README_template.md

Name_of_Your_Project

Setting up

• Using conda

  # create env
  conda env create --file docker/environment.yml
  
  # activate it
  conda activate NAMEOFYOURPROJECT
  
  # install this repo
  (NAMEOFYOURPROJECT) $ pip install -e .
  

• Using docker

  # pull image with [azureml image](https://hub.docker.com/_/microsoft-azureml?tab=description) as base with docker/environment.yml on top
  docker pull NAMEOFYOURPROJECT:latest
  
  # pull image with nvidia pytorch image as base
  # docker pull NAMEOFYOURPROJECT:latest-nvidia
  
  # run image
  docker run -it --gpus=all -v <PATH_TO_THIS_REPO>:<NAMEOFYOURPROJECT:latest
  
  # setup the repo (run inside the container)
  pip install -e .
  

• VSCode + Docker

  • Using Devcontainer

    • Connect to your remote Azure VM using VS Code

    • Open the workspace within a docker container for development, either using the popup as shown in the animation above, or by searching for (Re)Build and (Re)open in container in the command palette (hit Ctrl+Shift+P to open the command palette)

    • After setup is complete, it is time to set up the repository:

        pip install -e .
        pre-commit install
      

    • Note: By default, the devcontainer uses the azureml-conda base image. We can also use the nvidia base image by modifying the dockerfile line in devcontainer.json. Similarly, we can edit the docker files build argument therein itself.

  • Attach to a docker container

Running the code