COG is an open-source tool designed to package machine learning models into standard, production-ready containers. It simplifies the process of deploying machine learning models, whether on your own infrastructure or on Replicate.
Here are four key features of COG
- Simplified Docker Containers: COG simplifies the process of creating Docker containers for machine learning models. It uses a simple configuration file to define your environment and generates a Docker image incorporating best practices.
- CUDA Compatibility: COG understands the compatibility between different versions of CUDA, cuDNN, PyTorch, Tensorflow, and Python, and sets them up correctly for you, eliminating the complexities of managing these dependencies.
- Standard Python Inputs and Outputs: With COG, you can define the inputs and outputs for your model using standard Python. COG then generates an OpenAPI schema and validates the inputs and outputs with Pydantic.
- Automatic HTTP Prediction Server: COG uses your model’s types to dynamically generate a RESTful HTTP API using FastAPI. This allows for easy integration of your model into web applications and services.