Cortex makes deploying, scaling, and managing machine learning systems in production simple. We believe that developers in any organization should be able to add natural language processing, computer vision, and other machine learning capabilities to their applications without having to worry about infrastructure.

How it works:

Implement your predictor in predictor.py, configure your deployment in cortex.yaml, and run cortex deploy.

Here's how to deploy GPT-2 as a scalable text generation API:

demo

Don't forget to tag @cortexlabs in your comment, otherwise they may not be notified.

Authors community post
Share this project
Similar projects
TensorFlow Serving
A flexible, high-performance serving system for machine learning models, designed for production environments.
BentoML
BentoML is an open-source framework for high-performance ML model serving.
Deploying your ML Model with TorchServe
In this talk, Brad Heintz walks through how to use TorchServe to deploy trained models at scale without writing custom code.
Efficient Serverless Deployment of PyTorch Models on Azure
A tutorial for serving models cost-effectively at scale using Azure Functions and ONNX Runtime.