Virtual Environment¶

While the Docker images you will be using to run experiments on Run:ai would contain the conda environments you would need, you can also create these virtual environments within your development environment, and have it be persisted. The following set of commands allows you to create the conda environment and store the packages within your own workspace directory:

• First, have VSCode open the repository that you have cloned previously by heading over to the top left hand corner, selecting File > Open Folder..., and entering the path to the repository. In this case, you should be navigating to the folder /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/{{cookiecutter.repo_name}}.

• Now, let's initialise conda for the bash shell, and create the virtual environment specified in {{cookiecutter.repo_name}}-conda-env.yaml.

(base) $ conda env create -f {{cookiecutter.repo_name}}-conda-env.yaml \
            -p /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}-conda-env

• After creating the conda environment, let's create a permanent alias for easy activation.

(base) $ echo 'alias {{cookiecutter.repo_name}}-conda-env="conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}-conda-env"' >> ~/.bashrc
(base) $ source ~/.bashrc
(base) $ {{cookiecutter.repo_name}}-conda-env
({{cookiecutter.repo_name}}-conda-env) $ # conda environment has been activated

Reference(s):

• conda Docs - Managing environments
• StackOverflow - "Pip install killed - out of memory - how to get around it?"
• phoenixNAP - Linux alias Command: How to Use It With Examples

Jupyter Kernel for VSCode¶

While it is possible for VSCode to make use of different virtual Python environments, some other additional steps are required for the VSCode server to detect the conda environments that you would have created.

• Ensure that you are in a project folder which you intend to work on. You can open a folder through File > Open Folder.... In this case, you should be navigating to the folder /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/{{cookiecutter.repo_name}}.

• Install the VSCode extensions ms-python.python and ms-toolsai.jupyter. After installation of these extensions, restart VSCode by using the shortcut Ctrl + Shift + P, entering Developer: Reload Window in the prompt and pressing Enter following that.

• Ensure that you have ipykernel installed in the conda environment that you intend to use. This template by default lists the library as a dependency under {{cookiecutter.repo_name}}-conda-env.yaml. You can check for the library like so:

$ conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}-conda-env
$ conda list | grep "ipykernel"
ipykernel  X.X.X  pypi_0  pypi

• Now enter Ctrl + Shift + P again and execute Python: Select Interpreter. Provide the path to the Python executable within the conda environment that you intend to use, something like so: path/to/conda_env/bin/python.

• Open up any Jupyter notebook and click on the button that says Select Kernel on the top right hand corner. You will be presented with a selection of Python interpreters. Select the one that corresponds to the environment you intend to use.

• Test out the kernel by running the cells in the sample notebook provided under notebooks/sample-pytorch-notebook.ipynb.

Jupyter Kernel for JupyterLab¶

The same with the VSCode server, the JupyterLab server would not by default detect conda environments. You would have to specify to the JupyterLab installation the ipython kernel existing within your conda environment.

• Open up a terminal within JupyterLab.

• Activate the conda environment in question and ensure that you have ipykernel installed in the conda environment that you intend to use. This template by default lists the library as a dependency under {{cookiecutter.repo_name}}-conda-env.yaml. You can check for the library like so:

$ conda activate /<NAME_OF_DATA_SOURCE>/workspaces/<YOUR_HYPHENATED_NAME>/conda_envs/{{cookiecutter.repo_name}}-conda-env
$ conda list | grep "ipykernel"
ipykernel  6.9.2  pypi_0  pypi

• Within the conda environment, execute the following:

$ ipython kernel install --name "{{cookiecutter.repo_name}}-conda-env" --user

• Refresh the page.

• Open up the sample notebook provided under notebooks/sample-pytorch-notebook.ipynb.

• Within each Jupyter notebook, you can select the kernel of specific conda environments that you intend to use by heading to the toolbar under Kernel -> Change Kernel....

• Test out the kernel by running the cells in the sample notebook.

Reference(s):

• Jupyter Docs - Kernels (Programming Languages)