Kevin
05/13/2024, 9:40 PMMarvin
05/13/2024, 9:40 PMDockerfile that specifies the Python version you want, copies your flow file, and installs any dependencies your flow might need.
2. Build a Docker image using your Dockerfile.
3. (Optional) Use Prefect's DeploymentImage class to define your image for deployment.
4. Run the Docker container with the appropriate environment variables set, such as PREFECT_API_URL, which should point to your self-hosted Prefect API instance.
Here's an example of the Dockerfile you would write:
Dockerfile
FROM python:3.10
COPY ./requirements.txt /requirements.txt
RUN pip install -r /requirements.txt
And then run commands like:
bash
docker build -t my-flow-image:latest .
docker run -e PREFECT_API_URL=<http://host.docker.internal:4200/api> my-flow-image:latest
Remember to replace <http://host.docker.internal:4200/api> with the URL of your Prefect API. Also, if you're using Linux, you'll need to use your machine's IP address instead of host.docker.internal.
For a detailed guide on deploying your flow with Docker, consult the Prefect Docker Guide.
If you decide to create a deployment manifest and use Kubernetes for orchestration, you would define your flow image and other Kubernetes specifications in a .yaml file. An example deployment command using kubectl would be:
bash
kubectl apply -f deployment-manifest.yaml
For more advanced Docker image specifications when deploying your flow, you can use the optional keyword arguments for the DeploymentImage class provided by Prefect. To set a default Docker namespace for your images, you can also update your Prefect configuration:
bash
prefect config set PREFECT_DEFAULT_DOCKER_BUILD_NAMESPACE=<docker-registry-url>/<organization-or-username>
Please refer to this guideline for creating a deployment by leveraging the `DeploymentImage` class.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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