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Cog: Containers for machine learning

Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container.

You can deploy your packaged model to your own infrastructure, or to Replicate.

Highlights

  • πŸ“¦ Docker containers without the pain. Writing your own Dockerfile can be a bewildering process. With Cog, you define your environment with a simple configuration file and it generates a Docker image with all the best practices: Nvidia base images, efficient caching of dependencies, installing specific Python versions, sensible environment variable defaults, and so on.

  • 🀬️ No more CUDA hell. Cog knows which CUDA/cuDNN/PyTorch/Tensorflow/Python combos are compatible and will set it all up correctly for you.

  • βœ… Define the inputs and outputs for your model with standard Python. Then, Cog generates an OpenAPI schema and validates the inputs and outputs with Pydantic.

  • 🎁 Automatic HTTP prediction server: Your model's types are used to dynamically generate a RESTful HTTP API using FastAPI.

  • πŸ₯ž Automatic queue worker. Long-running deep learning models or batch processing is best architected with a queue. Cog models do this out of the box. Redis is currently supported, with more in the pipeline.

  • ☁️ Cloud storage. Files can be read and written directly to Amazon S3 and Google Cloud Storage. (Coming soon.)

  • πŸš€ Ready for production. Deploy your model anywhere that Docker images run. Your own infrastructure, or Replicate.

How it works

Define the Docker environment your model runs in with cog.yaml:

build:
  gpu: true
  system_packages:
    - "libgl1-mesa-glx"
    - "libglib2.0-0"
  python_version: "3.12"
  python_packages:
    - "torch==2.3"
predict: "predict.py:Predictor"

Define how predictions are run on your model with predict.py:

from cog import BasePredictor, Input, Path
import torch

class Predictor(BasePredictor):
    def setup(self):
        """Load the model into memory to make running multiple predictions efficient"""
        self.model = torch.load("./weights.pth")

    # The arguments and types the model takes as input
    def predict(self,
          image: Path = Input(description="Grayscale input image")
    ) -> Path:
        """Run a single prediction on the model"""
        processed_image = preprocess(image)
        output = self.model(processed_image)
        return postprocess(output)

Now, you can run predictions on this model:

$ cog predict -i [email protected]
--> Building Docker image...
--> Running Prediction...
--> Output written to output.jpg

Or, build a Docker image for deployment:

$ cog build -t my-colorization-model
--> Building Docker image...
--> Built my-colorization-model:latest

$ docker run -d -p 5000:5000 --gpus all my-colorization-model

$ curl http://localhost:5000/predictions -X POST \
    -H 'Content-Type: application/json' \
    -d '{"input": {"image": "https://.../input.jpg"}}'

Why are we building this?

It's really hard for researchers to ship machine learning models to production.

Part of the solution is Docker, but it is so complex to get it to work: Dockerfiles, pre-/post-processing, Flask servers, CUDA versions. More often than not the researcher has to sit down with an engineer to get the damn thing deployed.

Andreas and Ben created Cog. Andreas used to work at Spotify, where he built tools for building and deploying ML models with Docker. Ben worked at Docker, where he created Docker Compose.

We realized that, in addition to Spotify, other companies were also using Docker to build and deploy machine learning models. Uber and others have built similar systems. So, we're making an open source version so other people can do this too.

Hit us up if you're interested in using it or want to collaborate with us. We're on Discord or email us at [email protected].

Prerequisites

  • macOS, Linux or Windows 11. Cog works on macOS, Linux and Windows 11 with WSL 2
  • Docker. Cog uses Docker to create a container for your model. You'll need to install Docker before you can run Cog. If you install Docker Engine instead of Docker Desktop, you will need to install Buildx as well.

Install

If you're using macOS, you can install Cog using Homebrew:

brew install cog

You can also download and install the latest release using our install script:

# fish shell
sh (curl -fsSL https://cog.run/install.sh | psub)

# bash, zsh, and other shells
sh <(curl -fsSL https://cog.run/install.sh)

# download with wget and run in a separate command
wget -qO- https://cog.run/install.sh
sh ./install.sh

You can manually install the latest release of Cog directly from GitHub by running the following commands in a terminal:

sudo curl -o /usr/local/bin/cog -L "https://github.com/replicate/cog/releases/latest/download/cog_$(uname -s)_$(uname -m)"
sudo chmod +x /usr/local/bin/cog

Alternatively, you can build Cog from source and install it with these commands:

make
sudo make install

Or if you are on docker:

RUN sh -c "INSTALL_DIR=\"/usr/local/bin\" SUDO=\"\" $(curl -fsSL https://cog.run/install.sh)"

Upgrade

If you're using macOS and you previously installed Cog with Homebrew, run the following:

brew upgrade cog

Otherwise, you can upgrade to the latest version by running the same commands you used to install it.

Next steps

Need help?

Join us in #cog on Discord.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Ben Firshman
Ben Firshman

πŸ’» πŸ“–
Andreas Jansson
Andreas Jansson

πŸ’» πŸ“– 🚧
Zeke Sikelianos
Zeke Sikelianos

πŸ’» πŸ“– πŸ”§
Rory Byrne
Rory Byrne

πŸ’» πŸ“– ⚠️
Michael Floering
Michael Floering

πŸ’» πŸ“– πŸ€”
Ben Evans
Ben Evans

πŸ“–
shashank agarwal
shashank agarwal

πŸ’» πŸ“–
VictorXLR
VictorXLR

πŸ’» πŸ“– ⚠️
hung anna
hung anna

πŸ›
Brian Whitman
Brian Whitman

πŸ›
JimothyJohn
JimothyJohn

πŸ›
ericguizzo
ericguizzo

πŸ›
Dominic Baggott
Dominic Baggott

πŸ’» ⚠️
Dashiell Stander
Dashiell Stander

πŸ› πŸ’» ⚠️
Shuwei Liang
Shuwei Liang

πŸ› πŸ’¬
Eric Allam
Eric Allam

πŸ€”
IvΓ‘n Perdomo
IvΓ‘n Perdomo

πŸ›
Charles Frye
Charles Frye

πŸ“–
Luan Pham
Luan Pham

πŸ› πŸ“–
TommyDew
TommyDew

πŸ’»
Jesse Andrews
Jesse Andrews

πŸ’» πŸ“– ⚠️
Nick Stenning
Nick Stenning

πŸ’» πŸ“– 🎨 πŸš‡ ⚠️
Justin Merrell
Justin Merrell

πŸ“–
Rurik YlΓ€-Onnenvuori
Rurik YlΓ€-Onnenvuori

πŸ›
Youka
Youka

πŸ›
Clay Mullis
Clay Mullis

πŸ“–
Mattt
Mattt

πŸ’» πŸ“– πŸš‡
Eng Zer Jun
Eng Zer Jun

⚠️
BB
BB

πŸ’»
williamluer
williamluer

πŸ“–
Simon Eskildsen
Simon Eskildsen

πŸ’»
F
F

πŸ› πŸ’»
Philip Potter
Philip Potter

πŸ› πŸ’»
Joanne Chen
Joanne Chen

πŸ“–
technillogue
technillogue

πŸ’»
Aron Carroll
Aron Carroll

πŸ“– πŸ’» πŸ€”
Bohdan Mykhailenko
Bohdan Mykhailenko

πŸ“– πŸ›
Daniel Radu
Daniel Radu

πŸ“– πŸ›
Itay Etelis
Itay Etelis

πŸ’»
Gennaro Schiano
Gennaro Schiano

πŸ“–
AndrΓ© KnΓΆrig
AndrΓ© KnΓΆrig

πŸ“–

This project follows the all-contributors specification. Contributions of any kind welcome!