model.eval() + # self.seed = seed + # self.strategy = strategy + # self.cluster = cluster + + # Train and validation dataloaders + self.test_dataloader_class = dynamically_import_class( + test_dataloader_class + ) + test_dataloader_kwargs = ( + test_dataloader_kwargs + if test_dataloader_kwargs is not None else {} + ) + self.test_dataloader_kwargs = clear_key( + test_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' + ) + + # # Loggers + # self.logger = logger if logger is not None else ConsoleLogger() + + # # Metrics + # self.train_metrics = ( + # {} if train_metrics is None else train_metrics + # ) + # self.validation_metrics = ( + # self.train_metrics if validation_metrics is None + # else validation_metrics + # ) + + @monitor_exec + def execute( + self, + test_dataset: Dataset, + model: nn.Module = None, + ) -> Dict[str, Any]: + """Applies a torch model to a dataset for inference. + + Args: + test_dataset (Dataset[str, Any]): each item in this dataset is a + couple (item_unique_id, item) + model (nn.Module, optional): torch model. Overrides the existing + model, if given. Defaults to None. + + Returns: + Dict[str, Any]: maps each item ID to the corresponding predicted + value(s). + """ + if model is not None: + # Overrides existing "internal" model + self.model = model + + test_dataloader = self.test_dataloader_class( + test_dataset, **self.test_dataloader_kwargs + ) + + all_predictions = dict() + for samples_ids, samples in test_dataloader: + with torch.no_grad(): + pred = self.model(samples) + pred = self.transform_predictions(pred) + for idx, pre in zip(samples_ids, pred): + # For each item in the batch + if pre.numel() == 1: + pre = pre.item() + else: + pre = pre.to_dense().tolist() + all_predictions[idx] = pre + return all_predictions + + @abc.abstractmethod + def transform_predictions(self, batch: Batch) -> Batch: + """ + Post-process the predictions of the torch model (e.g., apply + threshold in case of multilabel classifier). + """ + + +class MulticlassTorchPredictor(TorchPredictor): + """ + Applies a pre-trained torch model to unseen data for + multiclass classification. + """ + + def transform_predictions(self, batch: Batch) -> Batch: + batch = batch.argmax(-1) + return batch + + +class MultilabelTorchPredictor(TorchPredictor): + """ + Applies a pre-trained torch model to unseen data for + multilabel classification, applying a threshold on the + output of the neural network. + """ + + threshold: float + + def __init__( + self, + model: Union[nn.Module, ModelLoader], + test_dataloader_class: str = 'torch.utils.data.DataLoader', + test_dataloader_kwargs: Optional[Dict] = None, + threshold: float = 0.5, + name: str = None + ) -> None: + super().__init__( + model, test_dataloader_class, test_dataloader_kwargs, name + ) + self.threshold = threshold + + def transform_predictions(self, batch: Batch) -> Batch: + return (batch > self.threshold).float() + + +class RegressionTorchPredictor(TorchPredictor): + """ + Applies a pre-trained torch model to unseen data for + regression, leaving untouched the output of the neural + network. + """ + + def transform_predictions(self, batch: Batch) -> Batch: + return batch diff --git a/src/itwinai/ptorch/mlflow.py b/src/itwinai/ptorch/mlflow.py new file mode 100644 index 00000000..18a014ff --- /dev/null +++ b/src/itwinai/ptorch/mlflow.py @@ -0,0 +1,77 @@ +from typing import Dict, Optional +import os + +import mlflow +import yaml + + +def _get_mlflow_logger_conf(pl_config: Dict) -> Optional[Dict]: + """Extract MLFLowLogger configuration from pytorch lightning + configuration file, if present. + + Args: + pl_config (Dict): lightning configuration loaded in memory. + + Returns: + Optional[Dict]: if present, MLFLowLogger constructor arguments + (under 'init_args' key). + """ + if isinstance(pl_config['trainer']['logger'], list): + # If multiple loggers are provided + for logger_conf in pl_config['trainer']['logger']: + if logger_conf['class_path'].endswith('MLFlowLogger'): + return logger_conf['init_args'] + elif pl_config['trainer']['logger']['class_path'].endswith('MLFlowLogger'): + return pl_config['trainer']['logger']['init_args'] + + +def _mlflow_log_pl_config(pl_config: Dict, local_yaml_path: str) -> None: + os.makedirs(os.path.dirname(local_yaml_path), exist_ok=True) + with open(local_yaml_path, 'w') as outfile: + yaml.dump(pl_config, outfile, default_flow_style=False) + mlflow.log_artifact(local_yaml_path) + + +def init_lightning_mlflow( + pl_config: Dict, + default_experiment_name: str = 'Default', + **autolog_kwargs +) -> None: + """Initialize mlflow for pytorch lightning, also setting up + auto-logging (mlflow.pytorch.autolog(...)). Creates a new mlflow + run and attaches it to the mlflow auto-logger. + + Args: + pl_config (Dict): pytorch lightning configuration loaded in memory. + default_experiment_name (str, optional): used as experiment name + if it is not given in the lightning conf. Defaults to 'Default'. + **autolog_kwargs (kwargs): args for mlflow.pytorch.autolog(...). + """ + mlflow_conf: Optional[Dict] = _get_mlflow_logger_conf(pl_config) + if not mlflow_conf: + return + + tracking_uri = mlflow_conf.get('tracking_uri') + if not tracking_uri: + save_path = mlflow_conf.get('save_dir') + tracking_uri = "file://" + os.path.abspath(save_path) + + experiment_name = mlflow_conf.get('experiment_name') + if not experiment_name: + experiment_name = default_experiment_name + + mlflow.set_tracking_uri(tracking_uri) + mlflow.set_experiment(experiment_name) + mlflow.pytorch.autolog(**autolog_kwargs) + mlflow.start_run() + + mlflow_conf['experiment_name'] = experiment_name + mlflow_conf['run_id'] = mlflow.active_run().info.run_id + + _mlflow_log_pl_config(pl_config, '.tmp/pl_config.yml') + + +def teardown_lightning_mlflow() -> None: + """End active mlflow run, if any.""" + if mlflow.active_run() is not None: + mlflow.end_run() diff --git a/src/itwinai/ptorch/models/__init__.py b/src/itwinai/ptorch/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/itwinai/ptorch/models/mnist.py b/src/itwinai/ptorch/models/mnist.py new file mode 100644 index 00000000..c222a5a3 --- /dev/null +++ b/src/itwinai/ptorch/models/mnist.py @@ -0,0 +1,75 @@ +import torch +import torch.nn as nn +import lightning as L + +from torch.nn import functional as F + + +class MNISTModel(L.LightningModule): + """ + Simple PL model for MNIST. + Adapted from + https://lightning.ai/docs/pytorch/stable/notebooks/lightning_examples/mnist-hello-world.html + """ + + def __init__( + self, + hidden_size: int = 64, + ): + super().__init__() + + # Automatically save constructor args as hyperparameters + self.save_hyperparameters() + + # Set our init args as class attributes + self.hidden_size = hidden_size + + # Hardcode some dataset specific attributes + self.num_classes = 10 + self.dims = (1, 28, 28) + channels, width, height = self.dims + + # Define PyTorch model + self.model = nn.Sequential( + nn.Flatten(), + nn.Linear(channels * width * height, hidden_size), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(hidden_size, self.num_classes), + ) + + def forward(self, x): + x = self.model(x) + return F.log_softmax(x, dim=1) + + def training_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = F.nll_loss(logits, y) + + # Log metrics with autolog + self.log("train_loss", loss, on_step=True, on_epoch=True) + + return loss + + def validation_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = F.nll_loss(logits, y) + # preds = torch.argmax(logits, dim=1) + self.log("val_loss", loss, prog_bar=True, on_step=True, on_epoch=True) + + def test_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = F.nll_loss(logits, y) + self.log("test_loss", loss) + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + x, _ = batch + logits = self(x) + preds = torch.argmax(logits, dim=1) + return preds diff --git a/src/itwinai/ptorch/trainer.py b/src/itwinai/ptorch/trainer.py new file mode 100644 index 00000000..31794c49 --- /dev/null +++ b/src/itwinai/ptorch/trainer.py @@ -0,0 +1,941 @@ +"""Provides training logic for PyTorch models via Trainer classes.""" + +from typing import ( + Optional, Dict, Union, Tuple, Type, List, Any +) +import time +import os +import sys + +import numpy as np +import torch +import torch.multiprocessing as mp +from torch.utils.data import DataLoader, Dataset +from torch.utils.data.distributed import DistributedSampler +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +import torch.nn as nn +from torch.optim.optimizer import Optimizer + +from ..components import Trainer, monitor_exec +from .utils import seed_worker, par_allgather_obj, clear_key +from .types import ( + Batch, Loss, LrScheduler, Metric +) +from .types import TorchDistributedStrategy as StrategyT +from ..loggers import LogMixin, Logger, ConsoleLogger +from ..utils import dynamically_import_class +from ..cluster import ClusterEnvironment + + +def preproc_dataloader(dataloader: DataLoader, gwsize, grank): + """Makes a Dataloader distributed.""" + sampler = DistributedSampler( + dataloader.dataset, + num_replicas=gwsize, + rank=grank, + shuffle=True + ) + # Recreate dataloader, with updated sampler + return DataLoader( + dataloader.dataset, + batch_size=dataloader.batch_size, + sampler=sampler, + num_workers=dataloader.num_workers, + collate_fn=dataloader.collate_fn, + pin_memory=dataloader.pin_memory, + drop_last=dataloader.drop_last, + timeout=dataloader.timeout, + worker_init_fn=seed_worker, # dataloader.worker_init_fn, + multiprocessing_context=dataloader.multiprocessing_context, + generator=dataloader.generator, + prefetch_factor=dataloader.prefetch_factor, + persistent_workers=dataloader.persistent_workers, + pin_memory_device=dataloader.pin_memory_device + ) + + +def distributed(func): + """The decorated function must have a standard signature. + Its first arguments must be: + model, train_dataloader, validation_dataloader, device (in this order). + + Additional args or kwargs are allowed consistently with the signature + of the decorated function. + """ + def dist_train( + model, train_dataloader, validation_dataloader=None, device='cpu', + *args, **kwargs + ): + if torch.cuda.is_available(): + dist.init_process_group(backend='nccl') + + if torch.cuda.is_available(): + lwsize = torch.cuda.device_count() # local world size - per node + gwsize = dist.get_world_size() # global world size - per run + grank = dist.get_rank() # global rank - assign per run + lrank = dist.get_rank() % lwsize # local rank - assign per node + else: + gwsize = 1 + grank = 0 + lrank = 0 + + device = torch.device( + 'cuda' if torch.cuda.is_available() else 'cpu', lrank) + if torch.cuda.is_available(): + torch.cuda.set_device(lrank) + + model = model.to(device) + model = DDP(model, device_ids=[device], output_device=device) + + train_dataloader = preproc_dataloader(train_dataloader, gwsize, grank) + if validation_dataloader is not None: + validation_dataloader = preproc_dataloader( + validation_dataloader, gwsize, grank) + + try: + func(model, train_dataloader, validation_dataloader, device, + *args, **kwargs) + finally: + if torch.cuda.is_available(): + dist.barrier() + dist.destroy_process_group() + return dist_train + + +class TorchTrainerMG(Trainer, LogMixin): + """ + Torch trainer for optionally distributed data-parallel (DDP) workload. + Multi-GPU distribution. + + Args: + model (nn.Module): neural network instance. + loss (Loss): torch loss function instance. + optimizer_class (str): path to optimizer class + (e.g., 'torch.optim.SGD') + optimizer_kwargs (Optional[Dict], optional): optimizer constructor + arguments (except from parameters). Defaults to None. + lr_scheduler_class (Optional[str], optional): path to learning + rate scheduler class. Defaults to None. + lr_scheduler_kwargs (Optional[Dict], optional): constructor arguments + of the learning rate scheduler, except for the optimizer. + Defaults to None. + train_dataloader_class (str, optional): train dataloader class path. + Defaults to 'torch.utils.data.DataLoader'. + train_dataloader_kwargs (Optional[Dict], optional): constructor + arguments of the train dataloader, except for the dataset + instance. Defaults to None. + validation_dataloader_class (str, optional): validation dataloader + class path. Defaults to 'torch.utils.data.DataLoader'. + validation_dataloader_kwargs (Optional[Dict], optional): constructor + arguments of the validation dataloader, except for the dataset + instance. If None, it replicates `train_dataloader_kwargs`. + Defaults to None. + epochs (int, optional): number of training epochs. Defaults to 1. + strategy (Optional[TorchDistributedStrategy], optional): distributed + strategy. Defaults to StrategyT.NONE.value. + backend (TorchDistributedBackend, optional): computing backend. + Defaults to BackendT.NCCL.value. + shuffle_dataset (bool, optional): whether shuffle dataset before + sampling batches from dataloader. Defaults to False. + use_cuda (bool, optional): whether to use GPU. Defaults to True. + benchrun (bool, optional): sets up a debug run. Defaults to False. + testrun (bool, optional): deterministic training seeding everything. + Defaults to False. + seed (Optional[int], optional): random seed. Defaults to None. + logger (Optional[List[Logger]], optional): logger. Defaults to None. + checkpoint_every (int, optional): how often (epochs) to checkpoint the + best model. Defaults to 10. + cluster (Optional[ClusterEnvironment], optional): cluster environment + object describing the context in which the trainer is executed. + Defaults to None. + train_metrics (Optional[Dict[str, Metric]], optional): + list of metrics computed in the training step on the predictions. + It's a dictionary with the form + ``{'metric_unique_name': CallableMetric}``. Defaults to None. + validation_metrics (Optional[Dict[str, Metric]], optional): same + as ``training_metrics``. If not given, it mirrors the training + metrics. Defaults to None. + + Raises: + RuntimeError: When trying to use DDP without CUDA support. + NotImplementedError: when trying to use a strategy different from the + ones provided by TorchDistributedStrategy. + """ + + model: nn.Module = None + loss: Loss = None + optimizer: Optimizer = None + lr_scheduler = None + _strategy: StrategyT = StrategyT.NONE.value + train_dataset: Dataset + validation_dataset: Dataset + train_dataloader: DataLoader = None + validation_dataloader: DataLoader = None + epoch_idx: int = 0 + train_glob_step: int = 0 + validation_glob_step: int = 0 + train_metrics: Dict[str, Metric] + validation_metrics: Dict[str, Metric] + + def __init__( + self, + model: nn.Module, + loss: Loss, + optimizer_class: str, + optimizer_kwargs: Optional[Dict] = None, + lr_scheduler_class: Optional[str] = None, + lr_scheduler_kwargs: Optional[Dict] = None, + train_dataloader_class: str = 'torch.utils.data.DataLoader', + train_dataloader_kwargs: Optional[Dict] = None, + validation_dataloader_class: str = 'torch.utils.data.DataLoader', + validation_dataloader_kwargs: Optional[Dict] = None, + epochs: int = 1, + strategy: str = StrategyT.NONE.value, + benchrun: bool = False, + testrun: bool = False, + seed: Optional[int] = None, + logger: Optional[List[Logger]] = None, + checkpoint_every: int = 10, + cluster: Optional[ClusterEnvironment] = None, + train_metrics: Optional[Dict[str, Metric]] = None, + validation_metrics: Optional[Dict[str, Metric]] = None + ) -> None: + """Sets up the distributed backend and loggers. + Makes the model a DDP model. + """ + super().__init__() + self.save_parameters(**self.locals2params(locals())) + self.model = model + self.loss = loss + self.epochs = epochs + self.testrun = testrun + self.seed = seed + self.strategy = strategy + self.benchrun = benchrun + self.cluster = cluster + # Checkpoint every n epochs + self.checkpoint_every = checkpoint_every + + # Train and validation dataloaders + self.train_dataloader_class = dynamically_import_class( + train_dataloader_class + ) + self.validation_dataloader_class = dynamically_import_class( + validation_dataloader_class + ) + train_dataloader_kwargs = ( + train_dataloader_kwargs + if train_dataloader_kwargs is not None else {} + ) + self.train_dataloader_kwargs = clear_key( + train_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' + ) + # If validation_dataloader_kwargs is not given, + # copy train_dataloader_kwargs + validation_dataloader_kwargs = ( + validation_dataloader_kwargs if validation_dataloader_kwargs + is not None else train_dataloader_kwargs + ) + self.validation_dataloader_kwargs = clear_key( + validation_dataloader_kwargs, 'validation_dataloader_kwargs', + 'dataset' + ) + + # Optimizer and scheduler + optim_class = dynamically_import_class(optimizer_class) + optimizer_kwargs = ( + optimizer_kwargs if optimizer_kwargs is not None else {} + ) + optimizer_kwargs = clear_key( + optimizer_kwargs, 'optimizer_kwargs', 'parameters' + ) + self.optimizer: Optimizer = optim_class( + self.model.parameters(), **optimizer_kwargs + ) + if lr_scheduler_class is not None: + scheduler_class = dynamically_import_class(lr_scheduler_class) + lr_scheduler_kwargs = ( + lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {} + ) + lr_scheduler_kwargs = clear_key( + lr_scheduler_kwargs, 'lr_scheduler_kwargs', 'optimizer' + ) + self.lr_scheduler: LrScheduler = scheduler_class( + self.optimizer, **lr_scheduler_kwargs + ) + + # Loggers + self.logger = logger if logger is not None else ConsoleLogger() + + # Metrics + self.train_metrics = ( + {} if train_metrics is None else train_metrics + ) + self.validation_metrics = ( + self.train_metrics if validation_metrics is None + else validation_metrics + ) + + @property + def strategy(self) -> Optional[str]: + return self._strategy + + @strategy.setter + def strategy(self, strategy_name) -> None: + if strategy_name not in StrategyT: + raise ValueError( + "Unrecognized 'strategy' field. Allowed values " + f"are: {StrategyT.list()}. Received '{strategy_name}'") + self._strategy = strategy_name + + @property + def global_step(self) -> int: + return self.train_glob_step + self.validation_glob_step + + def set_seed(self, seed: Optional[int] = None): + """Deterministic operations for reproducibility. + Sets the random seed. + + Args: + seed (Optional[int], optional): if not None, overrides + `self.seed`. Defaults to None. + """ + seed = seed if seed is not None else self.seed + np.random.seed(seed) + self.torch_rng = torch.Generator() + if seed is not None: + torch.manual_seed(seed) + self.torch_rng.manual_seed(seed) + if self.cluster.is_cuda_available(): + torch.cuda.manual_seed(seed) + + @monitor_exec + def execute( + self, + train_dataset: Dataset, + validation_dataset: Dataset, + model: nn.Module = None, + optimizer: Optimizer = None, + lr_scheduler: LrScheduler = None, + ) -> Any: + self.train_dataset = train_dataset + self.validation_dataset = validation_dataset + + # Update parameters passed for "interactive" use + if model is not None: + self.model = model + if optimizer is not None: + self.optimizer = optimizer + if lr_scheduler is not None: + self.lr_scheduler = lr_scheduler + + # Start training + if self.cluster.distributed: + # Make training distributed + result = mp.spawn(self._train, nprocs=self.cluster.ngpus_per_node) + else: + result = self._train(0) + + # Return value compliant with Executable.execute format + return result + + def _train( + self, + worker_id: int + ): + # Each worker has a different deterministic seed + # Here, 'worker' = replica of the training function + worker_seed = ( + self.seed + worker_id if self.seed is not None else self.seed + ) + self.set_seed(worker_seed) + + # Instantiate dataloaders + self.train_dataloader = self._instantiate_dataloader( + dataloader_class=self.train_dataloader_class, + dataset=self.train_dataset, + init_kwargs=self.train_dataloader_kwargs + ) + if self.validation_dataset is not None: + self.validation_dataloader = self._instantiate_dataloader( + dataloader_class=self.validation_dataloader_class, + dataset=self.validation_dataset, + init_kwargs=self.validation_dataloader_kwargs + ) + + # Launch actual training: + + # Single worker case + if not self.cluster.distributed: + with self.cluster.init_dist_gpu(worker_id) as device: + self.device: torch.device = device + self.model = self.model.to(self.device) + self.setup_logger() + self._setup_metrics() + try: + train_result = self.train() + except Exception as exc: + print(exc) + raise exc + finally: + print("INFO: Training ended") + self.destroy_logger() + train_result = None + return train_result + + # Init / connect to distributed backend + with self.cluster.init_dist_gpu(worker_id) as device: + self.device: torch.device = device + self._distribute_model() + self.setup_logger() + self._setup_metrics() + try: + train_result = self.train() + except Exception as exc: + print(exc) + raise exc + finally: + print("INFO: Training ended") + self.destroy_logger() + train_result = None + return train_result + + def _instantiate_dataloader( + self, + dataloader_class: Type, + dataset: Dataset, + init_kwargs: Dict + ) -> DataLoader: + """Make dataloader distributed if using distributed training strategy. + + Args: + dataloader_class (Type): some torch DataLoader type. + dataset (Dataset): torch dataset instance. + init_kwargs (Dict): constructor args. + """ + init_kwargs['generator'] = init_kwargs.get( + 'generator', self.torch_rng + ) + init_kwargs['worker_init_fn'] = init_kwargs.get( + 'worker_init_fn', seed_worker + ) + + if self.strategy == StrategyT.DDP.value and self.cluster.distributed: + sampler = DistributedSampler( + dataset=dataset, + num_replicas=self.cluster.global_world_size, + rank=self.cluster.global_rank, + shuffle=init_kwargs.get( + 'shuffle', False + ) + ) + # Overwrite existing sampler, if given. + # TODO: improve using wrapper: + # https://discuss.pytorch.org/t/how-to-use-my-own-sampler-when-i-already-use-distributedsampler/62143?page=2 + init_kwargs['sampler'] = sampler + if init_kwargs.get('shuffle') is not None: + # sampler option is mutually exclusive with shuffle + del init_kwargs['shuffle'] + + return dataloader_class(dataset, **init_kwargs) + + def _setup_metrics(self): + for m_name, metric in self.train_metrics.items(): + self.train_metrics[m_name] = metric.to(self.device) + for m_name, metric in self.validation_metrics.items(): + self.validation_metrics[m_name] = metric.to(self.device) + + def _distribute_model(self): + if self.cluster.distributed: + # Distribute model + self.model = self.model.to(self.device) + if self.strategy == StrategyT.NONE.value: + print( + "WARNING: A GPU cluster is available but no distributed " + "strategy was given... Falling back to single worker...") + if not self.cluster.is_main_worker(): + # Use only GPU:0 for single worker + sys.exit(0) + elif self.strategy == StrategyT.DDP.value: + self.model = DDP( + self.model, + device_ids=[self.device.index], + output_device=self.device + ) + else: + raise NotImplementedError("Only DDP strategy is implemented.") + else: + raise RuntimeError( + "Trying to distribute a model when a " + "distributed cluster is not available." + ) + + def setup_logger(self): + if self.cluster.is_main_worker(): + # Only setup loggers on main worker + if isinstance(self.logger, list): + for logger in self.logger: + logger.create_logger_context() + elif isinstance(self.logger, Logger): + self.logger.create_logger_context() + else: + raise TypeError( + "Unrecognized self.logger. Allowed types are 'list' and " + f"'Logger'. Received {type(self.logger)}" + ) + else: + self.logger = [] + + def destroy_logger(self): + if self.cluster.is_main_worker(): + if isinstance(self.logger, list): + for logger in self.logger: + logger.destroy_logger_context() + elif isinstance(self.logger, Logger): + self.logger.destroy_logger_context() + else: + raise TypeError( + "Unrecognized self.logger. Allowed types are 'list' and " + f"'Logger'. Received {type(self.logger)}" + ) + + def log( + self, + item: Union[Any, List[Any]], + identifier: Union[str, List[str]], + kind: str = 'metric', + step: Optional[int] = None, + batch_idx: Optional[int] = None, + every_worker: bool = False, + **kwargs + ) -> None: + if self.cluster.is_main_worker() or every_worker: + # Only log on main worker if not specified otherwise + if isinstance(self.logger, list): + for logger in self.logger: + logger.log( + item=item, + identifier=identifier, + kind=kind, + step=step, + batch_idx=batch_idx, + **kwargs + ) + elif isinstance(self.logger, Logger): + self.logger.log( + item=item, + identifier=identifier, + kind=kind, + step=step, + batch_idx=batch_idx, + **kwargs + ) + else: + raise TypeError( + "Unrecognized self.logger. Allowed types are 'list' and " + f"'Logger'. Received {type(self.logger)}" + ) + + def compute_metrics( + self, + metrics: Dict[str, Metric], + true: Batch, + pred: Batch, + logger_step: int, + batch_idx: Optional[int], + stage: str = 'train' + ) -> Dict[str, Any]: + """Compute and log metrics. + + Args: + metrics (Dict[str, Metric]): metrics dict. Can be + ``self.train_metrics`` or ``self.validation_metrics``. + true (Batch): true values. + pred (Batch): predicted values. + logger_step (int): global step to pass to the logger. + stage (str): 'train', 'validation'... + + Returns: + Dict[str, Any]: metric values. + """ + m_values = {} + for m_name, metric in metrics.items(): + # metric = metric.to(self.device) + m_val = metric(pred, true).detach().cpu().numpy() + self.log( + item=m_val, + identifier=f'{m_name}_{stage}', + kind='metric', + step=logger_step, + batch_idx=batch_idx + ) + m_values[m_name] = m_val + return m_values + + def training_step( + self, + batch: Batch, + batch_idx: int + ) -> Tuple[Loss, Dict[str, Any]]: + x, y = batch + x, y = x.to(self.device), y.to(self.device) + pred_y = self.model(x) + loss: Loss = self.loss(pred_y, y) + self.log( + item=loss.item(), + identifier='training_loss', + kind='metric', + step=self.train_glob_step, + batch_idx=batch_idx + ) + metrics: Dict[str, Any] = self.compute_metrics( + metrics=self.train_metrics, + true=y, + pred=pred_y, + logger_step=self.train_glob_step, + batch_idx=batch_idx, + stage='training' + ) + return loss, metrics + + def validation_step( + self, + batch: Batch, + batch_idx: int + ) -> Tuple[Loss, Dict[str, Any]]: + x, y = batch + x, y = x.to(self.device), y.to(self.device) + pred_y = self.model(x) + loss: Loss = self.loss(pred_y, y) + self.log( + item=loss.item(), + identifier='validation_loss', + kind='metric', + step=self.validation_glob_step, + batch_idx=batch_idx + ) + metrics: Dict[str, Any] = self.compute_metrics( + metrics=self.validation_metrics, + true=y, + pred=pred_y, + logger_step=self.validation_glob_step, + batch_idx=batch_idx, + stage='validation' + ) + return loss, metrics + + def training_epoch(self) -> Loss: + self.model.train() + train_losses = [] + for batch_idx, train_batch in enumerate(self.train_dataloader): + loss, metrics = self.training_step( + batch=train_batch, + batch_idx=batch_idx + ) + # TODO: merge and log batch metrics and loss into epoch metrics + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + train_losses.append(loss) + # Important: update counter + self.train_glob_step += 1 + + # Aggregate and log losses + avg_loss = torch.mean(torch.stack(train_losses)).detach().cpu() + self.log( + item=avg_loss.item(), + identifier='training_loss_epoch', + kind='metric', + step=self.train_glob_step, + ) + return avg_loss + + def validation_epoch(self) -> Loss: + if self.validation_dataloader is not None: + self.model.eval() + validation_losses = [] + for batch_idx, val_batch \ + in enumerate(self.validation_dataloader): + # TODO: merge and log batch metrics and loss into epoch metrics + loss, metrics = self.validation_step( + batch=val_batch, + batch_idx=batch_idx + ) + validation_losses.append(loss) + # Important: update counter + self.validation_glob_step += 1 + + # Aggregate and log losses + avg_loss = torch.mean( + torch.stack(validation_losses) + ).detach().cpu() + self.log( + item=avg_loss.item(), + identifier='validation_loss_epoch', + kind='metric', + step=self.validation_glob_step, + ) + return avg_loss + + def train(self): + + if self.optimizer is None: + raise ValueError("Undefined optimizer!") + + if self.loss is None: + raise ValueError("Undefined loss function!") + + st = time.time() + + # Resume state + self.start_epoch = 1 + self.best_loss = np.Inf + self.load_state() + + # start training/testing loop + if self.cluster.is_main_worker(): + print(f'TIMER: broadcast: {time.time()-st}s') + print('DEBUG: start training') + print('-'*56) + + ############################## + # Start training: run epochs # + ############################## + + et = time.time() + for self.epoch_idx in range(self.start_epoch, self.epochs + 1): + lt = time.time() + + ####################################################### + # Perform one training epoch and one validation epoch # + ####################################################### + + if self.benchrun and self.epoch_idx == self.epochs: + # TODO: move profiler into cluster environment + # profiling (done on last epoch - slower!) + with torch.autograd.profiler.profile( + use_cuda=self.cluster.is_cuda_available(), + profile_memory=True + ) as prof: + train_loss = self.training_epoch() + else: + train_loss = self.training_epoch() + val_loss = self.validation_epoch() + + ##################################### + # Save checkpoint if model improved # + ##################################### + + ref_loss = val_loss if val_loss is not None else train_loss + is_best = ref_loss < self.best_loss + if (self.epoch_idx % self.checkpoint_every == 0 + and not self.benchrun): + self.save_state( + loss_val=ref_loss, + is_best=is_best + ) + self.best_loss = min(ref_loss, self.best_loss) + + ########################### + # End of epoch operations # + ########################### + + # save first epoch timer + if self.epoch_idx == self.start_epoch: + first_ep_t = time.time()-lt + + # Final epoch + if self.epoch_idx + 1 == self.epochs: + self.train_dataloader.last_epoch = True + self.validation_dataloader.last_epoch = True + + if self.cluster.is_main_worker(): + print(f'TIMER: epoch time: {time.time()-lt}s') + if self.benchrun and self.epoch_idx == self.epochs: + print('-'*56) + print('benchmark of last epoch:') + what1 = ( + 'cuda' if self.cluster.is_cuda_available() else 'cpu' + ) + print( + prof.key_averages().table( + sort_by='self_'+str(what1)+'_time_total' + ) + ) + + ########################## + # Training has completed # + ########################## + + # save final state + if not self.benchrun: + self.save_state( + loss_val=ref_loss, + is_best=is_best + ) + if self.cluster.is_cuda_available() and self.cluster.distributed: + dist.barrier() + + ######################## + # Print training stats # + ######################## + + if self.cluster.is_main_worker(): + print('-'*56) + print('training results:') + print(f'TIMER: first epoch time: {first_ep_t}s') + print(f'TIMER: last epoch time: {time.time()-lt}s') + print( + f'TIMER: average epoch time: {(time.time()-et)/self.epochs}s') + print(f'TIMER: total epoch time: {time.time()-et}s') + if self.epoch_idx > 1: + print( + f'TIMER: total epoch-1 time: {time.time()-et-first_ep_t}s' + ) + print( + 'TIMER: average epoch-1 time: ' + f'{(time.time()-et-first_ep_t)/(self.epochs-1)}s') + if self.benchrun: + print( + f'TIMER: total epoch-2 time: {lt-first_ep_t}s') + print('TIMER: average epoch-2 time: ' + f'{(lt-first_ep_t)/(self.epochs-2)}s') + mem = int(torch.cuda.memory_reserved( + self.cluster.local_rank)/1024/1024) + print( + f'memory req: {mem} MB' + if self.cluster.is_cuda_available() + and self.cluster.distributed else 'memory req: - MB' + ) + if self.cluster.is_cuda_available(): + print( + f'memory summary:\n {torch.cuda.memory_summary(0)}') + + if self.cluster.is_main_worker(): + print(f'TIMER: final time: {time.time()-st} s') + + def save_state(self, loss_val: Any, is_best: bool): + """Save training state.""" + res_name = 'checkpoint.pth.tar' + rt = time.time() + + if (self.cluster.is_cuda_available() and self.cluster.distributed): + # find if is_best happened in any worker + is_best_m = par_allgather_obj( + is_best, self.cluster.global_world_size + ) + if any(is_best_m): + # TODO: is this strategy really good? Checkpointing when + # at least one worker improves the loss on their local + # data split is prone to overfitting, especially when + # the dataset in unbalanced! + + # find which rank is_best happened - select first rank + # if multiple + best_rank = np.where(np.array(is_best_m))[0][0] + if self.cluster.global_rank == best_rank: + self._save_sate( + epoch=self.epoch_idx+1, + loss_val=loss_val, + save_path=res_name + ) + print( + f'DEBUG: state in {self.cluster.global_rank} is ' + f'saved on epoch:{self.epoch_idx} ' + f'in {time.time()-rt} s') + else: + self._save_sate( + epoch=self.epoch_idx+1, + loss_val=loss_val, + save_path=res_name + ) + print( + f'DEBUG: state in {self.cluster.global_rank} ' + f'is saved on epoch:{self.epoch_idx} in {time.time()-rt} s') + + def _save_sate( + self, + epoch: int, + loss_val: Any, + save_path: str + ): + """Save state on disk.""" + sched = ( + self.lr_scheduler.state_dict() + if self.lr_scheduler is not None else None + ) + state = { + 'epoch': epoch, + 'state_dict': self.model.state_dict(), + 'best_loss': loss_val, + 'optimizer': self.optimizer.state_dict(), + 'lr_scheduler': sched + } + self.log( + item=state, + identifier=save_path, + kind='torch', + epoch_step=self.epoch_idx, + batch_step=0 + ) + + def load_state(self): + """Load training state.""" + res_name = 'checkpoint.pth.tar' + if os.path.isfile(res_name) and not self.benchrun: + try: + if (self.cluster.is_cuda_available() + and self.cluster.distributed): + dist.barrier() + # Map model to be loaded to specified single gpu. + # loc = ( + # {'cuda:%d' % 0: 'cuda:%d' % self.cluster.local_rank} + # if self.cluster.is_cuda_available() + # else {'cpu:%d' % 0: 'cpu:%d' % self.cluster.local_rank} + # ) + # checkpoint = torch.load(res_name, map_location=loc) + checkpoint = torch.load( + res_name, map_location=self.device + ) + else: + checkpoint = torch.load(res_name, map_location='cpu') + self.start_epoch = checkpoint['epoch'] + self.best_loss = checkpoint['best_loss'] + self.model.load_state_dict(checkpoint['state_dict']) + self.optimizer.load_state_dict(checkpoint['optimizer']) + if self.lr_scheduler is not None: + self.lr_scheduler.load_state_dict( + checkpoint['lr_scheduler'] + ) + if self.cluster.is_cuda_available(): + if self.cluster.is_main_worker(): + print( + f'WARNING: restarting from {self.start_epoch} ' + 'epoch') + else: + print( + f'WARNING: restarting from {self.start_epoch} epoch') + except Exception: + if self.cluster.is_cuda_available(): + if self.cluster.is_main_worker(): + print( + 'restart file cannot be loaded, restarting!') + else: + print( + 'WARNING: restart file cannot be loaded, restarting!') + + if self.start_epoch >= self.epochs + 1: + if self.cluster.is_cuda_available() and self.cluster.distributed: + if self.cluster.is_main_worker(): + print( + 'WARNING: given epochs are less than the ' + 'one in the restart file!') + print('WARNING: SYS.EXIT is issued') + sys.exit() + else: + print( + 'WARNING: given epochs are less than the ' + 'one in the restart file!') + print('WARNING: SYS.EXIT is issued') + sys.exit() diff --git a/src/itwinai/ptorch/types.py b/src/itwinai/ptorch/types.py new file mode 100644 index 00000000..6f6e5c9f --- /dev/null +++ b/src/itwinai/ptorch/types.py @@ -0,0 +1,64 @@ +"""Custom types definition.""" + +from typing import Callable +from enum import Enum, EnumMeta + +import torch +from torch import nn + +Loss = nn.Module +LrScheduler = nn.Module +Batch = torch.Tensor +Metric = Callable + + +class MetaEnum(EnumMeta): + def __contains__(cls, item): + try: + cls(item) + except ValueError: + return False + return True + + +class BaseEnum(Enum, metaclass=MetaEnum): + @classmethod + def list(cls): + return list(map(lambda c: c.value, cls)) + + +class TorchDistributedBackend(BaseEnum): + """ + Enum for torch distributed backends. + Reference: https://pytorch.org/docs/stable/distributed.html#backends + """ + DEFAULT = 'nccl' + GLOO = 'gloo' + NCCL = 'nccl' + MPI = 'mpi' + + +class TorchDistributedStrategy(BaseEnum): + DEFAULT = None + NONE = None + DDP = 'ddp' + + +class TorchLoss(BaseEnum): + """ + Torch loss class names. + TODO: complete from https://pytorch.org/docs/stable/nn.html#loss-functions + """ + L1 = 'L1Loss' + MSE = 'MSELoss' + CROSS_ENTROPY = 'CrossEntropyLoss' + NLLLOSS = 'NLLLoss' + + +class TorchOptimizer(BaseEnum): + """ + Torch optimizer class names. + TODO: complete from https://pytorch.org/docs/stable/optim.html#algorithms + """ + SGD = 'SGD' + ADAM = 'Adam' diff --git a/src/itwinai/ptorch/utils.py b/src/itwinai/ptorch/utils.py new file mode 100644 index 00000000..99bcd246 --- /dev/null +++ b/src/itwinai/ptorch/utils.py @@ -0,0 +1,84 @@ +from typing import Hashable, Dict +import time +import numpy as np +import random + +import torch +import torch.distributed as dist + + +def save_state( + epoch, distrib_model, loss_val, optimizer, res_name, grank, gwsize, + is_best, distributed: bool = True +): + """Save training state""" + rt = time.time() + # find if is_best happened in any worker + if torch.cuda.is_available() and distributed: + is_best_m = par_allgather_obj(is_best, gwsize) + + if torch.cuda.is_available() and distributed: + if any(is_best_m): + # find which rank is_best happened - select first rank if multiple + is_best_rank = np.where(np.array(is_best_m))[0][0] + + # collect state + state = {'epoch': epoch + 1, + 'state_dict': distrib_model.state_dict(), + 'best_loss': loss_val, + 'optimizer': optimizer.state_dict()} + + # write on worker with is_best + if grank == is_best_rank: + torch.save(state, './'+res_name) + print(f'DEBUG: state in {grank} is saved on ' + f'epoch:{epoch} in {time.time()-rt} s') + else: + # collect state + state = {'epoch': epoch + 1, + 'state_dict': distrib_model.state_dict(), + 'best_loss': loss_val, + 'optimizer': optimizer.state_dict()} + + torch.save(state, './'+res_name) + print( + f'DEBUG: state in {grank} is saved on epoch:{epoch} ' + f'in {time.time()-rt} s') + + +def seed_worker(worker_id): + """deterministic dataloader""" + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +def par_allgather_obj(obj, gwsize): + """gathers any object from the whole group in a list (to all workers)""" + res = [None]*gwsize + dist.all_gather_object(res, obj, group=None) + # print(f'ALLGATHER: {res}') + return res + + +def clear_key( + my_dict: Dict, + dict_name: str, + key: Hashable, + complain: bool = True +) -> Dict: + """Remove key from dictionary if present and complain. + + Args: + my_dict (Dict): Dictionary. + dict_name (str): name of the dictionary. + key (Hashable): Key to remove. + """ + if key in my_dict: + if complain: + print( + f"Field '{key}' should not be present " + f"in dictionary '{dict_name}'" + ) + del my_dict[key] + return my_dict From ac85999dbc835118db99f6686f7d878965e2bca1 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Fri, 1 Mar 2024 16:31:34 +0100 Subject: [PATCH 12/59] fixing tutorial sections --- docs/advanced_workflow.rst | 3 +++ docs/basic_workflow.rst | 11 +++++++---- docs/intermediate_workflow.rst | 12 ++++++++---- 3 files changed, 18 insertions(+), 8 deletions(-) diff --git a/docs/advanced_workflow.rst b/docs/advanced_workflow.rst index 140d8ee0..8ff74c36 100644 --- a/docs/advanced_workflow.rst +++ b/docs/advanced_workflow.rst @@ -17,3 +17,6 @@ The `tutorial_2_advanced_workflow.py` script is ... :undoc-members: :show-inheritance: + +.. literalinclude:: ../tutorials/ml-workflows/tutorial_2_advanced_workflow.py + :language: python diff --git a/docs/basic_workflow.rst b/docs/basic_workflow.rst index 7b6fca2d..cca169fe 100644 --- a/docs/basic_workflow.rst +++ b/docs/basic_workflow.rst @@ -12,8 +12,11 @@ The `tutorial_0_basic_workflow.py` script is ... .. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py .. :language: python -.. automodule:: tutorial_0_basic_workflow - :members: - :undoc-members: - :show-inheritance: +.. .. automodule:: tutorial_0_basic_workflow +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. literalinclude:: ../tutorials/ml-workflows/tutorial_0_basic_workflow.py + :language: python diff --git a/docs/intermediate_workflow.rst b/docs/intermediate_workflow.rst index 79c0472b..0a587705 100644 --- a/docs/intermediate_workflow.rst +++ b/docs/intermediate_workflow.rst @@ -12,8 +12,12 @@ The `tutorial_1_intermediate_workflow.py` script is ... .. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py .. :language: python -.. automodule:: tutorial_1_intermediate_workflow - :members: - :undoc-members: - :show-inheritance: +.. .. automodule:: tutorial_1_intermediate_workflow +.. :members: +.. :undoc-members: +.. :show-inheritance: + + +.. literalinclude:: ../tutorials/ml-workflows/tutorial_1_intermediate_workflow.py + :language: python From b0cef02655d5fef0a649f55e96ce2934ed1cd714 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 6 Mar 2024 13:14:31 +0100 Subject: [PATCH 13/59] fixes in pages appearance --- docs/index.rst | 2 +- docs/use_cases.rst | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index d56cb069..479a8525 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -52,7 +52,7 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi tutorials -**`interTwin Demo: itwinai integration with other DTE modules `_** +`**interTwin Demo:** itwinai integration with other DTE modules `_ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Indices and tables diff --git a/docs/use_cases.rst b/docs/use_cases.rst index dd59b82b..4401a68e 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -1,5 +1,3 @@ -.. _use-cases: - Integrated Use Cases ==================== From b6e919d2acc71ce95366c2d686889aa2c34b51d8 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 6 Mar 2024 15:11:27 +0100 Subject: [PATCH 14/59] fixing rendering bugs --- docs/3dgan_doc.rst | 4 +- docs/itwinai.torch.modules.rst | 78 +++++++++++++++++++++------------- docs/mnist_doc.rst | 4 +- docs/use_cases.rst | 4 +- 4 files changed, 55 insertions(+), 35 deletions(-) diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst index 1ddd592e..eb5db410 100644 --- a/docs/3dgan_doc.rst +++ b/docs/3dgan_doc.rst @@ -1,5 +1,5 @@ -3DGAN Documentation -==================== +3DGAN +===== The 3DGAN use case is a part of the use cases which... diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst index 883fd4e8..b4cf7b0a 100644 --- a/docs/itwinai.torch.modules.rst +++ b/docs/itwinai.torch.modules.rst @@ -1,34 +1,54 @@ itwinai.torch.modules ===================== -.. automodule:: itwinai.ptorch.cluster - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.inference - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.mlflow - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.trainer - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.types - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.utils - :members: - :undoc-members: - :show-inheritance: +.. literalinclude:: ../src/itwinai/ptorch/cluster.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/inference.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/mlflow.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/trainer.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/types.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/utils.py + :language: python + + + +.. .. automodule:: itwinai.ptorch.cluster +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.inference +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.mlflow +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.types +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.utils +.. :members: +.. :undoc-members: +.. :show-inheritance: diff --git a/docs/mnist_doc.rst b/docs/mnist_doc.rst index afedbb29..fd26c4e7 100644 --- a/docs/mnist_doc.rst +++ b/docs/mnist_doc.rst @@ -1,5 +1,5 @@ -MNIST Use Case -============== +MNIST +===== This section covers the MNIST use case, which utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: diff --git a/docs/use_cases.rst b/docs/use_cases.rst index 4401a68e..f2653f6b 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -10,7 +10,7 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - :caption: 3DGAN use case Documentation + .. :caption: 3DGAN use case Documentation 3dgan_doc @@ -22,7 +22,7 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - :caption: MNIST Documentation + .. :caption: MNIST Documentation mnist_doc From f9c0e335ddcb94c2cd9309ac6ca31f1e099c7f1b Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 6 Mar 2024 15:24:40 +0100 Subject: [PATCH 15/59] fixing pages appearance bugs --- docs/use_cases.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/use_cases.rst b/docs/use_cases.rst index f2653f6b..9cadd90a 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -10,7 +10,6 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - .. :caption: 3DGAN use case Documentation 3dgan_doc @@ -22,8 +21,9 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - .. :caption: MNIST Documentation mnist_doc + + From 5a3badc6eea6f558c7e0d20e7796c09ed1950844 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 7 Mar 2024 15:55:20 +0100 Subject: [PATCH 16/59] adding latest modifications --- docs/advanced_workflow.rst | 3 --- docs/basic_comp.rst | 3 --- docs/basic_workflow.rst | 3 --- docs/getting_started_with_itwinai.rst | 2 +- docs/index.rst | 4 +++- docs/intermediate_workflow.rst | 3 --- docs/itwinai.tf.modules.rst | 22 ++++++++++++++-------- docs/itwinai.torch.modules.rst | 22 ++++++++++++++++++++-- docs/tutorials.rst | 3 --- docs/use_cases.rst | 7 +++++-- 10 files changed, 43 insertions(+), 29 deletions(-) diff --git a/docs/advanced_workflow.rst b/docs/advanced_workflow.rst index 8ff74c36..121d3acc 100644 --- a/docs/advanced_workflow.rst +++ b/docs/advanced_workflow.rst @@ -1,9 +1,6 @@ Advanced workflow ================= -Description... - - tutorial_2_advanced_workflow.py +++++++++++++++++++++++++++++++ diff --git a/docs/basic_comp.rst b/docs/basic_comp.rst index d4fb3a18..5acfe66d 100644 --- a/docs/basic_comp.rst +++ b/docs/basic_comp.rst @@ -1,9 +1,6 @@ Basic components ================ -Description... - - basic_components.py +++++++++++++++++++ diff --git a/docs/basic_workflow.rst b/docs/basic_workflow.rst index cca169fe..cd77d328 100644 --- a/docs/basic_workflow.rst +++ b/docs/basic_workflow.rst @@ -1,9 +1,6 @@ Basic workflow ============== -Description... - - tutorial_0_basic_workflow.py ++++++++++++++++++++++++++++ diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 9956a89e..e559189a 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -4,7 +4,7 @@ Getting started with itwinai Installation ------------ -Requirements: +Requirements +++++++++++++ * Linux environment. diff --git a/docs/index.rst b/docs/index.rst index 479a8525..4b510081 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -40,6 +40,7 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi .. toctree:: :maxdepth: 2 + :hidden: :caption: 📚 Integrated Use-cases use_cases @@ -52,9 +53,10 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi tutorials -`**interTwin Demo:** itwinai integration with other DTE modules `_ +`interTwin Demo: itwinai integration with other DTE modules `_ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + Indices and tables ================== diff --git a/docs/intermediate_workflow.rst b/docs/intermediate_workflow.rst index 0a587705..860b12e5 100644 --- a/docs/intermediate_workflow.rst +++ b/docs/intermediate_workflow.rst @@ -1,9 +1,6 @@ Intermediate workflow ===================== -Description... - - tutorial_1_intermediate_workflow.py +++++++++++++++++++++++++++++++++++ diff --git a/docs/itwinai.tf.modules.rst b/docs/itwinai.tf.modules.rst index aad490d8..8b923bea 100644 --- a/docs/itwinai.tf.modules.rst +++ b/docs/itwinai.tf.modules.rst @@ -1,6 +1,19 @@ -itwinai.tensorflow.modules +itwinai Tensorflow Modules ========================== +trainer.py +++++++++++ + +.. literalinclude:: ../src/itwinai/tensorflow/trainer.py + :language: python + +utils.py +++++++++ + +.. literalinclude:: ../src/itwinai/tensorflow/utils.py + :language: python + + .. .. automodule:: itwinai.tensorflow.trainer .. :members: .. :undoc-members: @@ -11,10 +24,3 @@ itwinai.tensorflow.modules .. :undoc-members: .. :show-inheritance: -.. literalinclude:: ../src/itwinai/tensorflow/trainer.py - :language: python - -.. literalinclude:: ../src/itwinai/tensorflow/utils.py - :language: python - - diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst index b4cf7b0a..9b86f1f8 100644 --- a/docs/itwinai.torch.modules.rst +++ b/docs/itwinai.torch.modules.rst @@ -1,21 +1,39 @@ -itwinai.torch.modules -===================== +itwinai PyTorch Modules +======================= + +cluster.py +++++++++++ .. literalinclude:: ../src/itwinai/ptorch/cluster.py :language: python +inference.py +++++++++++++ + .. literalinclude:: ../src/itwinai/ptorch/inference.py :language: python +mlflow.py ++++++++++ + .. literalinclude:: ../src/itwinai/ptorch/mlflow.py :language: python +trainer.py +++++++++++ + .. literalinclude:: ../src/itwinai/ptorch/trainer.py :language: python +types.py +++++++++ + .. literalinclude:: ../src/itwinai/ptorch/types.py :language: python +utils.py +++++++++ + .. literalinclude:: ../src/itwinai/ptorch/utils.py :language: python diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 5a5772d5..623582f2 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -8,11 +8,8 @@ Here you can find a collection of tutorials for various complexity ML workflows. Tutorials --------- -Description... - .. toctree:: :maxdepth: 2 - :caption: Examples basic_comp basic_workflow diff --git a/docs/use_cases.rst b/docs/use_cases.rst index 9cadd90a..4a584702 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -6,7 +6,9 @@ Here you can find a collection of use cases including various projects. 3DGAN CERN use case ------------------- -Description of the use case etc... +The first ``interTwin`` use case integrated with ``itwinai`` framework is the DT for fast particle detector simulation. +3D Generative Adversarial Network (3DGAN) for generation of images of calorimeter depositions. +This project is based on the prototype `3DGAN `_ model developed at CERN and is implemented on PyTorch Lightning framework. .. toctree:: :maxdepth: 2 @@ -17,7 +19,8 @@ Description of the use case etc... MNIST use case -------------- -Description of the use case etc... +MNIST image classification is used to provide an example on +how to define an end-to-end digital twin workflow with the ``itwinai`` software. .. toctree:: :maxdepth: 2 From bcdf0f03ea9fb871b5eb69dad210599562f3a1c4 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Fri, 8 Mar 2024 11:16:27 +0100 Subject: [PATCH 17/59] Deleted duplicate folder after renaming src/itwinai/torch --- src/itwinai/torch/__init__.py | 0 src/itwinai/torch/cluster.py | 225 ------- src/itwinai/torch/data/__init__.py | 0 src/itwinai/torch/inference.py | 219 ------- src/itwinai/torch/mlflow.py | 77 --- src/itwinai/torch/models/__init__.py | 0 src/itwinai/torch/models/mnist.py | 75 --- src/itwinai/torch/trainer.py | 941 --------------------------- src/itwinai/torch/types.py | 64 -- src/itwinai/torch/utils.py | 84 --- 10 files changed, 1685 deletions(-) delete mode 100644 src/itwinai/torch/__init__.py delete mode 100644 src/itwinai/torch/cluster.py delete mode 100644 src/itwinai/torch/data/__init__.py delete mode 100644 src/itwinai/torch/inference.py delete mode 100644 src/itwinai/torch/mlflow.py delete mode 100644 src/itwinai/torch/models/__init__.py delete mode 100644 src/itwinai/torch/models/mnist.py delete mode 100644 src/itwinai/torch/trainer.py delete mode 100644 src/itwinai/torch/types.py delete mode 100644 src/itwinai/torch/utils.py diff --git a/src/itwinai/torch/__init__.py b/src/itwinai/torch/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/itwinai/torch/cluster.py b/src/itwinai/torch/cluster.py deleted file mode 100644 index aece16e2..00000000 --- a/src/itwinai/torch/cluster.py +++ /dev/null @@ -1,225 +0,0 @@ -"""Cluster environments where to run AI workflows. Partially adapted from: -https://github.com/facebookresearch/detr/blob/master/util/misc.py and -https://github.com/ramyamounir/Template/blob/main/lib/utils/distributed.py -""" - -from __future__ import annotations -from typing import Optional -import os -import signal -import subprocess -from pathlib import Path -from contextlib import contextmanager - -import numpy as np - -import torch -import torch.distributed as dist -import torch.backends.cudnn as cudnn - -from ..cluster import ( - ClusterEnvironment, - setup_for_distributed, - handle_sigusr1, - handle_sigterm -) -from .types import TorchDistributedBackend as BackendT - - -def fix_random_seeds(seed=31): - """ - Fix random seeds. - """ - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - np.random.seed(seed) - - -class TorchCluster(ClusterEnvironment): - def __init__(self) -> None: - super().__init__() - - def _set_backend(self, backend_name: str) -> None: - if backend_name not in BackendT: - raise ValueError( - "Unrecognized 'backend' field. Allowed values " - f"are: {BackendT.list()}. Received '{backend_name}'") - self._backend = backend_name - - def is_cuda_available(self) -> bool: - return self.use_cuda and torch.cuda.is_available() - - def is_main_worker(self) -> bool: - """Checks if the current process is the main/master process - in the whole job. - """ - return self.global_rank == 0 - - def cleanup_resources(self): - dist.barrier() - dist.destroy_process_group() - - -class LocalCluster(TorchCluster): - """Simple single node cluster with optional access to multiple GPUs.""" - - def __init__( - self, - backend: Optional[str] = None, - gpus: Optional[str] = '', - port: int = 49153, - rnd_seed: Optional[int] = 42 - ) -> None: - """Initialize local cluster for multi-GPU access. - - Args: - backend (Optional[str], optional): supported PyTorch backends. - If None, workload is not distributed. Defaults to None. - gpus (Optional[str], optional): list of visible GPU devices - (e.g., '1,2,3'). If empty string uses all available GPUs. - If None, CPU is used. Defaults to ''. - port (int, optional): TCP port used by the master process. - Defaults to 49153. - rnd_seed (Optional[int], optional): random seed to be setup after - all processes are setup. Defaults to 42. - """ - super().__init__() - self.backend = backend - self.gpus = gpus - self.port = port - self.dist_url = f'tcp://127.0.0.1:{self.port}' - self.rnd_seed = rnd_seed - - if self.gpus != '' and self.gpus is not None: - # Restrict the number of GPUs visible according to user needs - os.environ['CUDA_VISIBLE_DEVICES'] = self.gpus - - self.ngpus_per_node = torch.cuda.device_count() - self.global_rank = 0 - self.global_world_size = self.ngpus_per_node - - print(f"{self.ngpus_per_node} GPUs are available.") - self.distributed = True - # This flag tells whether the user wants to use the GPU(s) - self.use_cuda = ( - self.gpus is not None # GPU is not manually disabled - and torch.cuda.device_count() >= 1 # At least one GPU is selected - ) - if self.backend is None or self.ngpus_per_node <= 1: - print("Distributed has been disabled.") - self.distributed = False - self.dist_url = None - self.global_world_size = 1 - self.global_rank = 0 - if not self.is_cuda_available(): - print("CUDA disabled... Running on single CPU.") - self.use_cuda = False - self.distributed = False - self.dist_url = None - self.global_world_size = 1 - self.global_rank = 0 - - # Since single node case - self.local_world_size = self.global_world_size - - @contextmanager - def init_dist_gpu(self, worker_id) -> torch.device: - if self.distributed: - torch.cuda.set_device(worker_id) - self.global_rank += worker_id - # print(f'GLOBAL RANK: {self.global_rank}') - # Since single node case - self.local_rank = self.global_rank - # Simplification: worker ID mapped to GPU ID - self.gpu_id = worker_id - - try: - dist.init_process_group( - backend=self.backend, - init_method=self.dist_url, - world_size=self.global_world_size, - rank=self.global_rank - ) - fix_random_seeds(self.rnd_seed) - torch.cuda.set_device(self.gpu_id) - cudnn.benchmark = True - dist.barrier() - - setup_for_distributed(self.is_main_worker()) - print("SETUP DISTRIBUTED COMPLETE") - yield torch.device('cuda', worker_id) - finally: - self.cleanup_resources() - else: - # Distributed is disabled - # Since single node case - self.global_rank = 0 - self.local_rank = self.global_rank - if self.use_cuda: - torch.cuda.set_device(worker_id) - yield torch.device('cuda', worker_id) - else: - yield torch.device('cpu') - - -class SLURMCluster(TorchCluster): - """SLURM cluster with access to multi-node multi-GPU.""" - - def __init__( - self, - port: int = 49153, - backend: str = 'gloo', - rnd_seed: Optional[int] = 42 - ) -> None: - super().__init__() - self.port = port - self.backend = backend - self.rnd_seed = rnd_seed - if 'SLURM_JOB_ID' not in os.environ: - raise RuntimeError( - "'SLURM_JOB_ID' environment variable is not set. " - "Perhaps you are not running in a slurm cluster?" - ) - - self.ngpus_per_node = torch.cuda.device_count() - - # requeue job on SLURM preemption - signal.signal(signal.SIGUSR1, handle_sigusr1) - signal.signal(signal.SIGTERM, handle_sigterm) - - # find a common host name on all nodes - cmd = 'scontrol show hostnames ' + os.getenv('SLURM_JOB_NODELIST') - stdout = subprocess.check_output(cmd.split()) - host_name = stdout.decode().splitlines()[0] - self.dist_url = f'tcp://{host_name}:{self.port}' - - # distributed parameters - self.global_rank = int(os.getenv('SLURM_NODEID')) * self.ngpus_per_node - self.global_world_size = int( - os.getenv('SLURM_NNODES')) * self.ngpus_per_node - - @contextmanager - def init_dist_gpu(self): - import submitit - try: - job_env = submitit.JobEnvironment() - self.output_dir = Path( - str(self.output_dir).replace("%j", str(job_env.job_id))) - self.gpu = job_env.local_rank - self.global_rank = job_env.global_rank - - dist.init_process_group( - backend=self.backend, - init_method=self.dist_url, - world_size=self.global_world_size, - rank=self.global_rank - ) - fix_random_seeds(self.rnd_seed) - torch.cuda.set_device(self.gpu) - cudnn.benchmark = True - dist.barrier() - - setup_for_distributed(self.is_main_worker()) - yield - finally: - self.cleanup_resources() diff --git a/src/itwinai/torch/data/__init__.py b/src/itwinai/torch/data/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/itwinai/torch/inference.py b/src/itwinai/torch/inference.py deleted file mode 100644 index 02882f06..00000000 --- a/src/itwinai/torch/inference.py +++ /dev/null @@ -1,219 +0,0 @@ -from typing import Optional, Dict, Any, Union -import os -import abc - -import torch -from torch import nn -from torch.utils.data import DataLoader, Dataset - -from ..utils import dynamically_import_class -from .utils import clear_key -from ..components import Predictor, monitor_exec -from .types import TorchDistributedStrategy as StrategyT -from .types import Metric, Batch -from ..serialization import ModelLoader - - -class TorchModelLoader(ModelLoader): - """Loads a torch model from somewhere. - - Args: - model_uri (str): Can be a path on local filesystem - or an mlflow 'locator' in the form: - 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' - """ - - def __call__(self) -> nn.Module: - """"Loads model from model URI. - - Raises: - ValueError: if the model URI is not recognized - or the model is not found. - - Returns: - nn.Module: torch neural network. - """ - if os.path.exists(self.model_uri): - # Model is on local filesystem. - model = torch.load(self.model_uri) - return model.eval() - - if self.model_uri.startswith('mlflow+'): - # Model is on an MLFLow server - # Form is 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' - import mlflow - from mlflow import MlflowException - _, tracking_uri, run_id, artifact_path = self.model_uri.split('+') - mlflow.set_tracking_uri(tracking_uri) - - # Check that run exists - try: - mlflow.get_run(run_id) - except MlflowException: - raise ValueError(f"Run ID '{run_id}' was not found!") - - # Download model weights - ckpt_path = mlflow.artifacts.download_artifacts( - run_id=run_id, - artifact_path=artifact_path, - dst_path='tmp/', - tracking_uri=mlflow.get_tracking_uri() - ) - model = torch.load(ckpt_path) - return model.eval() - - raise ValueError( - 'Unrecognized model URI: model may not be there!' - ) - - -class TorchPredictor(Predictor): - """Applies a pre-trained torch model to unseen data.""" - - model: nn.Module = None - test_dataset: Dataset - test_dataloader: DataLoader = None - _strategy: StrategyT = StrategyT.NONE.value - epoch_idx: int = 0 - train_glob_step: int = 0 - validation_glob_step: int = 0 - train_metrics: Dict[str, Metric] - validation_metrics: Dict[str, Metric] - - def __init__( - self, - model: Union[nn.Module, ModelLoader], - test_dataloader_class: str = 'torch.utils.data.DataLoader', - test_dataloader_kwargs: Optional[Dict] = None, - # strategy: str = StrategyT.NONE.value, - # seed: Optional[int] = None, - # logger: Optional[List[Logger]] = None, - # cluster: Optional[ClusterEnvironment] = None, - # test_metrics: Optional[Dict[str, Metric]] = None, - name: str = None - ) -> None: - super().__init__(model=model, name=name) - self.save_parameters(**self.locals2params(locals())) - self.model = self.model.eval() - # self.seed = seed - # self.strategy = strategy - # self.cluster = cluster - - # Train and validation dataloaders - self.test_dataloader_class = dynamically_import_class( - test_dataloader_class - ) - test_dataloader_kwargs = ( - test_dataloader_kwargs - if test_dataloader_kwargs is not None else {} - ) - self.test_dataloader_kwargs = clear_key( - test_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' - ) - - # # Loggers - # self.logger = logger if logger is not None else ConsoleLogger() - - # # Metrics - # self.train_metrics = ( - # {} if train_metrics is None else train_metrics - # ) - # self.validation_metrics = ( - # self.train_metrics if validation_metrics is None - # else validation_metrics - # ) - - @monitor_exec - def execute( - self, - test_dataset: Dataset, - model: nn.Module = None, - ) -> Dict[str, Any]: - """Applies a torch model to a dataset for inference. - - Args: - test_dataset (Dataset[str, Any]): each item in this dataset is a - couple (item_unique_id, item) - model (nn.Module, optional): torch model. Overrides the existing - model, if given. Defaults to None. - - Returns: - Dict[str, Any]: maps each item ID to the corresponding predicted - value(s). - """ - if model is not None: - # Overrides existing "internal" model - self.model = model - - test_dataloader = self.test_dataloader_class( - test_dataset, **self.test_dataloader_kwargs - ) - - all_predictions = dict() - for samples_ids, samples in test_dataloader: - with torch.no_grad(): - pred = self.model(samples) - pred = self.transform_predictions(pred) - for idx, pre in zip(samples_ids, pred): - # For each item in the batch - if pre.numel() == 1: - pre = pre.item() - else: - pre = pre.to_dense().tolist() - all_predictions[idx] = pre - return all_predictions - - @abc.abstractmethod - def transform_predictions(self, batch: Batch) -> Batch: - """ - Post-process the predictions of the torch model (e.g., apply - threshold in case of multilabel classifier). - """ - - -class MulticlassTorchPredictor(TorchPredictor): - """ - Applies a pre-trained torch model to unseen data for - multiclass classification. - """ - - def transform_predictions(self, batch: Batch) -> Batch: - batch = batch.argmax(-1) - return batch - - -class MultilabelTorchPredictor(TorchPredictor): - """ - Applies a pre-trained torch model to unseen data for - multilabel classification, applying a threshold on the - output of the neural network. - """ - - threshold: float - - def __init__( - self, - model: Union[nn.Module, ModelLoader], - test_dataloader_class: str = 'torch.utils.data.DataLoader', - test_dataloader_kwargs: Optional[Dict] = None, - threshold: float = 0.5, - name: str = None - ) -> None: - super().__init__( - model, test_dataloader_class, test_dataloader_kwargs, name - ) - self.threshold = threshold - - def transform_predictions(self, batch: Batch) -> Batch: - return (batch > self.threshold).float() - - -class RegressionTorchPredictor(TorchPredictor): - """ - Applies a pre-trained torch model to unseen data for - regression, leaving untouched the output of the neural - network. - """ - - def transform_predictions(self, batch: Batch) -> Batch: - return batch diff --git a/src/itwinai/torch/mlflow.py b/src/itwinai/torch/mlflow.py deleted file mode 100644 index 18a014ff..00000000 --- a/src/itwinai/torch/mlflow.py +++ /dev/null @@ -1,77 +0,0 @@ -from typing import Dict, Optional -import os - -import mlflow -import yaml - - -def _get_mlflow_logger_conf(pl_config: Dict) -> Optional[Dict]: - """Extract MLFLowLogger configuration from pytorch lightning - configuration file, if present. - - Args: - pl_config (Dict): lightning configuration loaded in memory. - - Returns: - Optional[Dict]: if present, MLFLowLogger constructor arguments - (under 'init_args' key). - """ - if isinstance(pl_config['trainer']['logger'], list): - # If multiple loggers are provided - for logger_conf in pl_config['trainer']['logger']: - if logger_conf['class_path'].endswith('MLFlowLogger'): - return logger_conf['init_args'] - elif pl_config['trainer']['logger']['class_path'].endswith('MLFlowLogger'): - return pl_config['trainer']['logger']['init_args'] - - -def _mlflow_log_pl_config(pl_config: Dict, local_yaml_path: str) -> None: - os.makedirs(os.path.dirname(local_yaml_path), exist_ok=True) - with open(local_yaml_path, 'w') as outfile: - yaml.dump(pl_config, outfile, default_flow_style=False) - mlflow.log_artifact(local_yaml_path) - - -def init_lightning_mlflow( - pl_config: Dict, - default_experiment_name: str = 'Default', - **autolog_kwargs -) -> None: - """Initialize mlflow for pytorch lightning, also setting up - auto-logging (mlflow.pytorch.autolog(...)). Creates a new mlflow - run and attaches it to the mlflow auto-logger. - - Args: - pl_config (Dict): pytorch lightning configuration loaded in memory. - default_experiment_name (str, optional): used as experiment name - if it is not given in the lightning conf. Defaults to 'Default'. - **autolog_kwargs (kwargs): args for mlflow.pytorch.autolog(...). - """ - mlflow_conf: Optional[Dict] = _get_mlflow_logger_conf(pl_config) - if not mlflow_conf: - return - - tracking_uri = mlflow_conf.get('tracking_uri') - if not tracking_uri: - save_path = mlflow_conf.get('save_dir') - tracking_uri = "file://" + os.path.abspath(save_path) - - experiment_name = mlflow_conf.get('experiment_name') - if not experiment_name: - experiment_name = default_experiment_name - - mlflow.set_tracking_uri(tracking_uri) - mlflow.set_experiment(experiment_name) - mlflow.pytorch.autolog(**autolog_kwargs) - mlflow.start_run() - - mlflow_conf['experiment_name'] = experiment_name - mlflow_conf['run_id'] = mlflow.active_run().info.run_id - - _mlflow_log_pl_config(pl_config, '.tmp/pl_config.yml') - - -def teardown_lightning_mlflow() -> None: - """End active mlflow run, if any.""" - if mlflow.active_run() is not None: - mlflow.end_run() diff --git a/src/itwinai/torch/models/__init__.py b/src/itwinai/torch/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/itwinai/torch/models/mnist.py b/src/itwinai/torch/models/mnist.py deleted file mode 100644 index c222a5a3..00000000 --- a/src/itwinai/torch/models/mnist.py +++ /dev/null @@ -1,75 +0,0 @@ -import torch -import torch.nn as nn -import lightning as L - -from torch.nn import functional as F - - -class MNISTModel(L.LightningModule): - """ - Simple PL model for MNIST. - Adapted from - https://lightning.ai/docs/pytorch/stable/notebooks/lightning_examples/mnist-hello-world.html - """ - - def __init__( - self, - hidden_size: int = 64, - ): - super().__init__() - - # Automatically save constructor args as hyperparameters - self.save_hyperparameters() - - # Set our init args as class attributes - self.hidden_size = hidden_size - - # Hardcode some dataset specific attributes - self.num_classes = 10 - self.dims = (1, 28, 28) - channels, width, height = self.dims - - # Define PyTorch model - self.model = nn.Sequential( - nn.Flatten(), - nn.Linear(channels * width * height, hidden_size), - nn.ReLU(), - nn.Dropout(0.1), - nn.Linear(hidden_size, hidden_size), - nn.ReLU(), - nn.Dropout(0.1), - nn.Linear(hidden_size, self.num_classes), - ) - - def forward(self, x): - x = self.model(x) - return F.log_softmax(x, dim=1) - - def training_step(self, batch, batch_idx): - x, y = batch - logits = self(x) - loss = F.nll_loss(logits, y) - - # Log metrics with autolog - self.log("train_loss", loss, on_step=True, on_epoch=True) - - return loss - - def validation_step(self, batch, batch_idx): - x, y = batch - logits = self(x) - loss = F.nll_loss(logits, y) - # preds = torch.argmax(logits, dim=1) - self.log("val_loss", loss, prog_bar=True, on_step=True, on_epoch=True) - - def test_step(self, batch, batch_idx): - x, y = batch - logits = self(x) - loss = F.nll_loss(logits, y) - self.log("test_loss", loss) - - def predict_step(self, batch, batch_idx, dataloader_idx=0): - x, _ = batch - logits = self(x) - preds = torch.argmax(logits, dim=1) - return preds diff --git a/src/itwinai/torch/trainer.py b/src/itwinai/torch/trainer.py deleted file mode 100644 index 31794c49..00000000 --- a/src/itwinai/torch/trainer.py +++ /dev/null @@ -1,941 +0,0 @@ -"""Provides training logic for PyTorch models via Trainer classes.""" - -from typing import ( - Optional, Dict, Union, Tuple, Type, List, Any -) -import time -import os -import sys - -import numpy as np -import torch -import torch.multiprocessing as mp -from torch.utils.data import DataLoader, Dataset -from torch.utils.data.distributed import DistributedSampler -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.nn as nn -from torch.optim.optimizer import Optimizer - -from ..components import Trainer, monitor_exec -from .utils import seed_worker, par_allgather_obj, clear_key -from .types import ( - Batch, Loss, LrScheduler, Metric -) -from .types import TorchDistributedStrategy as StrategyT -from ..loggers import LogMixin, Logger, ConsoleLogger -from ..utils import dynamically_import_class -from ..cluster import ClusterEnvironment - - -def preproc_dataloader(dataloader: DataLoader, gwsize, grank): - """Makes a Dataloader distributed.""" - sampler = DistributedSampler( - dataloader.dataset, - num_replicas=gwsize, - rank=grank, - shuffle=True - ) - # Recreate dataloader, with updated sampler - return DataLoader( - dataloader.dataset, - batch_size=dataloader.batch_size, - sampler=sampler, - num_workers=dataloader.num_workers, - collate_fn=dataloader.collate_fn, - pin_memory=dataloader.pin_memory, - drop_last=dataloader.drop_last, - timeout=dataloader.timeout, - worker_init_fn=seed_worker, # dataloader.worker_init_fn, - multiprocessing_context=dataloader.multiprocessing_context, - generator=dataloader.generator, - prefetch_factor=dataloader.prefetch_factor, - persistent_workers=dataloader.persistent_workers, - pin_memory_device=dataloader.pin_memory_device - ) - - -def distributed(func): - """The decorated function must have a standard signature. - Its first arguments must be: - model, train_dataloader, validation_dataloader, device (in this order). - - Additional args or kwargs are allowed consistently with the signature - of the decorated function. - """ - def dist_train( - model, train_dataloader, validation_dataloader=None, device='cpu', - *args, **kwargs - ): - if torch.cuda.is_available(): - dist.init_process_group(backend='nccl') - - if torch.cuda.is_available(): - lwsize = torch.cuda.device_count() # local world size - per node - gwsize = dist.get_world_size() # global world size - per run - grank = dist.get_rank() # global rank - assign per run - lrank = dist.get_rank() % lwsize # local rank - assign per node - else: - gwsize = 1 - grank = 0 - lrank = 0 - - device = torch.device( - 'cuda' if torch.cuda.is_available() else 'cpu', lrank) - if torch.cuda.is_available(): - torch.cuda.set_device(lrank) - - model = model.to(device) - model = DDP(model, device_ids=[device], output_device=device) - - train_dataloader = preproc_dataloader(train_dataloader, gwsize, grank) - if validation_dataloader is not None: - validation_dataloader = preproc_dataloader( - validation_dataloader, gwsize, grank) - - try: - func(model, train_dataloader, validation_dataloader, device, - *args, **kwargs) - finally: - if torch.cuda.is_available(): - dist.barrier() - dist.destroy_process_group() - return dist_train - - -class TorchTrainerMG(Trainer, LogMixin): - """ - Torch trainer for optionally distributed data-parallel (DDP) workload. - Multi-GPU distribution. - - Args: - model (nn.Module): neural network instance. - loss (Loss): torch loss function instance. - optimizer_class (str): path to optimizer class - (e.g., 'torch.optim.SGD') - optimizer_kwargs (Optional[Dict], optional): optimizer constructor - arguments (except from parameters). Defaults to None. - lr_scheduler_class (Optional[str], optional): path to learning - rate scheduler class. Defaults to None. - lr_scheduler_kwargs (Optional[Dict], optional): constructor arguments - of the learning rate scheduler, except for the optimizer. - Defaults to None. - train_dataloader_class (str, optional): train dataloader class path. - Defaults to 'torch.utils.data.DataLoader'. - train_dataloader_kwargs (Optional[Dict], optional): constructor - arguments of the train dataloader, except for the dataset - instance. Defaults to None. - validation_dataloader_class (str, optional): validation dataloader - class path. Defaults to 'torch.utils.data.DataLoader'. - validation_dataloader_kwargs (Optional[Dict], optional): constructor - arguments of the validation dataloader, except for the dataset - instance. If None, it replicates `train_dataloader_kwargs`. - Defaults to None. - epochs (int, optional): number of training epochs. Defaults to 1. - strategy (Optional[TorchDistributedStrategy], optional): distributed - strategy. Defaults to StrategyT.NONE.value. - backend (TorchDistributedBackend, optional): computing backend. - Defaults to BackendT.NCCL.value. - shuffle_dataset (bool, optional): whether shuffle dataset before - sampling batches from dataloader. Defaults to False. - use_cuda (bool, optional): whether to use GPU. Defaults to True. - benchrun (bool, optional): sets up a debug run. Defaults to False. - testrun (bool, optional): deterministic training seeding everything. - Defaults to False. - seed (Optional[int], optional): random seed. Defaults to None. - logger (Optional[List[Logger]], optional): logger. Defaults to None. - checkpoint_every (int, optional): how often (epochs) to checkpoint the - best model. Defaults to 10. - cluster (Optional[ClusterEnvironment], optional): cluster environment - object describing the context in which the trainer is executed. - Defaults to None. - train_metrics (Optional[Dict[str, Metric]], optional): - list of metrics computed in the training step on the predictions. - It's a dictionary with the form - ``{'metric_unique_name': CallableMetric}``. Defaults to None. - validation_metrics (Optional[Dict[str, Metric]], optional): same - as ``training_metrics``. If not given, it mirrors the training - metrics. Defaults to None. - - Raises: - RuntimeError: When trying to use DDP without CUDA support. - NotImplementedError: when trying to use a strategy different from the - ones provided by TorchDistributedStrategy. - """ - - model: nn.Module = None - loss: Loss = None - optimizer: Optimizer = None - lr_scheduler = None - _strategy: StrategyT = StrategyT.NONE.value - train_dataset: Dataset - validation_dataset: Dataset - train_dataloader: DataLoader = None - validation_dataloader: DataLoader = None - epoch_idx: int = 0 - train_glob_step: int = 0 - validation_glob_step: int = 0 - train_metrics: Dict[str, Metric] - validation_metrics: Dict[str, Metric] - - def __init__( - self, - model: nn.Module, - loss: Loss, - optimizer_class: str, - optimizer_kwargs: Optional[Dict] = None, - lr_scheduler_class: Optional[str] = None, - lr_scheduler_kwargs: Optional[Dict] = None, - train_dataloader_class: str = 'torch.utils.data.DataLoader', - train_dataloader_kwargs: Optional[Dict] = None, - validation_dataloader_class: str = 'torch.utils.data.DataLoader', - validation_dataloader_kwargs: Optional[Dict] = None, - epochs: int = 1, - strategy: str = StrategyT.NONE.value, - benchrun: bool = False, - testrun: bool = False, - seed: Optional[int] = None, - logger: Optional[List[Logger]] = None, - checkpoint_every: int = 10, - cluster: Optional[ClusterEnvironment] = None, - train_metrics: Optional[Dict[str, Metric]] = None, - validation_metrics: Optional[Dict[str, Metric]] = None - ) -> None: - """Sets up the distributed backend and loggers. - Makes the model a DDP model. - """ - super().__init__() - self.save_parameters(**self.locals2params(locals())) - self.model = model - self.loss = loss - self.epochs = epochs - self.testrun = testrun - self.seed = seed - self.strategy = strategy - self.benchrun = benchrun - self.cluster = cluster - # Checkpoint every n epochs - self.checkpoint_every = checkpoint_every - - # Train and validation dataloaders - self.train_dataloader_class = dynamically_import_class( - train_dataloader_class - ) - self.validation_dataloader_class = dynamically_import_class( - validation_dataloader_class - ) - train_dataloader_kwargs = ( - train_dataloader_kwargs - if train_dataloader_kwargs is not None else {} - ) - self.train_dataloader_kwargs = clear_key( - train_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' - ) - # If validation_dataloader_kwargs is not given, - # copy train_dataloader_kwargs - validation_dataloader_kwargs = ( - validation_dataloader_kwargs if validation_dataloader_kwargs - is not None else train_dataloader_kwargs - ) - self.validation_dataloader_kwargs = clear_key( - validation_dataloader_kwargs, 'validation_dataloader_kwargs', - 'dataset' - ) - - # Optimizer and scheduler - optim_class = dynamically_import_class(optimizer_class) - optimizer_kwargs = ( - optimizer_kwargs if optimizer_kwargs is not None else {} - ) - optimizer_kwargs = clear_key( - optimizer_kwargs, 'optimizer_kwargs', 'parameters' - ) - self.optimizer: Optimizer = optim_class( - self.model.parameters(), **optimizer_kwargs - ) - if lr_scheduler_class is not None: - scheduler_class = dynamically_import_class(lr_scheduler_class) - lr_scheduler_kwargs = ( - lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {} - ) - lr_scheduler_kwargs = clear_key( - lr_scheduler_kwargs, 'lr_scheduler_kwargs', 'optimizer' - ) - self.lr_scheduler: LrScheduler = scheduler_class( - self.optimizer, **lr_scheduler_kwargs - ) - - # Loggers - self.logger = logger if logger is not None else ConsoleLogger() - - # Metrics - self.train_metrics = ( - {} if train_metrics is None else train_metrics - ) - self.validation_metrics = ( - self.train_metrics if validation_metrics is None - else validation_metrics - ) - - @property - def strategy(self) -> Optional[str]: - return self._strategy - - @strategy.setter - def strategy(self, strategy_name) -> None: - if strategy_name not in StrategyT: - raise ValueError( - "Unrecognized 'strategy' field. Allowed values " - f"are: {StrategyT.list()}. Received '{strategy_name}'") - self._strategy = strategy_name - - @property - def global_step(self) -> int: - return self.train_glob_step + self.validation_glob_step - - def set_seed(self, seed: Optional[int] = None): - """Deterministic operations for reproducibility. - Sets the random seed. - - Args: - seed (Optional[int], optional): if not None, overrides - `self.seed`. Defaults to None. - """ - seed = seed if seed is not None else self.seed - np.random.seed(seed) - self.torch_rng = torch.Generator() - if seed is not None: - torch.manual_seed(seed) - self.torch_rng.manual_seed(seed) - if self.cluster.is_cuda_available(): - torch.cuda.manual_seed(seed) - - @monitor_exec - def execute( - self, - train_dataset: Dataset, - validation_dataset: Dataset, - model: nn.Module = None, - optimizer: Optimizer = None, - lr_scheduler: LrScheduler = None, - ) -> Any: - self.train_dataset = train_dataset - self.validation_dataset = validation_dataset - - # Update parameters passed for "interactive" use - if model is not None: - self.model = model - if optimizer is not None: - self.optimizer = optimizer - if lr_scheduler is not None: - self.lr_scheduler = lr_scheduler - - # Start training - if self.cluster.distributed: - # Make training distributed - result = mp.spawn(self._train, nprocs=self.cluster.ngpus_per_node) - else: - result = self._train(0) - - # Return value compliant with Executable.execute format - return result - - def _train( - self, - worker_id: int - ): - # Each worker has a different deterministic seed - # Here, 'worker' = replica of the training function - worker_seed = ( - self.seed + worker_id if self.seed is not None else self.seed - ) - self.set_seed(worker_seed) - - # Instantiate dataloaders - self.train_dataloader = self._instantiate_dataloader( - dataloader_class=self.train_dataloader_class, - dataset=self.train_dataset, - init_kwargs=self.train_dataloader_kwargs - ) - if self.validation_dataset is not None: - self.validation_dataloader = self._instantiate_dataloader( - dataloader_class=self.validation_dataloader_class, - dataset=self.validation_dataset, - init_kwargs=self.validation_dataloader_kwargs - ) - - # Launch actual training: - - # Single worker case - if not self.cluster.distributed: - with self.cluster.init_dist_gpu(worker_id) as device: - self.device: torch.device = device - self.model = self.model.to(self.device) - self.setup_logger() - self._setup_metrics() - try: - train_result = self.train() - except Exception as exc: - print(exc) - raise exc - finally: - print("INFO: Training ended") - self.destroy_logger() - train_result = None - return train_result - - # Init / connect to distributed backend - with self.cluster.init_dist_gpu(worker_id) as device: - self.device: torch.device = device - self._distribute_model() - self.setup_logger() - self._setup_metrics() - try: - train_result = self.train() - except Exception as exc: - print(exc) - raise exc - finally: - print("INFO: Training ended") - self.destroy_logger() - train_result = None - return train_result - - def _instantiate_dataloader( - self, - dataloader_class: Type, - dataset: Dataset, - init_kwargs: Dict - ) -> DataLoader: - """Make dataloader distributed if using distributed training strategy. - - Args: - dataloader_class (Type): some torch DataLoader type. - dataset (Dataset): torch dataset instance. - init_kwargs (Dict): constructor args. - """ - init_kwargs['generator'] = init_kwargs.get( - 'generator', self.torch_rng - ) - init_kwargs['worker_init_fn'] = init_kwargs.get( - 'worker_init_fn', seed_worker - ) - - if self.strategy == StrategyT.DDP.value and self.cluster.distributed: - sampler = DistributedSampler( - dataset=dataset, - num_replicas=self.cluster.global_world_size, - rank=self.cluster.global_rank, - shuffle=init_kwargs.get( - 'shuffle', False - ) - ) - # Overwrite existing sampler, if given. - # TODO: improve using wrapper: - # https://discuss.pytorch.org/t/how-to-use-my-own-sampler-when-i-already-use-distributedsampler/62143?page=2 - init_kwargs['sampler'] = sampler - if init_kwargs.get('shuffle') is not None: - # sampler option is mutually exclusive with shuffle - del init_kwargs['shuffle'] - - return dataloader_class(dataset, **init_kwargs) - - def _setup_metrics(self): - for m_name, metric in self.train_metrics.items(): - self.train_metrics[m_name] = metric.to(self.device) - for m_name, metric in self.validation_metrics.items(): - self.validation_metrics[m_name] = metric.to(self.device) - - def _distribute_model(self): - if self.cluster.distributed: - # Distribute model - self.model = self.model.to(self.device) - if self.strategy == StrategyT.NONE.value: - print( - "WARNING: A GPU cluster is available but no distributed " - "strategy was given... Falling back to single worker...") - if not self.cluster.is_main_worker(): - # Use only GPU:0 for single worker - sys.exit(0) - elif self.strategy == StrategyT.DDP.value: - self.model = DDP( - self.model, - device_ids=[self.device.index], - output_device=self.device - ) - else: - raise NotImplementedError("Only DDP strategy is implemented.") - else: - raise RuntimeError( - "Trying to distribute a model when a " - "distributed cluster is not available." - ) - - def setup_logger(self): - if self.cluster.is_main_worker(): - # Only setup loggers on main worker - if isinstance(self.logger, list): - for logger in self.logger: - logger.create_logger_context() - elif isinstance(self.logger, Logger): - self.logger.create_logger_context() - else: - raise TypeError( - "Unrecognized self.logger. Allowed types are 'list' and " - f"'Logger'. Received {type(self.logger)}" - ) - else: - self.logger = [] - - def destroy_logger(self): - if self.cluster.is_main_worker(): - if isinstance(self.logger, list): - for logger in self.logger: - logger.destroy_logger_context() - elif isinstance(self.logger, Logger): - self.logger.destroy_logger_context() - else: - raise TypeError( - "Unrecognized self.logger. Allowed types are 'list' and " - f"'Logger'. Received {type(self.logger)}" - ) - - def log( - self, - item: Union[Any, List[Any]], - identifier: Union[str, List[str]], - kind: str = 'metric', - step: Optional[int] = None, - batch_idx: Optional[int] = None, - every_worker: bool = False, - **kwargs - ) -> None: - if self.cluster.is_main_worker() or every_worker: - # Only log on main worker if not specified otherwise - if isinstance(self.logger, list): - for logger in self.logger: - logger.log( - item=item, - identifier=identifier, - kind=kind, - step=step, - batch_idx=batch_idx, - **kwargs - ) - elif isinstance(self.logger, Logger): - self.logger.log( - item=item, - identifier=identifier, - kind=kind, - step=step, - batch_idx=batch_idx, - **kwargs - ) - else: - raise TypeError( - "Unrecognized self.logger. Allowed types are 'list' and " - f"'Logger'. Received {type(self.logger)}" - ) - - def compute_metrics( - self, - metrics: Dict[str, Metric], - true: Batch, - pred: Batch, - logger_step: int, - batch_idx: Optional[int], - stage: str = 'train' - ) -> Dict[str, Any]: - """Compute and log metrics. - - Args: - metrics (Dict[str, Metric]): metrics dict. Can be - ``self.train_metrics`` or ``self.validation_metrics``. - true (Batch): true values. - pred (Batch): predicted values. - logger_step (int): global step to pass to the logger. - stage (str): 'train', 'validation'... - - Returns: - Dict[str, Any]: metric values. - """ - m_values = {} - for m_name, metric in metrics.items(): - # metric = metric.to(self.device) - m_val = metric(pred, true).detach().cpu().numpy() - self.log( - item=m_val, - identifier=f'{m_name}_{stage}', - kind='metric', - step=logger_step, - batch_idx=batch_idx - ) - m_values[m_name] = m_val - return m_values - - def training_step( - self, - batch: Batch, - batch_idx: int - ) -> Tuple[Loss, Dict[str, Any]]: - x, y = batch - x, y = x.to(self.device), y.to(self.device) - pred_y = self.model(x) - loss: Loss = self.loss(pred_y, y) - self.log( - item=loss.item(), - identifier='training_loss', - kind='metric', - step=self.train_glob_step, - batch_idx=batch_idx - ) - metrics: Dict[str, Any] = self.compute_metrics( - metrics=self.train_metrics, - true=y, - pred=pred_y, - logger_step=self.train_glob_step, - batch_idx=batch_idx, - stage='training' - ) - return loss, metrics - - def validation_step( - self, - batch: Batch, - batch_idx: int - ) -> Tuple[Loss, Dict[str, Any]]: - x, y = batch - x, y = x.to(self.device), y.to(self.device) - pred_y = self.model(x) - loss: Loss = self.loss(pred_y, y) - self.log( - item=loss.item(), - identifier='validation_loss', - kind='metric', - step=self.validation_glob_step, - batch_idx=batch_idx - ) - metrics: Dict[str, Any] = self.compute_metrics( - metrics=self.validation_metrics, - true=y, - pred=pred_y, - logger_step=self.validation_glob_step, - batch_idx=batch_idx, - stage='validation' - ) - return loss, metrics - - def training_epoch(self) -> Loss: - self.model.train() - train_losses = [] - for batch_idx, train_batch in enumerate(self.train_dataloader): - loss, metrics = self.training_step( - batch=train_batch, - batch_idx=batch_idx - ) - # TODO: merge and log batch metrics and loss into epoch metrics - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - train_losses.append(loss) - # Important: update counter - self.train_glob_step += 1 - - # Aggregate and log losses - avg_loss = torch.mean(torch.stack(train_losses)).detach().cpu() - self.log( - item=avg_loss.item(), - identifier='training_loss_epoch', - kind='metric', - step=self.train_glob_step, - ) - return avg_loss - - def validation_epoch(self) -> Loss: - if self.validation_dataloader is not None: - self.model.eval() - validation_losses = [] - for batch_idx, val_batch \ - in enumerate(self.validation_dataloader): - # TODO: merge and log batch metrics and loss into epoch metrics - loss, metrics = self.validation_step( - batch=val_batch, - batch_idx=batch_idx - ) - validation_losses.append(loss) - # Important: update counter - self.validation_glob_step += 1 - - # Aggregate and log losses - avg_loss = torch.mean( - torch.stack(validation_losses) - ).detach().cpu() - self.log( - item=avg_loss.item(), - identifier='validation_loss_epoch', - kind='metric', - step=self.validation_glob_step, - ) - return avg_loss - - def train(self): - - if self.optimizer is None: - raise ValueError("Undefined optimizer!") - - if self.loss is None: - raise ValueError("Undefined loss function!") - - st = time.time() - - # Resume state - self.start_epoch = 1 - self.best_loss = np.Inf - self.load_state() - - # start training/testing loop - if self.cluster.is_main_worker(): - print(f'TIMER: broadcast: {time.time()-st}s') - print('DEBUG: start training') - print('-'*56) - - ############################## - # Start training: run epochs # - ############################## - - et = time.time() - for self.epoch_idx in range(self.start_epoch, self.epochs + 1): - lt = time.time() - - ####################################################### - # Perform one training epoch and one validation epoch # - ####################################################### - - if self.benchrun and self.epoch_idx == self.epochs: - # TODO: move profiler into cluster environment - # profiling (done on last epoch - slower!) - with torch.autograd.profiler.profile( - use_cuda=self.cluster.is_cuda_available(), - profile_memory=True - ) as prof: - train_loss = self.training_epoch() - else: - train_loss = self.training_epoch() - val_loss = self.validation_epoch() - - ##################################### - # Save checkpoint if model improved # - ##################################### - - ref_loss = val_loss if val_loss is not None else train_loss - is_best = ref_loss < self.best_loss - if (self.epoch_idx % self.checkpoint_every == 0 - and not self.benchrun): - self.save_state( - loss_val=ref_loss, - is_best=is_best - ) - self.best_loss = min(ref_loss, self.best_loss) - - ########################### - # End of epoch operations # - ########################### - - # save first epoch timer - if self.epoch_idx == self.start_epoch: - first_ep_t = time.time()-lt - - # Final epoch - if self.epoch_idx + 1 == self.epochs: - self.train_dataloader.last_epoch = True - self.validation_dataloader.last_epoch = True - - if self.cluster.is_main_worker(): - print(f'TIMER: epoch time: {time.time()-lt}s') - if self.benchrun and self.epoch_idx == self.epochs: - print('-'*56) - print('benchmark of last epoch:') - what1 = ( - 'cuda' if self.cluster.is_cuda_available() else 'cpu' - ) - print( - prof.key_averages().table( - sort_by='self_'+str(what1)+'_time_total' - ) - ) - - ########################## - # Training has completed # - ########################## - - # save final state - if not self.benchrun: - self.save_state( - loss_val=ref_loss, - is_best=is_best - ) - if self.cluster.is_cuda_available() and self.cluster.distributed: - dist.barrier() - - ######################## - # Print training stats # - ######################## - - if self.cluster.is_main_worker(): - print('-'*56) - print('training results:') - print(f'TIMER: first epoch time: {first_ep_t}s') - print(f'TIMER: last epoch time: {time.time()-lt}s') - print( - f'TIMER: average epoch time: {(time.time()-et)/self.epochs}s') - print(f'TIMER: total epoch time: {time.time()-et}s') - if self.epoch_idx > 1: - print( - f'TIMER: total epoch-1 time: {time.time()-et-first_ep_t}s' - ) - print( - 'TIMER: average epoch-1 time: ' - f'{(time.time()-et-first_ep_t)/(self.epochs-1)}s') - if self.benchrun: - print( - f'TIMER: total epoch-2 time: {lt-first_ep_t}s') - print('TIMER: average epoch-2 time: ' - f'{(lt-first_ep_t)/(self.epochs-2)}s') - mem = int(torch.cuda.memory_reserved( - self.cluster.local_rank)/1024/1024) - print( - f'memory req: {mem} MB' - if self.cluster.is_cuda_available() - and self.cluster.distributed else 'memory req: - MB' - ) - if self.cluster.is_cuda_available(): - print( - f'memory summary:\n {torch.cuda.memory_summary(0)}') - - if self.cluster.is_main_worker(): - print(f'TIMER: final time: {time.time()-st} s') - - def save_state(self, loss_val: Any, is_best: bool): - """Save training state.""" - res_name = 'checkpoint.pth.tar' - rt = time.time() - - if (self.cluster.is_cuda_available() and self.cluster.distributed): - # find if is_best happened in any worker - is_best_m = par_allgather_obj( - is_best, self.cluster.global_world_size - ) - if any(is_best_m): - # TODO: is this strategy really good? Checkpointing when - # at least one worker improves the loss on their local - # data split is prone to overfitting, especially when - # the dataset in unbalanced! - - # find which rank is_best happened - select first rank - # if multiple - best_rank = np.where(np.array(is_best_m))[0][0] - if self.cluster.global_rank == best_rank: - self._save_sate( - epoch=self.epoch_idx+1, - loss_val=loss_val, - save_path=res_name - ) - print( - f'DEBUG: state in {self.cluster.global_rank} is ' - f'saved on epoch:{self.epoch_idx} ' - f'in {time.time()-rt} s') - else: - self._save_sate( - epoch=self.epoch_idx+1, - loss_val=loss_val, - save_path=res_name - ) - print( - f'DEBUG: state in {self.cluster.global_rank} ' - f'is saved on epoch:{self.epoch_idx} in {time.time()-rt} s') - - def _save_sate( - self, - epoch: int, - loss_val: Any, - save_path: str - ): - """Save state on disk.""" - sched = ( - self.lr_scheduler.state_dict() - if self.lr_scheduler is not None else None - ) - state = { - 'epoch': epoch, - 'state_dict': self.model.state_dict(), - 'best_loss': loss_val, - 'optimizer': self.optimizer.state_dict(), - 'lr_scheduler': sched - } - self.log( - item=state, - identifier=save_path, - kind='torch', - epoch_step=self.epoch_idx, - batch_step=0 - ) - - def load_state(self): - """Load training state.""" - res_name = 'checkpoint.pth.tar' - if os.path.isfile(res_name) and not self.benchrun: - try: - if (self.cluster.is_cuda_available() - and self.cluster.distributed): - dist.barrier() - # Map model to be loaded to specified single gpu. - # loc = ( - # {'cuda:%d' % 0: 'cuda:%d' % self.cluster.local_rank} - # if self.cluster.is_cuda_available() - # else {'cpu:%d' % 0: 'cpu:%d' % self.cluster.local_rank} - # ) - # checkpoint = torch.load(res_name, map_location=loc) - checkpoint = torch.load( - res_name, map_location=self.device - ) - else: - checkpoint = torch.load(res_name, map_location='cpu') - self.start_epoch = checkpoint['epoch'] - self.best_loss = checkpoint['best_loss'] - self.model.load_state_dict(checkpoint['state_dict']) - self.optimizer.load_state_dict(checkpoint['optimizer']) - if self.lr_scheduler is not None: - self.lr_scheduler.load_state_dict( - checkpoint['lr_scheduler'] - ) - if self.cluster.is_cuda_available(): - if self.cluster.is_main_worker(): - print( - f'WARNING: restarting from {self.start_epoch} ' - 'epoch') - else: - print( - f'WARNING: restarting from {self.start_epoch} epoch') - except Exception: - if self.cluster.is_cuda_available(): - if self.cluster.is_main_worker(): - print( - 'restart file cannot be loaded, restarting!') - else: - print( - 'WARNING: restart file cannot be loaded, restarting!') - - if self.start_epoch >= self.epochs + 1: - if self.cluster.is_cuda_available() and self.cluster.distributed: - if self.cluster.is_main_worker(): - print( - 'WARNING: given epochs are less than the ' - 'one in the restart file!') - print('WARNING: SYS.EXIT is issued') - sys.exit() - else: - print( - 'WARNING: given epochs are less than the ' - 'one in the restart file!') - print('WARNING: SYS.EXIT is issued') - sys.exit() diff --git a/src/itwinai/torch/types.py b/src/itwinai/torch/types.py deleted file mode 100644 index 6f6e5c9f..00000000 --- a/src/itwinai/torch/types.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Custom types definition.""" - -from typing import Callable -from enum import Enum, EnumMeta - -import torch -from torch import nn - -Loss = nn.Module -LrScheduler = nn.Module -Batch = torch.Tensor -Metric = Callable - - -class MetaEnum(EnumMeta): - def __contains__(cls, item): - try: - cls(item) - except ValueError: - return False - return True - - -class BaseEnum(Enum, metaclass=MetaEnum): - @classmethod - def list(cls): - return list(map(lambda c: c.value, cls)) - - -class TorchDistributedBackend(BaseEnum): - """ - Enum for torch distributed backends. - Reference: https://pytorch.org/docs/stable/distributed.html#backends - """ - DEFAULT = 'nccl' - GLOO = 'gloo' - NCCL = 'nccl' - MPI = 'mpi' - - -class TorchDistributedStrategy(BaseEnum): - DEFAULT = None - NONE = None - DDP = 'ddp' - - -class TorchLoss(BaseEnum): - """ - Torch loss class names. - TODO: complete from https://pytorch.org/docs/stable/nn.html#loss-functions - """ - L1 = 'L1Loss' - MSE = 'MSELoss' - CROSS_ENTROPY = 'CrossEntropyLoss' - NLLLOSS = 'NLLLoss' - - -class TorchOptimizer(BaseEnum): - """ - Torch optimizer class names. - TODO: complete from https://pytorch.org/docs/stable/optim.html#algorithms - """ - SGD = 'SGD' - ADAM = 'Adam' diff --git a/src/itwinai/torch/utils.py b/src/itwinai/torch/utils.py deleted file mode 100644 index 99bcd246..00000000 --- a/src/itwinai/torch/utils.py +++ /dev/null @@ -1,84 +0,0 @@ -from typing import Hashable, Dict -import time -import numpy as np -import random - -import torch -import torch.distributed as dist - - -def save_state( - epoch, distrib_model, loss_val, optimizer, res_name, grank, gwsize, - is_best, distributed: bool = True -): - """Save training state""" - rt = time.time() - # find if is_best happened in any worker - if torch.cuda.is_available() and distributed: - is_best_m = par_allgather_obj(is_best, gwsize) - - if torch.cuda.is_available() and distributed: - if any(is_best_m): - # find which rank is_best happened - select first rank if multiple - is_best_rank = np.where(np.array(is_best_m))[0][0] - - # collect state - state = {'epoch': epoch + 1, - 'state_dict': distrib_model.state_dict(), - 'best_loss': loss_val, - 'optimizer': optimizer.state_dict()} - - # write on worker with is_best - if grank == is_best_rank: - torch.save(state, './'+res_name) - print(f'DEBUG: state in {grank} is saved on ' - f'epoch:{epoch} in {time.time()-rt} s') - else: - # collect state - state = {'epoch': epoch + 1, - 'state_dict': distrib_model.state_dict(), - 'best_loss': loss_val, - 'optimizer': optimizer.state_dict()} - - torch.save(state, './'+res_name) - print( - f'DEBUG: state in {grank} is saved on epoch:{epoch} ' - f'in {time.time()-rt} s') - - -def seed_worker(worker_id): - """deterministic dataloader""" - worker_seed = torch.initial_seed() % 2**32 - np.random.seed(worker_seed) - random.seed(worker_seed) - - -def par_allgather_obj(obj, gwsize): - """gathers any object from the whole group in a list (to all workers)""" - res = [None]*gwsize - dist.all_gather_object(res, obj, group=None) - # print(f'ALLGATHER: {res}') - return res - - -def clear_key( - my_dict: Dict, - dict_name: str, - key: Hashable, - complain: bool = True -) -> Dict: - """Remove key from dictionary if present and complain. - - Args: - my_dict (Dict): Dictionary. - dict_name (str): name of the dictionary. - key (Hashable): Key to remove. - """ - if key in my_dict: - if complain: - print( - f"Field '{key}' should not be present " - f"in dictionary '{dict_name}'" - ) - del my_dict[key] - return my_dict From 702b69baafdede9acf43ed97423333a344416584 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 20 Mar 2024 15:34:19 +0100 Subject: [PATCH 18/59] adding documentation.yml file for automatic updating on github pages --- .github/workflows/documentation.yml | 37 +++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 .github/workflows/documentation.yml diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml new file mode 100644 index 00000000..7b4d5cce --- /dev/null +++ b/.github/workflows/documentation.yml @@ -0,0 +1,37 @@ +name: Build and Deploy Sphinx Documentation + +on: + push: + branches: + - docs_dev + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.9' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r docs/requirements.txt + pip install sphinx sphinx-rtd-theme + + - name: Build documentation + run: | + cd docs + make html + + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./docs/_build/html From 0061f15498aed58f99380df9ceb1772065495fbb Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 20 Mar 2024 17:07:07 +0100 Subject: [PATCH 19/59] modifying documentation.yml file --- .github/workflows/documentation.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 7b4d5cce..fdf10745 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -21,7 +21,6 @@ jobs: - name: Install dependencies run: | - python -m pip install --upgrade pip pip install -r docs/requirements.txt pip install sphinx sphinx-rtd-theme From 00b4d93758d160f9a3a26fc340c60f2d3ca34b7c Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 20 Mar 2024 17:22:27 +0100 Subject: [PATCH 20/59] updating reqs file to solve bug in deployment --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 23528ceb..31776555 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -62,7 +62,7 @@ importlib-resources inquirer installer itsdangerous --e git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai +git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai jaraco.classes jeepney Jinja2 From 3419664eddf99ee4d6dfef821344811b2e625aed Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Tue, 13 Feb 2024 16:06:57 +0100 Subject: [PATCH 21/59] commiting docs functionality for testing deployment --- docs/3dgan_doc.rst | 44 ++++++++++ docs/Makefile | 20 +++++ docs/conf.py | 36 ++++++++ docs/index.rst | 23 ++++++ docs/itwinai.cli.rst | 7 ++ docs/itwinai.cluster.rst | 7 ++ docs/itwinai.components.rst | 8 ++ docs/itwinai.loggers.rst | 8 ++ docs/itwinai.parser.rst | 8 ++ docs/itwinai.pipeline.rst | 8 ++ docs/itwinai.serialization.rst | 8 ++ docs/itwinai.types.rst | 8 ++ docs/itwinai.utils.rst | 8 ++ docs/make.bat | 35 ++++++++ docs/mnist_doc.rst | 78 ++++++++++++++++++ docs/modules.rst | 16 ++++ docs/use_cases.rst | 20 +++++ {docs => docs_old}/.gitignore | 0 {docs => docs_old}/404.html | 0 {docs => docs_old}/Gemfile | 0 {docs => docs_old}/Gemfile.lock | 0 {docs => docs_old}/LICENSE | 0 {docs => docs_old}/README.md | 0 {docs => docs_old}/_config.yml | 0 {docs => docs_old}/docs/CLI.md | 0 {docs => docs_old}/docs/Concepts.md | 0 .../docs/How-to-use-this-software.md | 0 .../docs/img/Workflow DAG concept.png | Bin {docs => docs_old}/docs/img/cwl-workflow.png | Bin .../img/user-platform interaction full.png | Bin .../docs/img/user-platform interaction.png | Bin {docs => docs_old}/docs/use-cases/index.md | 0 {docs => docs_old}/docs/use-cases/mnist.md | 0 {docs => docs_old}/favicon.ico | Bin {docs => docs_old}/index.md | 0 use-cases/3dgan/__init__.py | 1 + 36 files changed, 343 insertions(+) create mode 100644 docs/3dgan_doc.rst create mode 100644 docs/Makefile create mode 100644 docs/conf.py create mode 100644 docs/index.rst create mode 100644 docs/itwinai.cli.rst create mode 100644 docs/itwinai.cluster.rst create mode 100644 docs/itwinai.components.rst create mode 100644 docs/itwinai.loggers.rst create mode 100644 docs/itwinai.parser.rst create mode 100644 docs/itwinai.pipeline.rst create mode 100644 docs/itwinai.serialization.rst create mode 100644 docs/itwinai.types.rst create mode 100644 docs/itwinai.utils.rst create mode 100644 docs/make.bat create mode 100644 docs/mnist_doc.rst create mode 100644 docs/modules.rst create mode 100644 docs/use_cases.rst rename {docs => docs_old}/.gitignore (100%) rename {docs => docs_old}/404.html (100%) rename {docs => docs_old}/Gemfile (100%) rename {docs => docs_old}/Gemfile.lock (100%) rename {docs => docs_old}/LICENSE (100%) rename {docs => docs_old}/README.md (100%) rename {docs => docs_old}/_config.yml (100%) rename {docs => docs_old}/docs/CLI.md (100%) rename {docs => docs_old}/docs/Concepts.md (100%) rename {docs => docs_old}/docs/How-to-use-this-software.md (100%) rename {docs => docs_old}/docs/img/Workflow DAG concept.png (100%) rename {docs => docs_old}/docs/img/cwl-workflow.png (100%) rename {docs => docs_old}/docs/img/user-platform interaction full.png (100%) rename {docs => docs_old}/docs/img/user-platform interaction.png (100%) rename {docs => docs_old}/docs/use-cases/index.md (100%) rename {docs => docs_old}/docs/use-cases/mnist.md (100%) rename {docs => docs_old}/favicon.ico (100%) rename {docs => docs_old}/index.md (100%) create mode 100644 use-cases/3dgan/__init__.py diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst new file mode 100644 index 00000000..1ddd592e --- /dev/null +++ b/docs/3dgan_doc.rst @@ -0,0 +1,44 @@ +3DGAN Documentation +==================== + +The 3DGAN use case is a part of the use cases which... + +.. literalinclude:: ../use-cases/3dgan/model.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/train.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/trainer.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/saver.py + :language: python + +.. literalinclude:: ../use-cases/3dgan/dataloader.py + :language: python + +.. .. automodule:: 3dgan.model +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.train +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.saver +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: 3dgan.dataloader +.. :members: +.. :undoc-members: +.. :show-inheritance: diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d4bb2cbb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..6bab8aec --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,36 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys +sys.path.insert(0, os.path.abspath('../use-cases/')) +sys.path.insert(0, os.path.abspath('../use-cases/3dgan/')) +sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch-lightning/')) +sys.path.insert(0, os.path.abspath('../src/itwinai')) +sys.path.insert(0, os.path.abspath('../')) + +project = 'itwinai' +copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' +author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' +release = '0.0.1' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode'] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'sphinx_rtd_theme' #'alabaster' +html_static_path = ['_static'] diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..9bf6cf33 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,23 @@ +.. itwinai documentation master file, created by + sphinx-quickstart on Fri Feb 9 13:58:30 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to itwinai's documentation! +=================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + modules + use_cases + 3dgan_doc + mnist_doc + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/itwinai.cli.rst b/docs/itwinai.cli.rst new file mode 100644 index 00000000..18e0d0ef --- /dev/null +++ b/docs/itwinai.cli.rst @@ -0,0 +1,7 @@ +itwinai.cli +=========== + +.. automodule:: itwinai.cli + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/itwinai.cluster.rst b/docs/itwinai.cluster.rst new file mode 100644 index 00000000..7360c981 --- /dev/null +++ b/docs/itwinai.cluster.rst @@ -0,0 +1,7 @@ +itwinai.cluster +=============== + +.. automodule:: itwinai.cluster + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/itwinai.components.rst b/docs/itwinai.components.rst new file mode 100644 index 00000000..db3b0956 --- /dev/null +++ b/docs/itwinai.components.rst @@ -0,0 +1,8 @@ +itwinai.components +================== + +.. automodule:: itwinai.components + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.loggers.rst b/docs/itwinai.loggers.rst new file mode 100644 index 00000000..513e3942 --- /dev/null +++ b/docs/itwinai.loggers.rst @@ -0,0 +1,8 @@ +itwinai.loggers +=============== + +.. automodule:: itwinai.loggers + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.parser.rst b/docs/itwinai.parser.rst new file mode 100644 index 00000000..f9c7d930 --- /dev/null +++ b/docs/itwinai.parser.rst @@ -0,0 +1,8 @@ +itwinai.parser +============== + +.. automodule:: itwinai.parser + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.pipeline.rst b/docs/itwinai.pipeline.rst new file mode 100644 index 00000000..b849240e --- /dev/null +++ b/docs/itwinai.pipeline.rst @@ -0,0 +1,8 @@ +itwinai.pipeline +================ + +.. automodule:: itwinai.pipeline + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.serialization.rst b/docs/itwinai.serialization.rst new file mode 100644 index 00000000..691a3721 --- /dev/null +++ b/docs/itwinai.serialization.rst @@ -0,0 +1,8 @@ +itwinai.serialization +===================== + +.. automodule:: itwinai.serialization + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.types.rst b/docs/itwinai.types.rst new file mode 100644 index 00000000..20367596 --- /dev/null +++ b/docs/itwinai.types.rst @@ -0,0 +1,8 @@ +itwinai.types +============= + +.. automodule:: itwinai.types + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/itwinai.utils.rst b/docs/itwinai.utils.rst new file mode 100644 index 00000000..b487da7f --- /dev/null +++ b/docs/itwinai.utils.rst @@ -0,0 +1,8 @@ +itwinai.utils +============= + +.. automodule:: itwinai.utils + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..32bb2452 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/mnist_doc.rst b/docs/mnist_doc.rst new file mode 100644 index 00000000..56dfa654 --- /dev/null +++ b/docs/mnist_doc.rst @@ -0,0 +1,78 @@ +MNIST Use Case +============== + +This section covers the MNIST use case, which utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: + +torch-lightning +--------------- + +.. toctree:: + :maxdepth: 5 + +dataloader.py ++++++++++++++ + +The `dataloader.py` script is responsible for loading the MNIST dataset and preparing it for training. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py + :language: python + +.. .. automodule:: torch-lightning.dataloader +.. :members: +.. :undoc-members: +.. :show-inheritance: + +pipeline.yaml ++++++++++++++ + +This YAML file defines the pipeline configuration for the MNIST use case. It includes settings for the model, training, and evaluation. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/pipeline.yaml + :language: yaml + +startscript ++++++++++++ + +The `startscript` is a shell script to initiate the training process. It sets up the environment and starts the training using the `train.py` script. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/startscript + :language: bash + +train.py +++++++++ + +This script contains the training loop and is where the model is trained using the data prepared by `dataloader.py`. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/train.py + :language: python + +.. .. automodule:: torch-lightning.train +.. :members: +.. :undoc-members: +.. :show-inheritance: + +trainer.py +++++++++++ + +The `trainer.py` file defines the `Trainer` class which sets up the training parameters and the training process. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/trainer.py + :language: python + +.. .. automodule:: torch-lightning.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +utils.py +++++++++ + +The `utils.py` script includes utility functions and classes that are used across the MNIST use case. + +.. literalinclude:: ../use-cases/mnist/torch-lightning/utils.py + :language: python + +.. .. automodule:: torch-lightning.utils +.. :members: +.. :undoc-members: +.. :show-inheritance: diff --git a/docs/modules.rst b/docs/modules.rst new file mode 100644 index 00000000..b220e0ff --- /dev/null +++ b/docs/modules.rst @@ -0,0 +1,16 @@ +itwinai +======= + +.. toctree:: + :maxdepth: 4 + + itwinai.cli + itwinai.cluster + itwinai.components + itwinai.loggers + itwinai.parser + itwinai.pipeline + itwinai.serialization + itwinai.types + itwinai.utils + diff --git a/docs/use_cases.rst b/docs/use_cases.rst new file mode 100644 index 00000000..de870cf6 --- /dev/null +++ b/docs/use_cases.rst @@ -0,0 +1,20 @@ +.. _use-cases: + +Integrated Use Cases +==================== + +Here you can find a collection of use cases including various projects. + +3DGAN CERN use case +------------------- + +Description of the use case etc... + +.. toctree:: + :maxdepth: 2 + :caption: 3DGAN use case Documentation Contents: + + 3dgan_doc + mnist_doc + + diff --git a/docs/.gitignore b/docs_old/.gitignore similarity index 100% rename from docs/.gitignore rename to docs_old/.gitignore diff --git a/docs/404.html b/docs_old/404.html similarity index 100% rename from docs/404.html rename to docs_old/404.html diff --git a/docs/Gemfile b/docs_old/Gemfile similarity index 100% rename from docs/Gemfile rename to docs_old/Gemfile diff --git a/docs/Gemfile.lock b/docs_old/Gemfile.lock similarity index 100% rename from docs/Gemfile.lock rename to docs_old/Gemfile.lock diff --git a/docs/LICENSE b/docs_old/LICENSE similarity index 100% rename from docs/LICENSE rename to docs_old/LICENSE diff --git a/docs/README.md b/docs_old/README.md similarity index 100% rename from docs/README.md rename to docs_old/README.md diff --git a/docs/_config.yml b/docs_old/_config.yml similarity index 100% rename from docs/_config.yml rename to docs_old/_config.yml diff --git a/docs/docs/CLI.md b/docs_old/docs/CLI.md similarity index 100% rename from docs/docs/CLI.md rename to docs_old/docs/CLI.md diff --git a/docs/docs/Concepts.md b/docs_old/docs/Concepts.md similarity index 100% rename from docs/docs/Concepts.md rename to docs_old/docs/Concepts.md diff --git a/docs/docs/How-to-use-this-software.md b/docs_old/docs/How-to-use-this-software.md similarity index 100% rename from docs/docs/How-to-use-this-software.md rename to docs_old/docs/How-to-use-this-software.md diff --git a/docs/docs/img/Workflow DAG concept.png b/docs_old/docs/img/Workflow DAG concept.png similarity index 100% rename from docs/docs/img/Workflow DAG concept.png rename to docs_old/docs/img/Workflow DAG concept.png diff --git a/docs/docs/img/cwl-workflow.png b/docs_old/docs/img/cwl-workflow.png similarity index 100% rename from docs/docs/img/cwl-workflow.png rename to docs_old/docs/img/cwl-workflow.png diff --git a/docs/docs/img/user-platform interaction full.png b/docs_old/docs/img/user-platform interaction full.png similarity index 100% rename from docs/docs/img/user-platform interaction full.png rename to docs_old/docs/img/user-platform interaction full.png diff --git a/docs/docs/img/user-platform interaction.png b/docs_old/docs/img/user-platform interaction.png similarity index 100% rename from docs/docs/img/user-platform interaction.png rename to docs_old/docs/img/user-platform interaction.png diff --git a/docs/docs/use-cases/index.md b/docs_old/docs/use-cases/index.md similarity index 100% rename from docs/docs/use-cases/index.md rename to docs_old/docs/use-cases/index.md diff --git a/docs/docs/use-cases/mnist.md b/docs_old/docs/use-cases/mnist.md similarity index 100% rename from docs/docs/use-cases/mnist.md rename to docs_old/docs/use-cases/mnist.md diff --git a/docs/favicon.ico b/docs_old/favicon.ico similarity index 100% rename from docs/favicon.ico rename to docs_old/favicon.ico diff --git a/docs/index.md b/docs_old/index.md similarity index 100% rename from docs/index.md rename to docs_old/index.md diff --git a/use-cases/3dgan/__init__.py b/use-cases/3dgan/__init__.py new file mode 100644 index 00000000..079a8283 --- /dev/null +++ b/use-cases/3dgan/__init__.py @@ -0,0 +1 @@ +## This file can be empty but must be present \ No newline at end of file From fca08314a9cc26b8265802cd919f08e71b85e937 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Tue, 13 Feb 2024 16:38:58 +0100 Subject: [PATCH 22/59] adding documentation deployment relevant files --- .readthedocs.yaml | 32 ++++++++ requirements.txt | 185 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 217 insertions(+) create mode 100644 .readthedocs.yaml create mode 100644 requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 00000000..39111812 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,32 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.9.12" + # You can also specify other tool versions: + # nodejs: "19" + # rust: "1.64" + # golang: "1.19" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..55918109 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,185 @@ +absl-py==1.2.0 +alabaster==0.7.16 +alembic==1.8.1 +antlr4-python3-runtime==4.9.3 +anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1693488585952/work +appdirs==1.4.4 +arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1696128962909/work +attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1683424013410/work +autopage==0.5.1 +Babel==2.14.0 +backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work +beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1680888073205/work +blessed @ file:///home/conda/feedstock_root/build_artifacts/blessed_1666523113356/work +blinker==1.6.3 +Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work +build @ file:///home/conda/feedstock_root/build_artifacts/python-build_1682868691601/work +CacheControl @ file:///home/conda/feedstock_root/build_artifacts/cachecontrol-split_1690307937243/work +certifi==2023.7.22 +cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1696001742886/work +charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1698005359935/work +cleo @ file:///home/conda/feedstock_root/build_artifacts/cleo_1669193745932/work +click @ file:///home/conda/feedstock_root/build_artifacts/click_1692311806742/work +cliff==4.0.0 +cloudpickle==2.2.1 +cmaes==0.8.2 +cmd2==2.4.2 +colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work +colorlog==6.7.0 +crashtest @ file:///home/conda/feedstock_root/build_artifacts/crashtest_1667467006729/work +croniter @ file:///home/conda/feedstock_root/build_artifacts/croniter_1685103477055/work +cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1695163786734/work +cycler==0.12.1 +databricks-cli==0.18.0 +dateutils @ file:///home/conda/feedstock_root/build_artifacts/dateutils_1603450192521/work +deepdiff @ file:///home/conda/feedstock_root/build_artifacts/deepdiff_1697589976995/work +distlib @ file:///home/conda/feedstock_root/build_artifacts/distlib_1689598491484/work +docker==6.1.3 +docker-pycreds==0.4.0 +docstring-parser==0.15 +docutils==0.20.1 +dulwich @ file:///home/conda/feedstock_root/build_artifacts/dulwich_1696599729805/work +einops==0.4.1 +entrypoints==0.4 +exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1692026125334/work +fastapi @ file:///home/conda/feedstock_root/build_artifacts/fastapi_1669571890356/work +filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1694629184119/work +Flask==2.3.3 +fonttools==4.37.4 +fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1697919321618/work +gast==0.4.0 +gitdb==4.0.9 +GitPython==3.1.27 +google==3.0.0 +greenlet==1.1.3 +gunicorn==21.2.0 +h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1664132893548/work +h5py==3.7.0 +idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work +imagesize==1.4.1 +importlib-metadata==5.0.0 +importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1695414790617/work +inquirer @ file:///home/conda/feedstock_root/build_artifacts/inquirer_1678135536462/work +installer @ file:///home/conda/feedstock_root/build_artifacts/python-installer_1679300325491/work +itsdangerous @ file:///home/conda/feedstock_root/build_artifacts/itsdangerous_1648147185463/work +-e git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai +jaraco.classes @ file:///home/conda/feedstock_root/build_artifacts/jaraco.classes_1689112411129/work +jeepney @ file:///home/conda/feedstock_root/build_artifacts/jeepney_1649085214306/work +Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work +joblib==1.3.2 +jsonargparse==4.26.1 +jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1669810440410/work +keyring @ file:///home/conda/feedstock_root/build_artifacts/keyring_1696001558711/work +kiwisolver==1.4.5 +libclang==14.0.6 +lightning @ file:///home/conda/feedstock_root/build_artifacts/lightning_1680807776127/work +lightning-cloud @ file:///home/conda/feedstock_root/build_artifacts/lightning-cloud_1695991149929/work +lightning-utilities @ file:///home/conda/feedstock_root/build_artifacts/lightning-utilities_1688117204588/work +Mako==1.2.3 +Markdown==3.5 +markdown-it-py @ file:///home/conda/feedstock_root/build_artifacts/markdown-it-py_1686175045316/work +MarkupSafe==2.1.1 +matplotlib==3.5.2 +mdurl @ file:///home/conda/feedstock_root/build_artifacts/mdurl_1639515908913/work +mlflow==2.7.1 +more-itertools @ file:///home/conda/feedstock_root/build_artifacts/more-itertools_1691086935839/work +msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1695464112187/work +mysqlclient==2.1.1 +numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1695290879892/work/dist/numpy-1.26.0-cp39-cp39-linux_x86_64.whl#sha256=2145d30cdd6d3c9c4a4be216bb158edeac64e1c9aba388e733918d65b9481735 +oauthlib==3.2.2 +omegaconf==2.3.0 +optuna==2.10.1 +ordered-set @ file:///home/conda/feedstock_root/build_artifacts/ordered-set_1643221357603/work +orjson @ file:///home/conda/feedstock_root/build_artifacts/orjson_1696973386080/work/target/wheels/orjson-3.9.8-cp39-cp39-linux_x86_64.whl#sha256=fb8cc3ab1dcff1e49ae7ab69473d2ddce4fc439c8e520c83f7c3bd12c2a0ee50 +packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1696202382185/work +pandas==2.1.1 +pathtools==0.1.2 +pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work +Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1675487166627/work +pkginfo @ file:///home/conda/feedstock_root/build_artifacts/pkginfo_1673281726124/work +pkgutil_resolve_name @ file:///home/conda/feedstock_root/build_artifacts/pkgutil-resolve-name_1694617248815/work +platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1696272223550/work +plotly==5.10.0 +poetry @ file:///home/conda/feedstock_root/build_artifacts/poetry_1692649045332/work +poetry-core @ file:///home/conda/feedstock_root/build_artifacts/poetry-core_1692602937170/work +poetry-plugin-export @ file:///home/conda/feedstock_root/build_artifacts/poetry-plugin-export_1692602977187/work +promise==2.3 +protobuf==4.24.4 +psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1695367186825/work +ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl +pyarrow==13.0.0 +pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work +pydantic @ file:///home/conda/feedstock_root/build_artifacts/pydantic_1697791018681/work +Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1691408637400/work +PyJWT @ file:///home/conda/feedstock_root/build_artifacts/pyjwt_1689721553971/work +PyMySQL==1.0.2 +pyparsing==3.1.1 +pyperclip==1.8.2 +pyproject_hooks @ file:///home/conda/feedstock_root/build_artifacts/pyproject_hooks_1670268672584/work +pyrsistent @ file:///home/conda/feedstock_root/build_artifacts/pyrsistent_1695501249085/work +PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work +python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work +python-editor==1.0.4 +python-multipart @ file:///home/conda/feedstock_root/build_artifacts/python-multipart_1679167423335/work +pytorch-lightning @ file:///home/conda/feedstock_root/build_artifacts/pytorch-lightning_1697150960795/work +pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1693930252784/work +PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1695373447169/work +querystring-parser==1.2.4 +rapidfuzz @ file:///home/conda/feedstock_root/build_artifacts/rapidfuzz_1696683887041/work +readchar @ file:///home/conda/feedstock_root/build_artifacts/readchar_1679045163536/work +requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work +requests-toolbelt @ file:///home/conda/feedstock_root/build_artifacts/requests-toolbelt_1682953341151/work +rich @ file:///home/conda/feedstock_root/build_artifacts/rich-split_1696096668362/work/dist +scikit-learn==1.3.2 +scipy==1.12.0 +SecretStorage @ file:///home/conda/feedstock_root/build_artifacts/secretstorage_1695551752261/work +sentry-sdk==1.9.10 +setproctitle==1.3.2 +shellingham @ file:///home/conda/feedstock_root/build_artifacts/shellingham_1698144360966/work +shortuuid==1.0.9 +six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work +smmap==5.0.0 +sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work +snowballstemmer==2.2.0 +soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1693929250441/work +Sphinx==7.2.6 +sphinx-rtd-theme==2.0.0 +sphinxcontrib-applehelp==1.0.8 +sphinxcontrib-devhelp==1.0.6 +sphinxcontrib-htmlhelp==2.0.5 +sphinxcontrib-jquery==4.1 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.7 +sphinxcontrib-serializinghtml==1.1.10 +SQLAlchemy==1.4.41 +sqlparse==0.4.4 +starlette @ file:///home/conda/feedstock_root/build_artifacts/starlette-recipe_1668726462865/work +starsessions @ file:///home/conda/feedstock_root/build_artifacts/starsessions_1659923701614/work +submitit==1.5.0 +tabulate==0.9.0 +tenacity==8.1.0 +tensorflow-io-gcs-filesystem==0.27.0 +threadpoolctl==3.2.0 +tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1644342247877/work +tomlkit @ file:///home/conda/feedstock_root/build_artifacts/tomlkit_1690472772788/work +torch==1.13.1 +torchaudio==0.13.1 +torchmetrics @ file:///home/conda/feedstock_root/build_artifacts/torchmetrics_1695676540578/work +torchvision==0.14.1 +tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work +traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1696377679271/work +trove-classifiers @ file:///home/conda/feedstock_root/build_artifacts/trove-classifiers_1697647699431/work +typer==0.9.0 +types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1689882883784/work +typeshed-client==2.4.0 +typing_extensions==4.5.0 +tzdata==2023.3 +urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1697720414277/work +uvicorn @ file:///home/conda/feedstock_root/build_artifacts/uvicorn-split_1695566113922/work +virtualenv @ file:///home/conda/feedstock_root/build_artifacts/virtualenv_1698092630749/work +wandb==0.15.12 +wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1696255154857/work +websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1696770128353/work +websockets @ file:///home/conda/feedstock_root/build_artifacts/websockets_1695409988029/work +Werkzeug==3.0.0 +zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work From 76ecd452b83211ec502b278efad42cfd59ca13d7 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 15 Feb 2024 16:12:38 +0100 Subject: [PATCH 23/59] updating readthedocs.yaml --- .readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 39111812..a1a43723 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,7 +9,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.9.12" + python: "3.9" # You can also specify other tool versions: # nodejs: "19" # rust: "1.64" From d7cef3acb27fbf170abc29e3568e890946aa0b07 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 15 Feb 2024 16:19:06 +0100 Subject: [PATCH 24/59] changing directory of requirements.txt --- requirements.txt => docs/requirements.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename requirements.txt => docs/requirements.txt (100%) diff --git a/requirements.txt b/docs/requirements.txt similarity index 100% rename from requirements.txt rename to docs/requirements.txt From 0a004f3eacf3f0691c63ea5db46c7dbfab6e5cf6 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 15 Feb 2024 16:37:05 +0100 Subject: [PATCH 25/59] updating reqs file --- docs/requirements.txt | 190 +++++++++++++++++++++--------------------- 1 file changed, 95 insertions(+), 95 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 55918109..23528ceb 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -2,146 +2,146 @@ absl-py==1.2.0 alabaster==0.7.16 alembic==1.8.1 antlr4-python3-runtime==4.9.3 -anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1693488585952/work +anyio appdirs==1.4.4 -arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1696128962909/work -attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1683424013410/work +arrow +attrs autopage==0.5.1 Babel==2.14.0 -backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work -beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1680888073205/work -blessed @ file:///home/conda/feedstock_root/build_artifacts/blessed_1666523113356/work +backports.functools-lru-cache +beautifulsoup4 +blessed blinker==1.6.3 -Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work -build @ file:///home/conda/feedstock_root/build_artifacts/python-build_1682868691601/work -CacheControl @ file:///home/conda/feedstock_root/build_artifacts/cachecontrol-split_1690307937243/work +Brotli +build +CacheControl certifi==2023.7.22 -cffi @ 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file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work -python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work +pyproject_hooks +pyrsistent +PySocks +python-dateutil python-editor==1.0.4 -python-multipart @ file:///home/conda/feedstock_root/build_artifacts/python-multipart_1679167423335/work -pytorch-lightning @ file:///home/conda/feedstock_root/build_artifacts/pytorch-lightning_1697150960795/work -pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1693930252784/work -PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1695373447169/work +python-multipart +pytorch-lightning +pytz +PyYAML querystring-parser==1.2.4 -rapidfuzz @ file:///home/conda/feedstock_root/build_artifacts/rapidfuzz_1696683887041/work -readchar @ file:///home/conda/feedstock_root/build_artifacts/readchar_1679045163536/work -requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work -requests-toolbelt @ file:///home/conda/feedstock_root/build_artifacts/requests-toolbelt_1682953341151/work -rich @ file:///home/conda/feedstock_root/build_artifacts/rich-split_1696096668362/work/dist +rapidfuzz +readchar +requests +requests-toolbelt +rich scikit-learn==1.3.2 scipy==1.12.0 -SecretStorage @ file:///home/conda/feedstock_root/build_artifacts/secretstorage_1695551752261/work +SecretStorage sentry-sdk==1.9.10 setproctitle==1.3.2 -shellingham @ file:///home/conda/feedstock_root/build_artifacts/shellingham_1698144360966/work +shellingham shortuuid==1.0.9 -six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work +six smmap==5.0.0 -sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work +sniffio snowballstemmer==2.2.0 -soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1693929250441/work +soupsieve Sphinx==7.2.6 sphinx-rtd-theme==2.0.0 sphinxcontrib-applehelp==1.0.8 @@ -153,33 +153,33 @@ sphinxcontrib-qthelp==1.0.7 sphinxcontrib-serializinghtml==1.1.10 SQLAlchemy==1.4.41 sqlparse==0.4.4 -starlette @ file:///home/conda/feedstock_root/build_artifacts/starlette-recipe_1668726462865/work -starsessions @ file:///home/conda/feedstock_root/build_artifacts/starsessions_1659923701614/work +starlette +starsessions submitit==1.5.0 tabulate==0.9.0 tenacity==8.1.0 tensorflow-io-gcs-filesystem==0.27.0 threadpoolctl==3.2.0 -tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1644342247877/work -tomlkit @ file:///home/conda/feedstock_root/build_artifacts/tomlkit_1690472772788/work +tomli +tomlkit torch==1.13.1 torchaudio==0.13.1 -torchmetrics @ file:///home/conda/feedstock_root/build_artifacts/torchmetrics_1695676540578/work +torchmetrics torchvision==0.14.1 -tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work -traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1696377679271/work -trove-classifiers @ file:///home/conda/feedstock_root/build_artifacts/trove-classifiers_1697647699431/work +tqdm +traitlets +trove-classifiers typer==0.9.0 -types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1689882883784/work +types-python-dateutil typeshed-client==2.4.0 typing_extensions==4.5.0 tzdata==2023.3 -urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1697720414277/work -uvicorn @ file:///home/conda/feedstock_root/build_artifacts/uvicorn-split_1695566113922/work -virtualenv @ file:///home/conda/feedstock_root/build_artifacts/virtualenv_1698092630749/work +urllib3 +uvicorn +virtualenv wandb==0.15.12 -wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1696255154857/work -websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1696770128353/work -websockets @ file:///home/conda/feedstock_root/build_artifacts/websockets_1695409988029/work +wcwidth +websocket-client +websockets Werkzeug==3.0.0 -zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work +zipp From 0e2e0e5425e2a4e9b00fa90600202a0ce3267fcd Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Fri, 16 Feb 2024 16:01:23 +0100 Subject: [PATCH 26/59] commiting changes and adding pages for tutorials --- docs/advanced_workflow.rst | 19 + docs/basic_comp.rst | 19 + docs/basic_workflow.rst | 19 + docs/conf.py | 2 + docs/index.rst | 25 +- docs/intermediate_workflow.rst | 19 + docs/mnist_doc.rst | 73 +++ docs/tutorials.rst | 22 + docs/use_cases.rst | 13 +- docs_old/.gitignore | 10 - docs_old/404.html | 12 - docs_old/Gemfile | 7 - docs_old/Gemfile.lock | 79 --- docs_old/LICENSE | 21 - docs_old/README.md | 181 ------ docs_old/_config.yml | 26 - docs_old/docs/CLI.md | 53 -- docs_old/docs/Concepts.md | 35 -- docs_old/docs/How-to-use-this-software.md | 540 ------------------ docs_old/docs/img/Workflow DAG concept.png | Bin 224511 -> 0 bytes docs_old/docs/img/cwl-workflow.png | Bin 375527 -> 0 bytes .../img/user-platform interaction full.png | Bin 211421 -> 0 bytes .../docs/img/user-platform interaction.png | Bin 30005 -> 0 bytes docs_old/docs/use-cases/index.md | 12 - docs_old/docs/use-cases/mnist.md | 185 ------ docs_old/favicon.ico | Bin 15406 -> 0 bytes docs_old/index.md | 64 --- 27 files changed, 207 insertions(+), 1229 deletions(-) create mode 100644 docs/advanced_workflow.rst create mode 100644 docs/basic_comp.rst create mode 100644 docs/basic_workflow.rst create mode 100644 docs/intermediate_workflow.rst create mode 100644 docs/tutorials.rst delete mode 100644 docs_old/.gitignore delete mode 100644 docs_old/404.html delete mode 100644 docs_old/Gemfile delete mode 100644 docs_old/Gemfile.lock delete mode 100644 docs_old/LICENSE delete mode 100644 docs_old/README.md delete mode 100644 docs_old/_config.yml delete mode 100644 docs_old/docs/CLI.md delete mode 100644 docs_old/docs/Concepts.md delete mode 100644 docs_old/docs/How-to-use-this-software.md delete mode 100644 docs_old/docs/img/Workflow DAG concept.png delete mode 100644 docs_old/docs/img/cwl-workflow.png delete mode 100644 docs_old/docs/img/user-platform interaction full.png delete mode 100644 docs_old/docs/img/user-platform interaction.png delete mode 100644 docs_old/docs/use-cases/index.md delete mode 100644 docs_old/docs/use-cases/mnist.md delete mode 100644 docs_old/favicon.ico delete mode 100644 docs_old/index.md diff --git a/docs/advanced_workflow.rst b/docs/advanced_workflow.rst new file mode 100644 index 00000000..140d8ee0 --- /dev/null +++ b/docs/advanced_workflow.rst @@ -0,0 +1,19 @@ +Advanced workflow +================= + +Description... + + +tutorial_2_advanced_workflow.py ++++++++++++++++++++++++++++++++ + +The `tutorial_2_advanced_workflow.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: tutorial_2_advanced_workflow + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/basic_comp.rst b/docs/basic_comp.rst new file mode 100644 index 00000000..d4fb3a18 --- /dev/null +++ b/docs/basic_comp.rst @@ -0,0 +1,19 @@ +Basic components +================ + +Description... + + +basic_components.py ++++++++++++++++++++ + +The `basic_components.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: basic_components + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/basic_workflow.rst b/docs/basic_workflow.rst new file mode 100644 index 00000000..7b6fca2d --- /dev/null +++ b/docs/basic_workflow.rst @@ -0,0 +1,19 @@ +Basic workflow +============== + +Description... + + +tutorial_0_basic_workflow.py +++++++++++++++++++++++++++++ + +The `tutorial_0_basic_workflow.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: tutorial_0_basic_workflow + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/conf.py b/docs/conf.py index 6bab8aec..e21e2464 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,6 +11,8 @@ sys.path.insert(0, os.path.abspath('../use-cases/')) sys.path.insert(0, os.path.abspath('../use-cases/3dgan/')) sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch-lightning/')) +sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch/')) +sys.path.insert(0, os.path.abspath('../tutorials/ml-workflows/')) sys.path.insert(0, os.path.abspath('../src/itwinai')) sys.path.insert(0, os.path.abspath('../')) diff --git a/docs/index.rst b/docs/index.rst index 9bf6cf33..29e47aa2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -6,18 +6,37 @@ Welcome to itwinai's documentation! =================================== +``itwinai`` is a framework for advanced AI/ML workflows in digital twins. + +This platform is intended to support general-purpose MLOps for digital twin use cases in `interTwin `_ project. + +Platform for machine learning workflows in digital twins +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +The goal of this platform is to provide ML researchers with an easy-to-use endpoint to manage general-purpose ML workflows, +with limited engineering overhead, while providing state-of-the-art MLOps best practices. + +The user is going to provide as input a set of configuration files, to fully describe ML workflows, in the context of digital twin applications. +``itwinai`` platform instantiates ML workflows with the configurations provided by the DT developer. +The execution of ML workflows produces as output a set of ML metrics, which are visualized by ``itwinai`` via `MLFlow `_. +As a result of ML training, the best model (on validation dataset) is saved to the Models Registry for future predictions. + +In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversimplifying all the rest (e.g., pre-processing, authentication, workflow execution). + + .. toctree:: :maxdepth: 2 :caption: Contents: modules use_cases - 3dgan_doc - mnist_doc + tutorials + Indices and tables ================== * :ref:`genindex` * :ref:`modindex` -* :ref:`search` +.. * :ref:`search` + diff --git a/docs/intermediate_workflow.rst b/docs/intermediate_workflow.rst new file mode 100644 index 00000000..79c0472b --- /dev/null +++ b/docs/intermediate_workflow.rst @@ -0,0 +1,19 @@ +Intermediate workflow +===================== + +Description... + + +tutorial_1_intermediate_workflow.py ++++++++++++++++++++++++++++++++++++ + +The `tutorial_1_intermediate_workflow.py` script is ... + +.. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py +.. :language: python + +.. automodule:: tutorial_1_intermediate_workflow + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/mnist_doc.rst b/docs/mnist_doc.rst index 56dfa654..afedbb29 100644 --- a/docs/mnist_doc.rst +++ b/docs/mnist_doc.rst @@ -76,3 +76,76 @@ The `utils.py` script includes utility functions and classes that are used acros .. :members: .. :undoc-members: .. :show-inheritance: + + +This section covers the MNIST use case, which utilizes the `torch` framework for training and evaluation. The following files are integral to this use case: + +torch +----- + +.. toctree:: + :maxdepth: 5 + +dataloader.py ++++++++++++++ + +The `dataloader.py` script is responsible for loading the MNIST dataset and preparing it for training. + +.. literalinclude:: ../use-cases/mnist/torch/dataloader.py + :language: python + + +Dockerfile +++++++++++ + +.. literalinclude:: ../use-cases/mnist/torch/Dockerfile + :language: bash + + +inference-pipeline.yaml ++++++++++++++++++++++++ + +This YAML file defines the pipeline configuration for the MNIST use case inference. + +.. literalinclude:: ../use-cases/mnist/torch/inference-pipeline.yaml + :language: yaml + +model.py +++++++++ + +The `model.py` script is responsible for loading a simple model. + +.. literalinclude:: ../use-cases/mnist/torch/model.py + :language: python + +pipeline.yaml ++++++++++++++ + +This YAML file defines the pipeline configuration for the MNIST use case. It includes settings for the model, training, and evaluation. + +.. literalinclude:: ../use-cases/mnist/torch/pipeline.yaml + :language: yaml + +startscript ++++++++++++ + +The `startscript` is a shell script to initiate the training process. It sets up the environment and starts the training using the `train.py` script. + +.. literalinclude:: ../use-cases/mnist/torch/startscript + :language: bash + +train.py +++++++++ + +This script contains the training loop and is where the model is trained using the data prepared by `dataloader.py`. + +.. literalinclude:: ../use-cases/mnist/torch/train.py + :language: python + +saver.py +++++++++ +... + +.. literalinclude:: ../use-cases/mnist/torch/saver.py + :language: python + diff --git a/docs/tutorials.rst b/docs/tutorials.rst new file mode 100644 index 00000000..5a5772d5 --- /dev/null +++ b/docs/tutorials.rst @@ -0,0 +1,22 @@ +.. _tutorials: + +ML workflow tutorials +===================== + +Here you can find a collection of tutorials for various complexity ML workflows. + +Tutorials +--------- + +Description... + +.. toctree:: + :maxdepth: 2 + :caption: Examples + + basic_comp + basic_workflow + intermediate_workflow + advanced_workflow + + \ No newline at end of file diff --git a/docs/use_cases.rst b/docs/use_cases.rst index de870cf6..dd59b82b 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -12,9 +12,20 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - :caption: 3DGAN use case Documentation Contents: + :caption: 3DGAN use case Documentation 3dgan_doc + + +MNIST use case +-------------- + +Description of the use case etc... + +.. toctree:: + :maxdepth: 2 + :caption: MNIST Documentation + mnist_doc diff --git a/docs_old/.gitignore b/docs_old/.gitignore deleted file mode 100644 index f55f6395..00000000 --- a/docs_old/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -# Copied from https://github.com/github/gitignore/blob/main/Jekyll.gitignore -# Ignore metadata generated by Jekyll -_site/ -.sass-cache/ -.jekyll-cache/ -.jekyll-metadata - -# Ignore folders generated by Bundler -.bundle/ -vendor/ diff --git a/docs_old/404.html b/docs_old/404.html deleted file mode 100644 index b8547546..00000000 --- a/docs_old/404.html +++ /dev/null @@ -1,12 +0,0 @@ ---- -layout: default -title: 404 -permalink: /404 -nav_exclude: true -search_exclude: true ---- - -

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    + Version: {{ release }} +
    +""" + +html_sidebars = { + '**': [ + html_footer # Adds the custom footer with version information + ] +} From 589795b2b18677f33029df2aa48244a6b87a52d3 Mon Sep 17 00:00:00 2001 From: Matteo Bunino <48362942+matbun@users.noreply.github.com> Date: Mon, 15 Apr 2024 17:43:27 +0200 Subject: [PATCH 47/59] Update .readthedocs.yaml --- .readthedocs.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index a1a43723..f89fc906 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,7 +9,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.9" + python: "3.12" # You can also specify other tool versions: # nodejs: "19" # rust: "1.64" @@ -27,6 +27,6 @@ sphinx: # Optional but recommended, declare the Python requirements required # to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html -python: - install: - - requirements: docs/requirements.txt \ No newline at end of file +# python: +# install: +# - requirements: docs/requirements.txt From 865fb700176135ed6b4323a88d140c995ae3561a Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 18 Apr 2024 16:59:48 +0200 Subject: [PATCH 48/59] fixing display structure in getting started page --- .github/workflows/documentation.yml | 12 ++++++------ docs/getting_started_with_itwinai.rst | 2 -- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 3695ef37..aeb26d82 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -1,11 +1,11 @@ name: Build and Deploy Sphinx Documentation -on: - push: - branches: - - docs_dev - tags: - - 'v*' +# on: +# push: +# branches: +# - docs_dev +# tags: +# - 'v*' jobs: build-and-deploy: diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index e76646c0..2833067a 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -11,7 +11,6 @@ Here you can find itwinai framework installation and usage instructions for HPC .. toctree:: :maxdepth: 2 :hidden: - :caption: 🌐 HPC systems hpc_setup @@ -23,7 +22,6 @@ Here you can find itwinai framework installation and usage instructions for HPC .. toctree:: :maxdepth: 2 :hidden: - :caption: 💻 Local systems local_setup From 47ba5360130d03327b23bfff1701d86563757b13 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 18 Apr 2024 17:26:35 +0200 Subject: [PATCH 49/59] new fixes similar to previous commit --- docs/getting_started_with_itwinai.rst | 2 -- docs/hpc_setup.rst | 4 ++-- docs/local_setup.rst | 4 ++-- 3 files changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 2833067a..127458de 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -10,7 +10,6 @@ Here you can find itwinai framework installation and usage instructions for HPC .. toctree:: :maxdepth: 2 - :hidden: hpc_setup @@ -21,7 +20,6 @@ Here you can find itwinai framework installation and usage instructions for HPC .. toctree:: :maxdepth: 2 - :hidden: local_setup diff --git a/docs/hpc_setup.rst b/docs/hpc_setup.rst index bbf32790..607c876c 100644 --- a/docs/hpc_setup.rst +++ b/docs/hpc_setup.rst @@ -1,5 +1,5 @@ -🌐 HPC systems ---------------- +.. 🌐 HPC systems +.. --------------- How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. diff --git a/docs/local_setup.rst b/docs/local_setup.rst index e16d9945..72b5f377 100644 --- a/docs/local_setup.rst +++ b/docs/local_setup.rst @@ -1,5 +1,5 @@ -💻 Local systems ------------------ +.. 💻 Local systems +.. ----------------- **Requirements** From ed768ba98128982a4361c382ef8cff936c356c61 Mon Sep 17 00:00:00 2001 From: VerderK <167095399+VerderK@users.noreply.github.com> Date: Fri, 19 Apr 2024 15:53:02 +0200 Subject: [PATCH 50/59] Update index.rst --- docs/index.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 90d8bc19..816c7a3e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -9,9 +9,9 @@ Welcome to itwinai's documentation! =================================== -``itwinai`` is a framework for advanced AI/ML workflows in digital twins. +``itwinai`` is a framework for advanced AI/ML workflows in Digital Twins (DTs). -This platform is intended to support general-purpose MLOps for digital twin use cases in `interTwin `_ project. +This platform is intended to support general-purpose MLOps for Digital Twin use cases in the `interTwin `_ project. Platform for machine learning workflows in digital twins ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ @@ -19,10 +19,10 @@ Platform for machine learning workflows in digital twins The goal of this platform is to provide ML researchers with an easy-to-use endpoint to manage general-purpose ML workflows, with limited engineering overhead, while providing state-of-the-art MLOps best practices. -The user is going to provide as input a set of configuration files, to fully describe ML workflows, in the context of digital twin applications. -``itwinai`` platform instantiates ML workflows with the configurations provided by the DT developer. -The execution of ML workflows produces as output a set of ML metrics, which are visualized by ``itwinai`` via `MLFlow `_. -As a result of ML training, the best model (on validation dataset) is saved to the Models Registry for future predictions. +The user can fully describe ML workflows for DT applications by providing a set of configuration files as input. +The ``itwinai`` platform instantiates ML workflows with the configurations provided by the DT developer. +The execution of ML workflows outputs a set of ML metrix, which are visualised by ``itwinai`` via `MLFlow `_. +The trained ML model that performed best on the validation dataset is saved to the Models Registry for future predictions. In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversimplifying all the rest (e.g., pre-processing, authentication, workflow execution). From 711d9eac9e9fc93f3d958a4f316345d6dc4ea4a5 Mon Sep 17 00:00:00 2001 From: VerderK <167095399+VerderK@users.noreply.github.com> Date: Fri, 19 Apr 2024 15:55:23 +0200 Subject: [PATCH 51/59] Update index.rst Text re-edit index --- docs/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/index.rst b/docs/index.rst index 816c7a3e..c3f67f14 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -65,5 +65,6 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` + .. * :ref:`search` From 151146c2ac997ad85c2e2bf9ce0e31b5e3b4ade4 Mon Sep 17 00:00:00 2001 From: VerderK <167095399+VerderK@users.noreply.github.com> Date: Mon, 22 Apr 2024 10:52:37 +0200 Subject: [PATCH 52/59] Update index.rst change 1 word --- docs/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index c3f67f14..82b192f8 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -24,7 +24,7 @@ The ``itwinai`` platform instantiates ML workflows with the configurations provi The execution of ML workflows outputs a set of ML metrix, which are visualised by ``itwinai`` via `MLFlow `_. The trained ML model that performed best on the validation dataset is saved to the Models Registry for future predictions. -In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversimplifying all the rest (e.g., pre-processing, authentication, workflow execution). +In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversimplifying the rest (e.g., pre-processing, authentication, workflow execution). .. toctree:: From 3b2a82cc545dd3326780e3334ef0ab8384e92d80 Mon Sep 17 00:00:00 2001 From: Matteo Bunino <48362942+matbun@users.noreply.github.com> Date: Mon, 22 Apr 2024 12:11:40 +0200 Subject: [PATCH 53/59] Update .readthedocs.yaml --- .readthedocs.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index f89fc906..b62c9172 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -27,6 +27,6 @@ sphinx: # Optional but recommended, declare the Python requirements required # to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html -# python: -# install: -# - requirements: docs/requirements.txt +python: + install: + - requirements: docs/requirements.txt From c2e61f5119a915464010ff76b43bc8935c6e7fa3 Mon Sep 17 00:00:00 2001 From: Matteo Bunino <48362942+matbun@users.noreply.github.com> Date: Mon, 22 Apr 2024 12:20:52 +0200 Subject: [PATCH 54/59] Update .readthedocs.yaml --- .readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index b62c9172..790af042 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,7 +9,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.12" + python: "3.10" # You can also specify other tool versions: # nodejs: "19" # rust: "1.64" From ce1b5af36b8fe2ef1878e226d6a9f2017adc44ba Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Mon, 22 Apr 2024 14:41:45 +0200 Subject: [PATCH 55/59] fixing getting started page --- docs/getting_started_with_itwinai.rst | 181 +++++++++++++++++++++++++- 1 file changed, 174 insertions(+), 7 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 127458de..42adaba2 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -7,20 +7,187 @@ Here you can find itwinai framework installation and usage instructions for HPC 🌐 HPC systems --------------- - -.. toctree:: - :maxdepth: 2 - hpc_setup +How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. +Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. + + +Environments +++++++++++++ + +Install PyTorch env (GPU support) on Juelich Super Computer (tested on HDFML system) + +.. code-block:: bash + + torch-gpu-jsc: env-files/torch/createEnvJSC.sh + sh env-files/torch/createEnvJSC.sh + + +Install Tensorflow env (GPU support) on Juelich Super Computer (tested on HDFML system) + +.. code-block:: bash + + tf-gpu-jsc: env-files/tensorflow/createEnvJSCTF.sh + sh env-files/tensorflow/createEnvJSCTF.sh + + +Setup ++++++ + +First, from the root of this `repository `_, build the environment containing pytorch, horovod and deepspeed. You can try with: + +.. code-block:: bash + + # Creates a Python venv called envAI_hdfml + make torch-gpu-jsc + + +Distributed training +++++++++++++++++++++ + +Each distributed strategy has its own SLURM job script, which should be used to run it: + +If you want to distribute the code in `train.py` with **torch DDP**, run from terminal: + +.. code-block:: bash + + sbatch ddp_slurm.sh + +If you want to distribute the code in `train.py` with **DeepSpeed**, run from terminal: + +.. code-block:: bash + + sbatch deepspeed_slurm.sh + +If you want to distribute the code in `train.py` with **Horovod**, run from terminal: + +.. code-block:: bash + + sbatch hvd_slurm.sh + +You can run all of them with: + +.. code-block:: bash + + bash runall.sh + + 💻 Local systems ----------------- -.. toctree:: - :maxdepth: 2 +**Requirements** + +* Linux environment. +* Windows and macOS were never tested. - local_setup + +Micromamba installation ++++++++++++++++++++++++ + +To manage Conda environments we use micromamba, a light weight version of conda. + +It is suggested to refer to the `Manual installation guide `_. + +Consider that Micromamba can eat a lot of space when building environments because packages are cached on +the local filesystem after being downloaded. To clear cache you can use `micromamba clean -a`. +Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage +space and it would be cleverer to install Micromamba in another location with more storage space. +Thus by changing the `$MAMBA_ROOT_PREFIX` variable. See a complete installation example for Linux below, where the +default `$MAMBA_ROOT_PREFIX` is overridden: + + +.. code-block:: bash + + cd $HOME + + # Download micromamba (This command is for Linux Intel (x86_64) systems. Find the right one for your system!) + curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba + + # Install micromamba in a custom directory + MAMBA_ROOT_PREFIX='my-mamba-root' + ./bin/micromamba shell init $MAMBA_ROOT_PREFIX + + # To invoke micromamba from Makefile, you need to add explicitly to $PATH + echo 'PATH="$(dirname $MAMBA_EXE):$PATH"' >> ~/.bashrc + +**Reference**: `Micromamba installation guide `_. + + +Environment setup ++++++++++++++++++ + +**Requirements:** + +* Linux environment. Windows and macOS were never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +Tensorflow +++++++++++ + +Installation: + +.. code-block:: bash + + # Install TensorFlow 2.13 + make tf-2.13 + + # Activate env + micromamba activate ./.venv-tf + +Other TF versions are available, using the following targets `tf-2.10`, and `tf-2.11`. + + +PyTorch (+ Lightning) ++++++++++++++++++++++ + +Installation: + +.. code-block:: bash + + # Install PyTorch + lightning + make torch-gpu + + # Activate env + micromamba activate ./.venv-pytorch + +Other also CPU-only version is available at the target `torch-cpu`. + + +Development environment ++++++++++++++++++++++++ + +This is for developers only. To have it, update the installed `itwinai` package adding the `dev` extra: + +.. code-block:: bash + + pip install -e .[dev] + + +**Test with `pytest`** +To run tests on itwinai package: + +.. code-block:: bash + + # Activate env + micromamba activate ./.venv-pytorch # or ./.venv-tf + + pytest -v -m "not slurm" tests/ + + +However, some tests are intended to be executed only on an HPC system, where SLURM is available. They are marked with "slurm" tag. To run also those tests, use the dedicated job script: + +.. code-block:: bash + + sbatch tests/slurm_tests_startscript + + # Upon completion, check the output: + cat job.err + cat job.out + + From e96679de18527fcdae1e4de2efcf8b24fda20531 Mon Sep 17 00:00:00 2001 From: VerderK <167095399+VerderK@users.noreply.github.com> Date: Fri, 26 Apr 2024 17:00:35 +0200 Subject: [PATCH 56/59] Text review getting_started_with_itwinai.rst --- docs/getting_started_with_itwinai.rst | 41 +++++++++++++-------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 42adaba2..76851b6d 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -1,14 +1,13 @@ Getting started with itwinai ============================ - -Here you can find itwinai framework installation and usage instructions for HPC and local systems. +In this section, we will run you through the installation and give some instructions for the use of the itwinai framework for HPC and local systems. 🌐 HPC systems --------------- -How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. +Here, we lay out how to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. @@ -34,38 +33,38 @@ Install Tensorflow env (GPU support) on Juelich Super Computer (tested on HDFML Setup +++++ -First, from the root of this `repository `_, build the environment containing pytorch, horovod and deepspeed. You can try with: +First, from the root of `this repository `_, build the environment containing pytorch, horovod, and deepspeed. You can try with: .. code-block:: bash - # Creates a Python venv called envAI_hdfml + # Creates a Python environment called envAI_hdfml make torch-gpu-jsc Distributed training ++++++++++++++++++++ -Each distributed strategy has its own SLURM job script, which should be used to run it: + Each distributed strategy is described with a SLURM job script used to run that strategy. -If you want to distribute the code in `train.py` with **torch DDP**, run from terminal: +So if you want to distribute the code in `train.py` with, for example, **torch DDP**, run from terminal: .. code-block:: bash sbatch ddp_slurm.sh -If you want to distribute the code in `train.py` with **DeepSpeed**, run from terminal: +Similarly, if you want to distribute the code in `train.py` with **DeepSpeed**, run from terminal: .. code-block:: bash sbatch deepspeed_slurm.sh -If you want to distribute the code in `train.py` with **Horovod**, run from terminal: +To distribute the code in `train.py` with **Horovod**, run from terminal: .. code-block:: bash sbatch hvd_slurm.sh -You can run all of them with: +Finally, you can run all of them with: .. code-block:: bash @@ -81,22 +80,20 @@ You can run all of them with: **Requirements** * Linux environment. -* Windows and macOS were never tested. + +Windows and macOS were never tested. Micromamba installation +++++++++++++++++++++++ -To manage Conda environments we use micromamba, a light weight version of conda. +To manage Conda environments we use micromamba, a lightweight version of Conda. -It is suggested to refer to the `Manual installation guide `_. +In order to install micromamba, please refer to the `Manual installation guide `_. -Consider that Micromamba can eat a lot of space when building environments because packages are cached on -the local filesystem after being downloaded. To clear cache you can use `micromamba clean -a`. -Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage -space and it would be cleverer to install Micromamba in another location with more storage space. -Thus by changing the `$MAMBA_ROOT_PREFIX` variable. See a complete installation example for Linux below, where the -default `$MAMBA_ROOT_PREFIX` is overridden: +Consider that Micromamba can eat a lot of space when building environments because packages are cached on the local filesystem after being downloaded. To clear cache, you can use `micromamba clean -a`. +Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage space so it is recommended to install Micromamba in another location with more storage space by changing the `$MAMBA_ROOT_PREFIX` variable. +Below is a complete installation example where the default `$MAMBA_ROOT_PREFIX` is overridden for Linux: .. code-block:: bash @@ -138,7 +135,7 @@ Installation: # Activate env micromamba activate ./.venv-tf -Other TF versions are available, using the following targets `tf-2.10`, and `tf-2.11`. +Other TensorFlow versions are available, using the following targets `tf-2.10`, and `tf-2.11`. PyTorch (+ Lightning) @@ -154,7 +151,7 @@ Installation: # Activate env micromamba activate ./.venv-pytorch -Other also CPU-only version is available at the target `torch-cpu`. +Other similarly CPU-only version is available at the target `torch-cpu`. Development environment @@ -178,7 +175,7 @@ To run tests on itwinai package: pytest -v -m "not slurm" tests/ -However, some tests are intended to be executed only on an HPC system, where SLURM is available. They are marked with "slurm" tag. To run also those tests, use the dedicated job script: +However, some tests are intended to be executed only on HPC systems, where SLURM is available. They are marked with "slurm" tags. To run these tests, use the dedicated job script: .. code-block:: bash From 92a697ebbd6ffd37c1789b65221aae54190efc61 Mon Sep 17 00:00:00 2001 From: VerderK <167095399+VerderK@users.noreply.github.com> Date: Mon, 29 Apr 2024 11:01:43 +0200 Subject: [PATCH 57/59] Update 3dgan_doc.rst --- docs/3dgan_doc.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst index 69f62774..1b8added 100644 --- a/docs/3dgan_doc.rst +++ b/docs/3dgan_doc.rst @@ -1,7 +1,7 @@ 3DGAN ===== -This section covers the CERN use case, which utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: +This section covers the CERN use case that utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: itwinai x 3DGAN --------------- From 3db8d596146e4fcb80519b439ec9a9ba640fce15 Mon Sep 17 00:00:00 2001 From: VerderK <167095399+VerderK@users.noreply.github.com> Date: Mon, 29 Apr 2024 15:15:24 +0200 Subject: [PATCH 58/59] Update getting_started_with_itwinai.rst punctuation --- docs/getting_started_with_itwinai.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 76851b6d..dc69a5b2 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -7,7 +7,7 @@ In this section, we will run you through the installation and give some instruct 🌐 HPC systems --------------- -Here, we lay out how to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. +Here, we lay out how to use torch DistributedDataParallel (DDP), Horovod, and DeepSpeed from the same client code. Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. From b0a092c7a08bc5f38a7dcd14c0a9fcf3ec53b7e5 Mon Sep 17 00:00:00 2001 From: Matteo Bunino Date: Mon, 29 Apr 2024 21:58:24 +0200 Subject: [PATCH 59/59] Fix torch naming problem --- docs/conf.py | 9 ++++--- docs/itwinai.torch.modules.rst | 24 +++++++++---------- src/itwinai/{ptorch => torch}/__init__.py | 0 src/itwinai/{ptorch => torch}/cluster.py | 0 .../{ptorch => torch}/data/__init__.py | 0 src/itwinai/{ptorch => torch}/distributed.py | 0 src/itwinai/{ptorch => torch}/engine.py | 0 src/itwinai/{ptorch => torch}/inference.py | 0 src/itwinai/{ptorch => torch}/mlflow.py | 0 .../{ptorch => torch}/models/__init__.py | 0 src/itwinai/{ptorch => torch}/models/mnist.py | 0 src/itwinai/{ptorch => torch}/trainer.py | 0 src/itwinai/{ptorch => torch}/types.py | 0 src/itwinai/{ptorch => torch}/utils.py | 0 14 files changed, 18 insertions(+), 15 deletions(-) rename src/itwinai/{ptorch => torch}/__init__.py (100%) rename src/itwinai/{ptorch => torch}/cluster.py (100%) rename src/itwinai/{ptorch => torch}/data/__init__.py (100%) rename src/itwinai/{ptorch => torch}/distributed.py (100%) rename src/itwinai/{ptorch => torch}/engine.py (100%) rename src/itwinai/{ptorch => torch}/inference.py (100%) rename src/itwinai/{ptorch => torch}/mlflow.py (100%) rename src/itwinai/{ptorch => torch}/models/__init__.py (100%) rename src/itwinai/{ptorch => torch}/models/mnist.py (100%) rename src/itwinai/{ptorch => torch}/trainer.py (100%) rename src/itwinai/{ptorch => torch}/types.py (100%) rename src/itwinai/{ptorch => torch}/utils.py (100%) diff --git a/docs/conf.py b/docs/conf.py index 97ce4672..f4c9b297 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,7 +17,7 @@ sys.path.insert(0, os.path.abspath('../tutorials/ml-workflows/')) sys.path.insert(0, os.path.abspath('../src/itwinai')) sys.path.insert(0, os.path.abspath('../src/itwinai/tensorflow')) -sys.path.insert(0, os.path.abspath('../src/itwinai/ptorch')) +sys.path.insert(0, os.path.abspath('../src/itwinai/torch')) sys.path.insert(0, os.path.abspath('../')) project = 'itwinai' @@ -29,7 +29,8 @@ # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode'] #'myst_parser' +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', + 'sphinx.ext.viewcode'] # 'myst_parser' templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] @@ -39,17 +40,19 @@ # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + def get_git_tag(): try: return subprocess.check_output(['git', 'describe', '--tags', '--abbrev=0']).decode('utf-8').strip() except subprocess.CalledProcessError: return 'unknown' + # Set the version to the latest tag version = get_git_tag() release = version -html_theme = 'sphinx_rtd_theme' #'alabaster' +html_theme = 'sphinx_rtd_theme' # 'alabaster' html_static_path = ['_static'] html_context = { diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst index 9b86f1f8..d551af40 100644 --- a/docs/itwinai.torch.modules.rst +++ b/docs/itwinai.torch.modules.rst @@ -4,67 +4,67 @@ itwinai PyTorch Modules cluster.py ++++++++++ -.. literalinclude:: ../src/itwinai/ptorch/cluster.py +.. literalinclude:: ../src/itwinai/torch/cluster.py :language: python inference.py ++++++++++++ -.. literalinclude:: ../src/itwinai/ptorch/inference.py +.. literalinclude:: ../src/itwinai/torch/inference.py :language: python mlflow.py +++++++++ -.. literalinclude:: ../src/itwinai/ptorch/mlflow.py +.. literalinclude:: ../src/itwinai/torch/mlflow.py :language: python trainer.py ++++++++++ -.. literalinclude:: ../src/itwinai/ptorch/trainer.py +.. literalinclude:: ../src/itwinai/torch/trainer.py :language: python types.py ++++++++ -.. literalinclude:: ../src/itwinai/ptorch/types.py +.. literalinclude:: ../src/itwinai/torch/types.py :language: python utils.py ++++++++ -.. literalinclude:: ../src/itwinai/ptorch/utils.py +.. literalinclude:: ../src/itwinai/torch/utils.py :language: python -.. .. automodule:: itwinai.ptorch.cluster +.. .. automodule:: itwinai.torch.cluster .. :members: .. :undoc-members: .. :show-inheritance: -.. .. automodule:: itwinai.ptorch.inference +.. .. automodule:: itwinai.torch.inference .. :members: .. :undoc-members: .. :show-inheritance: -.. .. automodule:: itwinai.ptorch.mlflow +.. .. automodule:: itwinai.torch.mlflow .. :members: .. :undoc-members: .. :show-inheritance: -.. .. automodule:: itwinai.ptorch.trainer +.. .. automodule:: itwinai.torch.trainer .. :members: .. :undoc-members: .. :show-inheritance: -.. .. automodule:: itwinai.ptorch.types +.. .. automodule:: itwinai.torch.types .. :members: .. :undoc-members: .. :show-inheritance: -.. .. automodule:: itwinai.ptorch.utils +.. .. automodule:: itwinai.torch.utils .. :members: .. :undoc-members: .. :show-inheritance: diff --git a/src/itwinai/ptorch/__init__.py b/src/itwinai/torch/__init__.py similarity index 100% rename from src/itwinai/ptorch/__init__.py rename to src/itwinai/torch/__init__.py diff --git a/src/itwinai/ptorch/cluster.py b/src/itwinai/torch/cluster.py similarity index 100% rename from src/itwinai/ptorch/cluster.py rename to src/itwinai/torch/cluster.py diff --git a/src/itwinai/ptorch/data/__init__.py b/src/itwinai/torch/data/__init__.py similarity index 100% rename from src/itwinai/ptorch/data/__init__.py rename to src/itwinai/torch/data/__init__.py diff --git a/src/itwinai/ptorch/distributed.py b/src/itwinai/torch/distributed.py similarity index 100% rename from src/itwinai/ptorch/distributed.py rename to src/itwinai/torch/distributed.py diff --git a/src/itwinai/ptorch/engine.py b/src/itwinai/torch/engine.py similarity index 100% rename from src/itwinai/ptorch/engine.py rename to src/itwinai/torch/engine.py diff --git a/src/itwinai/ptorch/inference.py b/src/itwinai/torch/inference.py similarity index 100% rename from src/itwinai/ptorch/inference.py rename to src/itwinai/torch/inference.py diff --git a/src/itwinai/ptorch/mlflow.py b/src/itwinai/torch/mlflow.py similarity index 100% rename from src/itwinai/ptorch/mlflow.py rename to src/itwinai/torch/mlflow.py diff --git a/src/itwinai/ptorch/models/__init__.py b/src/itwinai/torch/models/__init__.py similarity index 100% rename from src/itwinai/ptorch/models/__init__.py rename to src/itwinai/torch/models/__init__.py diff --git a/src/itwinai/ptorch/models/mnist.py b/src/itwinai/torch/models/mnist.py similarity index 100% rename from src/itwinai/ptorch/models/mnist.py rename to src/itwinai/torch/models/mnist.py diff --git a/src/itwinai/ptorch/trainer.py b/src/itwinai/torch/trainer.py similarity index 100% rename from src/itwinai/ptorch/trainer.py rename to src/itwinai/torch/trainer.py diff --git a/src/itwinai/ptorch/types.py b/src/itwinai/torch/types.py similarity index 100% rename from src/itwinai/ptorch/types.py rename to src/itwinai/torch/types.py diff --git a/src/itwinai/ptorch/utils.py b/src/itwinai/torch/utils.py similarity index 100% rename from src/itwinai/ptorch/utils.py rename to src/itwinai/torch/utils.py

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zb;2lA+h%5G@d-B&L7;J$f%00qe+1HDsxrOE7(1UgG!%90$;DRldu_Aqo9`^bYXl_Y zGn4wWB+f~SeZhe^Igzib)DPgb0PDJczicb7!^bmHA_ z_Zl%G`AU3=9C%BE8H}@BZsO@GUd5y@&?X4~DV~^Z#_Ka3F;$G0*>2=&@lw@cHzT$^ hCD>8?-@6E{seF`-B-0hx*FgOGX-)kT`RbQ~{y!RhR7L;* diff --git a/docs_old/docs/use-cases/index.md b/docs_old/docs/use-cases/index.md deleted file mode 100644 index 681caeb6..00000000 --- a/docs_old/docs/use-cases/index.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -layout: default -title: Use cases -nav_order: 4 -has_children: true ---- - -# Use cases - -Digital twin use cases employing machine learning workflows, currently integrated -in this prototype. -To integrate a new use case, please refer to [this page](../How-to-use-this-software). diff --git a/docs_old/docs/use-cases/mnist.md b/docs_old/docs/use-cases/mnist.md deleted file mode 100644 index e22f7ab1..00000000 --- a/docs_old/docs/use-cases/mnist.md +++ /dev/null @@ -1,185 +0,0 @@ ---- -layout: default -title: MNIST -parent: Use cases -nav_order: 1 ---- - -# MNIST: toy example for DT workflows -{: .no_toc } - -## Table of contents -{: .no_toc .text-delta } - -1. TOC -{:toc} - ---- - -Of course MNIST images classification is not a digital twin. Still, it is useful to -provide an example on how to define an end-to-end digital twin workflow with the -software provided in this repository. - -The MNIST use case implements two workflows: - -1. Training workflow: train a neural network to classify MNIST images, and save the trained -neural network to the Models Registry. - - ```mermaid - flowchart LR - %% Nodes - preproc(Pre-processing) - ai(ML training) - reg[(Models Registry:\npre-trained ML models)] - - %% Workflow - preproc --> ai - - %% Connections - ai -.-> |Saves to| reg - ``` - - This workflow is executed by running the command: - - ```bash - micromamba run -p ./.venv python run-workflow.py -f ./use-cases/mnist/training-workflow.yml - ``` - -1. Inference workflow: use the pre-trained neural network to classify unseen images (the test set, in this case). - - ```mermaid - flowchart LR - %% Nodes - preproc(Pre-processing) - ai_depl(ML inference) - pred[(Predictions)] - - %% Workflow - preproc --> ai_depl - - %% Connections - ai_depl -.-> |Saves to| pred - ``` - - This workflow is executed by running the command: - - ```bash - micromamba run -p ./.venv python run-workflow.py -f ./use-cases/mnist/inference-workflow.yml - ``` - -The interactions among workflows and their steps can be described in more details as the following, where conceptual ordering -among different workflow steps is represented by solid arrows: - -```mermaid -graph TD - %% Nodes - remote_repo[(Remote repo)] - preproc(Pre-processing) - ai(ML training) - ai_depl(ML inference) - train_set[(Train dataset)] - test_set[(Test dataset)] - ml_logs[(ML logs)] - reg[(Models Registry:\npre-trained ML models)] - pred[(Predictions)] - - %% Workflow - preproc --> ai ---> ai_depl - - %% Connections - preproc -.-> |Fetches| remote_repo - preproc -.-> |Stores| train_set - preproc -.-> |Stores| test_set - ai -.-> |Trains/validates model| train_set - ai -.-> |Tests model| test_set - ai -.-> |Stores model to| reg - ai -.-> |Logs| ml_logs - ai_depl -.-> |Loads from| reg - ai_depl -.-> |Predict from| test_set - ai_depl -.-> |Stores| pred -``` - -## Workflow steps - -Here we explain in more details how the workflow steps have been configured. -Configuration files and Python scripts are organized under `use-cases/mnist/` -folder, in the core repository. - -### Pre-processing - -This step is implemented by executing `mnist-preproc.py` script in its dedicated micromamba environment, defined by -`preproc-env.yml`. This solution gives full freedom to the DT developer to implement any preprocessing logic, adaptable -to any custom dataset format. - -### ML training - -Is the step in which a neural network is trained on the training dataset, and -validated on the validation dataset. -The mentioned datasets are a result from a split of the pre-processed training -dataset, produced by the pre-processing step. -This step completes the **training workflow**, and results into ML logs and a -trained neural network, which is saved to -the Models Registry. The training workflow can be re-run multiple times with different (hyper)parameters, with the goal -of optimizing some ML validation metric. The neural network with the best validation performances is used to make -predictions on unseen data, in the inference step. - -ML training logic is implemented by the `itwinai` library, requiring the DT developer to produce only a set of YAML -configuration files. For the moment, we assume that the neural network and the training code is already present in -`itwinai` library. - -Both ML training and inference are implemented by commands executed in the same virtual environment. At the moment, -only PyTorch is supported. The corresponding virtual environment definition, used by the `itwinai` library, -is located at `ai/pytorch-env.yml`. - -The ML training logic provided by `itwinai` library is accessed via the -[itwinai CLI](../CLI). - -The DT developer must provide a training configuration file, following some -rules explained in [this section](../How-to-use-this-software#ml-training-configuration). For MNIST use case, the -training configuration is provided by `mnist-ai-train.yml` file. - -Training command is automatically managed by the workflow runner, but it can also -be triggered from withing the ai environment running the following command: - -```bash -micromamba activate ./ai/.venv-pytorch && \ - itwinai train --train-dataset $TRAINING_DATASET_PATH \ - --ml-logs $MLFLOW_TRACKING_URI \ - --config ./use-cases/mnist/mnist-ai-train.yml -``` - -While training is running, the produced ML logs can be inspected in real-time from MLFlow UI by running the command in -the training virtual environment (Conda): - -```bash -micromamba activate ./ai/.venv-pytorch && \ - itwinai mlflow-ui --path $PATH_TO_ML_LOGS -``` - -### ML inference - -A pre-trained neural network is applied to a set of data which was not used to train it. In fact, this is defined as -"unseen" data, from the neural network perspective. An example of ML inference is the application of a trained neural -network to make predictions on new data, to support decision making. *Example*: forecast fire risk maps in Sicily in -August 2023, starting from newly-collected satellite images, to alert local authorities in case of elevated fire risk. - -To select a pre-trained ML model, the DT developer must retrieve the `RUN ID` of -the training run created by MLFLow for some specific training. - -The, the DT developer can update the inference configuration file -`mnist-ai-inference.yml` and run inference workflow. - -Inference/prediction command is automatically managed by the workflow runner, but it can also be triggered from within -the ai environment running the following command: - -```bash -micromamba activate ./ai/.venv-pytorch && \ - itwinai predict --input-dataset $UNSEEN_EXAMPLES_DATASET_PATH \ - --predictions-location $PREDICTIONS_LOCATION \ - --ml-logs $PATH_TO_ML_LOGS \ - --config ./use-cases/mnist/mnist-ai-inference.yml -``` - -## References - -To learn more on how to use this software, e.g., to deploy a new use case, please refer to [this guide](../How-to-use-this-software). diff --git a/docs_old/favicon.ico b/docs_old/favicon.ico deleted file mode 100644 index 1665919207a78e8012a58e4425fd3fb90f51c3f4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 15406 zcmeHNU1(fI6rR~MB$33l52nTjS+R)~MN*bZkd`ipL`oD96-sF-rKz?rqDAaO!TL}e z4Q*Al6#q6=@Q)&GL{P+neUQFbL8`GTt)eqv`yT1xmezvd+#-eTPzCU8mF%w1EgD z(&b2k(o!_s5B?E6nPEP&jCEerfQr|_(_ryn*0BxST8a6iU@tjmIon!wP^YtvU!F1~ z_Gt#Q)}rR$AgZ<@E8o!2AZ=}JqFFa%hcafY(&gP>zk6U$^aTe9oBI!!N{BWEk>Rgiowq@C}#YMoj;gQ`J@?>psDd6$`|XuMhA7y_@Wt?nbhn3 zp}ZmElg2kV$9(pCe~o-zDc6}OI1c9eR03*-jy2#nz{kNOVD_8LXO^*!ZP>O{bPE(E zz!Tu0KESq=p=_aa4Fvaqr@%q&I@?i}I;blj!fv5XfSvjZ%~Kb32B8=lod*|c%S(T z?csXIccPbX6a|}+NJKVo-Yln2pU&>%d>9=amHz&IX=-Zn!a z_#ySY@m=}g;72yQ&dkuzkStoXC|7@a4AuYx*KB)zq(=1CBeUDi#6eFUz3McXlRtLs znAFtN*lnb3n!UtUr(+KXvduA=_sW?3a^>$Fo6dsOt5@4>Wcqv@Dfcd0o$h@vuzh0K zef&?Sxt#o=Lx*fOGB(W~YOmY=O?BDjf%G$5?_|pe${jae9 zRr~$p8er>xDqp$vr`qc;x|@m>bH~7pOL_Mn+OPKhOAOVFKWM+&@fR^vGybG~QZ?i6 zG=lUvlrk31=ljE8pg+(ji-}7qOC4_UHi`og-^~QrJw`NZggq_A_oow_i4{%EWMYSM ze}6l1{HyXjC&3Y|S`RMKISDl7Py{6lz&C??zF)(#jf}$`o06qieSzA!NUhSxZx(2{ea5+drDQ*eW zQ{W`n3rn%()J+?-MVqCfI23&bF2#8J^^rDcD-JG}I0lb`&w;s~`q?g3J#En@Z5KKZ z^^ko6Tuy)S>o08+1F_^o>@(#qg8giiyPg<`C7*fVyj64Dont~Q#NnX8X<0ZyVj6nk9*7etlzV&X=cdZw_qE{lf-M)~Y&V0ESc#d$_?0oBzu(WR6Mf@S(Q9ABef=M~XQ6vWB(mv4(YOy#b&Xj} zjg5`6a^*@H930Hv?OP9G$H&Lzz<~o2i^Zg_t}ZJFOkU5#O3cK*0L(bnt8Kcx`Fhd! z?-1QLoQpeiW^T^$b^kfhkL*qTuYvz#*tc(=jE#+1A-g`0jEu;xUAv^UwRLtZX{^Ld z?EH>6J1hPM_{y67qV`_&#cukbUc4mg@y}%N^Dkv;YRVO_TjAv7q$CoFAU;LR=DW$e zzd9oNo~@!@x)L0_Wj-GIWI~emzjV04wv|m!Ps_G#+fsKxvySV`H=if|$W6;c_n$2Y zJLjTle^TE0VM+igFQUJ7b#-~-%Ji!bDZfeG+ul+rx(s z`^|y*)^^38x>J1i`=ar;zL}JtNB!@joD(!WJS-g@9j@3iW9~dBu(j2J+3aUSSP=fIV`6DY;~owdJlWVkN*pOF^7&(Q{L#lfW_ z&I8w0?hobkC+emR+M>;JP!viBz>JqlF(1@HT?61KxDv$JBL)uR|2M4TW6s~rIWWIP Y&6%F{T!YVZ>Hjm Beware! As the code is frequently changed, the docs are unstable and may not reflect the actual state of the code base. -> Therefore, if you are looking for a more stable version, check out our -> [releases](https://github.com/interTwin-eu/T6.5-AI-and-ML/releases). - -Additional resources include: - -- Detailed instructions on [How to use this software](docs/How-to-use-this-software). -- Roadmap towards a prototype for T6.5 AI workflows for -digital twins here: [Prototype for T6.5](https://github.com/interTwin-eu/T6.5-AI-and-ML/wiki/Prototype-for-T6.5). - -## Platform for machine learning workflows in digital twins - -The goal of this platform is to provide ML researchers with an easy-to-use endpoint -to manage general-purpose machine learning (ML) workflows, with limited engineering overhead, -while providing state-of-the-art MLOps best practices. - -We call this platform `itwinai`. - -The user is going to provide as input a set of configuration files, to fully -describe ML workflows, in the context of digital twin (DT) applications. -`itwinai` platform instantiates ML workflows with the configurations -provided by the DT developer. The execution of ML workflows produces as output a -set of ML metrics, which are visualized by `itwinai` via -[MLFlow](https://mlflow.org/). -As a result of ML training, the best model (on validation dataset) -is saved to the *Models Registry* for future predictions. - -![image](docs/img/user-platform%20interaction%20full.png) - -### Simulating a whole DT workflow - -A DT workflow is more than ML. Generally speaking, MLOps -(i.e., ML model lifecycle management), -can be considered just as a step of a larger DT workflow. - -![image](docs/img/cwl-workflow.png) - -In `itwinai` platform, we focus mainly on the MLOps step, simulating or oversimplifying all the rest -(e.g., pre-processing, authentication, workflow execution). - -For further details on how to define a DT workflow in `itwinai`, follow [this guide](docs/How-to-use-this-software#2-define-a-dt-workflow). - -### How to integrate a new use case - -To integrate an existing use case in this platform, the ML engineer rewrites -the ML experiments according to a format supported by `itwinai`. -Some examples can be found by looking at the use cases -already integrated [here](docs/use-cases/index), located under `use-cases/` -in the code repository. - -A detailed guide on how to integrate a new use case in `itwinai` can be found [here](docs/How-to-use-this-software). From 44511b552e7eea83da2069411cecf6d9026cff60 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Mon, 26 Feb 2024 15:01:53 +0100 Subject: [PATCH 27/59] adding installation instructions in docs --- docs/getting_started_with_itwinai.rst | 138 ++++++++++++++++++++++++++ docs/index.rst | 23 ++++- 2 files changed, 160 insertions(+), 1 deletion(-) create mode 100644 docs/getting_started_with_itwinai.rst diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst new file mode 100644 index 00000000..4ba53df3 --- /dev/null +++ b/docs/getting_started_with_itwinai.rst @@ -0,0 +1,138 @@ +Getting started with itwinai +============================ + +Installation +------------ + +Requirements: ++++++++++++++ + +* Linux environment. +* Windows and macOS were never tested. + + +Micromamba installation ++++++++++++++++++++++++ + +To manage Conda environments we use micromamba, a light weight version of conda. + +It is suggested to refer to the `Manual installation guide `_. + +Consider that Micromamba can eat a lot of space when building environments because packages are cached on +the local filesystem after being downloaded. To clear cache you can use `micromamba clean -a`. +Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage +space and it would be cleverer to install Micromamba in another location with more storage space. +Thus by changing the `$MAMBA_ROOT_PREFIX` variable. See a complete installation example for Linux below, where the +default `$MAMBA_ROOT_PREFIX` is overridden: + + +.. code-block:: bash + + cd $HOME + + # Download micromamba (This command is for Linux Intel (x86_64) systems. Find the right one for your system!) + curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba + + # Install micromamba in a custom directory + MAMBA_ROOT_PREFIX='my-mamba-root' + ./bin/micromamba shell init $MAMBA_ROOT_PREFIX + + # To invoke micromamba from Makefile, you need to add explicitly to $PATH + echo 'PATH="$(dirname $MAMBA_EXE):$PATH"' >> ~/.bashrc + +**Reference**: `Micromamba installation guide `_. + + +Workflow orchestrator ++++++++++++++++++++++ + +Install the (custom) orchestrator virtual environment. + +.. code-block:: bash + + source ~/.bashrc + # Create local env + make + + # Activate env + micromamba activate ./.venv + +To run tests on workflows use: + +.. code-block:: bash + + # Activate env + micromamba activate ./.venv + + pytest tests/ + + +Development env setup +--------------------- + +Requirements: + +* Linux, macOS environment. Windows was never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +Installation: + +.. code-block:: bash + + make dev-env + + # Activate env + micromamba activate ./.venv-dev + +To run tests on itwinai package: + +.. code-block:: bash + + # Activate env + micromamba activate ./.venv-dev + + pytest tests/ai/ + + +AI environment setup +-------------------- + +Requirements: + +* Linux, macOS environment. Windows was never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +**NOTE**: this environment gets automatically setup when a workflow is executed! + +However, you can also set it up explicitly with: + +.. code-block:: bash + + make ai-env + + # Activate env + micromamba activate ./ai/.venv-pytorch + +Updating the environment files +++++++++++++++++++++++++++++++ + +The files under `ai/env-files/` are of two categories: + +* Simple environment definition, such as `pytorch-env.yml` +and `pytorch-env-gpu.yml` +* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, +generated by [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/). + +**When you install the ai environment, install it from the lock file!** + +When the "simple" environment file (e.g., `pytorch-env.yml`) changes, +lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): + +.. code-block:: bash + + micromamba activate ./.venv + + make lock-ai + diff --git a/docs/index.rst b/docs/index.rst index 29e47aa2..35529c1f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -26,13 +26,34 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi .. toctree:: :maxdepth: 2 - :caption: Contents: + :hidden: + :caption: 💡 Installation + + getting_started_with_itwinai + +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: 🪄 itwinai Modules modules + +.. toctree:: + :maxdepth: 2 + :caption: 📚 Integrated Use-cases + use_cases + +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: 🚀 Tutorials + tutorials +`interTwin Demo: itwinai integration with other DTE modules `_ + Indices and tables ================== From d43e35168b5c9878678dd8487ee62f1efd5a8b26 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Mon, 26 Feb 2024 15:16:49 +0100 Subject: [PATCH 28/59] adding latest changes to docs --- docs/getting_started_with_itwinai.rst | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 4ba53df3..2e0388b7 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -120,15 +120,12 @@ Updating the environment files The files under `ai/env-files/` are of two categories: -* Simple environment definition, such as `pytorch-env.yml` -and `pytorch-env-gpu.yml` -* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, -generated by [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/). +* Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` +* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by ```conda-lock ```_. **When you install the ai environment, install it from the lock file!** -When the "simple" environment file (e.g., `pytorch-env.yml`) changes, -lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): +When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): .. code-block:: bash From 02ed18df815900adcc43cd379d545b4c7b87dbc8 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 29 Feb 2024 15:42:21 +0100 Subject: [PATCH 29/59] adding new pages for itwinai modules and other modifications --- docs/conf.py | 6 +- docs/getting_started_with_itwinai.rst | 167 +++++++++++++++++++------- docs/index.rst | 3 +- docs/itwinai.tf.modules.rst | 20 +++ docs/itwinai.torch.modules.rst | 34 ++++++ docs/modules.rst | 7 ++ 6 files changed, 188 insertions(+), 49 deletions(-) create mode 100644 docs/itwinai.tf.modules.rst create mode 100644 docs/itwinai.torch.modules.rst diff --git a/docs/conf.py b/docs/conf.py index e21e2464..64a10acb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -14,10 +14,12 @@ sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch/')) sys.path.insert(0, os.path.abspath('../tutorials/ml-workflows/')) sys.path.insert(0, os.path.abspath('../src/itwinai')) +sys.path.insert(0, os.path.abspath('../src/itwinai/tensorflow')) +sys.path.insert(0, os.path.abspath('../src/itwinai/ptorch')) sys.path.insert(0, os.path.abspath('../')) project = 'itwinai' -copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' +copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki on behalf of CERN' author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' release = '0.0.1' @@ -29,7 +31,7 @@ templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - +autodoc_mock_imports = ["mlflow"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 2e0388b7..9956a89e 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -43,93 +43,168 @@ default `$MAMBA_ROOT_PREFIX` is overridden: **Reference**: `Micromamba installation guide `_. -Workflow orchestrator -+++++++++++++++++++++ +Environment setup +----------------- + +**Requirements:** + +* Linux environment. Windows and macOS were never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +Tensorflow +++++++++++ -Install the (custom) orchestrator virtual environment. +Installation: .. code-block:: bash - source ~/.bashrc - # Create local env - make + # Install TensorFlow 2.13 + make tf-2.13 # Activate env - micromamba activate ./.venv + micromamba activate ./.venv-tf -To run tests on workflows use: +Other TF versions are available, using the following targets `tf-2.10`, and `tf-2.11`. -.. code-block:: bash - # Activate env - micromamba activate ./.venv +PyTorch (+ Lightning) ++++++++++++++++++++++ - pytest tests/ +Installation: +.. code-block:: bash -Development env setup ---------------------- + # Install PyTorch + lightning + make torch-gpu -Requirements: + # Activate env + micromamba activate ./.venv-pytorch -* Linux, macOS environment. Windows was never tested. -* Micromamba: see the installation instructions above. -* VS Code, for development. +Other also CPU-only version is available at the target `torch-cpu`. -Installation: + +Development environment ++++++++++++++++++++++++ + +This is for developers only. To have it, update the installed `itwinai` package adding the `dev` extra: .. code-block:: bash - make dev-env + pip install -e .[dev] - # Activate env - micromamba activate ./.venv-dev +**Test with `pytest`** To run tests on itwinai package: .. code-block:: bash # Activate env - micromamba activate ./.venv-dev + micromamba activate ./.venv-pytorch # or ./.venv-tf - pytest tests/ai/ + pytest -v -m "not slurm" tests/ -AI environment setup --------------------- +However, some tests are intended to be executed only on an HPC system, where SLURM is available. They are marked with "slurm" tag. To run also those tests, use the dedicated job script: -Requirements: +.. code-block:: bash -* Linux, macOS environment. Windows was never tested. -* Micromamba: see the installation instructions above. -* VS Code, for development. + sbatch tests/slurm_tests_startscript -**NOTE**: this environment gets automatically setup when a workflow is executed! + # Upon completion, check the output: + cat job.err + cat job.out -However, you can also set it up explicitly with: -.. code-block:: bash - make ai-env - # Activate env - micromamba activate ./ai/.venv-pytorch +.. Workflow orchestrator +.. +++++++++++++++++++++ -Updating the environment files -++++++++++++++++++++++++++++++ +.. Install the (custom) orchestrator virtual environment. -The files under `ai/env-files/` are of two categories: +.. .. code-block:: bash -* Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` -* Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by ```conda-lock ```_. +.. source ~/.bashrc +.. # Create local env +.. make -**When you install the ai environment, install it from the lock file!** +.. # Activate env +.. micromamba activate ./.venv -When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with [`conda-lock`](https://conda.github.io/conda-lock/cli/gen/): +.. To run tests on workflows use: -.. code-block:: bash +.. .. code-block:: bash + +.. # Activate env +.. micromamba activate ./.venv + +.. pytest tests/ + + +.. Development env setup +.. --------------------- + +.. Requirements: + +.. * Linux, macOS environment. Windows was never tested. +.. * Micromamba: see the installation instructions above. +.. * VS Code, for development. + +.. Installation: + +.. .. code-block:: bash + +.. make dev-env + +.. # Activate env +.. micromamba activate ./.venv-dev + +.. To run tests on itwinai package: + +.. .. code-block:: bash + +.. # Activate env +.. micromamba activate ./.venv-dev + +.. pytest tests/ai/ + + +.. AI environment setup +.. -------------------- + +.. Requirements: + +.. * Linux, macOS environment. Windows was never tested. +.. * Micromamba: see the installation instructions above. +.. * VS Code, for development. + +.. **NOTE**: this environment gets automatically setup when a workflow is executed! + +.. However, you can also set it up explicitly with: + +.. .. code-block:: bash + +.. make ai-env + +.. # Activate env +.. micromamba activate ./ai/.venv-pytorch + +.. Updating the environment files +.. ++++++++++++++++++++++++++++++ + +.. The files under `ai/env-files/` are of two categories: + +.. * Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` +.. * Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by `conda-lock `_. + +.. **When you install the ai environment, install it from the lock file!** + +.. When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with `conda-lock `_: + +.. .. code-block:: bash - micromamba activate ./.venv +.. micromamba activate ./.venv - make lock-ai +.. make lock-ai diff --git a/docs/index.rst b/docs/index.rst index 35529c1f..d56cb069 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -52,7 +52,8 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi tutorials -`interTwin Demo: itwinai integration with other DTE modules `_ +**`interTwin Demo: itwinai integration with other DTE modules `_** ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Indices and tables ================== diff --git a/docs/itwinai.tf.modules.rst b/docs/itwinai.tf.modules.rst new file mode 100644 index 00000000..aad490d8 --- /dev/null +++ b/docs/itwinai.tf.modules.rst @@ -0,0 +1,20 @@ +itwinai.tensorflow.modules +========================== + +.. .. automodule:: itwinai.tensorflow.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.tensorflow.utils +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. literalinclude:: ../src/itwinai/tensorflow/trainer.py + :language: python + +.. literalinclude:: ../src/itwinai/tensorflow/utils.py + :language: python + + diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst new file mode 100644 index 00000000..883fd4e8 --- /dev/null +++ b/docs/itwinai.torch.modules.rst @@ -0,0 +1,34 @@ +itwinai.torch.modules +===================== + +.. automodule:: itwinai.ptorch.cluster + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.inference + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.mlflow + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.trainer + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.types + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: itwinai.ptorch.utils + :members: + :undoc-members: + :show-inheritance: + + diff --git a/docs/modules.rst b/docs/modules.rst index b220e0ff..a9e96c97 100644 --- a/docs/modules.rst +++ b/docs/modules.rst @@ -14,3 +14,10 @@ itwinai itwinai.types itwinai.utils + +.. toctree:: + :maxdepth: 4 + + itwinai.tf.modules + itwinai.torch.modules + From e809b43511413099697cb86ad6a76114a2924509 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 29 Feb 2024 15:49:41 +0100 Subject: [PATCH 30/59] modified src/itwinai/torch directory name to solve namespace conflict --- src/itwinai/ptorch/__init__.py | 0 src/itwinai/ptorch/cluster.py | 225 ++++++ src/itwinai/ptorch/data/__init__.py | 0 src/itwinai/ptorch/inference.py | 219 ++++++ src/itwinai/ptorch/mlflow.py | 77 +++ src/itwinai/ptorch/models/__init__.py | 0 src/itwinai/ptorch/models/mnist.py | 75 ++ src/itwinai/ptorch/trainer.py | 941 ++++++++++++++++++++++++++ src/itwinai/ptorch/types.py | 64 ++ src/itwinai/ptorch/utils.py | 84 +++ 10 files changed, 1685 insertions(+) create mode 100644 src/itwinai/ptorch/__init__.py create mode 100644 src/itwinai/ptorch/cluster.py create mode 100644 src/itwinai/ptorch/data/__init__.py create mode 100644 src/itwinai/ptorch/inference.py create mode 100644 src/itwinai/ptorch/mlflow.py create mode 100644 src/itwinai/ptorch/models/__init__.py create mode 100644 src/itwinai/ptorch/models/mnist.py create mode 100644 src/itwinai/ptorch/trainer.py create mode 100644 src/itwinai/ptorch/types.py create mode 100644 src/itwinai/ptorch/utils.py diff --git a/src/itwinai/ptorch/__init__.py b/src/itwinai/ptorch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/itwinai/ptorch/cluster.py b/src/itwinai/ptorch/cluster.py new file mode 100644 index 00000000..aece16e2 --- /dev/null +++ b/src/itwinai/ptorch/cluster.py @@ -0,0 +1,225 @@ +"""Cluster environments where to run AI workflows. Partially adapted from: +https://github.com/facebookresearch/detr/blob/master/util/misc.py and +https://github.com/ramyamounir/Template/blob/main/lib/utils/distributed.py +""" + +from __future__ import annotations +from typing import Optional +import os +import signal +import subprocess +from pathlib import Path +from contextlib import contextmanager + +import numpy as np + +import torch +import torch.distributed as dist +import torch.backends.cudnn as cudnn + +from ..cluster import ( + ClusterEnvironment, + setup_for_distributed, + handle_sigusr1, + handle_sigterm +) +from .types import TorchDistributedBackend as BackendT + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + + +class TorchCluster(ClusterEnvironment): + def __init__(self) -> None: + super().__init__() + + def _set_backend(self, backend_name: str) -> None: + if backend_name not in BackendT: + raise ValueError( + "Unrecognized 'backend' field. Allowed values " + f"are: {BackendT.list()}. Received '{backend_name}'") + self._backend = backend_name + + def is_cuda_available(self) -> bool: + return self.use_cuda and torch.cuda.is_available() + + def is_main_worker(self) -> bool: + """Checks if the current process is the main/master process + in the whole job. + """ + return self.global_rank == 0 + + def cleanup_resources(self): + dist.barrier() + dist.destroy_process_group() + + +class LocalCluster(TorchCluster): + """Simple single node cluster with optional access to multiple GPUs.""" + + def __init__( + self, + backend: Optional[str] = None, + gpus: Optional[str] = '', + port: int = 49153, + rnd_seed: Optional[int] = 42 + ) -> None: + """Initialize local cluster for multi-GPU access. + + Args: + backend (Optional[str], optional): supported PyTorch backends. + If None, workload is not distributed. Defaults to None. + gpus (Optional[str], optional): list of visible GPU devices + (e.g., '1,2,3'). If empty string uses all available GPUs. + If None, CPU is used. Defaults to ''. + port (int, optional): TCP port used by the master process. + Defaults to 49153. + rnd_seed (Optional[int], optional): random seed to be setup after + all processes are setup. Defaults to 42. + """ + super().__init__() + self.backend = backend + self.gpus = gpus + self.port = port + self.dist_url = f'tcp://127.0.0.1:{self.port}' + self.rnd_seed = rnd_seed + + if self.gpus != '' and self.gpus is not None: + # Restrict the number of GPUs visible according to user needs + os.environ['CUDA_VISIBLE_DEVICES'] = self.gpus + + self.ngpus_per_node = torch.cuda.device_count() + self.global_rank = 0 + self.global_world_size = self.ngpus_per_node + + print(f"{self.ngpus_per_node} GPUs are available.") + self.distributed = True + # This flag tells whether the user wants to use the GPU(s) + self.use_cuda = ( + self.gpus is not None # GPU is not manually disabled + and torch.cuda.device_count() >= 1 # At least one GPU is selected + ) + if self.backend is None or self.ngpus_per_node <= 1: + print("Distributed has been disabled.") + self.distributed = False + self.dist_url = None + self.global_world_size = 1 + self.global_rank = 0 + if not self.is_cuda_available(): + print("CUDA disabled... Running on single CPU.") + self.use_cuda = False + self.distributed = False + self.dist_url = None + self.global_world_size = 1 + self.global_rank = 0 + + # Since single node case + self.local_world_size = self.global_world_size + + @contextmanager + def init_dist_gpu(self, worker_id) -> torch.device: + if self.distributed: + torch.cuda.set_device(worker_id) + self.global_rank += worker_id + # print(f'GLOBAL RANK: {self.global_rank}') + # Since single node case + self.local_rank = self.global_rank + # Simplification: worker ID mapped to GPU ID + self.gpu_id = worker_id + + try: + dist.init_process_group( + backend=self.backend, + init_method=self.dist_url, + world_size=self.global_world_size, + rank=self.global_rank + ) + fix_random_seeds(self.rnd_seed) + torch.cuda.set_device(self.gpu_id) + cudnn.benchmark = True + dist.barrier() + + setup_for_distributed(self.is_main_worker()) + print("SETUP DISTRIBUTED COMPLETE") + yield torch.device('cuda', worker_id) + finally: + self.cleanup_resources() + else: + # Distributed is disabled + # Since single node case + self.global_rank = 0 + self.local_rank = self.global_rank + if self.use_cuda: + torch.cuda.set_device(worker_id) + yield torch.device('cuda', worker_id) + else: + yield torch.device('cpu') + + +class SLURMCluster(TorchCluster): + """SLURM cluster with access to multi-node multi-GPU.""" + + def __init__( + self, + port: int = 49153, + backend: str = 'gloo', + rnd_seed: Optional[int] = 42 + ) -> None: + super().__init__() + self.port = port + self.backend = backend + self.rnd_seed = rnd_seed + if 'SLURM_JOB_ID' not in os.environ: + raise RuntimeError( + "'SLURM_JOB_ID' environment variable is not set. " + "Perhaps you are not running in a slurm cluster?" + ) + + self.ngpus_per_node = torch.cuda.device_count() + + # requeue job on SLURM preemption + signal.signal(signal.SIGUSR1, handle_sigusr1) + signal.signal(signal.SIGTERM, handle_sigterm) + + # find a common host name on all nodes + cmd = 'scontrol show hostnames ' + os.getenv('SLURM_JOB_NODELIST') + stdout = subprocess.check_output(cmd.split()) + host_name = stdout.decode().splitlines()[0] + self.dist_url = f'tcp://{host_name}:{self.port}' + + # distributed parameters + self.global_rank = int(os.getenv('SLURM_NODEID')) * self.ngpus_per_node + self.global_world_size = int( + os.getenv('SLURM_NNODES')) * self.ngpus_per_node + + @contextmanager + def init_dist_gpu(self): + import submitit + try: + job_env = submitit.JobEnvironment() + self.output_dir = Path( + str(self.output_dir).replace("%j", str(job_env.job_id))) + self.gpu = job_env.local_rank + self.global_rank = job_env.global_rank + + dist.init_process_group( + backend=self.backend, + init_method=self.dist_url, + world_size=self.global_world_size, + rank=self.global_rank + ) + fix_random_seeds(self.rnd_seed) + torch.cuda.set_device(self.gpu) + cudnn.benchmark = True + dist.barrier() + + setup_for_distributed(self.is_main_worker()) + yield + finally: + self.cleanup_resources() diff --git a/src/itwinai/ptorch/data/__init__.py b/src/itwinai/ptorch/data/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/itwinai/ptorch/inference.py b/src/itwinai/ptorch/inference.py new file mode 100644 index 00000000..02882f06 --- /dev/null +++ b/src/itwinai/ptorch/inference.py @@ -0,0 +1,219 @@ +from typing import Optional, Dict, Any, Union +import os +import abc + +import torch +from torch import nn +from torch.utils.data import DataLoader, Dataset + +from ..utils import dynamically_import_class +from .utils import clear_key +from ..components import Predictor, monitor_exec +from .types import TorchDistributedStrategy as StrategyT +from .types import Metric, Batch +from ..serialization import ModelLoader + + +class TorchModelLoader(ModelLoader): + """Loads a torch model from somewhere. + + Args: + model_uri (str): Can be a path on local filesystem + or an mlflow 'locator' in the form: + 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' + """ + + def __call__(self) -> nn.Module: + """"Loads model from model URI. + + Raises: + ValueError: if the model URI is not recognized + or the model is not found. + + Returns: + nn.Module: torch neural network. + """ + if os.path.exists(self.model_uri): + # Model is on local filesystem. + model = torch.load(self.model_uri) + return model.eval() + + if self.model_uri.startswith('mlflow+'): + # Model is on an MLFLow server + # Form is 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' + import mlflow + from mlflow import MlflowException + _, tracking_uri, run_id, artifact_path = self.model_uri.split('+') + mlflow.set_tracking_uri(tracking_uri) + + # Check that run exists + try: + mlflow.get_run(run_id) + except MlflowException: + raise ValueError(f"Run ID '{run_id}' was not found!") + + # Download model weights + ckpt_path = mlflow.artifacts.download_artifacts( + run_id=run_id, + artifact_path=artifact_path, + dst_path='tmp/', + tracking_uri=mlflow.get_tracking_uri() + ) + model = torch.load(ckpt_path) + return model.eval() + + raise ValueError( + 'Unrecognized model URI: model may not be there!' + ) + + +class TorchPredictor(Predictor): + """Applies a pre-trained torch model to unseen data.""" + + model: nn.Module = None + test_dataset: Dataset + test_dataloader: DataLoader = None + _strategy: StrategyT = StrategyT.NONE.value + epoch_idx: int = 0 + train_glob_step: int = 0 + validation_glob_step: int = 0 + train_metrics: Dict[str, Metric] + validation_metrics: Dict[str, Metric] + + def __init__( + self, + model: Union[nn.Module, ModelLoader], + test_dataloader_class: str = 'torch.utils.data.DataLoader', + test_dataloader_kwargs: Optional[Dict] = None, + # strategy: str = StrategyT.NONE.value, + # seed: Optional[int] = None, + # logger: Optional[List[Logger]] = None, + # cluster: Optional[ClusterEnvironment] = None, + # test_metrics: Optional[Dict[str, Metric]] = None, + name: str = None + ) -> None: + super().__init__(model=model, name=name) + self.save_parameters(**self.locals2params(locals())) + self.model = self.model.eval() + # self.seed = seed + # self.strategy = strategy + # self.cluster = cluster + + # Train and validation dataloaders + self.test_dataloader_class = dynamically_import_class( + test_dataloader_class + ) + test_dataloader_kwargs = ( + test_dataloader_kwargs + if test_dataloader_kwargs is not None else {} + ) + self.test_dataloader_kwargs = clear_key( + test_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' + ) + + # # Loggers + # self.logger = logger if logger is not None else ConsoleLogger() + + # # Metrics + # self.train_metrics = ( + # {} if train_metrics is None else train_metrics + # ) + # self.validation_metrics = ( + # self.train_metrics if validation_metrics is None + # else validation_metrics + # ) + + @monitor_exec + def execute( + self, + test_dataset: Dataset, + model: nn.Module = None, + ) -> Dict[str, Any]: + """Applies a torch model to a dataset for inference. + + Args: + test_dataset (Dataset[str, Any]): each item in this dataset is a + couple (item_unique_id, item) + model (nn.Module, optional): torch model. Overrides the existing + model, if given. Defaults to None. + + Returns: + Dict[str, Any]: maps each item ID to the corresponding predicted + value(s). + """ + if model is not None: + # Overrides existing "internal" model + self.model = model + + test_dataloader = self.test_dataloader_class( + test_dataset, **self.test_dataloader_kwargs + ) + + all_predictions = dict() + for samples_ids, samples in test_dataloader: + with torch.no_grad(): + pred = self.model(samples) + pred = self.transform_predictions(pred) + for idx, pre in zip(samples_ids, pred): + # For each item in the batch + if pre.numel() == 1: + pre = pre.item() + else: + pre = pre.to_dense().tolist() + all_predictions[idx] = pre + return all_predictions + + @abc.abstractmethod + def transform_predictions(self, batch: Batch) -> Batch: + """ + Post-process the predictions of the torch model (e.g., apply + threshold in case of multilabel classifier). + """ + + +class MulticlassTorchPredictor(TorchPredictor): + """ + Applies a pre-trained torch model to unseen data for + multiclass classification. + """ + + def transform_predictions(self, batch: Batch) -> Batch: + batch = batch.argmax(-1) + return batch + + +class MultilabelTorchPredictor(TorchPredictor): + """ + Applies a pre-trained torch model to unseen data for + multilabel classification, applying a threshold on the + output of the neural network. + """ + + threshold: float + + def __init__( + self, + model: Union[nn.Module, ModelLoader], + test_dataloader_class: str = 'torch.utils.data.DataLoader', + test_dataloader_kwargs: Optional[Dict] = None, + threshold: float = 0.5, + name: str = None + ) -> None: + super().__init__( + model, test_dataloader_class, test_dataloader_kwargs, name + ) + self.threshold = threshold + + def transform_predictions(self, batch: Batch) -> Batch: + return (batch > self.threshold).float() + + +class RegressionTorchPredictor(TorchPredictor): + """ + Applies a pre-trained torch model to unseen data for + regression, leaving untouched the output of the neural + network. + """ + + def transform_predictions(self, batch: Batch) -> Batch: + return batch diff --git a/src/itwinai/ptorch/mlflow.py b/src/itwinai/ptorch/mlflow.py new file mode 100644 index 00000000..18a014ff --- /dev/null +++ b/src/itwinai/ptorch/mlflow.py @@ -0,0 +1,77 @@ +from typing import Dict, Optional +import os + +import mlflow +import yaml + + +def _get_mlflow_logger_conf(pl_config: Dict) -> Optional[Dict]: + """Extract MLFLowLogger configuration from pytorch lightning + configuration file, if present. + + Args: + pl_config (Dict): lightning configuration loaded in memory. + + Returns: + Optional[Dict]: if present, MLFLowLogger constructor arguments + (under 'init_args' key). + """ + if isinstance(pl_config['trainer']['logger'], list): + # If multiple loggers are provided + for logger_conf in pl_config['trainer']['logger']: + if logger_conf['class_path'].endswith('MLFlowLogger'): + return logger_conf['init_args'] + elif pl_config['trainer']['logger']['class_path'].endswith('MLFlowLogger'): + return pl_config['trainer']['logger']['init_args'] + + +def _mlflow_log_pl_config(pl_config: Dict, local_yaml_path: str) -> None: + os.makedirs(os.path.dirname(local_yaml_path), exist_ok=True) + with open(local_yaml_path, 'w') as outfile: + yaml.dump(pl_config, outfile, default_flow_style=False) + mlflow.log_artifact(local_yaml_path) + + +def init_lightning_mlflow( + pl_config: Dict, + default_experiment_name: str = 'Default', + **autolog_kwargs +) -> None: + """Initialize mlflow for pytorch lightning, also setting up + auto-logging (mlflow.pytorch.autolog(...)). Creates a new mlflow + run and attaches it to the mlflow auto-logger. + + Args: + pl_config (Dict): pytorch lightning configuration loaded in memory. + default_experiment_name (str, optional): used as experiment name + if it is not given in the lightning conf. Defaults to 'Default'. + **autolog_kwargs (kwargs): args for mlflow.pytorch.autolog(...). + """ + mlflow_conf: Optional[Dict] = _get_mlflow_logger_conf(pl_config) + if not mlflow_conf: + return + + tracking_uri = mlflow_conf.get('tracking_uri') + if not tracking_uri: + save_path = mlflow_conf.get('save_dir') + tracking_uri = "file://" + os.path.abspath(save_path) + + experiment_name = mlflow_conf.get('experiment_name') + if not experiment_name: + experiment_name = default_experiment_name + + mlflow.set_tracking_uri(tracking_uri) + mlflow.set_experiment(experiment_name) + mlflow.pytorch.autolog(**autolog_kwargs) + mlflow.start_run() + + mlflow_conf['experiment_name'] = experiment_name + mlflow_conf['run_id'] = mlflow.active_run().info.run_id + + _mlflow_log_pl_config(pl_config, '.tmp/pl_config.yml') + + +def teardown_lightning_mlflow() -> None: + """End active mlflow run, if any.""" + if mlflow.active_run() is not None: + mlflow.end_run() diff --git a/src/itwinai/ptorch/models/__init__.py b/src/itwinai/ptorch/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/itwinai/ptorch/models/mnist.py b/src/itwinai/ptorch/models/mnist.py new file mode 100644 index 00000000..c222a5a3 --- /dev/null +++ b/src/itwinai/ptorch/models/mnist.py @@ -0,0 +1,75 @@ +import torch +import torch.nn as nn +import lightning as L + +from torch.nn import functional as F + + +class MNISTModel(L.LightningModule): + """ + Simple PL model for MNIST. + Adapted from + https://lightning.ai/docs/pytorch/stable/notebooks/lightning_examples/mnist-hello-world.html + """ + + def __init__( + self, + hidden_size: int = 64, + ): + super().__init__() + + # Automatically save constructor args as hyperparameters + self.save_hyperparameters() + + # Set our init args as class attributes + self.hidden_size = hidden_size + + # Hardcode some dataset specific attributes + self.num_classes = 10 + self.dims = (1, 28, 28) + channels, width, height = self.dims + + # Define PyTorch model + self.model = nn.Sequential( + nn.Flatten(), + nn.Linear(channels * width * height, hidden_size), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(hidden_size, self.num_classes), + ) + + def forward(self, x): + x = self.model(x) + return F.log_softmax(x, dim=1) + + def training_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = F.nll_loss(logits, y) + + # Log metrics with autolog + self.log("train_loss", loss, on_step=True, on_epoch=True) + + return loss + + def validation_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = F.nll_loss(logits, y) + # preds = torch.argmax(logits, dim=1) + self.log("val_loss", loss, prog_bar=True, on_step=True, on_epoch=True) + + def test_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = F.nll_loss(logits, y) + self.log("test_loss", loss) + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + x, _ = batch + logits = self(x) + preds = torch.argmax(logits, dim=1) + return preds diff --git a/src/itwinai/ptorch/trainer.py b/src/itwinai/ptorch/trainer.py new file mode 100644 index 00000000..31794c49 --- /dev/null +++ b/src/itwinai/ptorch/trainer.py @@ -0,0 +1,941 @@ +"""Provides training logic for PyTorch models via Trainer classes.""" + +from typing import ( + Optional, Dict, Union, Tuple, Type, List, Any +) +import time +import os +import sys + +import numpy as np +import torch +import torch.multiprocessing as mp +from torch.utils.data import DataLoader, Dataset +from torch.utils.data.distributed import DistributedSampler +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +import torch.nn as nn +from torch.optim.optimizer import Optimizer + +from ..components import Trainer, monitor_exec +from .utils import seed_worker, par_allgather_obj, clear_key +from .types import ( + Batch, Loss, LrScheduler, Metric +) +from .types import TorchDistributedStrategy as StrategyT +from ..loggers import LogMixin, Logger, ConsoleLogger +from ..utils import dynamically_import_class +from ..cluster import ClusterEnvironment + + +def preproc_dataloader(dataloader: DataLoader, gwsize, grank): + """Makes a Dataloader distributed.""" + sampler = DistributedSampler( + dataloader.dataset, + num_replicas=gwsize, + rank=grank, + shuffle=True + ) + # Recreate dataloader, with updated sampler + return DataLoader( + dataloader.dataset, + batch_size=dataloader.batch_size, + sampler=sampler, + num_workers=dataloader.num_workers, + collate_fn=dataloader.collate_fn, + pin_memory=dataloader.pin_memory, + drop_last=dataloader.drop_last, + timeout=dataloader.timeout, + worker_init_fn=seed_worker, # dataloader.worker_init_fn, + multiprocessing_context=dataloader.multiprocessing_context, + generator=dataloader.generator, + prefetch_factor=dataloader.prefetch_factor, + persistent_workers=dataloader.persistent_workers, + pin_memory_device=dataloader.pin_memory_device + ) + + +def distributed(func): + """The decorated function must have a standard signature. + Its first arguments must be: + model, train_dataloader, validation_dataloader, device (in this order). + + Additional args or kwargs are allowed consistently with the signature + of the decorated function. + """ + def dist_train( + model, train_dataloader, validation_dataloader=None, device='cpu', + *args, **kwargs + ): + if torch.cuda.is_available(): + dist.init_process_group(backend='nccl') + + if torch.cuda.is_available(): + lwsize = torch.cuda.device_count() # local world size - per node + gwsize = dist.get_world_size() # global world size - per run + grank = dist.get_rank() # global rank - assign per run + lrank = dist.get_rank() % lwsize # local rank - assign per node + else: + gwsize = 1 + grank = 0 + lrank = 0 + + device = torch.device( + 'cuda' if torch.cuda.is_available() else 'cpu', lrank) + if torch.cuda.is_available(): + torch.cuda.set_device(lrank) + + model = model.to(device) + model = DDP(model, device_ids=[device], output_device=device) + + train_dataloader = preproc_dataloader(train_dataloader, gwsize, grank) + if validation_dataloader is not None: + validation_dataloader = preproc_dataloader( + validation_dataloader, gwsize, grank) + + try: + func(model, train_dataloader, validation_dataloader, device, + *args, **kwargs) + finally: + if torch.cuda.is_available(): + dist.barrier() + dist.destroy_process_group() + return dist_train + + +class TorchTrainerMG(Trainer, LogMixin): + """ + Torch trainer for optionally distributed data-parallel (DDP) workload. + Multi-GPU distribution. + + Args: + model (nn.Module): neural network instance. + loss (Loss): torch loss function instance. + optimizer_class (str): path to optimizer class + (e.g., 'torch.optim.SGD') + optimizer_kwargs (Optional[Dict], optional): optimizer constructor + arguments (except from parameters). Defaults to None. + lr_scheduler_class (Optional[str], optional): path to learning + rate scheduler class. Defaults to None. + lr_scheduler_kwargs (Optional[Dict], optional): constructor arguments + of the learning rate scheduler, except for the optimizer. + Defaults to None. + train_dataloader_class (str, optional): train dataloader class path. + Defaults to 'torch.utils.data.DataLoader'. + train_dataloader_kwargs (Optional[Dict], optional): constructor + arguments of the train dataloader, except for the dataset + instance. Defaults to None. + validation_dataloader_class (str, optional): validation dataloader + class path. Defaults to 'torch.utils.data.DataLoader'. + validation_dataloader_kwargs (Optional[Dict], optional): constructor + arguments of the validation dataloader, except for the dataset + instance. If None, it replicates `train_dataloader_kwargs`. + Defaults to None. + epochs (int, optional): number of training epochs. Defaults to 1. + strategy (Optional[TorchDistributedStrategy], optional): distributed + strategy. Defaults to StrategyT.NONE.value. + backend (TorchDistributedBackend, optional): computing backend. + Defaults to BackendT.NCCL.value. + shuffle_dataset (bool, optional): whether shuffle dataset before + sampling batches from dataloader. Defaults to False. + use_cuda (bool, optional): whether to use GPU. Defaults to True. + benchrun (bool, optional): sets up a debug run. Defaults to False. + testrun (bool, optional): deterministic training seeding everything. + Defaults to False. + seed (Optional[int], optional): random seed. Defaults to None. + logger (Optional[List[Logger]], optional): logger. Defaults to None. + checkpoint_every (int, optional): how often (epochs) to checkpoint the + best model. Defaults to 10. + cluster (Optional[ClusterEnvironment], optional): cluster environment + object describing the context in which the trainer is executed. + Defaults to None. + train_metrics (Optional[Dict[str, Metric]], optional): + list of metrics computed in the training step on the predictions. + It's a dictionary with the form + ``{'metric_unique_name': CallableMetric}``. Defaults to None. + validation_metrics (Optional[Dict[str, Metric]], optional): same + as ``training_metrics``. If not given, it mirrors the training + metrics. Defaults to None. + + Raises: + RuntimeError: When trying to use DDP without CUDA support. + NotImplementedError: when trying to use a strategy different from the + ones provided by TorchDistributedStrategy. + """ + + model: nn.Module = None + loss: Loss = None + optimizer: Optimizer = None + lr_scheduler = None + _strategy: StrategyT = StrategyT.NONE.value + train_dataset: Dataset + validation_dataset: Dataset + train_dataloader: DataLoader = None + validation_dataloader: DataLoader = None + epoch_idx: int = 0 + train_glob_step: int = 0 + validation_glob_step: int = 0 + train_metrics: Dict[str, Metric] + validation_metrics: Dict[str, Metric] + + def __init__( + self, + model: nn.Module, + loss: Loss, + optimizer_class: str, + optimizer_kwargs: Optional[Dict] = None, + lr_scheduler_class: Optional[str] = None, + lr_scheduler_kwargs: Optional[Dict] = None, + train_dataloader_class: str = 'torch.utils.data.DataLoader', + train_dataloader_kwargs: Optional[Dict] = None, + validation_dataloader_class: str = 'torch.utils.data.DataLoader', + validation_dataloader_kwargs: Optional[Dict] = None, + epochs: int = 1, + strategy: str = StrategyT.NONE.value, + benchrun: bool = False, + testrun: bool = False, + seed: Optional[int] = None, + logger: Optional[List[Logger]] = None, + checkpoint_every: int = 10, + cluster: Optional[ClusterEnvironment] = None, + train_metrics: Optional[Dict[str, Metric]] = None, + validation_metrics: Optional[Dict[str, Metric]] = None + ) -> None: + """Sets up the distributed backend and loggers. + Makes the model a DDP model. + """ + super().__init__() + self.save_parameters(**self.locals2params(locals())) + self.model = model + self.loss = loss + self.epochs = epochs + self.testrun = testrun + self.seed = seed + self.strategy = strategy + self.benchrun = benchrun + self.cluster = cluster + # Checkpoint every n epochs + self.checkpoint_every = checkpoint_every + + # Train and validation dataloaders + self.train_dataloader_class = dynamically_import_class( + train_dataloader_class + ) + self.validation_dataloader_class = dynamically_import_class( + validation_dataloader_class + ) + train_dataloader_kwargs = ( + train_dataloader_kwargs + if train_dataloader_kwargs is not None else {} + ) + self.train_dataloader_kwargs = clear_key( + train_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' + ) + # If validation_dataloader_kwargs is not given, + # copy train_dataloader_kwargs + validation_dataloader_kwargs = ( + validation_dataloader_kwargs if validation_dataloader_kwargs + is not None else train_dataloader_kwargs + ) + self.validation_dataloader_kwargs = clear_key( + validation_dataloader_kwargs, 'validation_dataloader_kwargs', + 'dataset' + ) + + # Optimizer and scheduler + optim_class = dynamically_import_class(optimizer_class) + optimizer_kwargs = ( + optimizer_kwargs if optimizer_kwargs is not None else {} + ) + optimizer_kwargs = clear_key( + optimizer_kwargs, 'optimizer_kwargs', 'parameters' + ) + self.optimizer: Optimizer = optim_class( + self.model.parameters(), **optimizer_kwargs + ) + if lr_scheduler_class is not None: + scheduler_class = dynamically_import_class(lr_scheduler_class) + lr_scheduler_kwargs = ( + lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {} + ) + lr_scheduler_kwargs = clear_key( + lr_scheduler_kwargs, 'lr_scheduler_kwargs', 'optimizer' + ) + self.lr_scheduler: LrScheduler = scheduler_class( + self.optimizer, **lr_scheduler_kwargs + ) + + # Loggers + self.logger = logger if logger is not None else ConsoleLogger() + + # Metrics + self.train_metrics = ( + {} if train_metrics is None else train_metrics + ) + self.validation_metrics = ( + self.train_metrics if validation_metrics is None + else validation_metrics + ) + + @property + def strategy(self) -> Optional[str]: + return self._strategy + + @strategy.setter + def strategy(self, strategy_name) -> None: + if strategy_name not in StrategyT: + raise ValueError( + "Unrecognized 'strategy' field. Allowed values " + f"are: {StrategyT.list()}. Received '{strategy_name}'") + self._strategy = strategy_name + + @property + def global_step(self) -> int: + return self.train_glob_step + self.validation_glob_step + + def set_seed(self, seed: Optional[int] = None): + """Deterministic operations for reproducibility. + Sets the random seed. + + Args: + seed (Optional[int], optional): if not None, overrides + `self.seed`. Defaults to None. + """ + seed = seed if seed is not None else self.seed + np.random.seed(seed) + self.torch_rng = torch.Generator() + if seed is not None: + torch.manual_seed(seed) + self.torch_rng.manual_seed(seed) + if self.cluster.is_cuda_available(): + torch.cuda.manual_seed(seed) + + @monitor_exec + def execute( + self, + train_dataset: Dataset, + validation_dataset: Dataset, + model: nn.Module = None, + optimizer: Optimizer = None, + lr_scheduler: LrScheduler = None, + ) -> Any: + self.train_dataset = train_dataset + self.validation_dataset = validation_dataset + + # Update parameters passed for "interactive" use + if model is not None: + self.model = model + if optimizer is not None: + self.optimizer = optimizer + if lr_scheduler is not None: + self.lr_scheduler = lr_scheduler + + # Start training + if self.cluster.distributed: + # Make training distributed + result = mp.spawn(self._train, nprocs=self.cluster.ngpus_per_node) + else: + result = self._train(0) + + # Return value compliant with Executable.execute format + return result + + def _train( + self, + worker_id: int + ): + # Each worker has a different deterministic seed + # Here, 'worker' = replica of the training function + worker_seed = ( + self.seed + worker_id if self.seed is not None else self.seed + ) + self.set_seed(worker_seed) + + # Instantiate dataloaders + self.train_dataloader = self._instantiate_dataloader( + dataloader_class=self.train_dataloader_class, + dataset=self.train_dataset, + init_kwargs=self.train_dataloader_kwargs + ) + if self.validation_dataset is not None: + self.validation_dataloader = self._instantiate_dataloader( + dataloader_class=self.validation_dataloader_class, + dataset=self.validation_dataset, + init_kwargs=self.validation_dataloader_kwargs + ) + + # Launch actual training: + + # Single worker case + if not self.cluster.distributed: + with self.cluster.init_dist_gpu(worker_id) as device: + self.device: torch.device = device + self.model = self.model.to(self.device) + self.setup_logger() + self._setup_metrics() + try: + train_result = self.train() + except Exception as exc: + print(exc) + raise exc + finally: + print("INFO: Training ended") + self.destroy_logger() + train_result = None + return train_result + + # Init / connect to distributed backend + with self.cluster.init_dist_gpu(worker_id) as device: + self.device: torch.device = device + self._distribute_model() + self.setup_logger() + self._setup_metrics() + try: + train_result = self.train() + except Exception as exc: + print(exc) + raise exc + finally: + print("INFO: Training ended") + self.destroy_logger() + train_result = None + return train_result + + def _instantiate_dataloader( + self, + dataloader_class: Type, + dataset: Dataset, + init_kwargs: Dict + ) -> DataLoader: + """Make dataloader distributed if using distributed training strategy. + + Args: + dataloader_class (Type): some torch DataLoader type. + dataset (Dataset): torch dataset instance. + init_kwargs (Dict): constructor args. + """ + init_kwargs['generator'] = init_kwargs.get( + 'generator', self.torch_rng + ) + init_kwargs['worker_init_fn'] = init_kwargs.get( + 'worker_init_fn', seed_worker + ) + + if self.strategy == StrategyT.DDP.value and self.cluster.distributed: + sampler = DistributedSampler( + dataset=dataset, + num_replicas=self.cluster.global_world_size, + rank=self.cluster.global_rank, + shuffle=init_kwargs.get( + 'shuffle', False + ) + ) + # Overwrite existing sampler, if given. + # TODO: improve using wrapper: + # https://discuss.pytorch.org/t/how-to-use-my-own-sampler-when-i-already-use-distributedsampler/62143?page=2 + init_kwargs['sampler'] = sampler + if init_kwargs.get('shuffle') is not None: + # sampler option is mutually exclusive with shuffle + del init_kwargs['shuffle'] + + return dataloader_class(dataset, **init_kwargs) + + def _setup_metrics(self): + for m_name, metric in self.train_metrics.items(): + self.train_metrics[m_name] = metric.to(self.device) + for m_name, metric in self.validation_metrics.items(): + self.validation_metrics[m_name] = metric.to(self.device) + + def _distribute_model(self): + if self.cluster.distributed: + # Distribute model + self.model = self.model.to(self.device) + if self.strategy == StrategyT.NONE.value: + print( + "WARNING: A GPU cluster is available but no distributed " + "strategy was given... Falling back to single worker...") + if not self.cluster.is_main_worker(): + # Use only GPU:0 for single worker + sys.exit(0) + elif self.strategy == StrategyT.DDP.value: + self.model = DDP( + self.model, + device_ids=[self.device.index], + output_device=self.device + ) + else: + raise NotImplementedError("Only DDP strategy is implemented.") + else: + raise RuntimeError( + "Trying to distribute a model when a " + "distributed cluster is not available." + ) + + def setup_logger(self): + if self.cluster.is_main_worker(): + # Only setup loggers on main worker + if isinstance(self.logger, list): + for logger in self.logger: + logger.create_logger_context() + elif isinstance(self.logger, Logger): + self.logger.create_logger_context() + else: + raise TypeError( + "Unrecognized self.logger. Allowed types are 'list' and " + f"'Logger'. Received {type(self.logger)}" + ) + else: + self.logger = [] + + def destroy_logger(self): + if self.cluster.is_main_worker(): + if isinstance(self.logger, list): + for logger in self.logger: + logger.destroy_logger_context() + elif isinstance(self.logger, Logger): + self.logger.destroy_logger_context() + else: + raise TypeError( + "Unrecognized self.logger. Allowed types are 'list' and " + f"'Logger'. Received {type(self.logger)}" + ) + + def log( + self, + item: Union[Any, List[Any]], + identifier: Union[str, List[str]], + kind: str = 'metric', + step: Optional[int] = None, + batch_idx: Optional[int] = None, + every_worker: bool = False, + **kwargs + ) -> None: + if self.cluster.is_main_worker() or every_worker: + # Only log on main worker if not specified otherwise + if isinstance(self.logger, list): + for logger in self.logger: + logger.log( + item=item, + identifier=identifier, + kind=kind, + step=step, + batch_idx=batch_idx, + **kwargs + ) + elif isinstance(self.logger, Logger): + self.logger.log( + item=item, + identifier=identifier, + kind=kind, + step=step, + batch_idx=batch_idx, + **kwargs + ) + else: + raise TypeError( + "Unrecognized self.logger. Allowed types are 'list' and " + f"'Logger'. Received {type(self.logger)}" + ) + + def compute_metrics( + self, + metrics: Dict[str, Metric], + true: Batch, + pred: Batch, + logger_step: int, + batch_idx: Optional[int], + stage: str = 'train' + ) -> Dict[str, Any]: + """Compute and log metrics. + + Args: + metrics (Dict[str, Metric]): metrics dict. Can be + ``self.train_metrics`` or ``self.validation_metrics``. + true (Batch): true values. + pred (Batch): predicted values. + logger_step (int): global step to pass to the logger. + stage (str): 'train', 'validation'... + + Returns: + Dict[str, Any]: metric values. + """ + m_values = {} + for m_name, metric in metrics.items(): + # metric = metric.to(self.device) + m_val = metric(pred, true).detach().cpu().numpy() + self.log( + item=m_val, + identifier=f'{m_name}_{stage}', + kind='metric', + step=logger_step, + batch_idx=batch_idx + ) + m_values[m_name] = m_val + return m_values + + def training_step( + self, + batch: Batch, + batch_idx: int + ) -> Tuple[Loss, Dict[str, Any]]: + x, y = batch + x, y = x.to(self.device), y.to(self.device) + pred_y = self.model(x) + loss: Loss = self.loss(pred_y, y) + self.log( + item=loss.item(), + identifier='training_loss', + kind='metric', + step=self.train_glob_step, + batch_idx=batch_idx + ) + metrics: Dict[str, Any] = self.compute_metrics( + metrics=self.train_metrics, + true=y, + pred=pred_y, + logger_step=self.train_glob_step, + batch_idx=batch_idx, + stage='training' + ) + return loss, metrics + + def validation_step( + self, + batch: Batch, + batch_idx: int + ) -> Tuple[Loss, Dict[str, Any]]: + x, y = batch + x, y = x.to(self.device), y.to(self.device) + pred_y = self.model(x) + loss: Loss = self.loss(pred_y, y) + self.log( + item=loss.item(), + identifier='validation_loss', + kind='metric', + step=self.validation_glob_step, + batch_idx=batch_idx + ) + metrics: Dict[str, Any] = self.compute_metrics( + metrics=self.validation_metrics, + true=y, + pred=pred_y, + logger_step=self.validation_glob_step, + batch_idx=batch_idx, + stage='validation' + ) + return loss, metrics + + def training_epoch(self) -> Loss: + self.model.train() + train_losses = [] + for batch_idx, train_batch in enumerate(self.train_dataloader): + loss, metrics = self.training_step( + batch=train_batch, + batch_idx=batch_idx + ) + # TODO: merge and log batch metrics and loss into epoch metrics + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + train_losses.append(loss) + # Important: update counter + self.train_glob_step += 1 + + # Aggregate and log losses + avg_loss = torch.mean(torch.stack(train_losses)).detach().cpu() + self.log( + item=avg_loss.item(), + identifier='training_loss_epoch', + kind='metric', + step=self.train_glob_step, + ) + return avg_loss + + def validation_epoch(self) -> Loss: + if self.validation_dataloader is not None: + self.model.eval() + validation_losses = [] + for batch_idx, val_batch \ + in enumerate(self.validation_dataloader): + # TODO: merge and log batch metrics and loss into epoch metrics + loss, metrics = self.validation_step( + batch=val_batch, + batch_idx=batch_idx + ) + validation_losses.append(loss) + # Important: update counter + self.validation_glob_step += 1 + + # Aggregate and log losses + avg_loss = torch.mean( + torch.stack(validation_losses) + ).detach().cpu() + self.log( + item=avg_loss.item(), + identifier='validation_loss_epoch', + kind='metric', + step=self.validation_glob_step, + ) + return avg_loss + + def train(self): + + if self.optimizer is None: + raise ValueError("Undefined optimizer!") + + if self.loss is None: + raise ValueError("Undefined loss function!") + + st = time.time() + + # Resume state + self.start_epoch = 1 + self.best_loss = np.Inf + self.load_state() + + # start training/testing loop + if self.cluster.is_main_worker(): + print(f'TIMER: broadcast: {time.time()-st}s') + print('DEBUG: start training') + print('-'*56) + + ############################## + # Start training: run epochs # + ############################## + + et = time.time() + for self.epoch_idx in range(self.start_epoch, self.epochs + 1): + lt = time.time() + + ####################################################### + # Perform one training epoch and one validation epoch # + ####################################################### + + if self.benchrun and self.epoch_idx == self.epochs: + # TODO: move profiler into cluster environment + # profiling (done on last epoch - slower!) + with torch.autograd.profiler.profile( + use_cuda=self.cluster.is_cuda_available(), + profile_memory=True + ) as prof: + train_loss = self.training_epoch() + else: + train_loss = self.training_epoch() + val_loss = self.validation_epoch() + + ##################################### + # Save checkpoint if model improved # + ##################################### + + ref_loss = val_loss if val_loss is not None else train_loss + is_best = ref_loss < self.best_loss + if (self.epoch_idx % self.checkpoint_every == 0 + and not self.benchrun): + self.save_state( + loss_val=ref_loss, + is_best=is_best + ) + self.best_loss = min(ref_loss, self.best_loss) + + ########################### + # End of epoch operations # + ########################### + + # save first epoch timer + if self.epoch_idx == self.start_epoch: + first_ep_t = time.time()-lt + + # Final epoch + if self.epoch_idx + 1 == self.epochs: + self.train_dataloader.last_epoch = True + self.validation_dataloader.last_epoch = True + + if self.cluster.is_main_worker(): + print(f'TIMER: epoch time: {time.time()-lt}s') + if self.benchrun and self.epoch_idx == self.epochs: + print('-'*56) + print('benchmark of last epoch:') + what1 = ( + 'cuda' if self.cluster.is_cuda_available() else 'cpu' + ) + print( + prof.key_averages().table( + sort_by='self_'+str(what1)+'_time_total' + ) + ) + + ########################## + # Training has completed # + ########################## + + # save final state + if not self.benchrun: + self.save_state( + loss_val=ref_loss, + is_best=is_best + ) + if self.cluster.is_cuda_available() and self.cluster.distributed: + dist.barrier() + + ######################## + # Print training stats # + ######################## + + if self.cluster.is_main_worker(): + print('-'*56) + print('training results:') + print(f'TIMER: first epoch time: {first_ep_t}s') + print(f'TIMER: last epoch time: {time.time()-lt}s') + print( + f'TIMER: average epoch time: {(time.time()-et)/self.epochs}s') + print(f'TIMER: total epoch time: {time.time()-et}s') + if self.epoch_idx > 1: + print( + f'TIMER: total epoch-1 time: {time.time()-et-first_ep_t}s' + ) + print( + 'TIMER: average epoch-1 time: ' + f'{(time.time()-et-first_ep_t)/(self.epochs-1)}s') + if self.benchrun: + print( + f'TIMER: total epoch-2 time: {lt-first_ep_t}s') + print('TIMER: average epoch-2 time: ' + f'{(lt-first_ep_t)/(self.epochs-2)}s') + mem = int(torch.cuda.memory_reserved( + self.cluster.local_rank)/1024/1024) + print( + f'memory req: {mem} MB' + if self.cluster.is_cuda_available() + and self.cluster.distributed else 'memory req: - MB' + ) + if self.cluster.is_cuda_available(): + print( + f'memory summary:\n {torch.cuda.memory_summary(0)}') + + if self.cluster.is_main_worker(): + print(f'TIMER: final time: {time.time()-st} s') + + def save_state(self, loss_val: Any, is_best: bool): + """Save training state.""" + res_name = 'checkpoint.pth.tar' + rt = time.time() + + if (self.cluster.is_cuda_available() and self.cluster.distributed): + # find if is_best happened in any worker + is_best_m = par_allgather_obj( + is_best, self.cluster.global_world_size + ) + if any(is_best_m): + # TODO: is this strategy really good? Checkpointing when + # at least one worker improves the loss on their local + # data split is prone to overfitting, especially when + # the dataset in unbalanced! + + # find which rank is_best happened - select first rank + # if multiple + best_rank = np.where(np.array(is_best_m))[0][0] + if self.cluster.global_rank == best_rank: + self._save_sate( + epoch=self.epoch_idx+1, + loss_val=loss_val, + save_path=res_name + ) + print( + f'DEBUG: state in {self.cluster.global_rank} is ' + f'saved on epoch:{self.epoch_idx} ' + f'in {time.time()-rt} s') + else: + self._save_sate( + epoch=self.epoch_idx+1, + loss_val=loss_val, + save_path=res_name + ) + print( + f'DEBUG: state in {self.cluster.global_rank} ' + f'is saved on epoch:{self.epoch_idx} in {time.time()-rt} s') + + def _save_sate( + self, + epoch: int, + loss_val: Any, + save_path: str + ): + """Save state on disk.""" + sched = ( + self.lr_scheduler.state_dict() + if self.lr_scheduler is not None else None + ) + state = { + 'epoch': epoch, + 'state_dict': self.model.state_dict(), + 'best_loss': loss_val, + 'optimizer': self.optimizer.state_dict(), + 'lr_scheduler': sched + } + self.log( + item=state, + identifier=save_path, + kind='torch', + epoch_step=self.epoch_idx, + batch_step=0 + ) + + def load_state(self): + """Load training state.""" + res_name = 'checkpoint.pth.tar' + if os.path.isfile(res_name) and not self.benchrun: + try: + if (self.cluster.is_cuda_available() + and self.cluster.distributed): + dist.barrier() + # Map model to be loaded to specified single gpu. + # loc = ( + # {'cuda:%d' % 0: 'cuda:%d' % self.cluster.local_rank} + # if self.cluster.is_cuda_available() + # else {'cpu:%d' % 0: 'cpu:%d' % self.cluster.local_rank} + # ) + # checkpoint = torch.load(res_name, map_location=loc) + checkpoint = torch.load( + res_name, map_location=self.device + ) + else: + checkpoint = torch.load(res_name, map_location='cpu') + self.start_epoch = checkpoint['epoch'] + self.best_loss = checkpoint['best_loss'] + self.model.load_state_dict(checkpoint['state_dict']) + self.optimizer.load_state_dict(checkpoint['optimizer']) + if self.lr_scheduler is not None: + self.lr_scheduler.load_state_dict( + checkpoint['lr_scheduler'] + ) + if self.cluster.is_cuda_available(): + if self.cluster.is_main_worker(): + print( + f'WARNING: restarting from {self.start_epoch} ' + 'epoch') + else: + print( + f'WARNING: restarting from {self.start_epoch} epoch') + except Exception: + if self.cluster.is_cuda_available(): + if self.cluster.is_main_worker(): + print( + 'restart file cannot be loaded, restarting!') + else: + print( + 'WARNING: restart file cannot be loaded, restarting!') + + if self.start_epoch >= self.epochs + 1: + if self.cluster.is_cuda_available() and self.cluster.distributed: + if self.cluster.is_main_worker(): + print( + 'WARNING: given epochs are less than the ' + 'one in the restart file!') + print('WARNING: SYS.EXIT is issued') + sys.exit() + else: + print( + 'WARNING: given epochs are less than the ' + 'one in the restart file!') + print('WARNING: SYS.EXIT is issued') + sys.exit() diff --git a/src/itwinai/ptorch/types.py b/src/itwinai/ptorch/types.py new file mode 100644 index 00000000..6f6e5c9f --- /dev/null +++ b/src/itwinai/ptorch/types.py @@ -0,0 +1,64 @@ +"""Custom types definition.""" + +from typing import Callable +from enum import Enum, EnumMeta + +import torch +from torch import nn + +Loss = nn.Module +LrScheduler = nn.Module +Batch = torch.Tensor +Metric = Callable + + +class MetaEnum(EnumMeta): + def __contains__(cls, item): + try: + cls(item) + except ValueError: + return False + return True + + +class BaseEnum(Enum, metaclass=MetaEnum): + @classmethod + def list(cls): + return list(map(lambda c: c.value, cls)) + + +class TorchDistributedBackend(BaseEnum): + """ + Enum for torch distributed backends. + Reference: https://pytorch.org/docs/stable/distributed.html#backends + """ + DEFAULT = 'nccl' + GLOO = 'gloo' + NCCL = 'nccl' + MPI = 'mpi' + + +class TorchDistributedStrategy(BaseEnum): + DEFAULT = None + NONE = None + DDP = 'ddp' + + +class TorchLoss(BaseEnum): + """ + Torch loss class names. + TODO: complete from https://pytorch.org/docs/stable/nn.html#loss-functions + """ + L1 = 'L1Loss' + MSE = 'MSELoss' + CROSS_ENTROPY = 'CrossEntropyLoss' + NLLLOSS = 'NLLLoss' + + +class TorchOptimizer(BaseEnum): + """ + Torch optimizer class names. + TODO: complete from https://pytorch.org/docs/stable/optim.html#algorithms + """ + SGD = 'SGD' + ADAM = 'Adam' diff --git a/src/itwinai/ptorch/utils.py b/src/itwinai/ptorch/utils.py new file mode 100644 index 00000000..99bcd246 --- /dev/null +++ b/src/itwinai/ptorch/utils.py @@ -0,0 +1,84 @@ +from typing import Hashable, Dict +import time +import numpy as np +import random + +import torch +import torch.distributed as dist + + +def save_state( + epoch, distrib_model, loss_val, optimizer, res_name, grank, gwsize, + is_best, distributed: bool = True +): + """Save training state""" + rt = time.time() + # find if is_best happened in any worker + if torch.cuda.is_available() and distributed: + is_best_m = par_allgather_obj(is_best, gwsize) + + if torch.cuda.is_available() and distributed: + if any(is_best_m): + # find which rank is_best happened - select first rank if multiple + is_best_rank = np.where(np.array(is_best_m))[0][0] + + # collect state + state = {'epoch': epoch + 1, + 'state_dict': distrib_model.state_dict(), + 'best_loss': loss_val, + 'optimizer': optimizer.state_dict()} + + # write on worker with is_best + if grank == is_best_rank: + torch.save(state, './'+res_name) + print(f'DEBUG: state in {grank} is saved on ' + f'epoch:{epoch} in {time.time()-rt} s') + else: + # collect state + state = {'epoch': epoch + 1, + 'state_dict': distrib_model.state_dict(), + 'best_loss': loss_val, + 'optimizer': optimizer.state_dict()} + + torch.save(state, './'+res_name) + print( + f'DEBUG: state in {grank} is saved on epoch:{epoch} ' + f'in {time.time()-rt} s') + + +def seed_worker(worker_id): + """deterministic dataloader""" + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +def par_allgather_obj(obj, gwsize): + """gathers any object from the whole group in a list (to all workers)""" + res = [None]*gwsize + dist.all_gather_object(res, obj, group=None) + # print(f'ALLGATHER: {res}') + return res + + +def clear_key( + my_dict: Dict, + dict_name: str, + key: Hashable, + complain: bool = True +) -> Dict: + """Remove key from dictionary if present and complain. + + Args: + my_dict (Dict): Dictionary. + dict_name (str): name of the dictionary. + key (Hashable): Key to remove. + """ + if key in my_dict: + if complain: + print( + f"Field '{key}' should not be present " + f"in dictionary '{dict_name}'" + ) + del my_dict[key] + return my_dict From a031224c619a3928e8d1c380bfcfb1acb81db90a Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Fri, 1 Mar 2024 16:31:34 +0100 Subject: [PATCH 31/59] fixing tutorial sections --- docs/advanced_workflow.rst | 3 +++ docs/basic_workflow.rst | 11 +++++++---- docs/intermediate_workflow.rst | 12 ++++++++---- 3 files changed, 18 insertions(+), 8 deletions(-) diff --git a/docs/advanced_workflow.rst b/docs/advanced_workflow.rst index 140d8ee0..8ff74c36 100644 --- a/docs/advanced_workflow.rst +++ b/docs/advanced_workflow.rst @@ -17,3 +17,6 @@ The `tutorial_2_advanced_workflow.py` script is ... :undoc-members: :show-inheritance: + +.. literalinclude:: ../tutorials/ml-workflows/tutorial_2_advanced_workflow.py + :language: python diff --git a/docs/basic_workflow.rst b/docs/basic_workflow.rst index 7b6fca2d..cca169fe 100644 --- a/docs/basic_workflow.rst +++ b/docs/basic_workflow.rst @@ -12,8 +12,11 @@ The `tutorial_0_basic_workflow.py` script is ... .. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py .. :language: python -.. automodule:: tutorial_0_basic_workflow - :members: - :undoc-members: - :show-inheritance: +.. .. automodule:: tutorial_0_basic_workflow +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. literalinclude:: ../tutorials/ml-workflows/tutorial_0_basic_workflow.py + :language: python diff --git a/docs/intermediate_workflow.rst b/docs/intermediate_workflow.rst index 79c0472b..0a587705 100644 --- a/docs/intermediate_workflow.rst +++ b/docs/intermediate_workflow.rst @@ -12,8 +12,12 @@ The `tutorial_1_intermediate_workflow.py` script is ... .. .. literalinclude:: ../use-cases/mnist/torch-lightning/dataloader.py .. :language: python -.. automodule:: tutorial_1_intermediate_workflow - :members: - :undoc-members: - :show-inheritance: +.. .. automodule:: tutorial_1_intermediate_workflow +.. :members: +.. :undoc-members: +.. :show-inheritance: + + +.. literalinclude:: ../tutorials/ml-workflows/tutorial_1_intermediate_workflow.py + :language: python From 17dc0105f2aa85c039672d0ed94f829970a30167 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 6 Mar 2024 13:14:31 +0100 Subject: [PATCH 32/59] fixes in pages appearance --- docs/index.rst | 2 +- docs/use_cases.rst | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index d56cb069..479a8525 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -52,7 +52,7 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi tutorials -**`interTwin Demo: itwinai integration with other DTE modules `_** +`**interTwin Demo:** itwinai integration with other DTE modules `_ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Indices and tables diff --git a/docs/use_cases.rst b/docs/use_cases.rst index dd59b82b..4401a68e 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -1,5 +1,3 @@ -.. _use-cases: - Integrated Use Cases ==================== From 267ec7f1dbe0aff3900057b9b778f5d92b224622 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 6 Mar 2024 15:11:27 +0100 Subject: [PATCH 33/59] fixing rendering bugs --- docs/3dgan_doc.rst | 4 +- docs/itwinai.torch.modules.rst | 78 +++++++++++++++++++++------------- docs/mnist_doc.rst | 4 +- docs/use_cases.rst | 4 +- 4 files changed, 55 insertions(+), 35 deletions(-) diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst index 1ddd592e..eb5db410 100644 --- a/docs/3dgan_doc.rst +++ b/docs/3dgan_doc.rst @@ -1,5 +1,5 @@ -3DGAN Documentation -==================== +3DGAN +===== The 3DGAN use case is a part of the use cases which... diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst index 883fd4e8..b4cf7b0a 100644 --- a/docs/itwinai.torch.modules.rst +++ b/docs/itwinai.torch.modules.rst @@ -1,34 +1,54 @@ itwinai.torch.modules ===================== -.. automodule:: itwinai.ptorch.cluster - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.inference - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.mlflow - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.trainer - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.types - :members: - :undoc-members: - :show-inheritance: - -.. automodule:: itwinai.ptorch.utils - :members: - :undoc-members: - :show-inheritance: +.. literalinclude:: ../src/itwinai/ptorch/cluster.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/inference.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/mlflow.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/trainer.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/types.py + :language: python + +.. literalinclude:: ../src/itwinai/ptorch/utils.py + :language: python + + + +.. .. automodule:: itwinai.ptorch.cluster +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.inference +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.mlflow +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.trainer +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.types +.. :members: +.. :undoc-members: +.. :show-inheritance: + +.. .. automodule:: itwinai.ptorch.utils +.. :members: +.. :undoc-members: +.. :show-inheritance: diff --git a/docs/mnist_doc.rst b/docs/mnist_doc.rst index afedbb29..fd26c4e7 100644 --- a/docs/mnist_doc.rst +++ b/docs/mnist_doc.rst @@ -1,5 +1,5 @@ -MNIST Use Case -============== +MNIST +===== This section covers the MNIST use case, which utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: diff --git a/docs/use_cases.rst b/docs/use_cases.rst index 4401a68e..f2653f6b 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -10,7 +10,7 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - :caption: 3DGAN use case Documentation + .. :caption: 3DGAN use case Documentation 3dgan_doc @@ -22,7 +22,7 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - :caption: MNIST Documentation + .. :caption: MNIST Documentation mnist_doc From b6c8d112768543e22e0bf142adb6e90803e81d16 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 6 Mar 2024 15:24:40 +0100 Subject: [PATCH 34/59] fixing pages appearance bugs --- docs/use_cases.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/use_cases.rst b/docs/use_cases.rst index f2653f6b..9cadd90a 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -10,7 +10,6 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - .. :caption: 3DGAN use case Documentation 3dgan_doc @@ -22,8 +21,9 @@ Description of the use case etc... .. toctree:: :maxdepth: 2 - .. :caption: MNIST Documentation mnist_doc + + From c4b65e39de8f36ce281200ae82f8f17cb95b6c89 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 7 Mar 2024 15:55:20 +0100 Subject: [PATCH 35/59] adding latest modifications --- docs/advanced_workflow.rst | 3 --- docs/basic_comp.rst | 3 --- docs/basic_workflow.rst | 3 --- docs/getting_started_with_itwinai.rst | 2 +- docs/index.rst | 4 +++- docs/intermediate_workflow.rst | 3 --- docs/itwinai.tf.modules.rst | 22 ++++++++++++++-------- docs/itwinai.torch.modules.rst | 22 ++++++++++++++++++++-- docs/tutorials.rst | 3 --- docs/use_cases.rst | 7 +++++-- 10 files changed, 43 insertions(+), 29 deletions(-) diff --git a/docs/advanced_workflow.rst b/docs/advanced_workflow.rst index 8ff74c36..121d3acc 100644 --- a/docs/advanced_workflow.rst +++ b/docs/advanced_workflow.rst @@ -1,9 +1,6 @@ Advanced workflow ================= -Description... - - tutorial_2_advanced_workflow.py +++++++++++++++++++++++++++++++ diff --git a/docs/basic_comp.rst b/docs/basic_comp.rst index d4fb3a18..5acfe66d 100644 --- a/docs/basic_comp.rst +++ b/docs/basic_comp.rst @@ -1,9 +1,6 @@ Basic components ================ -Description... - - basic_components.py +++++++++++++++++++ diff --git a/docs/basic_workflow.rst b/docs/basic_workflow.rst index cca169fe..cd77d328 100644 --- a/docs/basic_workflow.rst +++ b/docs/basic_workflow.rst @@ -1,9 +1,6 @@ Basic workflow ============== -Description... - - tutorial_0_basic_workflow.py ++++++++++++++++++++++++++++ diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 9956a89e..e559189a 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -4,7 +4,7 @@ Getting started with itwinai Installation ------------ -Requirements: +Requirements +++++++++++++ * Linux environment. diff --git a/docs/index.rst b/docs/index.rst index 479a8525..4b510081 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -40,6 +40,7 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi .. toctree:: :maxdepth: 2 + :hidden: :caption: 📚 Integrated Use-cases use_cases @@ -52,9 +53,10 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi tutorials -`**interTwin Demo:** itwinai integration with other DTE modules `_ +`interTwin Demo: itwinai integration with other DTE modules `_ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + Indices and tables ================== diff --git a/docs/intermediate_workflow.rst b/docs/intermediate_workflow.rst index 0a587705..860b12e5 100644 --- a/docs/intermediate_workflow.rst +++ b/docs/intermediate_workflow.rst @@ -1,9 +1,6 @@ Intermediate workflow ===================== -Description... - - tutorial_1_intermediate_workflow.py +++++++++++++++++++++++++++++++++++ diff --git a/docs/itwinai.tf.modules.rst b/docs/itwinai.tf.modules.rst index aad490d8..8b923bea 100644 --- a/docs/itwinai.tf.modules.rst +++ b/docs/itwinai.tf.modules.rst @@ -1,6 +1,19 @@ -itwinai.tensorflow.modules +itwinai Tensorflow Modules ========================== +trainer.py +++++++++++ + +.. literalinclude:: ../src/itwinai/tensorflow/trainer.py + :language: python + +utils.py +++++++++ + +.. literalinclude:: ../src/itwinai/tensorflow/utils.py + :language: python + + .. .. automodule:: itwinai.tensorflow.trainer .. :members: .. :undoc-members: @@ -11,10 +24,3 @@ itwinai.tensorflow.modules .. :undoc-members: .. :show-inheritance: -.. literalinclude:: ../src/itwinai/tensorflow/trainer.py - :language: python - -.. literalinclude:: ../src/itwinai/tensorflow/utils.py - :language: python - - diff --git a/docs/itwinai.torch.modules.rst b/docs/itwinai.torch.modules.rst index b4cf7b0a..9b86f1f8 100644 --- a/docs/itwinai.torch.modules.rst +++ b/docs/itwinai.torch.modules.rst @@ -1,21 +1,39 @@ -itwinai.torch.modules -===================== +itwinai PyTorch Modules +======================= + +cluster.py +++++++++++ .. literalinclude:: ../src/itwinai/ptorch/cluster.py :language: python +inference.py +++++++++++++ + .. literalinclude:: ../src/itwinai/ptorch/inference.py :language: python +mlflow.py ++++++++++ + .. literalinclude:: ../src/itwinai/ptorch/mlflow.py :language: python +trainer.py +++++++++++ + .. literalinclude:: ../src/itwinai/ptorch/trainer.py :language: python +types.py +++++++++ + .. literalinclude:: ../src/itwinai/ptorch/types.py :language: python +utils.py +++++++++ + .. literalinclude:: ../src/itwinai/ptorch/utils.py :language: python diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 5a5772d5..623582f2 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -8,11 +8,8 @@ Here you can find a collection of tutorials for various complexity ML workflows. Tutorials --------- -Description... - .. toctree:: :maxdepth: 2 - :caption: Examples basic_comp basic_workflow diff --git a/docs/use_cases.rst b/docs/use_cases.rst index 9cadd90a..4a584702 100644 --- a/docs/use_cases.rst +++ b/docs/use_cases.rst @@ -6,7 +6,9 @@ Here you can find a collection of use cases including various projects. 3DGAN CERN use case ------------------- -Description of the use case etc... +The first ``interTwin`` use case integrated with ``itwinai`` framework is the DT for fast particle detector simulation. +3D Generative Adversarial Network (3DGAN) for generation of images of calorimeter depositions. +This project is based on the prototype `3DGAN `_ model developed at CERN and is implemented on PyTorch Lightning framework. .. toctree:: :maxdepth: 2 @@ -17,7 +19,8 @@ Description of the use case etc... MNIST use case -------------- -Description of the use case etc... +MNIST image classification is used to provide an example on +how to define an end-to-end digital twin workflow with the ``itwinai`` software. .. toctree:: :maxdepth: 2 From da8faaf1ffc965f33afc9d930fe2e121c60963e0 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Fri, 8 Mar 2024 11:16:27 +0100 Subject: [PATCH 36/59] Deleted duplicate folder after renaming src/itwinai/torch --- src/itwinai/torch/__init__.py | 0 src/itwinai/torch/cluster.py | 225 ------- src/itwinai/torch/data/__init__.py | 0 src/itwinai/torch/inference.py | 219 ------- src/itwinai/torch/mlflow.py | 77 --- src/itwinai/torch/models/__init__.py | 0 src/itwinai/torch/models/mnist.py | 75 --- src/itwinai/torch/trainer.py | 941 --------------------------- src/itwinai/torch/types.py | 64 -- src/itwinai/torch/utils.py | 84 --- 10 files changed, 1685 deletions(-) delete mode 100644 src/itwinai/torch/__init__.py delete mode 100644 src/itwinai/torch/cluster.py delete mode 100644 src/itwinai/torch/data/__init__.py delete mode 100644 src/itwinai/torch/inference.py delete mode 100644 src/itwinai/torch/mlflow.py delete mode 100644 src/itwinai/torch/models/__init__.py delete mode 100644 src/itwinai/torch/models/mnist.py delete mode 100644 src/itwinai/torch/trainer.py delete mode 100644 src/itwinai/torch/types.py delete mode 100644 src/itwinai/torch/utils.py diff --git a/src/itwinai/torch/__init__.py b/src/itwinai/torch/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/itwinai/torch/cluster.py b/src/itwinai/torch/cluster.py deleted file mode 100644 index aece16e2..00000000 --- a/src/itwinai/torch/cluster.py +++ /dev/null @@ -1,225 +0,0 @@ -"""Cluster environments where to run AI workflows. Partially adapted from: -https://github.com/facebookresearch/detr/blob/master/util/misc.py and -https://github.com/ramyamounir/Template/blob/main/lib/utils/distributed.py -""" - -from __future__ import annotations -from typing import Optional -import os -import signal -import subprocess -from pathlib import Path -from contextlib import contextmanager - -import numpy as np - -import torch -import torch.distributed as dist -import torch.backends.cudnn as cudnn - -from ..cluster import ( - ClusterEnvironment, - setup_for_distributed, - handle_sigusr1, - handle_sigterm -) -from .types import TorchDistributedBackend as BackendT - - -def fix_random_seeds(seed=31): - """ - Fix random seeds. - """ - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - np.random.seed(seed) - - -class TorchCluster(ClusterEnvironment): - def __init__(self) -> None: - super().__init__() - - def _set_backend(self, backend_name: str) -> None: - if backend_name not in BackendT: - raise ValueError( - "Unrecognized 'backend' field. Allowed values " - f"are: {BackendT.list()}. Received '{backend_name}'") - self._backend = backend_name - - def is_cuda_available(self) -> bool: - return self.use_cuda and torch.cuda.is_available() - - def is_main_worker(self) -> bool: - """Checks if the current process is the main/master process - in the whole job. - """ - return self.global_rank == 0 - - def cleanup_resources(self): - dist.barrier() - dist.destroy_process_group() - - -class LocalCluster(TorchCluster): - """Simple single node cluster with optional access to multiple GPUs.""" - - def __init__( - self, - backend: Optional[str] = None, - gpus: Optional[str] = '', - port: int = 49153, - rnd_seed: Optional[int] = 42 - ) -> None: - """Initialize local cluster for multi-GPU access. - - Args: - backend (Optional[str], optional): supported PyTorch backends. - If None, workload is not distributed. Defaults to None. - gpus (Optional[str], optional): list of visible GPU devices - (e.g., '1,2,3'). If empty string uses all available GPUs. - If None, CPU is used. Defaults to ''. - port (int, optional): TCP port used by the master process. - Defaults to 49153. - rnd_seed (Optional[int], optional): random seed to be setup after - all processes are setup. Defaults to 42. - """ - super().__init__() - self.backend = backend - self.gpus = gpus - self.port = port - self.dist_url = f'tcp://127.0.0.1:{self.port}' - self.rnd_seed = rnd_seed - - if self.gpus != '' and self.gpus is not None: - # Restrict the number of GPUs visible according to user needs - os.environ['CUDA_VISIBLE_DEVICES'] = self.gpus - - self.ngpus_per_node = torch.cuda.device_count() - self.global_rank = 0 - self.global_world_size = self.ngpus_per_node - - print(f"{self.ngpus_per_node} GPUs are available.") - self.distributed = True - # This flag tells whether the user wants to use the GPU(s) - self.use_cuda = ( - self.gpus is not None # GPU is not manually disabled - and torch.cuda.device_count() >= 1 # At least one GPU is selected - ) - if self.backend is None or self.ngpus_per_node <= 1: - print("Distributed has been disabled.") - self.distributed = False - self.dist_url = None - self.global_world_size = 1 - self.global_rank = 0 - if not self.is_cuda_available(): - print("CUDA disabled... Running on single CPU.") - self.use_cuda = False - self.distributed = False - self.dist_url = None - self.global_world_size = 1 - self.global_rank = 0 - - # Since single node case - self.local_world_size = self.global_world_size - - @contextmanager - def init_dist_gpu(self, worker_id) -> torch.device: - if self.distributed: - torch.cuda.set_device(worker_id) - self.global_rank += worker_id - # print(f'GLOBAL RANK: {self.global_rank}') - # Since single node case - self.local_rank = self.global_rank - # Simplification: worker ID mapped to GPU ID - self.gpu_id = worker_id - - try: - dist.init_process_group( - backend=self.backend, - init_method=self.dist_url, - world_size=self.global_world_size, - rank=self.global_rank - ) - fix_random_seeds(self.rnd_seed) - torch.cuda.set_device(self.gpu_id) - cudnn.benchmark = True - dist.barrier() - - setup_for_distributed(self.is_main_worker()) - print("SETUP DISTRIBUTED COMPLETE") - yield torch.device('cuda', worker_id) - finally: - self.cleanup_resources() - else: - # Distributed is disabled - # Since single node case - self.global_rank = 0 - self.local_rank = self.global_rank - if self.use_cuda: - torch.cuda.set_device(worker_id) - yield torch.device('cuda', worker_id) - else: - yield torch.device('cpu') - - -class SLURMCluster(TorchCluster): - """SLURM cluster with access to multi-node multi-GPU.""" - - def __init__( - self, - port: int = 49153, - backend: str = 'gloo', - rnd_seed: Optional[int] = 42 - ) -> None: - super().__init__() - self.port = port - self.backend = backend - self.rnd_seed = rnd_seed - if 'SLURM_JOB_ID' not in os.environ: - raise RuntimeError( - "'SLURM_JOB_ID' environment variable is not set. " - "Perhaps you are not running in a slurm cluster?" - ) - - self.ngpus_per_node = torch.cuda.device_count() - - # requeue job on SLURM preemption - signal.signal(signal.SIGUSR1, handle_sigusr1) - signal.signal(signal.SIGTERM, handle_sigterm) - - # find a common host name on all nodes - cmd = 'scontrol show hostnames ' + os.getenv('SLURM_JOB_NODELIST') - stdout = subprocess.check_output(cmd.split()) - host_name = stdout.decode().splitlines()[0] - self.dist_url = f'tcp://{host_name}:{self.port}' - - # distributed parameters - self.global_rank = int(os.getenv('SLURM_NODEID')) * self.ngpus_per_node - self.global_world_size = int( - os.getenv('SLURM_NNODES')) * self.ngpus_per_node - - @contextmanager - def init_dist_gpu(self): - import submitit - try: - job_env = submitit.JobEnvironment() - self.output_dir = Path( - str(self.output_dir).replace("%j", str(job_env.job_id))) - self.gpu = job_env.local_rank - self.global_rank = job_env.global_rank - - dist.init_process_group( - backend=self.backend, - init_method=self.dist_url, - world_size=self.global_world_size, - rank=self.global_rank - ) - fix_random_seeds(self.rnd_seed) - torch.cuda.set_device(self.gpu) - cudnn.benchmark = True - dist.barrier() - - setup_for_distributed(self.is_main_worker()) - yield - finally: - self.cleanup_resources() diff --git a/src/itwinai/torch/data/__init__.py b/src/itwinai/torch/data/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/itwinai/torch/inference.py b/src/itwinai/torch/inference.py deleted file mode 100644 index 02882f06..00000000 --- a/src/itwinai/torch/inference.py +++ /dev/null @@ -1,219 +0,0 @@ -from typing import Optional, Dict, Any, Union -import os -import abc - -import torch -from torch import nn -from torch.utils.data import DataLoader, Dataset - -from ..utils import dynamically_import_class -from .utils import clear_key -from ..components import Predictor, monitor_exec -from .types import TorchDistributedStrategy as StrategyT -from .types import Metric, Batch -from ..serialization import ModelLoader - - -class TorchModelLoader(ModelLoader): - """Loads a torch model from somewhere. - - Args: - model_uri (str): Can be a path on local filesystem - or an mlflow 'locator' in the form: - 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' - """ - - def __call__(self) -> nn.Module: - """"Loads model from model URI. - - Raises: - ValueError: if the model URI is not recognized - or the model is not found. - - Returns: - nn.Module: torch neural network. - """ - if os.path.exists(self.model_uri): - # Model is on local filesystem. - model = torch.load(self.model_uri) - return model.eval() - - if self.model_uri.startswith('mlflow+'): - # Model is on an MLFLow server - # Form is 'mlflow+MLFLOW_TRACKING_URI+RUN_ID+ARTIFACT_PATH' - import mlflow - from mlflow import MlflowException - _, tracking_uri, run_id, artifact_path = self.model_uri.split('+') - mlflow.set_tracking_uri(tracking_uri) - - # Check that run exists - try: - mlflow.get_run(run_id) - except MlflowException: - raise ValueError(f"Run ID '{run_id}' was not found!") - - # Download model weights - ckpt_path = mlflow.artifacts.download_artifacts( - run_id=run_id, - artifact_path=artifact_path, - dst_path='tmp/', - tracking_uri=mlflow.get_tracking_uri() - ) - model = torch.load(ckpt_path) - return model.eval() - - raise ValueError( - 'Unrecognized model URI: model may not be there!' - ) - - -class TorchPredictor(Predictor): - """Applies a pre-trained torch model to unseen data.""" - - model: nn.Module = None - test_dataset: Dataset - test_dataloader: DataLoader = None - _strategy: StrategyT = StrategyT.NONE.value - epoch_idx: int = 0 - train_glob_step: int = 0 - validation_glob_step: int = 0 - train_metrics: Dict[str, Metric] - validation_metrics: Dict[str, Metric] - - def __init__( - self, - model: Union[nn.Module, ModelLoader], - test_dataloader_class: str = 'torch.utils.data.DataLoader', - test_dataloader_kwargs: Optional[Dict] = None, - # strategy: str = StrategyT.NONE.value, - # seed: Optional[int] = None, - # logger: Optional[List[Logger]] = None, - # cluster: Optional[ClusterEnvironment] = None, - # test_metrics: Optional[Dict[str, Metric]] = None, - name: str = None - ) -> None: - super().__init__(model=model, name=name) - self.save_parameters(**self.locals2params(locals())) - self.model = self.model.eval() - # self.seed = seed - # self.strategy = strategy - # self.cluster = cluster - - # Train and validation dataloaders - self.test_dataloader_class = dynamically_import_class( - test_dataloader_class - ) - test_dataloader_kwargs = ( - test_dataloader_kwargs - if test_dataloader_kwargs is not None else {} - ) - self.test_dataloader_kwargs = clear_key( - test_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' - ) - - # # Loggers - # self.logger = logger if logger is not None else ConsoleLogger() - - # # Metrics - # self.train_metrics = ( - # {} if train_metrics is None else train_metrics - # ) - # self.validation_metrics = ( - # self.train_metrics if validation_metrics is None - # else validation_metrics - # ) - - @monitor_exec - def execute( - self, - test_dataset: Dataset, - model: nn.Module = None, - ) -> Dict[str, Any]: - """Applies a torch model to a dataset for inference. - - Args: - test_dataset (Dataset[str, Any]): each item in this dataset is a - couple (item_unique_id, item) - model (nn.Module, optional): torch model. Overrides the existing - model, if given. Defaults to None. - - Returns: - Dict[str, Any]: maps each item ID to the corresponding predicted - value(s). - """ - if model is not None: - # Overrides existing "internal" model - self.model = model - - test_dataloader = self.test_dataloader_class( - test_dataset, **self.test_dataloader_kwargs - ) - - all_predictions = dict() - for samples_ids, samples in test_dataloader: - with torch.no_grad(): - pred = self.model(samples) - pred = self.transform_predictions(pred) - for idx, pre in zip(samples_ids, pred): - # For each item in the batch - if pre.numel() == 1: - pre = pre.item() - else: - pre = pre.to_dense().tolist() - all_predictions[idx] = pre - return all_predictions - - @abc.abstractmethod - def transform_predictions(self, batch: Batch) -> Batch: - """ - Post-process the predictions of the torch model (e.g., apply - threshold in case of multilabel classifier). - """ - - -class MulticlassTorchPredictor(TorchPredictor): - """ - Applies a pre-trained torch model to unseen data for - multiclass classification. - """ - - def transform_predictions(self, batch: Batch) -> Batch: - batch = batch.argmax(-1) - return batch - - -class MultilabelTorchPredictor(TorchPredictor): - """ - Applies a pre-trained torch model to unseen data for - multilabel classification, applying a threshold on the - output of the neural network. - """ - - threshold: float - - def __init__( - self, - model: Union[nn.Module, ModelLoader], - test_dataloader_class: str = 'torch.utils.data.DataLoader', - test_dataloader_kwargs: Optional[Dict] = None, - threshold: float = 0.5, - name: str = None - ) -> None: - super().__init__( - model, test_dataloader_class, test_dataloader_kwargs, name - ) - self.threshold = threshold - - def transform_predictions(self, batch: Batch) -> Batch: - return (batch > self.threshold).float() - - -class RegressionTorchPredictor(TorchPredictor): - """ - Applies a pre-trained torch model to unseen data for - regression, leaving untouched the output of the neural - network. - """ - - def transform_predictions(self, batch: Batch) -> Batch: - return batch diff --git a/src/itwinai/torch/mlflow.py b/src/itwinai/torch/mlflow.py deleted file mode 100644 index 18a014ff..00000000 --- a/src/itwinai/torch/mlflow.py +++ /dev/null @@ -1,77 +0,0 @@ -from typing import Dict, Optional -import os - -import mlflow -import yaml - - -def _get_mlflow_logger_conf(pl_config: Dict) -> Optional[Dict]: - """Extract MLFLowLogger configuration from pytorch lightning - configuration file, if present. - - Args: - pl_config (Dict): lightning configuration loaded in memory. - - Returns: - Optional[Dict]: if present, MLFLowLogger constructor arguments - (under 'init_args' key). - """ - if isinstance(pl_config['trainer']['logger'], list): - # If multiple loggers are provided - for logger_conf in pl_config['trainer']['logger']: - if logger_conf['class_path'].endswith('MLFlowLogger'): - return logger_conf['init_args'] - elif pl_config['trainer']['logger']['class_path'].endswith('MLFlowLogger'): - return pl_config['trainer']['logger']['init_args'] - - -def _mlflow_log_pl_config(pl_config: Dict, local_yaml_path: str) -> None: - os.makedirs(os.path.dirname(local_yaml_path), exist_ok=True) - with open(local_yaml_path, 'w') as outfile: - yaml.dump(pl_config, outfile, default_flow_style=False) - mlflow.log_artifact(local_yaml_path) - - -def init_lightning_mlflow( - pl_config: Dict, - default_experiment_name: str = 'Default', - **autolog_kwargs -) -> None: - """Initialize mlflow for pytorch lightning, also setting up - auto-logging (mlflow.pytorch.autolog(...)). Creates a new mlflow - run and attaches it to the mlflow auto-logger. - - Args: - pl_config (Dict): pytorch lightning configuration loaded in memory. - default_experiment_name (str, optional): used as experiment name - if it is not given in the lightning conf. Defaults to 'Default'. - **autolog_kwargs (kwargs): args for mlflow.pytorch.autolog(...). - """ - mlflow_conf: Optional[Dict] = _get_mlflow_logger_conf(pl_config) - if not mlflow_conf: - return - - tracking_uri = mlflow_conf.get('tracking_uri') - if not tracking_uri: - save_path = mlflow_conf.get('save_dir') - tracking_uri = "file://" + os.path.abspath(save_path) - - experiment_name = mlflow_conf.get('experiment_name') - if not experiment_name: - experiment_name = default_experiment_name - - mlflow.set_tracking_uri(tracking_uri) - mlflow.set_experiment(experiment_name) - mlflow.pytorch.autolog(**autolog_kwargs) - mlflow.start_run() - - mlflow_conf['experiment_name'] = experiment_name - mlflow_conf['run_id'] = mlflow.active_run().info.run_id - - _mlflow_log_pl_config(pl_config, '.tmp/pl_config.yml') - - -def teardown_lightning_mlflow() -> None: - """End active mlflow run, if any.""" - if mlflow.active_run() is not None: - mlflow.end_run() diff --git a/src/itwinai/torch/models/__init__.py b/src/itwinai/torch/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/itwinai/torch/models/mnist.py b/src/itwinai/torch/models/mnist.py deleted file mode 100644 index c222a5a3..00000000 --- a/src/itwinai/torch/models/mnist.py +++ /dev/null @@ -1,75 +0,0 @@ -import torch -import torch.nn as nn -import lightning as L - -from torch.nn import functional as F - - -class MNISTModel(L.LightningModule): - """ - Simple PL model for MNIST. - Adapted from - https://lightning.ai/docs/pytorch/stable/notebooks/lightning_examples/mnist-hello-world.html - """ - - def __init__( - self, - hidden_size: int = 64, - ): - super().__init__() - - # Automatically save constructor args as hyperparameters - self.save_hyperparameters() - - # Set our init args as class attributes - self.hidden_size = hidden_size - - # Hardcode some dataset specific attributes - self.num_classes = 10 - self.dims = (1, 28, 28) - channels, width, height = self.dims - - # Define PyTorch model - self.model = nn.Sequential( - nn.Flatten(), - nn.Linear(channels * width * height, hidden_size), - nn.ReLU(), - nn.Dropout(0.1), - nn.Linear(hidden_size, hidden_size), - nn.ReLU(), - nn.Dropout(0.1), - nn.Linear(hidden_size, self.num_classes), - ) - - def forward(self, x): - x = self.model(x) - return F.log_softmax(x, dim=1) - - def training_step(self, batch, batch_idx): - x, y = batch - logits = self(x) - loss = F.nll_loss(logits, y) - - # Log metrics with autolog - self.log("train_loss", loss, on_step=True, on_epoch=True) - - return loss - - def validation_step(self, batch, batch_idx): - x, y = batch - logits = self(x) - loss = F.nll_loss(logits, y) - # preds = torch.argmax(logits, dim=1) - self.log("val_loss", loss, prog_bar=True, on_step=True, on_epoch=True) - - def test_step(self, batch, batch_idx): - x, y = batch - logits = self(x) - loss = F.nll_loss(logits, y) - self.log("test_loss", loss) - - def predict_step(self, batch, batch_idx, dataloader_idx=0): - x, _ = batch - logits = self(x) - preds = torch.argmax(logits, dim=1) - return preds diff --git a/src/itwinai/torch/trainer.py b/src/itwinai/torch/trainer.py deleted file mode 100644 index 31794c49..00000000 --- a/src/itwinai/torch/trainer.py +++ /dev/null @@ -1,941 +0,0 @@ -"""Provides training logic for PyTorch models via Trainer classes.""" - -from typing import ( - Optional, Dict, Union, Tuple, Type, List, Any -) -import time -import os -import sys - -import numpy as np -import torch -import torch.multiprocessing as mp -from torch.utils.data import DataLoader, Dataset -from torch.utils.data.distributed import DistributedSampler -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.nn as nn -from torch.optim.optimizer import Optimizer - -from ..components import Trainer, monitor_exec -from .utils import seed_worker, par_allgather_obj, clear_key -from .types import ( - Batch, Loss, LrScheduler, Metric -) -from .types import TorchDistributedStrategy as StrategyT -from ..loggers import LogMixin, Logger, ConsoleLogger -from ..utils import dynamically_import_class -from ..cluster import ClusterEnvironment - - -def preproc_dataloader(dataloader: DataLoader, gwsize, grank): - """Makes a Dataloader distributed.""" - sampler = DistributedSampler( - dataloader.dataset, - num_replicas=gwsize, - rank=grank, - shuffle=True - ) - # Recreate dataloader, with updated sampler - return DataLoader( - dataloader.dataset, - batch_size=dataloader.batch_size, - sampler=sampler, - num_workers=dataloader.num_workers, - collate_fn=dataloader.collate_fn, - pin_memory=dataloader.pin_memory, - drop_last=dataloader.drop_last, - timeout=dataloader.timeout, - worker_init_fn=seed_worker, # dataloader.worker_init_fn, - multiprocessing_context=dataloader.multiprocessing_context, - generator=dataloader.generator, - prefetch_factor=dataloader.prefetch_factor, - persistent_workers=dataloader.persistent_workers, - pin_memory_device=dataloader.pin_memory_device - ) - - -def distributed(func): - """The decorated function must have a standard signature. - Its first arguments must be: - model, train_dataloader, validation_dataloader, device (in this order). - - Additional args or kwargs are allowed consistently with the signature - of the decorated function. - """ - def dist_train( - model, train_dataloader, validation_dataloader=None, device='cpu', - *args, **kwargs - ): - if torch.cuda.is_available(): - dist.init_process_group(backend='nccl') - - if torch.cuda.is_available(): - lwsize = torch.cuda.device_count() # local world size - per node - gwsize = dist.get_world_size() # global world size - per run - grank = dist.get_rank() # global rank - assign per run - lrank = dist.get_rank() % lwsize # local rank - assign per node - else: - gwsize = 1 - grank = 0 - lrank = 0 - - device = torch.device( - 'cuda' if torch.cuda.is_available() else 'cpu', lrank) - if torch.cuda.is_available(): - torch.cuda.set_device(lrank) - - model = model.to(device) - model = DDP(model, device_ids=[device], output_device=device) - - train_dataloader = preproc_dataloader(train_dataloader, gwsize, grank) - if validation_dataloader is not None: - validation_dataloader = preproc_dataloader( - validation_dataloader, gwsize, grank) - - try: - func(model, train_dataloader, validation_dataloader, device, - *args, **kwargs) - finally: - if torch.cuda.is_available(): - dist.barrier() - dist.destroy_process_group() - return dist_train - - -class TorchTrainerMG(Trainer, LogMixin): - """ - Torch trainer for optionally distributed data-parallel (DDP) workload. - Multi-GPU distribution. - - Args: - model (nn.Module): neural network instance. - loss (Loss): torch loss function instance. - optimizer_class (str): path to optimizer class - (e.g., 'torch.optim.SGD') - optimizer_kwargs (Optional[Dict], optional): optimizer constructor - arguments (except from parameters). Defaults to None. - lr_scheduler_class (Optional[str], optional): path to learning - rate scheduler class. Defaults to None. - lr_scheduler_kwargs (Optional[Dict], optional): constructor arguments - of the learning rate scheduler, except for the optimizer. - Defaults to None. - train_dataloader_class (str, optional): train dataloader class path. - Defaults to 'torch.utils.data.DataLoader'. - train_dataloader_kwargs (Optional[Dict], optional): constructor - arguments of the train dataloader, except for the dataset - instance. Defaults to None. - validation_dataloader_class (str, optional): validation dataloader - class path. Defaults to 'torch.utils.data.DataLoader'. - validation_dataloader_kwargs (Optional[Dict], optional): constructor - arguments of the validation dataloader, except for the dataset - instance. If None, it replicates `train_dataloader_kwargs`. - Defaults to None. - epochs (int, optional): number of training epochs. Defaults to 1. - strategy (Optional[TorchDistributedStrategy], optional): distributed - strategy. Defaults to StrategyT.NONE.value. - backend (TorchDistributedBackend, optional): computing backend. - Defaults to BackendT.NCCL.value. - shuffle_dataset (bool, optional): whether shuffle dataset before - sampling batches from dataloader. Defaults to False. - use_cuda (bool, optional): whether to use GPU. Defaults to True. - benchrun (bool, optional): sets up a debug run. Defaults to False. - testrun (bool, optional): deterministic training seeding everything. - Defaults to False. - seed (Optional[int], optional): random seed. Defaults to None. - logger (Optional[List[Logger]], optional): logger. Defaults to None. - checkpoint_every (int, optional): how often (epochs) to checkpoint the - best model. Defaults to 10. - cluster (Optional[ClusterEnvironment], optional): cluster environment - object describing the context in which the trainer is executed. - Defaults to None. - train_metrics (Optional[Dict[str, Metric]], optional): - list of metrics computed in the training step on the predictions. - It's a dictionary with the form - ``{'metric_unique_name': CallableMetric}``. Defaults to None. - validation_metrics (Optional[Dict[str, Metric]], optional): same - as ``training_metrics``. If not given, it mirrors the training - metrics. Defaults to None. - - Raises: - RuntimeError: When trying to use DDP without CUDA support. - NotImplementedError: when trying to use a strategy different from the - ones provided by TorchDistributedStrategy. - """ - - model: nn.Module = None - loss: Loss = None - optimizer: Optimizer = None - lr_scheduler = None - _strategy: StrategyT = StrategyT.NONE.value - train_dataset: Dataset - validation_dataset: Dataset - train_dataloader: DataLoader = None - validation_dataloader: DataLoader = None - epoch_idx: int = 0 - train_glob_step: int = 0 - validation_glob_step: int = 0 - train_metrics: Dict[str, Metric] - validation_metrics: Dict[str, Metric] - - def __init__( - self, - model: nn.Module, - loss: Loss, - optimizer_class: str, - optimizer_kwargs: Optional[Dict] = None, - lr_scheduler_class: Optional[str] = None, - lr_scheduler_kwargs: Optional[Dict] = None, - train_dataloader_class: str = 'torch.utils.data.DataLoader', - train_dataloader_kwargs: Optional[Dict] = None, - validation_dataloader_class: str = 'torch.utils.data.DataLoader', - validation_dataloader_kwargs: Optional[Dict] = None, - epochs: int = 1, - strategy: str = StrategyT.NONE.value, - benchrun: bool = False, - testrun: bool = False, - seed: Optional[int] = None, - logger: Optional[List[Logger]] = None, - checkpoint_every: int = 10, - cluster: Optional[ClusterEnvironment] = None, - train_metrics: Optional[Dict[str, Metric]] = None, - validation_metrics: Optional[Dict[str, Metric]] = None - ) -> None: - """Sets up the distributed backend and loggers. - Makes the model a DDP model. - """ - super().__init__() - self.save_parameters(**self.locals2params(locals())) - self.model = model - self.loss = loss - self.epochs = epochs - self.testrun = testrun - self.seed = seed - self.strategy = strategy - self.benchrun = benchrun - self.cluster = cluster - # Checkpoint every n epochs - self.checkpoint_every = checkpoint_every - - # Train and validation dataloaders - self.train_dataloader_class = dynamically_import_class( - train_dataloader_class - ) - self.validation_dataloader_class = dynamically_import_class( - validation_dataloader_class - ) - train_dataloader_kwargs = ( - train_dataloader_kwargs - if train_dataloader_kwargs is not None else {} - ) - self.train_dataloader_kwargs = clear_key( - train_dataloader_kwargs, 'train_dataloader_kwargs', 'dataset' - ) - # If validation_dataloader_kwargs is not given, - # copy train_dataloader_kwargs - validation_dataloader_kwargs = ( - validation_dataloader_kwargs if validation_dataloader_kwargs - is not None else train_dataloader_kwargs - ) - self.validation_dataloader_kwargs = clear_key( - validation_dataloader_kwargs, 'validation_dataloader_kwargs', - 'dataset' - ) - - # Optimizer and scheduler - optim_class = dynamically_import_class(optimizer_class) - optimizer_kwargs = ( - optimizer_kwargs if optimizer_kwargs is not None else {} - ) - optimizer_kwargs = clear_key( - optimizer_kwargs, 'optimizer_kwargs', 'parameters' - ) - self.optimizer: Optimizer = optim_class( - self.model.parameters(), **optimizer_kwargs - ) - if lr_scheduler_class is not None: - scheduler_class = dynamically_import_class(lr_scheduler_class) - lr_scheduler_kwargs = ( - lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {} - ) - lr_scheduler_kwargs = clear_key( - lr_scheduler_kwargs, 'lr_scheduler_kwargs', 'optimizer' - ) - self.lr_scheduler: LrScheduler = scheduler_class( - self.optimizer, **lr_scheduler_kwargs - ) - - # Loggers - self.logger = logger if logger is not None else ConsoleLogger() - - # Metrics - self.train_metrics = ( - {} if train_metrics is None else train_metrics - ) - self.validation_metrics = ( - self.train_metrics if validation_metrics is None - else validation_metrics - ) - - @property - def strategy(self) -> Optional[str]: - return self._strategy - - @strategy.setter - def strategy(self, strategy_name) -> None: - if strategy_name not in StrategyT: - raise ValueError( - "Unrecognized 'strategy' field. Allowed values " - f"are: {StrategyT.list()}. Received '{strategy_name}'") - self._strategy = strategy_name - - @property - def global_step(self) -> int: - return self.train_glob_step + self.validation_glob_step - - def set_seed(self, seed: Optional[int] = None): - """Deterministic operations for reproducibility. - Sets the random seed. - - Args: - seed (Optional[int], optional): if not None, overrides - `self.seed`. Defaults to None. - """ - seed = seed if seed is not None else self.seed - np.random.seed(seed) - self.torch_rng = torch.Generator() - if seed is not None: - torch.manual_seed(seed) - self.torch_rng.manual_seed(seed) - if self.cluster.is_cuda_available(): - torch.cuda.manual_seed(seed) - - @monitor_exec - def execute( - self, - train_dataset: Dataset, - validation_dataset: Dataset, - model: nn.Module = None, - optimizer: Optimizer = None, - lr_scheduler: LrScheduler = None, - ) -> Any: - self.train_dataset = train_dataset - self.validation_dataset = validation_dataset - - # Update parameters passed for "interactive" use - if model is not None: - self.model = model - if optimizer is not None: - self.optimizer = optimizer - if lr_scheduler is not None: - self.lr_scheduler = lr_scheduler - - # Start training - if self.cluster.distributed: - # Make training distributed - result = mp.spawn(self._train, nprocs=self.cluster.ngpus_per_node) - else: - result = self._train(0) - - # Return value compliant with Executable.execute format - return result - - def _train( - self, - worker_id: int - ): - # Each worker has a different deterministic seed - # Here, 'worker' = replica of the training function - worker_seed = ( - self.seed + worker_id if self.seed is not None else self.seed - ) - self.set_seed(worker_seed) - - # Instantiate dataloaders - self.train_dataloader = self._instantiate_dataloader( - dataloader_class=self.train_dataloader_class, - dataset=self.train_dataset, - init_kwargs=self.train_dataloader_kwargs - ) - if self.validation_dataset is not None: - self.validation_dataloader = self._instantiate_dataloader( - dataloader_class=self.validation_dataloader_class, - dataset=self.validation_dataset, - init_kwargs=self.validation_dataloader_kwargs - ) - - # Launch actual training: - - # Single worker case - if not self.cluster.distributed: - with self.cluster.init_dist_gpu(worker_id) as device: - self.device: torch.device = device - self.model = self.model.to(self.device) - self.setup_logger() - self._setup_metrics() - try: - train_result = self.train() - except Exception as exc: - print(exc) - raise exc - finally: - print("INFO: Training ended") - self.destroy_logger() - train_result = None - return train_result - - # Init / connect to distributed backend - with self.cluster.init_dist_gpu(worker_id) as device: - self.device: torch.device = device - self._distribute_model() - self.setup_logger() - self._setup_metrics() - try: - train_result = self.train() - except Exception as exc: - print(exc) - raise exc - finally: - print("INFO: Training ended") - self.destroy_logger() - train_result = None - return train_result - - def _instantiate_dataloader( - self, - dataloader_class: Type, - dataset: Dataset, - init_kwargs: Dict - ) -> DataLoader: - """Make dataloader distributed if using distributed training strategy. - - Args: - dataloader_class (Type): some torch DataLoader type. - dataset (Dataset): torch dataset instance. - init_kwargs (Dict): constructor args. - """ - init_kwargs['generator'] = init_kwargs.get( - 'generator', self.torch_rng - ) - init_kwargs['worker_init_fn'] = init_kwargs.get( - 'worker_init_fn', seed_worker - ) - - if self.strategy == StrategyT.DDP.value and self.cluster.distributed: - sampler = DistributedSampler( - dataset=dataset, - num_replicas=self.cluster.global_world_size, - rank=self.cluster.global_rank, - shuffle=init_kwargs.get( - 'shuffle', False - ) - ) - # Overwrite existing sampler, if given. - # TODO: improve using wrapper: - # https://discuss.pytorch.org/t/how-to-use-my-own-sampler-when-i-already-use-distributedsampler/62143?page=2 - init_kwargs['sampler'] = sampler - if init_kwargs.get('shuffle') is not None: - # sampler option is mutually exclusive with shuffle - del init_kwargs['shuffle'] - - return dataloader_class(dataset, **init_kwargs) - - def _setup_metrics(self): - for m_name, metric in self.train_metrics.items(): - self.train_metrics[m_name] = metric.to(self.device) - for m_name, metric in self.validation_metrics.items(): - self.validation_metrics[m_name] = metric.to(self.device) - - def _distribute_model(self): - if self.cluster.distributed: - # Distribute model - self.model = self.model.to(self.device) - if self.strategy == StrategyT.NONE.value: - print( - "WARNING: A GPU cluster is available but no distributed " - "strategy was given... Falling back to single worker...") - if not self.cluster.is_main_worker(): - # Use only GPU:0 for single worker - sys.exit(0) - elif self.strategy == StrategyT.DDP.value: - self.model = DDP( - self.model, - device_ids=[self.device.index], - output_device=self.device - ) - else: - raise NotImplementedError("Only DDP strategy is implemented.") - else: - raise RuntimeError( - "Trying to distribute a model when a " - "distributed cluster is not available." - ) - - def setup_logger(self): - if self.cluster.is_main_worker(): - # Only setup loggers on main worker - if isinstance(self.logger, list): - for logger in self.logger: - logger.create_logger_context() - elif isinstance(self.logger, Logger): - self.logger.create_logger_context() - else: - raise TypeError( - "Unrecognized self.logger. Allowed types are 'list' and " - f"'Logger'. Received {type(self.logger)}" - ) - else: - self.logger = [] - - def destroy_logger(self): - if self.cluster.is_main_worker(): - if isinstance(self.logger, list): - for logger in self.logger: - logger.destroy_logger_context() - elif isinstance(self.logger, Logger): - self.logger.destroy_logger_context() - else: - raise TypeError( - "Unrecognized self.logger. Allowed types are 'list' and " - f"'Logger'. Received {type(self.logger)}" - ) - - def log( - self, - item: Union[Any, List[Any]], - identifier: Union[str, List[str]], - kind: str = 'metric', - step: Optional[int] = None, - batch_idx: Optional[int] = None, - every_worker: bool = False, - **kwargs - ) -> None: - if self.cluster.is_main_worker() or every_worker: - # Only log on main worker if not specified otherwise - if isinstance(self.logger, list): - for logger in self.logger: - logger.log( - item=item, - identifier=identifier, - kind=kind, - step=step, - batch_idx=batch_idx, - **kwargs - ) - elif isinstance(self.logger, Logger): - self.logger.log( - item=item, - identifier=identifier, - kind=kind, - step=step, - batch_idx=batch_idx, - **kwargs - ) - else: - raise TypeError( - "Unrecognized self.logger. Allowed types are 'list' and " - f"'Logger'. Received {type(self.logger)}" - ) - - def compute_metrics( - self, - metrics: Dict[str, Metric], - true: Batch, - pred: Batch, - logger_step: int, - batch_idx: Optional[int], - stage: str = 'train' - ) -> Dict[str, Any]: - """Compute and log metrics. - - Args: - metrics (Dict[str, Metric]): metrics dict. Can be - ``self.train_metrics`` or ``self.validation_metrics``. - true (Batch): true values. - pred (Batch): predicted values. - logger_step (int): global step to pass to the logger. - stage (str): 'train', 'validation'... - - Returns: - Dict[str, Any]: metric values. - """ - m_values = {} - for m_name, metric in metrics.items(): - # metric = metric.to(self.device) - m_val = metric(pred, true).detach().cpu().numpy() - self.log( - item=m_val, - identifier=f'{m_name}_{stage}', - kind='metric', - step=logger_step, - batch_idx=batch_idx - ) - m_values[m_name] = m_val - return m_values - - def training_step( - self, - batch: Batch, - batch_idx: int - ) -> Tuple[Loss, Dict[str, Any]]: - x, y = batch - x, y = x.to(self.device), y.to(self.device) - pred_y = self.model(x) - loss: Loss = self.loss(pred_y, y) - self.log( - item=loss.item(), - identifier='training_loss', - kind='metric', - step=self.train_glob_step, - batch_idx=batch_idx - ) - metrics: Dict[str, Any] = self.compute_metrics( - metrics=self.train_metrics, - true=y, - pred=pred_y, - logger_step=self.train_glob_step, - batch_idx=batch_idx, - stage='training' - ) - return loss, metrics - - def validation_step( - self, - batch: Batch, - batch_idx: int - ) -> Tuple[Loss, Dict[str, Any]]: - x, y = batch - x, y = x.to(self.device), y.to(self.device) - pred_y = self.model(x) - loss: Loss = self.loss(pred_y, y) - self.log( - item=loss.item(), - identifier='validation_loss', - kind='metric', - step=self.validation_glob_step, - batch_idx=batch_idx - ) - metrics: Dict[str, Any] = self.compute_metrics( - metrics=self.validation_metrics, - true=y, - pred=pred_y, - logger_step=self.validation_glob_step, - batch_idx=batch_idx, - stage='validation' - ) - return loss, metrics - - def training_epoch(self) -> Loss: - self.model.train() - train_losses = [] - for batch_idx, train_batch in enumerate(self.train_dataloader): - loss, metrics = self.training_step( - batch=train_batch, - batch_idx=batch_idx - ) - # TODO: merge and log batch metrics and loss into epoch metrics - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - train_losses.append(loss) - # Important: update counter - self.train_glob_step += 1 - - # Aggregate and log losses - avg_loss = torch.mean(torch.stack(train_losses)).detach().cpu() - self.log( - item=avg_loss.item(), - identifier='training_loss_epoch', - kind='metric', - step=self.train_glob_step, - ) - return avg_loss - - def validation_epoch(self) -> Loss: - if self.validation_dataloader is not None: - self.model.eval() - validation_losses = [] - for batch_idx, val_batch \ - in enumerate(self.validation_dataloader): - # TODO: merge and log batch metrics and loss into epoch metrics - loss, metrics = self.validation_step( - batch=val_batch, - batch_idx=batch_idx - ) - validation_losses.append(loss) - # Important: update counter - self.validation_glob_step += 1 - - # Aggregate and log losses - avg_loss = torch.mean( - torch.stack(validation_losses) - ).detach().cpu() - self.log( - item=avg_loss.item(), - identifier='validation_loss_epoch', - kind='metric', - step=self.validation_glob_step, - ) - return avg_loss - - def train(self): - - if self.optimizer is None: - raise ValueError("Undefined optimizer!") - - if self.loss is None: - raise ValueError("Undefined loss function!") - - st = time.time() - - # Resume state - self.start_epoch = 1 - self.best_loss = np.Inf - self.load_state() - - # start training/testing loop - if self.cluster.is_main_worker(): - print(f'TIMER: broadcast: {time.time()-st}s') - print('DEBUG: start training') - print('-'*56) - - ############################## - # Start training: run epochs # - ############################## - - et = time.time() - for self.epoch_idx in range(self.start_epoch, self.epochs + 1): - lt = time.time() - - ####################################################### - # Perform one training epoch and one validation epoch # - ####################################################### - - if self.benchrun and self.epoch_idx == self.epochs: - # TODO: move profiler into cluster environment - # profiling (done on last epoch - slower!) - with torch.autograd.profiler.profile( - use_cuda=self.cluster.is_cuda_available(), - profile_memory=True - ) as prof: - train_loss = self.training_epoch() - else: - train_loss = self.training_epoch() - val_loss = self.validation_epoch() - - ##################################### - # Save checkpoint if model improved # - ##################################### - - ref_loss = val_loss if val_loss is not None else train_loss - is_best = ref_loss < self.best_loss - if (self.epoch_idx % self.checkpoint_every == 0 - and not self.benchrun): - self.save_state( - loss_val=ref_loss, - is_best=is_best - ) - self.best_loss = min(ref_loss, self.best_loss) - - ########################### - # End of epoch operations # - ########################### - - # save first epoch timer - if self.epoch_idx == self.start_epoch: - first_ep_t = time.time()-lt - - # Final epoch - if self.epoch_idx + 1 == self.epochs: - self.train_dataloader.last_epoch = True - self.validation_dataloader.last_epoch = True - - if self.cluster.is_main_worker(): - print(f'TIMER: epoch time: {time.time()-lt}s') - if self.benchrun and self.epoch_idx == self.epochs: - print('-'*56) - print('benchmark of last epoch:') - what1 = ( - 'cuda' if self.cluster.is_cuda_available() else 'cpu' - ) - print( - prof.key_averages().table( - sort_by='self_'+str(what1)+'_time_total' - ) - ) - - ########################## - # Training has completed # - ########################## - - # save final state - if not self.benchrun: - self.save_state( - loss_val=ref_loss, - is_best=is_best - ) - if self.cluster.is_cuda_available() and self.cluster.distributed: - dist.barrier() - - ######################## - # Print training stats # - ######################## - - if self.cluster.is_main_worker(): - print('-'*56) - print('training results:') - print(f'TIMER: first epoch time: {first_ep_t}s') - print(f'TIMER: last epoch time: {time.time()-lt}s') - print( - f'TIMER: average epoch time: {(time.time()-et)/self.epochs}s') - print(f'TIMER: total epoch time: {time.time()-et}s') - if self.epoch_idx > 1: - print( - f'TIMER: total epoch-1 time: {time.time()-et-first_ep_t}s' - ) - print( - 'TIMER: average epoch-1 time: ' - f'{(time.time()-et-first_ep_t)/(self.epochs-1)}s') - if self.benchrun: - print( - f'TIMER: total epoch-2 time: {lt-first_ep_t}s') - print('TIMER: average epoch-2 time: ' - f'{(lt-first_ep_t)/(self.epochs-2)}s') - mem = int(torch.cuda.memory_reserved( - self.cluster.local_rank)/1024/1024) - print( - f'memory req: {mem} MB' - if self.cluster.is_cuda_available() - and self.cluster.distributed else 'memory req: - MB' - ) - if self.cluster.is_cuda_available(): - print( - f'memory summary:\n {torch.cuda.memory_summary(0)}') - - if self.cluster.is_main_worker(): - print(f'TIMER: final time: {time.time()-st} s') - - def save_state(self, loss_val: Any, is_best: bool): - """Save training state.""" - res_name = 'checkpoint.pth.tar' - rt = time.time() - - if (self.cluster.is_cuda_available() and self.cluster.distributed): - # find if is_best happened in any worker - is_best_m = par_allgather_obj( - is_best, self.cluster.global_world_size - ) - if any(is_best_m): - # TODO: is this strategy really good? Checkpointing when - # at least one worker improves the loss on their local - # data split is prone to overfitting, especially when - # the dataset in unbalanced! - - # find which rank is_best happened - select first rank - # if multiple - best_rank = np.where(np.array(is_best_m))[0][0] - if self.cluster.global_rank == best_rank: - self._save_sate( - epoch=self.epoch_idx+1, - loss_val=loss_val, - save_path=res_name - ) - print( - f'DEBUG: state in {self.cluster.global_rank} is ' - f'saved on epoch:{self.epoch_idx} ' - f'in {time.time()-rt} s') - else: - self._save_sate( - epoch=self.epoch_idx+1, - loss_val=loss_val, - save_path=res_name - ) - print( - f'DEBUG: state in {self.cluster.global_rank} ' - f'is saved on epoch:{self.epoch_idx} in {time.time()-rt} s') - - def _save_sate( - self, - epoch: int, - loss_val: Any, - save_path: str - ): - """Save state on disk.""" - sched = ( - self.lr_scheduler.state_dict() - if self.lr_scheduler is not None else None - ) - state = { - 'epoch': epoch, - 'state_dict': self.model.state_dict(), - 'best_loss': loss_val, - 'optimizer': self.optimizer.state_dict(), - 'lr_scheduler': sched - } - self.log( - item=state, - identifier=save_path, - kind='torch', - epoch_step=self.epoch_idx, - batch_step=0 - ) - - def load_state(self): - """Load training state.""" - res_name = 'checkpoint.pth.tar' - if os.path.isfile(res_name) and not self.benchrun: - try: - if (self.cluster.is_cuda_available() - and self.cluster.distributed): - dist.barrier() - # Map model to be loaded to specified single gpu. - # loc = ( - # {'cuda:%d' % 0: 'cuda:%d' % self.cluster.local_rank} - # if self.cluster.is_cuda_available() - # else {'cpu:%d' % 0: 'cpu:%d' % self.cluster.local_rank} - # ) - # checkpoint = torch.load(res_name, map_location=loc) - checkpoint = torch.load( - res_name, map_location=self.device - ) - else: - checkpoint = torch.load(res_name, map_location='cpu') - self.start_epoch = checkpoint['epoch'] - self.best_loss = checkpoint['best_loss'] - self.model.load_state_dict(checkpoint['state_dict']) - self.optimizer.load_state_dict(checkpoint['optimizer']) - if self.lr_scheduler is not None: - self.lr_scheduler.load_state_dict( - checkpoint['lr_scheduler'] - ) - if self.cluster.is_cuda_available(): - if self.cluster.is_main_worker(): - print( - f'WARNING: restarting from {self.start_epoch} ' - 'epoch') - else: - print( - f'WARNING: restarting from {self.start_epoch} epoch') - except Exception: - if self.cluster.is_cuda_available(): - if self.cluster.is_main_worker(): - print( - 'restart file cannot be loaded, restarting!') - else: - print( - 'WARNING: restart file cannot be loaded, restarting!') - - if self.start_epoch >= self.epochs + 1: - if self.cluster.is_cuda_available() and self.cluster.distributed: - if self.cluster.is_main_worker(): - print( - 'WARNING: given epochs are less than the ' - 'one in the restart file!') - print('WARNING: SYS.EXIT is issued') - sys.exit() - else: - print( - 'WARNING: given epochs are less than the ' - 'one in the restart file!') - print('WARNING: SYS.EXIT is issued') - sys.exit() diff --git a/src/itwinai/torch/types.py b/src/itwinai/torch/types.py deleted file mode 100644 index 6f6e5c9f..00000000 --- a/src/itwinai/torch/types.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Custom types definition.""" - -from typing import Callable -from enum import Enum, EnumMeta - -import torch -from torch import nn - -Loss = nn.Module -LrScheduler = nn.Module -Batch = torch.Tensor -Metric = Callable - - -class MetaEnum(EnumMeta): - def __contains__(cls, item): - try: - cls(item) - except ValueError: - return False - return True - - -class BaseEnum(Enum, metaclass=MetaEnum): - @classmethod - def list(cls): - return list(map(lambda c: c.value, cls)) - - -class TorchDistributedBackend(BaseEnum): - """ - Enum for torch distributed backends. - Reference: https://pytorch.org/docs/stable/distributed.html#backends - """ - DEFAULT = 'nccl' - GLOO = 'gloo' - NCCL = 'nccl' - MPI = 'mpi' - - -class TorchDistributedStrategy(BaseEnum): - DEFAULT = None - NONE = None - DDP = 'ddp' - - -class TorchLoss(BaseEnum): - """ - Torch loss class names. - TODO: complete from https://pytorch.org/docs/stable/nn.html#loss-functions - """ - L1 = 'L1Loss' - MSE = 'MSELoss' - CROSS_ENTROPY = 'CrossEntropyLoss' - NLLLOSS = 'NLLLoss' - - -class TorchOptimizer(BaseEnum): - """ - Torch optimizer class names. - TODO: complete from https://pytorch.org/docs/stable/optim.html#algorithms - """ - SGD = 'SGD' - ADAM = 'Adam' diff --git a/src/itwinai/torch/utils.py b/src/itwinai/torch/utils.py deleted file mode 100644 index 99bcd246..00000000 --- a/src/itwinai/torch/utils.py +++ /dev/null @@ -1,84 +0,0 @@ -from typing import Hashable, Dict -import time -import numpy as np -import random - -import torch -import torch.distributed as dist - - -def save_state( - epoch, distrib_model, loss_val, optimizer, res_name, grank, gwsize, - is_best, distributed: bool = True -): - """Save training state""" - rt = time.time() - # find if is_best happened in any worker - if torch.cuda.is_available() and distributed: - is_best_m = par_allgather_obj(is_best, gwsize) - - if torch.cuda.is_available() and distributed: - if any(is_best_m): - # find which rank is_best happened - select first rank if multiple - is_best_rank = np.where(np.array(is_best_m))[0][0] - - # collect state - state = {'epoch': epoch + 1, - 'state_dict': distrib_model.state_dict(), - 'best_loss': loss_val, - 'optimizer': optimizer.state_dict()} - - # write on worker with is_best - if grank == is_best_rank: - torch.save(state, './'+res_name) - print(f'DEBUG: state in {grank} is saved on ' - f'epoch:{epoch} in {time.time()-rt} s') - else: - # collect state - state = {'epoch': epoch + 1, - 'state_dict': distrib_model.state_dict(), - 'best_loss': loss_val, - 'optimizer': optimizer.state_dict()} - - torch.save(state, './'+res_name) - print( - f'DEBUG: state in {grank} is saved on epoch:{epoch} ' - f'in {time.time()-rt} s') - - -def seed_worker(worker_id): - """deterministic dataloader""" - worker_seed = torch.initial_seed() % 2**32 - np.random.seed(worker_seed) - random.seed(worker_seed) - - -def par_allgather_obj(obj, gwsize): - """gathers any object from the whole group in a list (to all workers)""" - res = [None]*gwsize - dist.all_gather_object(res, obj, group=None) - # print(f'ALLGATHER: {res}') - return res - - -def clear_key( - my_dict: Dict, - dict_name: str, - key: Hashable, - complain: bool = True -) -> Dict: - """Remove key from dictionary if present and complain. - - Args: - my_dict (Dict): Dictionary. - dict_name (str): name of the dictionary. - key (Hashable): Key to remove. - """ - if key in my_dict: - if complain: - print( - f"Field '{key}' should not be present " - f"in dictionary '{dict_name}'" - ) - del my_dict[key] - return my_dict From 9922c684406eeb64269e2daa176cccdad9332d7b Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 20 Mar 2024 15:34:19 +0100 Subject: [PATCH 37/59] adding documentation.yml file for automatic updating on github pages --- .github/workflows/documentation.yml | 37 +++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 .github/workflows/documentation.yml diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml new file mode 100644 index 00000000..7b4d5cce --- /dev/null +++ b/.github/workflows/documentation.yml @@ -0,0 +1,37 @@ +name: Build and Deploy Sphinx Documentation + +on: + push: + branches: + - docs_dev + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.9' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r docs/requirements.txt + pip install sphinx sphinx-rtd-theme + + - name: Build documentation + run: | + cd docs + make html + + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./docs/_build/html From a84c3d7e92210d00f133d00c769c2e8508138a81 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 20 Mar 2024 17:07:07 +0100 Subject: [PATCH 38/59] modifying documentation.yml file --- .github/workflows/documentation.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 7b4d5cce..fdf10745 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -21,7 +21,6 @@ jobs: - name: Install dependencies run: | - python -m pip install --upgrade pip pip install -r docs/requirements.txt pip install sphinx sphinx-rtd-theme From 94cea66d4c3610428b77c0485dc18e3e15eb2123 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Wed, 20 Mar 2024 17:22:27 +0100 Subject: [PATCH 39/59] updating reqs file to solve bug in deployment --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 23528ceb..31776555 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -62,7 +62,7 @@ importlib-resources inquirer installer itsdangerous --e git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai +git+https://github.com/interTwin-eu/itwinai.git@a8f9ccb035c7736553eaafb12e06fd7b3fc73fb6#egg=itwinai jaraco.classes jeepney Jinja2 From a2d76d5834ef10b8e821cf94e78dca155e03f016 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 21 Mar 2024 13:03:20 +0100 Subject: [PATCH 40/59] testing automated docs update --- docs/index.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/index.rst b/docs/index.rst index 4b510081..d74f9e1c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,6 +3,9 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. +🚧 UNDER CONSTRUCTION 🚧 +========================= + Welcome to itwinai's documentation! =================================== From 3eb5dae1059fd6396e25155f7f6befee42d625ed Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 21 Mar 2024 15:21:03 +0100 Subject: [PATCH 41/59] updating getting started page --- docs/conf.py | 2 +- docs/getting_started_with_itwinai.rst | 66 +++++++++++++++++++++++++-- docs/index.rst | 2 +- 3 files changed, 65 insertions(+), 5 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 64a10acb..acf1d227 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,7 +19,7 @@ sys.path.insert(0, os.path.abspath('../')) project = 'itwinai' -copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki on behalf of CERN' +copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki, Rakesh Sarma on behalf of CERN & JSC' author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' release = '0.0.1' diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index e559189a..9deb4fa1 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -4,8 +4,68 @@ Getting started with itwinai Installation ------------ -Requirements -+++++++++++++ + +🌐 HPC systems ++++++++++++++++ +How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. +Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. + + +**Environments** +* Install PyTorch env (GPU support) on Juelich Super Computer (tested on HDFML system) + +.. code-block:: bash + torch-gpu-jsc: env-files/torch/createEnvJSC.sh + sh env-files/torch/createEnvJSC.sh + + +* Install Tensorflow env (GPU support) on Juelich Super Computer (tested on HDFML system) + +.. code-block:: bash + tf-gpu-jsc: env-files/tensorflow/createEnvJSCTF.sh + sh env-files/tensorflow/createEnvJSCTF.sh + + + +**Setup** +First, from the root of this `repository `_, build the environment containing pytorch, horovod and deepspeed. You can try with: + +.. code-block:: bash + + # Creates a Python venv called envAI_hdfml + make torch-gpu-jsc + + +**Distributed training** +Each distributed strategy has its own SLURM job script, which should be used to run it: + +* If you want to distribute the code in `train.py` with **torch DDP**, run from terminal: + +.. code-block:: bash + sbatch ddp_slurm.sh + +* If you want to distribute the code in `train.py` with **DeepSpeed**, run from terminal: + +.. code-block:: bash + sbatch deepspeed_slurm.sh + +* If you want to distribute the code in `train.py` with **Horovod**, run from terminal: + +.. code-block:: bash + sbatch hvd_slurm.sh + +* You can run all of them with: + +.. code-block:: bash + bash runall.sh + + + + +💻 Local systems ++++++++++++++++++ + +**Requirements** * Linux environment. * Windows and macOS were never tested. @@ -44,7 +104,7 @@ default `$MAMBA_ROOT_PREFIX` is overridden: Environment setup ------------------ ++++++++++++++++++ **Requirements:** diff --git a/docs/index.rst b/docs/index.rst index d74f9e1c..90d8bc19 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -57,7 +57,7 @@ In ``itwinai`` platform, we focus mainly on the MLOps step, simulating or oversi `interTwin Demo: itwinai integration with other DTE modules `_ -+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Indices and tables From be08188c2d3ff6fc365b090f9bfc385dd3edf412 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 21 Mar 2024 16:54:45 +0100 Subject: [PATCH 42/59] fixing pages and adding new content --- docs/3dgan_doc.rst | 90 ++++++++- docs/conf.py | 4 +- docs/getting_started_with_itwinai.rst | 274 ++------------------------ docs/hpc_setup.rst | 60 ++++++ docs/local_setup.rst | 206 +++++++++++++++++++ 5 files changed, 367 insertions(+), 267 deletions(-) create mode 100644 docs/hpc_setup.rst create mode 100644 docs/local_setup.rst diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst index eb5db410..90af7fad 100644 --- a/docs/3dgan_doc.rst +++ b/docs/3dgan_doc.rst @@ -1,23 +1,107 @@ 3DGAN ===== -The 3DGAN use case is a part of the use cases which... +This section covers the CERN use case, which utilizes the `torch-lightning` framework for training and evaluation. The following files are integral to this use case: + +itwinai x 3DGAN +--------------- + + +.. toctree:: + :maxdepth: 5 + + ../use-cases/3dgan/README.md + + +model.py +++++++++ .. literalinclude:: ../use-cases/3dgan/model.py :language: python -.. literalinclude:: ../use-cases/3dgan/train.py - :language: python +trainer.py +++++++++++ .. literalinclude:: ../use-cases/3dgan/trainer.py :language: python + +saver.py +++++++++ + .. literalinclude:: ../use-cases/3dgan/saver.py :language: python + +dataloader.py ++++++++++++++ + .. literalinclude:: ../use-cases/3dgan/dataloader.py :language: python + +cern-pipeline.yaml +++++++++++++++++++ + +This YAML file defines the pipeline configuration for the CERN use case. + +.. literalinclude:: ../use-cases/3dgan/cern-pipeline.yaml + :language: yaml + + +inference-pipeline.yaml ++++++++++++++++++++++++ + +This YAML file defines the pipeline configuration for the CERN use case inference. + +.. literalinclude:: ../use-cases/3dgan/inference-pipeline.yaml + :language: yaml + + +Dockerfile +++++++++++ + +.. literalinclude:: ../use-cases/3dgan/Dockerfile + :language: bash + + +pipeline.yaml ++++++++++++++ + +This YAML file defines the pipeline configuration for the CERN use case. It includes settings for the model, training, and evaluation. + +.. literalinclude:: ../use-cases/3dgan/pipeline.yaml + :language: yaml + + + +This section covers the CERN use case integration with `interLink `_ using ``itwinai``. The following files are integral to this use case: + +interLink x 3DGAN +----------------- + +.. toctree:: + :maxdepth: 5 + + ../use-cases/3dgan/interLink/README.md + + +3dgan-inference-cpu.yaml +++++++++++++++++++++++++ + +.. literalinclude:: ../use-cases/3dgan/interLink/3dgan-inference-cpu.yaml + :language: yaml + + +3dgan-inference.yaml +++++++++++++++++++++++++ + +.. literalinclude:: ../use-cases/3dgan/interLink/3dgan-inference.yaml + :language: yaml + + + + .. .. automodule:: 3dgan.model .. :members: .. :undoc-members: diff --git a/docs/conf.py b/docs/conf.py index acf1d227..5d0b1f4b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -21,12 +21,12 @@ project = 'itwinai' copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki, Rakesh Sarma on behalf of CERN & JSC' author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' -release = '0.0.1' +release = '0.0.2' # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode'] +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode', 'myst_parser'] templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 9deb4fa1..92d012f7 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -1,270 +1,20 @@ Getting started with itwinai ============================ -Installation ------------- +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: 🌐 HPC systems + + hpc_setup -🌐 HPC systems -+++++++++++++++ -How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. -Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: 💻 Local systems + + local_setup -**Environments** -* Install PyTorch env (GPU support) on Juelich Super Computer (tested on HDFML system) - -.. code-block:: bash - torch-gpu-jsc: env-files/torch/createEnvJSC.sh - sh env-files/torch/createEnvJSC.sh - - -* Install Tensorflow env (GPU support) on Juelich Super Computer (tested on HDFML system) - -.. code-block:: bash - tf-gpu-jsc: env-files/tensorflow/createEnvJSCTF.sh - sh env-files/tensorflow/createEnvJSCTF.sh - - - -**Setup** -First, from the root of this `repository `_, build the environment containing pytorch, horovod and deepspeed. You can try with: - -.. code-block:: bash - - # Creates a Python venv called envAI_hdfml - make torch-gpu-jsc - - -**Distributed training** -Each distributed strategy has its own SLURM job script, which should be used to run it: - -* If you want to distribute the code in `train.py` with **torch DDP**, run from terminal: - -.. code-block:: bash - sbatch ddp_slurm.sh - -* If you want to distribute the code in `train.py` with **DeepSpeed**, run from terminal: - -.. code-block:: bash - sbatch deepspeed_slurm.sh - -* If you want to distribute the code in `train.py` with **Horovod**, run from terminal: - -.. code-block:: bash - sbatch hvd_slurm.sh - -* You can run all of them with: - -.. code-block:: bash - bash runall.sh - - - - -💻 Local systems -+++++++++++++++++ - -**Requirements** - -* Linux environment. -* Windows and macOS were never tested. - - -Micromamba installation -+++++++++++++++++++++++ - -To manage Conda environments we use micromamba, a light weight version of conda. - -It is suggested to refer to the `Manual installation guide `_. - -Consider that Micromamba can eat a lot of space when building environments because packages are cached on -the local filesystem after being downloaded. To clear cache you can use `micromamba clean -a`. -Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage -space and it would be cleverer to install Micromamba in another location with more storage space. -Thus by changing the `$MAMBA_ROOT_PREFIX` variable. See a complete installation example for Linux below, where the -default `$MAMBA_ROOT_PREFIX` is overridden: - - -.. code-block:: bash - - cd $HOME - - # Download micromamba (This command is for Linux Intel (x86_64) systems. Find the right one for your system!) - curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba - - # Install micromamba in a custom directory - MAMBA_ROOT_PREFIX='my-mamba-root' - ./bin/micromamba shell init $MAMBA_ROOT_PREFIX - - # To invoke micromamba from Makefile, you need to add explicitly to $PATH - echo 'PATH="$(dirname $MAMBA_EXE):$PATH"' >> ~/.bashrc - -**Reference**: `Micromamba installation guide `_. - - -Environment setup -+++++++++++++++++ - -**Requirements:** - -* Linux environment. Windows and macOS were never tested. -* Micromamba: see the installation instructions above. -* VS Code, for development. - -Tensorflow -++++++++++ - -Installation: - -.. code-block:: bash - - # Install TensorFlow 2.13 - make tf-2.13 - - # Activate env - micromamba activate ./.venv-tf - -Other TF versions are available, using the following targets `tf-2.10`, and `tf-2.11`. - - -PyTorch (+ Lightning) -+++++++++++++++++++++ - -Installation: - -.. code-block:: bash - - # Install PyTorch + lightning - make torch-gpu - - # Activate env - micromamba activate ./.venv-pytorch - -Other also CPU-only version is available at the target `torch-cpu`. - - -Development environment -+++++++++++++++++++++++ - -This is for developers only. To have it, update the installed `itwinai` package adding the `dev` extra: - -.. code-block:: bash - - pip install -e .[dev] - - -**Test with `pytest`** -To run tests on itwinai package: - -.. code-block:: bash - - # Activate env - micromamba activate ./.venv-pytorch # or ./.venv-tf - - pytest -v -m "not slurm" tests/ - - -However, some tests are intended to be executed only on an HPC system, where SLURM is available. They are marked with "slurm" tag. To run also those tests, use the dedicated job script: - -.. code-block:: bash - - sbatch tests/slurm_tests_startscript - - # Upon completion, check the output: - cat job.err - cat job.out - - - - -.. Workflow orchestrator -.. +++++++++++++++++++++ - -.. Install the (custom) orchestrator virtual environment. - -.. .. code-block:: bash - -.. source ~/.bashrc -.. # Create local env -.. make - -.. # Activate env -.. micromamba activate ./.venv - -.. To run tests on workflows use: - -.. .. code-block:: bash - -.. # Activate env -.. micromamba activate ./.venv - -.. pytest tests/ - - -.. Development env setup -.. --------------------- - -.. Requirements: - -.. * Linux, macOS environment. Windows was never tested. -.. * Micromamba: see the installation instructions above. -.. * VS Code, for development. - -.. Installation: - -.. .. code-block:: bash - -.. make dev-env - -.. # Activate env -.. micromamba activate ./.venv-dev - -.. To run tests on itwinai package: - -.. .. code-block:: bash - -.. # Activate env -.. micromamba activate ./.venv-dev - -.. pytest tests/ai/ - - -.. AI environment setup -.. -------------------- - -.. Requirements: - -.. * Linux, macOS environment. Windows was never tested. -.. * Micromamba: see the installation instructions above. -.. * VS Code, for development. - -.. **NOTE**: this environment gets automatically setup when a workflow is executed! - -.. However, you can also set it up explicitly with: - -.. .. code-block:: bash - -.. make ai-env - -.. # Activate env -.. micromamba activate ./ai/.venv-pytorch - -.. Updating the environment files -.. ++++++++++++++++++++++++++++++ - -.. The files under `ai/env-files/` are of two categories: - -.. * Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` -.. * Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by `conda-lock `_. - -.. **When you install the ai environment, install it from the lock file!** - -.. When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with `conda-lock `_: - -.. .. code-block:: bash - -.. micromamba activate ./.venv - -.. make lock-ai diff --git a/docs/hpc_setup.rst b/docs/hpc_setup.rst new file mode 100644 index 00000000..55e12682 --- /dev/null +++ b/docs/hpc_setup.rst @@ -0,0 +1,60 @@ +🌐 HPC systems +--------------- +How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the same client code. +Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. + + +**Environments** + +Install PyTorch env (GPU support) on Juelich Super Computer (tested on HDFML system) + +.. code-block:: bash + + torch-gpu-jsc: env-files/torch/createEnvJSC.sh + sh env-files/torch/createEnvJSC.sh + + +Install Tensorflow env (GPU support) on Juelich Super Computer (tested on HDFML system) + +.. code-block:: bash + + tf-gpu-jsc: env-files/tensorflow/createEnvJSCTF.sh + sh env-files/tensorflow/createEnvJSCTF.sh + + + +**Setup** +First, from the root of this `repository `_, build the environment containing pytorch, horovod and deepspeed. You can try with: + +.. code-block:: bash + + # Creates a Python venv called envAI_hdfml + make torch-gpu-jsc + + +**Distributed training** +Each distributed strategy has its own SLURM job script, which should be used to run it: + +If you want to distribute the code in `train.py` with **torch DDP**, run from terminal: + +.. code-block:: bash + + sbatch ddp_slurm.sh + +If you want to distribute the code in `train.py` with **DeepSpeed**, run from terminal: + +.. code-block:: bash + + sbatch deepspeed_slurm.sh + +If you want to distribute the code in `train.py` with **Horovod**, run from terminal: + +.. code-block:: bash + + sbatch hvd_slurm.sh + +You can run all of them with: + +.. code-block:: bash + + bash runall.sh \ No newline at end of file diff --git a/docs/local_setup.rst b/docs/local_setup.rst new file mode 100644 index 00000000..e3a3c74c --- /dev/null +++ b/docs/local_setup.rst @@ -0,0 +1,206 @@ +💻 Local systems +----------------- + +**Requirements** + +* Linux environment. +* Windows and macOS were never tested. + + +Micromamba installation ++++++++++++++++++++++++ + +To manage Conda environments we use micromamba, a light weight version of conda. + +It is suggested to refer to the `Manual installation guide `_. + +Consider that Micromamba can eat a lot of space when building environments because packages are cached on +the local filesystem after being downloaded. To clear cache you can use `micromamba clean -a`. +Micromamba data are kept under the `$HOME` location. However, in some systems, `$HOME` has a limited storage +space and it would be cleverer to install Micromamba in another location with more storage space. +Thus by changing the `$MAMBA_ROOT_PREFIX` variable. See a complete installation example for Linux below, where the +default `$MAMBA_ROOT_PREFIX` is overridden: + + +.. code-block:: bash + + cd $HOME + + # Download micromamba (This command is for Linux Intel (x86_64) systems. Find the right one for your system!) + curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba + + # Install micromamba in a custom directory + MAMBA_ROOT_PREFIX='my-mamba-root' + ./bin/micromamba shell init $MAMBA_ROOT_PREFIX + + # To invoke micromamba from Makefile, you need to add explicitly to $PATH + echo 'PATH="$(dirname $MAMBA_EXE):$PATH"' >> ~/.bashrc + +**Reference**: `Micromamba installation guide `_. + + +Environment setup ++++++++++++++++++ + +**Requirements:** + +* Linux environment. Windows and macOS were never tested. +* Micromamba: see the installation instructions above. +* VS Code, for development. + +Tensorflow +++++++++++ + +Installation: + +.. code-block:: bash + + # Install TensorFlow 2.13 + make tf-2.13 + + # Activate env + micromamba activate ./.venv-tf + +Other TF versions are available, using the following targets `tf-2.10`, and `tf-2.11`. + + +PyTorch (+ Lightning) ++++++++++++++++++++++ + +Installation: + +.. code-block:: bash + + # Install PyTorch + lightning + make torch-gpu + + # Activate env + micromamba activate ./.venv-pytorch + +Other also CPU-only version is available at the target `torch-cpu`. + + +Development environment ++++++++++++++++++++++++ + +This is for developers only. To have it, update the installed `itwinai` package adding the `dev` extra: + +.. code-block:: bash + + pip install -e .[dev] + + +**Test with `pytest`** +To run tests on itwinai package: + +.. code-block:: bash + + # Activate env + micromamba activate ./.venv-pytorch # or ./.venv-tf + + pytest -v -m "not slurm" tests/ + + +However, some tests are intended to be executed only on an HPC system, where SLURM is available. They are marked with "slurm" tag. To run also those tests, use the dedicated job script: + +.. code-block:: bash + + sbatch tests/slurm_tests_startscript + + # Upon completion, check the output: + cat job.err + cat job.out + + + + +.. Workflow orchestrator +.. +++++++++++++++++++++ + +.. Install the (custom) orchestrator virtual environment. + +.. .. code-block:: bash + +.. source ~/.bashrc +.. # Create local env +.. make + +.. # Activate env +.. micromamba activate ./.venv + +.. To run tests on workflows use: + +.. .. code-block:: bash + +.. # Activate env +.. micromamba activate ./.venv + +.. pytest tests/ + + +.. Development env setup +.. --------------------- + +.. Requirements: + +.. * Linux, macOS environment. Windows was never tested. +.. * Micromamba: see the installation instructions above. +.. * VS Code, for development. + +.. Installation: + +.. .. code-block:: bash + +.. make dev-env + +.. # Activate env +.. micromamba activate ./.venv-dev + +.. To run tests on itwinai package: + +.. .. code-block:: bash + +.. # Activate env +.. micromamba activate ./.venv-dev + +.. pytest tests/ai/ + + +.. AI environment setup +.. -------------------- + +.. Requirements: + +.. * Linux, macOS environment. Windows was never tested. +.. * Micromamba: see the installation instructions above. +.. * VS Code, for development. + +.. **NOTE**: this environment gets automatically setup when a workflow is executed! + +.. However, you can also set it up explicitly with: + +.. .. code-block:: bash + +.. make ai-env + +.. # Activate env +.. micromamba activate ./ai/.venv-pytorch + +.. Updating the environment files +.. ++++++++++++++++++++++++++++++ + +.. The files under `ai/env-files/` are of two categories: + +.. * Simple environment definition, such as `pytorch-env.yml` and `pytorch-env-gpu.yml` +.. * Lockfiles, such as `pytorch-lock.yml` and `pytorch-gpu-lock.yml`, generated by `conda-lock `_. + +.. **When you install the ai environment, install it from the lock file!** + +.. When the "simple" environment file (e.g., `pytorch-env.yml`) changes, lock it with `conda-lock `_: + +.. .. code-block:: bash + +.. micromamba activate ./.venv + +.. make lock-ai + From 5cce36e6e71c1673b1427202824edd36da016060 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 21 Mar 2024 17:10:35 +0100 Subject: [PATCH 43/59] bug fixes --- docs/3dgan_doc.rst | 4 ---- docs/conf.py | 2 +- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/docs/3dgan_doc.rst b/docs/3dgan_doc.rst index 90af7fad..69f62774 100644 --- a/docs/3dgan_doc.rst +++ b/docs/3dgan_doc.rst @@ -9,8 +9,6 @@ itwinai x 3DGAN .. toctree:: :maxdepth: 5 - - ../use-cases/3dgan/README.md model.py @@ -82,8 +80,6 @@ interLink x 3DGAN .. toctree:: :maxdepth: 5 - - ../use-cases/3dgan/interLink/README.md 3dgan-inference-cpu.yaml diff --git a/docs/conf.py b/docs/conf.py index 5d0b1f4b..ab367bb7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -26,7 +26,7 @@ # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode', 'myst_parser'] +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.viewcode'] #'myst_parser' templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] From 662a6abf5a682c01d66bdcc0c438ce461806e151 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Thu, 21 Mar 2024 17:25:27 +0100 Subject: [PATCH 44/59] fixing content rendering --- docs/getting_started_with_itwinai.rst | 10 ++++++++++ docs/hpc_setup.rst | 13 +++++++++---- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 92d012f7..557d99a3 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -2,6 +2,12 @@ Getting started with itwinai ============================ +Here you can find itwinai framework installation and usage instructions for HPC and local systems. + + +HPC systems +----------- + .. toctree:: :maxdepth: 2 :hidden: @@ -10,6 +16,10 @@ Getting started with itwinai hpc_setup + +Local systems +------------- + .. toctree:: :maxdepth: 2 :hidden: diff --git a/docs/hpc_setup.rst b/docs/hpc_setup.rst index 55e12682..c2ef7a79 100644 --- a/docs/hpc_setup.rst +++ b/docs/hpc_setup.rst @@ -4,7 +4,8 @@ How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. -**Environments** +Environments +++++++++++++ Install PyTorch env (GPU support) on Juelich Super Computer (tested on HDFML system) @@ -23,7 +24,9 @@ Install Tensorflow env (GPU support) on Juelich Super Computer (tested on HDFML -**Setup** +Setup ++++++ + First, from the root of this `repository `_, build the environment containing pytorch, horovod and deepspeed. You can try with: .. code-block:: bash @@ -32,7 +35,9 @@ First, from the root of this `repository Date: Fri, 22 Mar 2024 16:15:51 +0100 Subject: [PATCH 45/59] latest fixes in rendering --- docs/getting_started_with_itwinai.rst | 8 ++++---- docs/hpc_setup.rst | 4 ++++ docs/local_setup.rst | 4 ++++ 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/docs/getting_started_with_itwinai.rst b/docs/getting_started_with_itwinai.rst index 557d99a3..e76646c0 100644 --- a/docs/getting_started_with_itwinai.rst +++ b/docs/getting_started_with_itwinai.rst @@ -5,8 +5,8 @@ Getting started with itwinai Here you can find itwinai framework installation and usage instructions for HPC and local systems. -HPC systems ------------ +🌐 HPC systems +--------------- .. toctree:: :maxdepth: 2 @@ -17,8 +17,8 @@ HPC systems -Local systems -------------- +💻 Local systems +----------------- .. toctree:: :maxdepth: 2 diff --git a/docs/hpc_setup.rst b/docs/hpc_setup.rst index c2ef7a79..bbf32790 100644 --- a/docs/hpc_setup.rst +++ b/docs/hpc_setup.rst @@ -4,6 +4,10 @@ How to use torch `DistributedDataParallel` (DDP), Horovod and DeepSpeed from the Note that the environment is tested on the HDFML system at JSC. For other systems, the module versions might need change accordingly. +.. toctree:: + :maxdepth: 5 + + Environments ++++++++++++ diff --git a/docs/local_setup.rst b/docs/local_setup.rst index e3a3c74c..e16d9945 100644 --- a/docs/local_setup.rst +++ b/docs/local_setup.rst @@ -7,6 +7,10 @@ * Windows and macOS were never tested. +.. toctree:: + :maxdepth: 5 + + Micromamba installation +++++++++++++++++++++++ From dfc6e32a80a6f372e78a7da923ad78277e5a07c6 Mon Sep 17 00:00:00 2001 From: KalliopiTsolaki Date: Mon, 15 Apr 2024 16:16:58 +0200 Subject: [PATCH 46/59] Add version feature to docs --- .github/workflows/documentation.yml | 2 ++ docs/conf.py | 32 ++++++++++++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index fdf10745..3695ef37 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -4,6 +4,8 @@ on: push: branches: - docs_dev + tags: + - 'v*' jobs: build-and-deploy: diff --git a/docs/conf.py b/docs/conf.py index ab367bb7..97ce4672 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -8,6 +8,8 @@ import os import sys +import subprocess + sys.path.insert(0, os.path.abspath('../use-cases/')) sys.path.insert(0, os.path.abspath('../use-cases/3dgan/')) sys.path.insert(0, os.path.abspath('../use-cases/mnist/torch-lightning/')) @@ -21,7 +23,8 @@ project = 'itwinai' copyright = '2024, Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki, Rakesh Sarma on behalf of CERN & JSC' author = 'Matteo Bunino, Alexander Zoechbauer, Kalliopi Tsolaki' -release = '0.0.2' +# version = '0.0' # short version +# release = '0.0.2' # full version # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -36,5 +39,32 @@ # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output +def get_git_tag(): + try: + return subprocess.check_output(['git', 'describe', '--tags', '--abbrev=0']).decode('utf-8').strip() + except subprocess.CalledProcessError: + return 'unknown' + +# Set the version to the latest tag +version = get_git_tag() +release = version + html_theme = 'sphinx_rtd_theme' #'alabaster' html_static_path = ['_static'] + +html_context = { + 'display_version': True, + 'release': release +} + +html_footer = """ +