✨ Add `.devcontainer` Configuration for TorchServe Development Environment

yhna940 commented 1 month ago

Description

This PR introduces a .devcontainer/devcontainer.json configuration for setting up a Docker-based development environment in VS Code, specifically for TorchServe. Many libraries, such as Accelerate and PyTorch, support similar Dev Container environments, allowing for streamlined development with a consistent toolchain across contributors and platforms.

Type of change

Please delete options that are not relevant.

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[x] New feature (non-breaking change which adds functionality)
[ ] This change requires a documentation update

Feature/Issue validation/testing

Visual Studio Code Dev Containers

The Dev Containers extension in VS Code allows developers to use a Docker container as a fully-featured development environment. This provides a reproducible, isolated setup that can mirror production configurations, enabling:

Consistent toolchain and OS across development and deployment.
Easy swapping between environments, minimizing local machine impacts.
Faster onboarding for new team members with a ready-to-go environment.
Safe exploration of new technologies or codebases without local setup risks.

Once initialized, VS Code operates as if all tools and files were local, ensuring a seamless workflow.

Install the Dev Containers Extension
In Visual Studio Code, search for "Dev Containers" in the Extensions marketplace and install it.

Open in Container
After installation, you will see a pop-up similar to the one in the screenshot. Click the "Reopen in Container" button to start the development environment inside a Docker container.

If you want to customize the development environment further, modify the appropriate fields in .devcontainer/devcontainer.json. For example, to use a GPU-based container, you can adjust the configuration as shown below:

{
    "name": "TorchServe Dev Environment",
    "build": {
        "dockerfile": "../docker/Dockerfile",
        "context": "..",
        "args": {
            "BASE_IMAGE": "nvidia/cuda:12.1.1-base-ubuntu20.04",
            "PYTHON_VERSION": "3.9",
            "BRANCH_NAME": "master",
            "REPO_URL": "https://github.com/pytorch/serve.git",
            "CUDA_VERSION": "cu121"
        }
    },
    "customizations": {
        "vscode": {
            "extensions": [
                "ms-python.python"
            ]
        }
    },
    "runArgs": [
        "--gpus", "all",
        "--ipc", "host"
    ]
}

With this setup, you can run a GPU-enabled TorchServe container for accelerated model serving.

Checklist:

[x] Did you have fun?
[ ] Have you added tests that prove your fix is effective or that this feature works?
[ ] Has code been commented, particularly in hard-to-understand areas?
[ ] Have you made corresponding changes to the documentation?

mreso commented 1 month ago

Hi @yhna940 thanks for the contribution! Could you add the same description you added to the PR to serve/docker/README.md and also add a line referring to that section into serve/CONTRIBUTING.md? Thank would be very helpful for users to discover the feature.

yhna940 commented 1 month ago

Hi @yhna940 thanks for the contribution! Could you add the same description you added to the PR to serve/docker/README.md and also add a line referring to that section into serve/CONTRIBUTING.md? Thank would be very helpful for users to discover the feature.

Hi @mreso , thank you for the review! I have updated the files as requested. Please let me know if there are any additional changes needed :)

yhna940 commented 4 days ago

Hello @mreso, I hope you're doing well! I wanted to follow up on the status of this PR. Are there any additional changes or updates you'd like me to make? Please let me know if there's anything else I can do to help move this forward. Thank you for your time and feedback! 😊

pytorch / serve