Deploy ANY Open Source LLM on AWS? (Easily) -Anktechsol
Deploying open-source Language Learning Models (LLMs) on AWS can be a straightforward process if you know the right approach. This guide will walk you through several methods, each with varying levels of complexity, to help you deploy your LLM efficiently.
1. Use as an API on Amazon Bedrock
If your LLM is hosted on Amazon Bedrock, such as LLAMA2–13B, you can use it directly via an API without the need for deployment. This method is the simplest and quickest way to leverage your LLM.
Steps:
- Access the Amazon Bedrock service.
- Use the provided API to integrate the LLM into your application.
2. Deploy from Amazon SageMaker Jumpstart
Amazon SageMaker Jumpstart offers a variety of popular open-source LLMs. The Jumpstart team has prepared deployment scripts verified on different instance types, using an optimized serving stack. This ensures a smooth deployment process.
Steps:
- Search for the desired model in Jumpstart.
- Deploy the model from the SageMaker Studio console or run the included iPython notebook.
Details:
- Search Models in Jumpstart: Use the SageMaker Studio to browse available models.
- Deploy a Model: Follow the instructions provided in the notebook or console to deploy your model.
3. Deploy Using Example Notebooks in DeepJava Library
The DeepJava Library provides sample scripts to deploy popular models on Amazon SageMaker. These scripts utilize high-performance containers and offer various hyper-parameter choices.
Steps:
- Access the DeepJava Library.
- Select the relevant example notebook for your model.
- Run the notebook to deploy the model on SageMaker.
Details:
- High-Performance Containers: Ensure optimal performance with pre-configured containers.
- Hyper-Parameter Choices: Customize your deployment with different parameters
4. Deploy Using `deploy.py` from Hugging Face Hub
For models available on the Hugging Face hub, you can use the `deploy.py` script. Visit the model card on Hugging Face, click "Deploy -> Amazon SageMaker," and you'll get a `deploy.py` code snippet.
Steps:
- Visit the model card on Hugging Face.
- Click "Deploy -> Amazon SageMaker" to generate the `deploy.py` script.
- Adjust deployment details such as the target instance type, number of GPUs, and IAM roles.
Details:
- Instance Type: Choose an instance type suitable for your model's requirements (e.g., g5.12xlarge for a 40B model).
- CUDA Version: Ensure compatibility with the required CUDA version. If needed, extend the Deep Learning Container (DLC) or bring your own container.
Example API Parameters for Inference:
```json{"inputs": "write a snake game","parameters": {"max_new_tokens": 512}}```
5. Bring Your Own Container to SageMaker Inference
If your model isn’t on Hugging Face or requires a special performant container like vLLM, you can bring your own container into SageMaker Inference. Ensure your container responds to calls on `:8080/invocations` and returns `200 OK` to `:8080/ping`.
Steps:
- Create a Docker container with your model.- Ensure the container listens and responds to the specified endpoints.- Deploy the container on SageMaker Inference.
Details:
- Custom Containers: Use your own Docker container to meet specific performance and compatibility needs.- Endpoint Configuration: Configure your container to handle inference requests and health checks.
Conclusion:
Deploying open-source LLMs on AWS can be done using various methods, from simple API integrations to more complex custom container deployments. By understanding the specific needs of your project and choosing the appropriate approach, you can efficiently leverage AWS services to deploy and run your LLMs. Whether you opt for Amazon Bedrock, SageMaker Jumpstart, example notebooks, the Hugging Face Hub, or your own container, AWS provides robust solutions to meet your deployment needs.