/serverless-template-gptj

test

Primary LanguagePythonMIT LicenseMIT

🍌 Banana Serverless

This repo gives a basic framework for serving ML models in production using simple HTTP servers.

Quickstart:

The repo is already set up to run a basic HuggingFace GPTJ model.

  1. Run pip3 install -r requirements.txt to download dependencies.
  2. Run python3 server.py to start the server.
  3. Run python3 test.py in a different terminal session to test against it.

Make it your own:

  1. Edit app.py to load and run your model.
  2. Make sure to test with test.py!

if deploying using Docker:

  1. Edit download.py (or the Dockerfile itself) with scripts download your custom model weights at build time.

Move to prod:

At this point, you have a functioning http server for your ML model. You can use it as is, or package it up with our provided Dockerfile and deploy it to your favorite container hosting provider!

If Banana is your favorite GPU hosting provider (and we sure hope it is), read on!

🍌

Deploy to Banana Serverless:

Three steps:

  1. Create your own copy of this template repo. Either:
  • Click "Fork" on this repo (creates a public repo)
  • Create your own repo and copy the template files into it (to create a private repo)

  1. Install the Banana Github App to your new repo.

  2. Login in to the Banana Dashboard and setup your account by saving your payment details and linking your Github.

From then onward, any pushes to the default repo branch (usually "main" or "master") trigger Banana to build and deploy your server, using the Dockerfile. Throughout the build we'll sprinkle in some secret sauce to make your server extra snappy 🔥

It'll then be deployed on our Serverless GPU cluster and callable with any of our serverside SDKs:

View deployment progress on our dashboard


Use Banana for scale.