vLLM Endpoint | Serverless Worker

 

🚀 | This serverless worker utilizes vLLM (very Large Language Model) behind the scenes and is integrated into RunPod's serverless environment. It supports dynamic auto-scaling using the built-in RunPod autoscaling feature.

Docker Arguments:

1. HUGGING_FACE_HUB_TOKEN: Your private Hugging Face token. This token is required for downloading models that necessitate agreement to an End User License Agreement (EULA), such as the llama2 family of models.
2. MODEL_NAME: The Hugging Face model to use. Please ensure that the chosen model is supported by vLLM. Refer to the list of supported models for compatibility.
3. TOKENIZER: (Optional) The specified tokenizer to use. If you want to use the default tokenizer for the model, do not provide this docker argument at all.
4. STREAMING: Whether to use HTTP Streaming or not. Specify True if you want to enable HTTP Streaming; otherwise, omit this argument.

llama2 7B Chat:

docker build . --platform linux/amd64 --build-arg HUGGING_FACE_HUB_TOKEN=your_hugging_face_token_here --build-arg MODEL_NAME=meta-llama/Llama-2-7b-chat-hf --build-arg TOKENIZER=hf-internal-testing/llama-tokenizer --build-arg STREAMING=True

llama2 13B Chat:

docker build . --platform linux/amd64 --build-arg HUGGING_FACE_HUB_TOKEN=your_hugging_face_token_here --build-arg MODEL_NAME=meta-llama/Llama-2-13b-chat-hf --build-arg TOKENIZER=hf-internal-testing/llama-tokenizer --build-arg STREAMING=True

Please make sure to replace your_hugging_face_token_here with your actual Hugging Face token to enable model downloads that require it.

Ensure that you have Docker installed and properly set up before running the docker build commands. Once built, you can deploy this serverless worker in your desired environment with confidence that it will automatically scale based on demand. For further inquiries or assistance, feel free to contact our support team.

📖 | Getting Started

1. Clone this repository.
2. (Optional) Add DockerHub credentials to GitHub Secrets.
3. Add your code to the src directory.
4. Update the handler.py file to load models and process requests.
5. Add any dependencies to the requirements.txt file.
6. Add any other build time scripts to thebuilder directory, for example, downloading models.
7. Update the Dockerfile to include any additional dependencies.

CI/CD

This repository is setup to automatically build and push a docker image to the GitHub Container Registry. You will need to add the following to the GitHub Secrets for this repository to enable this functionality:

• DOCKERHUB_USERNAME | Your DockerHub username for logging in.
• DOCKERHUB_TOKEN | Your DockerHub token for logging in.
• DOCKERHUB_REPO | The name of the repository you want to push to.
• DOCKERHUB_IMG | The name of the image you want to push to.

The CD-docker_dev.yml file will build the image and push it to the dev tag, while the CD-docker_release.yml file will build the image on releases and tag it with the release version.

The CI-test_worker.yml file will test the worker using the input provided by the --test_input argument when calling the file containing your handler. Be sure to update this workflow to install any dependencies you need to run your tests.

💡 | Best Practices

System dempendency installation, model caching, and other shell tasks should be added to the builder/setup.sh this will allow you to easily setup your Dockerfile as well as run CI/CD tasks.

Models should be part of your docker image, this can be accomplished by either copying them into the image or downloading them during the build process.

If using the input validation utility from the runpod python package, create a schemas python file where you can define the schemas, then import that file into your handler.py file.

🔗 | Links

🐳 Docker Container