PyDocker

A simple Python Docker workspace template for developing Python projects within Docker containers.

You could use this template to start/containerize a development environment for your Python project.

Usage

1. Congfigure

   docker-compose.yml

   build:
     context: .
     dockerfile: Dockerfile
     args:
       PROJECT_NAME: "my-project" # Name you project and  init for you
       UBUNTU_VERSION: "22.04"    # Choose ubuntu version
       PYTHON_VERSION: "3.10"     # Choose python version
   develop:
     watch:
       - action: sync
         path: ./my-project       # Don't forget to modify the default folder name to your project name here!

2. Start the development environment:

   $ docker compose up -d 

   Now you will find that your {PROJECT_NAME} folder appears in the current directory and the container is running.

   From,

   .
   ├── ...
   ├── Dockerfile
   ├── docker-compose.yml
   

   to,

   .
   ├── ...
   ├── {PROJECT_NAME}
       # See more details in https://docs.astral.sh/uv/concepts/projects/init/#applications.
       ├── (Default uv project structure)
   ├── Dockerfile
   ├── docker-compose.yml
   

3. Access docker container terminal with:

   $ docker exec -it dev /bin/bash

   Exit terminal with CTRL + D.

4. Stop docker container with:

   $ docker compose down

5. Add packages

   • See more details in UV Document.

   e.g. Install cuda available torch:

   $ uv lock --index https://download.pytorch.org/whl/cu118
   $ uv add numpy==1.26.4  torch==2.1.2

   P.S. Torch-CUDA-PYTHON compatible version table

Q&A

1. How to use GPU in container?

   For gpu usage, ensure your system meets the following requirements:

   1. Windows users need to enable WSL

      To run Docker with GPU support on Windows, you must enable Windows Subsystem for Linux (WSL).

      Follow the instructions here: Setting up WSL.

   2. Install Docker

      Windows users: Download and install Docker Desktop.

      Linux users: Install Docker Engine following the instructions here.

   3. Install CUDA Toolkit

      Ensure that your system has the CUDA Toolkit installed, which is necessary for GPU acceleration.

      Refer to the guide: Installing CUDA Toolkit.

   4. Install NVIDIA Container Toolkit

      This is required to enable Docker containers to use the GPU.

      Follow the instructions to install the NVIDIA Container Toolkit here.

   5 Test your gpu util with test_gpu_util.py.

2. Do I need to reinstall packages in the container once it closed?

   No need. The packages you intalled in the container will still be remained. All packages will be kept in the {your-project-name}/.venv which will be mounted to the container.