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Dockerfile (528, 2022-10-07)
LICENSE (1063, 2022-10-07)
app.py (1359, 2022-10-07)
client.py (270, 2022-10-07)
download.py (702, 2022-10-07)
requirements.txt (38, 2022-10-07)
server.py (1271, 2022-10-07)
test.py (272, 2022-10-07)

# 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](https://huggingface.co/EleutherAI/gpt-j-6B) 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: 3. 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: - Log in to the [Banana App](https://app.banana.dev) - Select your customized repo for deploy! It'll then be built from the dockerfile, optimized, then deployed on our Serverless GPU cluster and callable with any of our SDKs: - [Python](https://github.com/bananaml/banana-python-sdk) - [Node JS / Typescript](https://github.com/bananaml/banana-node-sdk) - [Go](https://github.com/bananaml/banana-go) You can monitor buildtime and runtime logs by clicking the logs button in the model view on the Banana Dashboard](https://app.banana.dev)
## Use Banana for scale.

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