Deploying Gradio AI Apps with Docker

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Gradio and Docker: More Flexibility for AI Applications

Gradio, a Python library for creating interactive web applications for machine learning models, offers developers a variety of options for presenting and using their projects. In addition to local execution and free hosting on Hugging Face Spaces, Gradio also allows applications to run in Docker containers on your own hardware. This option provides additional flexibility and control over the deployment environment.

Advantages of Docker Integration

Using Docker for Gradio applications offers several advantages:

• Consistency: Docker ensures that the application always works the same regardless of the deployment target by bundling the application and its dependencies in a container.
• Portability: Docker containers can be easily moved between different systems and cloud environments.
• Scalability: Docker combines well with orchestration systems like Kubernetes, allowing the application to be scaled as needed.

Dockerizing a Gradio Application

Dockerizing a Gradio application is straightforward. A simple example illustrates the necessary steps:

• Creating the Gradio application: A Python file (e.g., app.py) contains the code of the Gradio application.
• Creating the Dockerfile: The Dockerfile defines the build and execution environment of the container. It is based on a Python image, copies the application files, installs Gradio and all other dependencies, exposes port 7860, and defines the start command for the application.
• Building and running the Docker container: The container is created and started using the docker build and docker run commands. The application is then accessible via the specified port.

Important Notes for Docker Usage

There are a few things to keep in mind when running Gradio applications in Docker:

• Network Configuration: The environment variable GRADIO_SERVER_NAME must be set to 0.0.0.0 to allow connections from outside the container. Port 7860 must be exposed in the Dockerfile (EXPOSE 7860).
• Session Persistence: When using multiple replicas of the application, e.g., in AWS ECS, enabling "stickiness" (sessionAffinity: ClientIP) is important to ensure that all requests from a user are directed to the same instance. This is necessary for the correct processing of events in Gradio.
• Proxy Configuration: When using a proxy like Nginx, it must be configured accordingly to ensure the correct functioning of the Gradio application.

Gradio as an API Endpoint

Every Gradio application can serve as an API endpoint for downstream processes. Information about the available endpoints and their parameters can be obtained via the application's API documentation, which is accessible via a link in the application's footer. The endpoints can be accessed with various clients, such as Python or JavaScript.

Conclusion

Docker integration and API functionality significantly expand the possible uses of Gradio. Developers can flexibly deploy their AI applications on their own hardware, integrate them into existing systems, and thus optimally utilize the advantages of Docker, such as consistency, portability, and scalability. The easy creation of API endpoints also enables seamless integration of the applications into complex workflows.

Bibliographie: https://www.gradio.app/guides/deploying-gradio-with-docker https://www.gradio.app/guides/using-hugging-face-integrations https://huggingface.co/docs/hub/spaces-sdks-gradio https://www.gradio.app/guides/sharing-your-app https://huggingface.co/docs/hub/spaces-sdks-docker https://discuss.huggingface.co/t/deploy-gradio-app-on-my-own-server-machine-fails/41808 https://blog.runpod.io/run-huggingface-spaces-on-runpod/ https://huggingface.co/learn/cookbook/enterprise_cookbook_gradio ```