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cog.yaml reference

cog.yaml defines how to build a Docker image and how to run predictions on your model inside that image.

It has three keys: build, image, and predict. It looks a bit like this:

build:
  python_version: "3.8"
  python_packages:
    - pytorch==1.4.0
  system_packages:
    - "ffmpeg"
    - "libavcodec-dev"
predict: "predict.py:JazzSoloComposerPredictor"

Tip: Run cog init to generate an annotated cog.yaml file that can be used as a starting point for setting up your model.

build

This stanza describes how to build the Docker image your model runs in. It contains various options within it:

cuda

Cog automatically picks the correct version of CUDA to install, but this lets you override it for whatever reason.

gpu

Enable GPUs for this model. When enabled, the nvidia-docker base image will be used, and Cog will automatically figure out what versions of CUDA and cuDNN to use based on the version of Python, PyTorch, and Tensorflow that you are using.

For example:

build:
  gpu: true

When you use cog run or cog predict, Cog will automatically pass the --gpus=all flag to Docker. When you run a Docker image built with Cog, you'll need to pass this option to docker run.

python_packages

A list of Python packages to install, in the format package==version. For example:

build:
  python_packages:
    - pillow==8.3.1
    - tensorflow==2.5.0

python_version

The minor (3.8) or patch (3.8.1) version of Python to use. For example:

build:
  python_version: "3.8.1"

run

A list of setup commands to run in the environment after your system packages and Python packages have been installed. If you're familiar with Docker, it's like a RUN instruction in your Dockerfile.

For example:

build:
  run:
    - curl -L https://github.com/cowsay-org/cowsay/archive/refs/tags/v3.7.0.tar.gz | tar -xzf -
    - cd cowsay-3.7.0 && make install

Your code is not available to commands in run. This is so we can build your image efficiently when running locally.

system_packages

A list of Ubuntu APT packages to install. For example:

build:
  system_packages:
    - "ffmpeg"
    - "libavcodec-dev"

image

The name given to built Docker images. If you want to push to a registry, this should also include the registry name.

For example:

image: "registry.hooli.corp/jazz-solo-model"

If you don't provide this, a name will be generated from the directory name.

predictor

The pointer to the cog.Predictor object in your code, which defines how predictions are run on your model.

For example:

predict: "predict.py:HotdogPredictor"

See the Python API documentation for more information.