https://github.com/ronylpatil/fastapi
Develop & Dockerize ML Microservices using FastAPI + Docker
https://github.com/ronylpatil/fastapi
docker fastapi machine-learning microservice
Last synced: about 1 year ago
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Develop & Dockerize ML Microservices using FastAPI + Docker
- Host: GitHub
- URL: https://github.com/ronylpatil/fastapi
- Owner: ronylpatil
- License: other
- Created: 2024-02-19T13:10:21.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-18T05:45:02.000Z (over 1 year ago)
- Last Synced: 2024-10-19T14:17:57.265Z (over 1 year ago)
- Topics: docker, fastapi, machine-learning, microservice
- Language: Python
- Homepage: https://medium.com/@ronilpatil/build-ml-microservices-using-fastapi-e7c8a0dd5ef0
- Size: 1.16 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Docker.md
- License: LICENSE
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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/)