Utilizing Terraform, Docker, FastAPI, and PyTorch to Deploy a Custom PyTorch Model on SageMaker

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Deploying A Custom Pytorch Model to SageMaker using Terraform, Docker, FastAPI and Pytorch

Deploying A Custom Pytorch Model to SageMaker using Terraform, Docker, FastAPI and Pytorch

In this article, we will walk you through the process of deploying a custom Pytorch model to Amazon SageMaker using Terraform, Docker, FastAPI, and Pytorch.

Step 1: Prepare Your Pytorch Model

First, you need to train and save your custom Pytorch model. Make sure to save the model weights and any necessary files.

Step 2: Create a FastAPI Service

Next, you will create a FastAPI service that will serve as the interface between your Pytorch model and SageMaker. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python.

Step 3: Build a Docker Image

Create a Dockerfile that installs all the necessary dependencies for your FastAPI service and Pytorch model. This Docker image will be used to run your service in SageMaker.

Step 4: Use Terraform to Deploy to SageMaker

Use Terraform to create the necessary resources in SageMaker to deploy your custom Pytorch model. This may include creating a model, endpoint configuration, and endpoint.

Step 5: Test Your Deployment

Once your model is deployed to SageMaker, you can test it using the SageMaker API or by sending requests to the endpoint you created.

Conclusion

Deploying a custom Pytorch model to SageMaker using Terraform, Docker, FastAPI, and Pytorch can be a complex process, but with the right tools and steps, you can successfully deploy and test your model in a production environment.

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@Tetrax
7 months ago

Hi, Thank you so much for making this tutorial. I have a question, I see that you have defined a "/invocations" endpoint, is it possible to have multiple post/get endpoints and use it? And I have a pretrained CLIP model from transformers library Im using. Essentially my endpoint would take in a video, read frame by frame and insert these embeddings into a vector database. Currently I have it wrapped in a fastapi application deployed to a CPU instance on EC2. My application would benefit a ton from having a GPU and I was wondering if there are any other ways to deploy this on a GPU instance or any recommendations at all :)) I would really appreciate your input!

@aminasgharisooreh9243
7 months ago

thank you, please continue producing videos

@samsantechsamsan9024
7 months ago

How to make the final request via postman?

@huytube6
7 months ago

Great content! However, it would be even more helpful if you could provide detailed explanations of the steps involved in configuring AWS IAM for Terraform..