Summary

You define your entire environment within a simple, easy-to-read configuration file that includes optimized Nvidia base images and efficient caching for critical dependencies. This ensures your system has the best possible setup ready for immediate use. Get started by utilizing this simple tool with specific Python versions, sensible environment variables, and sensible defaults. You can customize the environment by editing the cog.yaml reference directly in the file.

With Cog, you can start deploying any model with the standard interface, training your own model with a notebook, or deploying to Windows 11 with minimal setup time. You can also deploy to specific environments or run prediction interfaces directly from the Predictor interface. If you are fine-tuning a model, you can use an HTTP API to serve your predictions or train additional models efficiently.

Title

Cog

Description

Cog

Keywords

model, docker, image, using, models, http, input, https, building, environment, build, curl, works, upgrade, need, prediction, path

NS Lookup

A 104.18.16.10, A 104.18.17.10

Dates

Created 2026-03-09

Updated 2026-04-22

Summarized 2026-04-24