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* add comet_ml callback * fix typo * add comet to readme * add link to notebook * train for more epochs * add TrainingCallbacks in doc * apply isort and black * prepare release * typing * Update README.md * Update README.md * update gitignore * add viz in notebooks
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| ********************************** | ||
| TrainingCallbacks | ||
| ********************************** | ||
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| .. automodule:: | ||
| pythae.trainers.training_callbacks | ||
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| .. autoclass:: pythae.trainers.training_callbacks.TrainingCallback | ||
| :members: | ||
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| .. autoclass:: pythae.trainers.training_callbacks.CallbackHandler | ||
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| .. autoclass:: pythae.trainers.training_callbacks.MetricConsolePrinterCallback | ||
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| .. autoclass:: pythae.trainers.training_callbacks.ProgressBarCallback | ||
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| .. autoclass:: pythae.trainers.training_callbacks.WandbCallback | ||
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| .. autoclass:: pythae.trainers.training_callbacks.MLFlowCallback | ||
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| .. autoclass:: pythae.trainers.training_callbacks.CometCallback | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Tutorial - Comet ml experiments monitoring\n", | ||
| "\n", | ||
| "In this notebook, we will see how to monitor your experiments using the integrated **comet_ml** callbacks." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Install the library\n", | ||
| "%pip install pythae" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Train your Pythae model" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import torchvision.datasets as datasets\n", | ||
| "\n", | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 2" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "mnist_trainset = datasets.MNIST(root='../data', train=True, download=True, transform=None)\n", | ||
| "\n", | ||
| "train_dataset = mnist_trainset.data[:-10000].reshape(-1, 1, 28, 28) / 255.\n", | ||
| "eval_dataset = mnist_trainset.data[-10000:].reshape(-1, 1, 28, 28) / 255." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pythae.models import BetaVAE, BetaVAEConfig\n", | ||
| "from pythae.trainers import BaseTrainerConfig\n", | ||
| "from pythae.pipelines.training import TrainingPipeline\n", | ||
| "from pythae.models.nn.benchmarks.mnist import Encoder_ResNet_VAE_MNIST, Decoder_ResNet_AE_MNIST" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "training_config = BaseTrainerConfig(\n", | ||
| " output_dir='my_model',\n", | ||
| " learning_rate=1e-4,\n", | ||
| " batch_size=100,\n", | ||
| " num_epochs=10, # Change this to train the model a bit more,\n", | ||
| " steps_predict=3\n", | ||
| ")\n", | ||
| "\n", | ||
| "\n", | ||
| "model_config = BetaVAEConfig(\n", | ||
| " input_dim=(1, 28, 28),\n", | ||
| " latent_dim=16,\n", | ||
| " beta=2.\n", | ||
| "\n", | ||
| ")\n", | ||
| "\n", | ||
| "model = BetaVAE(\n", | ||
| " model_config=model_config,\n", | ||
| " encoder=Encoder_ResNet_VAE_MNIST(model_config), \n", | ||
| " decoder=Decoder_ResNet_AE_MNIST(model_config) \n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Before lauching the pipeline, you will need to build your `CometCallback`\n", | ||
| "\n", | ||
| "To be able to access this feature you will need:\n", | ||
| "- a valid comet_ml acccount\n", | ||
| "- the `comet_ml` package installed in your virtual env. You can install it by running (`pip install comet_ml`)\n", | ||
| "- Your `api_key` when setting up the `CometCallback`. Note that you may need to run `comet init --api-key` to set up your api-key locally and be able to synchronize your offline runs." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Before being allowed to monitor your experiments you may need to run the following\n", | ||
| "# !pip install comet_ml" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Create you callback\n", | ||
| "from pythae.trainers.training_callbacks import CometCallback\n", | ||
| "\n", | ||
| "callbacks = [] # the TrainingPipeline expects a list of callbacks\n", | ||
| "\n", | ||
| "comet_cb = CometCallback() # Build the callback \n", | ||
| "\n", | ||
| "# SetUp the callback \n", | ||
| "comet_cb.setup(\n", | ||
| " training_config=training_config, # training config\n", | ||
| " model_config=model_config, # model config\n", | ||
| " api_key=\"your_comet_api_key\", # specify your comet api-key\n", | ||
| " project_name=\"your_comet_project\", # specify your wandb project\n", | ||
| " #offline_run=True, # run in offline mode\n", | ||
| " #offline_directory='my_offline_runs' # set the directory to store the offline runs\n", | ||
| ")\n", | ||
| "\n", | ||
| "callbacks.append(comet_cb) # Add it to the callbacks list" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "pipeline = TrainingPipeline(\n", | ||
| " training_config=training_config,\n", | ||
| " model=model\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "pipeline(\n", | ||
| " train_data=train_dataset,\n", | ||
| " eval_data=eval_dataset,\n", | ||
| " callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!\n", | ||
| ")\n", | ||
| "# You can log to https://comet.com/your_comet_username/your_comet_project to monitor your training" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Or you can alternatively ability to view the Comet UI in the jupyter notebook\n", | ||
| "import comet_ml\n", | ||
| "\n", | ||
| "experiment = comet_ml.get_global_experiment()\n", | ||
| "experiment.display()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "interpreter": { | ||
| "hash": "3efa06c4da850a09a4898b773c7e91b0da3286dbbffa369a8099a14a8fa43098" | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": "Python 3.8.11 64-bit ('pythae_dev': conda)", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.13" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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