https://github.com/princetp/imageiq
ImageIQ is a FastAPI application designed to serve a machine learning model that processes text and image inputs to generate predictions. It's built to be scalable and efficient, suitable for real-time data processing.
https://github.com/princetp/imageiq
docker fastapi pillow python uvicorn
Last synced: 3 months ago
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ImageIQ is a FastAPI application designed to serve a machine learning model that processes text and image inputs to generate predictions. It's built to be scalable and efficient, suitable for real-time data processing.
- Host: GitHub
- URL: https://github.com/princetp/imageiq
- Owner: PrinceTp
- Created: 2024-05-29T05:59:19.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-29T18:22:20.000Z (about 1 year ago)
- Last Synced: 2025-01-18T15:51:26.925Z (5 months ago)
- Topics: docker, fastapi, pillow, python, uvicorn
- Language: Dockerfile
- Homepage:
- Size: 19.5 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.Docker.md
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README
### Building and running your application
When you're ready, start your application by running:
`docker compose up --build`.
Your application will be available at http://localhost:8000.
### Deploying your application to the cloud
First, build your image, e.g.: `docker build -t myapp .`.
If your cloud uses a different CPU architecture than your development
machine (e.g., you are on a Mac M1 and your cloud provider is amd64),
you'll want to build the image for that platform, e.g.:
`docker build --platform=linux/amd64 -t myapp .`.
Then, push it to your registry, e.g. `docker push myregistry.com/myapp`.
Consult Docker's [getting started](https://docs.docker.com/go/get-started-sharing/)
docs for more detail on building and pushing.
### References
* [Docker's Python guide](https://docs.docker.com/language/python/)