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Deploy Hugging Face Models to AWS Lambda in teps

Linda Hamilton
Release: 2024-11-29 21:24:11
Original
791 people have browsed it

Ever wanted to deploy a Hugging Face model to AWS Lambda but got stuck with container builds, cold starts, and model caching? Here's how to do it in under 5 minutes using Scaffoldly.

TL;DR

  1. Create an EFS filesystem named .cache in AWS:

    • Go to AWS EFS Console
    • Click "Create File System"
    • Name it .cache
    • Select any VPC (Scaffoldly will take care of the rest!)
  2. Create your app from the python-huggingface branch:

     npx scaffoldly create app --template python-huggingface
    
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  3. Deploy it:

     cd my-app && npx scaffoldly deploy
    
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That's it! You'll get a Hugging Face model running on Lambda (using openai-community/gpt2 as an example), complete with proper caching and container deployment.

Pro-Tip: For the EFS setup, you can customize it down to a Single AZ in Burstable mode for even more cost savings. Scaffoldly will match the Lambda Function to the EFS's VPC, Subnets, and Security Group.

✨ Check out the Live Demo and the example code!

The Problem

Deploying ML models to AWS Lambda traditionally involves:

  • Building and managing Docker containers
  • Figuring out model caching and storage
  • Dealing with Lambda's size limits
  • Managing cold starts
  • Setting up API endpoints

It's a lot of infrastructure work when you just want to serve a model!

The Solution

Scaffoldly handles all this complexity with a simple configuration file. Here's a complete application that serves a Hugging Face model (using openai-community/gpt2 as an example):

# app.py
from flask import Flask
from transformers import pipeline
app = Flask(__name__)
generator = pipeline('text-generation', model='openai-community/gpt2')
@app.route("/")
def hello_world():
    output = generator("Hello, world,")
    return output[0]['generated_text']
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// requirements.txt
Flask ~= 3.0
gunicorn ~= 23.0
torch ~= 2.5
numpy ~= 2.1
transformers ~= 4.46
huggingface_hub[cli] ~= 0.26
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// scaffoldly.json
{
  "name": "python-huggingface",
  "runtime": "python:3.12",
  "handler": "localhost:8000",
  "files": ["app.py"],
  "packages": ["pip:requirements.txt"],
  "resources": ["arn::elasticfilesystem:::file-system/.cache"],
  "schedules": {
    "@immediately": "huggingface-cli download openai-community/gpt2"
  },
  "scripts": {
    "start": "gunicorn app:app"
  },
  "memorySize": 1024
}
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How It Works

Scaffoldly does some clever things behind the scenes:

  1. Smart Container Building:

    • Automatically creates a Docker container optimized for Lambda
    • Handles all Python dependencies including PyTorch
    • Pushes to ECR without you writing any Docker commands
  2. Efficient Model Handling:

    • Uses Amazon EFS to cache the model files
    • Pre-downloads models after deployment for faster cold starts
    • Mounts the cache automatically in Lambda
  3. Lambda-Ready Setup:

    • Rus up a proper WSGI server (gunicorn)
    • Creates a public Lambda Function URL
    • Proxies Function URL requests to gunicorn
    • Manages IAM roles and permissions

What deploy looks like

Here's output from a npx scaffoldly deploy command I ran on this example:

Deploy Hugging Face Models to AWS Lambda in teps

Real World Performance & Costs

Costs: ~$0.20/day for AWS Lambda, ECR, and EFS

Cold Start: ~20s for first request (model loading)

Warm Requests: 5-20s (CPU-based inference)

While this setup uses CPU inference (which is slower than GPU), it's an incredibly cost-effective way to experiment with ML models or serve low-traffic endpoints.

Customizing for Other Models

Want to use a different model? Just update two files:

  1. Change the model in app.py:
 npx scaffoldly create app --template python-huggingface
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  1. Update the download in scaffoldly.json:
 cd my-app && npx scaffoldly deploy
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Using Private or Gated Models

Scaffoldly supports private and gated models via the HF_TOKEN environment variable. You can add your Hugging Face token in several ways:

  • Local Development: Add to your shell profile (.bashrc, .zprofile, etc.):
# app.py
from flask import Flask
from transformers import pipeline
app = Flask(__name__)
generator = pipeline('text-generation', model='openai-community/gpt2')
@app.route("/")
def hello_world():
    output = generator("Hello, world,")
    return output[0]['generated_text']
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  • CI/CD: Add as a GitHub Actions Secret:
// requirements.txt
Flask ~= 3.0
gunicorn ~= 23.0
torch ~= 2.5
numpy ~= 2.1
transformers ~= 4.46
huggingface_hub[cli] ~= 0.26
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The token will be automatically used for both downloading and accessing your private or gated models.

CI/CD Bonus

Scaffoldly even generates a GitHub Action for automated deployments:

// scaffoldly.json
{
  "name": "python-huggingface",
  "runtime": "python:3.12",
  "handler": "localhost:8000",
  "files": ["app.py"],
  "packages": ["pip:requirements.txt"],
  "resources": ["arn::elasticfilesystem:::file-system/.cache"],
  "schedules": {
    "@immediately": "huggingface-cli download openai-community/gpt2"
  },
  "scripts": {
    "start": "gunicorn app:app"
  },
  "memorySize": 1024
}
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Try It Yourself

The complete example is available on GitHub:
scaffoldly/scaffoldly-examples#python-huggingface

And you can create your own copy of this example by running:

generator = pipeline('text-generation', model='your-model-here')
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You can see it running live (though responses might be slow due to CPU inference):
Live Demo

What's Next?

  • Try deploying different Hugging Face models
  • Join the Scaffoldly Community on Discord
  • Check out other examples
  • Star our repos if you found this useful!
    • The scaffoldly toolchain
    • The Scaffoldly Examples repository

Licenses

Scaffoldly is Open Source, and welcome contributions from the community.

  • The examples are licensed with the Apache-2.0 license.
  • The scaffoldly toolchain is licensed with the FSL-1.1-Apache-2.0 license.

What other models do you want to run in AWS Lambda? Let me know in the comments!

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