Practical overview
When it fits
Use Cog when a machine-learning model needs a standardized Docker packaging and serving workflow, especially when GPU-oriented dependencies and an HTTP inference interface are part of the deployment plan.
Who it is for
- Machine-learning researchers packaging models for production use
- Engineers building Docker-based model-serving workflows
- Teams deploying PyTorch or TensorFlow models to self-managed infrastructure or Replicate
Documented uses
- Define a reproducible model container environment
- Generate a Docker image for a machine-learning model
- Describe typed inference inputs and outputs
- Expose a packaged model through a REST inference endpoint
- Prepare models for self-managed deployment or Replicate
Requirements & limits
The repository is publicly accessible and describes Cog as open-source under the Apache-2.0 license. Docker is required, and Windows use is documented through WSL 2.
- Docker is a core prerequisite for creating and running the model container.
- The environment definition includes GPU settings, system packages, Python version and Python dependencies.
- The runner uses standard Python types to define model inputs and outputs.
- Cog generates an HTTP inference server and OpenAPI-related input and output handling.
- The documented deployment targets are infrastructure where Docker images run and Replicate.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Package a model and build its Docker image
Define a model environment and runner, then generate a deployable Docker image.
Before you start
- macOS or Linux, or Windows 11 with WSL 2.
- Docker; Docker Engine installations also require Buildx.
- A model and Python runner implementation.
- A cog.yaml configuration file.
Steps
Define the Docker environment in cog.yaml, including packages, Python version, dependencies and runner entry point.
Define the model inputs, outputs and inference logic in a Python runner.
Run Cog’s documented build workflow to generate a tagged Docker image.
Run the resulting image with Docker and GPU access as shown in the project documentation.
Expected outcome
A standardized Docker image containing the model and an HTTP inference service.
Guide limitations
- The evidence does not specify the full cog.yaml schema beyond the example fields.
- GPU execution depends on the environment and Docker configuration.
- This recipe describes the documented workflow; it is not a verification of a successful local build.
Prepare a model for deployment to Replicate
Package a model with Cog in the format identified by the project for Replicate deployment.
Before you start
- A model runner and cog.yaml configuration.
- Docker and a supported operating environment.
- A model environment described through Cog’s configuration workflow.
Steps
Define the model environment in cog.yaml.
Define the model’s typed inputs and outputs in the Python runner.
Build the packaged Docker image with Cog.
Use the resulting package as the deployment artifact for Replicate.
Expected outcome
A Cog-packaged model identified by the project as deployable to Replicate.
Guide limitations
- The evidence does not document authentication, account requirements, pricing or the final Replicate publishing steps.
- This recipe does not establish deployment success or service compatibility.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Cog | Define a reproducible model container environment | Cloud, Docker, Self-hosted, Server | The repository is publicly accessible and describes Cog as open-source under the Apache-2.0 license. Docker is required, and Windows use is documented through WSL 2. |
| Morphingdb | Import a libtorch model and create predictions from values in a PostgreSQL table. | Docker, Linux, Self-hosted, Server | The README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension. |
| Gateway — pyadav | Send chat-completion requests to a provider through a common server endpoint. | Server | The repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- replicate/cog · Official homepage title · retained in its source language