Run and deploy with Docker container 🐳¶
Containerization¶
To containerize your FastServe application, a Docker example is provided in the examples/docker-compose-example directory. The example is about face recognition and includes a Dockerfile for creating a Docker image and a docker-compose.yml for easy deployment. Here's a quick overview:
- Dockerfile: Defines the environment, installs dependencies from
requirements.txt, and specifies the command to run your FastServe application. - docker-compose.yml: Simplifies the deployment of your FastServe application by defining services, networks, and volumes.
To use the example, navigate to the examples/docker-compose-example directory and run:
docker-compose up --build
This will build the Docker image and start your FastServe application in a container, making it accessible on the specified port.
Note: We provide an example using face recognition. If you need to use other models, you will likely need to change the requirements.txt or the Dockerfile. Don't worry; this example is intended to serve as a quick start. Feel free to modify it as needed.
Passing Arguments to Uvicorn in run_server()¶
FastServe leverages Uvicorn, a lightning-fast ASGI server, to serve machine learning models, making FastServe highly efficient and scalable.
The run_server() method supports passing additional arguments to uvicorn by utilizing *args and **kwargs. This feature allows you to customize the server's behavior without modifying the source code. For example:
app.run_server(host='0.0.0.0', port=8000, log_level='info')
In this example, host, port, and log_level are passed directly to uvicorn.run() to specify the server's IP address, port, and logging level. You can pass any argument supported by uvicorn.run() to run_server() in this manner.