Documenting PyTorch support

docs/en/1-Experiments/Kubeflow.md

@@ -54,7 +54,7 @@ You will need to choose an image. There are JupyterLab, RStudio, Ubuntu remote
 desktop, and SAS images available. The SAS image is only available for StatCan
 employees (due to license limitations), the others are available for everyone.
 Select the drop down menu to select additional options within these (for
-instance, CPU, PyTorch, and TensorFlow images for JupyterLab).
+instance, CPU, and TensorFlow images for JupyterLab).
 
 Check the name of the images and choose one that matches what you want to do. Don't know
 which one to choose? Check out your options [here](./Selecting-an-Image.md).

docs/en/1-Experiments/Selecting-an-Image.md

@@ -21,11 +21,20 @@ When selecting an image, you have 3 main options:
     data transformation, numerical simulation, statistical
     modelling, machine learning and more.
 
-The jupyter notebooks are great launchpads for analytics including machine learning. The `jupyterlab-cpu` image gives a good core experience for python, including common packages such as `numpy`, `pandas` and `scikit-learn`. If you're interested specifically in using **_TensorFlow_** or **_PyTorch_**, we also have `jupyterlab-tensorflow` and `jupyterlab-pytorch` which come with those tools pre-installed.
+The jupyter notebooks are great launchpads for analytics including machine learning. The `jupyterlab-cpu` image gives a good core experience for python, including common packages such as `numpy`, `pandas` and `scikit-learn`. If you're interested specifically in using **_TensorFlow_**, we also have `jupyterlab-tensorflow` which come with those tools pre-installed.
 
-For the `jupyterlab-pytorch` image, the PyTorch packages (torch, torchvision, and torchaudio) are installed in the `torch` conda environment. You must activate this environment to use PyTorch.
+For users interested in working with PyTorch, there is support for quickly installing the required packaged.
+In the terminal on your created notebook, run the following command:
+`mamba clean -a -y && mamba create -n torch && mamba install -n torch -c conda-forge -c pytorch -c nvidia -y --file /usr/local/bin/requirements.txt`
+The PyTorch packages (torch, torchvision, and torchaudio) will be installed in the `torch` conda environment.
+You must activate this environment using `mamba activate torch` to use PyTorch.
+The environment can also be added to the to the JupyterLab Launcher with the following command:
 
-For the `jupyterlab-cpu`, `jupyterlab-tensorflow`, and `jupyterlab-pytorch` images, in the default shell the `conda activate` command may not work. This is due to the environment not being initialized properly. In this case run `bash`, you should see the AAW logo and a few instructions appear. After this `conda activate` should work properly. If you see the AAW logo on startup it means the environment is correctly initialized and `conda activate` should work properly. A fix for this bug is in the works, once this is fixed this paragraph will be removed.
+```bash
+python -m ipykernel install --user --name "torch" --display-name "PyTorch"
+```
+
+For the `jupyterlab-cpu`, and `jupyterlab-tensorflow` images, in the default shell the `conda activate` command may not work. This is due to the environment not being initialized properly. In this case run `bash`, you should see the AAW logo and a few instructions appear. After this `conda activate` should work properly. If you see the AAW logo on startup it means the environment is correctly initialized and `conda activate` should work properly. A fix for this bug is in the works, once this is fixed this paragraph will be removed.
 
 Each image comes pre-loaded with VS Code in the browser if you prefer a full IDE experience.