Merge branch 'master' of github.com:Linardos/GaNDLF

docs/customize.md

@@ -120,9 +120,9 @@ This file contains mid-level information regarding various parameters that can b
 - These are various parameters that control the overall training process.
 - `verbose`: generate verbose messages on console; generally used for debugging.
 - `batch_size`: batch size to be used for training.
-- `in_memory`: this is to enable or disable lazy loading. If set to True, all data is loaded on RAM once during data loading, resulting in faster training. If set to False, data gets read into RAM on-the-go when needed, which slows down training but lessens the memory load. The latter is recommended if the user's RAM has limited capacity.
+- `in_memory`: this is to enable or disable lazy loading. If set to `True`, all data is loaded onto the RAM at once during the construction of the dataloader (either training/validation/testing), thus resulting in faster training. If set to `False`, data gets read into RAM on-the-go when needed (also called ["lazy loading"](https://en.wikipedia.org/wiki/Lazy_loading)), which slows down training but lessens the memory load. The latter is recommended if the user's RAM has limited capacity.
 - `num_epochs`: number of epochs to train for.
-- `patience`: number of epochs to wait for improvement before early stopping.
+- `patience`: number of epochs to wait for improvement in the validation loss before early stopping.
 - `learning_rate`: learning rate to be used for training.
 - `scheduler`: learning rate scheduler to be used for training, more details are [here](https://github.com/mlcommons/GaNDLF/blob/master/GANDLF/schedulers/__init__.py); can take the following sub-parameters:
     - `type`: `triangle`, `triangle_modified`, `exp`, `step`, `reduce-on-plateau`, `cosineannealing`, `triangular`, `triangular2`, `exp_range`

docs/setup.md

@@ -32,70 +32,58 @@ First, instantiate your environment
 (venv_gandlf) $> ### subsequent commands go here
 ```
 
-You may install pytorch to be compatible with CUDA, ROCm, or CPU-only. An exhaustive list of PyTorch installations can be found here: https://pytorch.org/get-started/previous-versions/#v210
+You may install pytorch to be compatible with CUDA, ROCm, or CPU-only. An exhaustive list of PyTorch installations for the specific version compatible with GaNDLF can be found here: https://pytorch.org/get-started/previous-versions/#v231
 Use one of the following depending on your needs:
 - CUDA 12.1
 ```bash
- pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121
-```
-- CUDA 11.8
-```bash
- pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu118
-```
-- ROCm 5.7
-```bash
- pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/rocm5.7
-```
-- CPU-only
-```bash
- pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cpu
+(venv_gandlf) $> pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
 ```
 
 ### Optional Dependencies 
 
 The following dependencies are **optional**, and are only needed to access specific features of GaNDLF.
 
 ```bash
-pip install openvino-dev==2023.0.1 # [OPTIONAL] to generate post-training optimized models for inference
-pip install mlcube_docker # [OPTIONAL] to deploy GaNDLF models as MLCube-compliant Docker containers
+(venv_gandlf) $> pip install openvino-dev==2023.0.1 # [OPTIONAL] to generate post-training optimized models for inference
+(venv_gandlf) $> pip install mlcube_docker # [OPTIONAL] to deploy GaNDLF models as MLCube-compliant Docker containers
 ```
 
 ### Install from Package Managers
 
 This option is recommended for most users, and allows for the quickest way to get started with GaNDLF.
 
 ```bash
-pip install gandlf # this will give you the latest stable release
+(venv_gandlf) $> pip install gandlf # this will give you the latest stable release
 ```
 You can also use conda
 ```bash
-conda install -c conda-forge gandlf -y
+(venv_gandlf) $> conda install -c conda-forge gandlf -y
 ```
 
 If you are interested in running the latest version of GaNDLF, you can install the nightly build by running the following command:
 
 ```bash
-pip install --pre gandlf
+(venv_gandlf) $> pip install --pre gandlf
 ```
 
 You can also use conda
 ```bash
-conda install -c conda-forge/label/gandlf_dev -c conda-forge gandlf -y
+(venv_gandlf) $> conda install -c conda-forge/label/gandlf_dev -c conda-forge gandlf -y
 ```
 
 ### Install from Sources
 
 Use this option if you want to [contribute to GaNDLF](https://github.com/mlcommons/GaNDLF/blob/master/CONTRIBUTING.md), or are interested to make other code-level changes for your own use.
 
 ```bash
-git clone https://github.com/mlcommons/GaNDLF.git
-cd GaNDLF
-pip install -e .
+(venv_gandlf) $> git clone https://github.com/mlcommons/GaNDLF.git
+(venv_gandlf) $> cd GaNDLF
+(venv_gandlf) $> pip install -e .
 ```
 
 Test your installation:
 ```bash
-gandlf --version
+(venv_gandlf) $> gandlf verify-install
 ```
 
 ## Docker Installation