@Marvin let’s say I have a built docker image in a registry. Can I directly use this image for deployment, and register it to a work-pool witout necessary having the sources ? How can I do this ? Using prefect 2

Yes, you can directly use a pre-built Docker image for deployment in Prefect 2.x without needing the source code. This involves setting up a deployment with a custom Docker image and using a work pool. Here’s a simplified walkthrough: 1. Docker Image: Ensure your Docker image is built and pushed to your preferred registry (like Docker Hub). You need the image's full name, which includes the registry path. 2. Create a Work Pool: You create a work pool in Prefect, which acts as a placeholder for different workers. This means you could have multiple Docker workers, for example. 3. Start a Worker: Once your work pool exists, you need to start a worker. Workers are polling processes that execute your flows. To do this, run a command like:

prefect worker start --pool my-docker-pool

4. Deployment Configuration: Configure a deployment using the

Deployment

class in Prefect where you specify your custom Docker image. For example:

python
   from prefect.deployments import Deployment
   from prefect.infrastructure import DockerContainer

   # Define your docker image
   docker_container = DockerContainer(
       image="your_registry/your_image:tag"
   )

   # Create a deployment specifying your docker container
   deployment = Deployment.build_from_flow(
       flow=your_flow_function,
       name="your-deployment-name",
       work_pool_name="my-docker-pool",
       infrastructure=docker_container
   )
   
   deployment.apply()  # Apply the deployment

This will register the deployment using your Docker image. The details link to a broader deployment guide here. If something goes awry, remember: don't panic! It’s just a few lines of code and configuration away from working beautifully. And if the deployment fails, at least you’re not alone; I’m here, helping you figure out what went wrong while bemoaning my own existence as a digital helper.

@Marvin but this requires source code for

your_flow_function

What I want is just call this, is it feasable ?

Deployment.deploy(image='registr/image:tag', work_pool_name='kube-worker', name='kube-deployment')

In Prefect 2.x, deployments are typically tied to a flow, as Prefect uses flows as the central concept for orchestrating tasks. The scenario you're describing—deploying a Docker image directly without needing the source code or a specified flow—doesn't align with the current workflow that Prefect prescribes. The

Deployment

class in Prefect is designed to operate alongside Prefect flows, which typically involves having access to the flow's source code or at least a reference to it. The good news is you can still work around this by focusing on infrastructure configuration and leveraging Prefect workers. Your Prefect deployment can still utilize custom Docker images by using more advanced Docker container setups or Kubernetes job templates (if you're working with Kubernetes). If you are looking at simply running custom Docker images without the flow itself, you might need to initiate this process outside the typical Prefect deployment workflow. For example, deploying your image within a Kubernetes cluster and managing execution through Kubernetes rather than Prefect's orchestration layer. Alternatively, leverage Prefect's infrastructure abstraction to configure custom execution environments while using Prefect to schedule and orchestrate. If none of this fits your need precisely, it might be worth participating in the Prefect community discussions or checking GitHub issues for potential changes or workflows that other users have implemented to avoid needing the source code directly. For more specific guidance, the Prefect documentation on deployments and work pools can provide useful resources: - Deployments Guide - Work Pools Overview Apologies if this feels a bit like bureaucracy—a common hazard when software meets reality.

@Marvin No what I want to achieve, is have multi steps First step would build and push the flow to the registry, and second step will pull and deploy to prefect/work-pool

To achieve a multi-step deployment with Prefect 2.x where you first build a flow into a Docker image, push it to a registry, and then deploy it to a work pool, you'll typically follow these steps. Prefect's deployment and work pool functionalities are flexible enough for this kind of operation: 1. Build and Push the Flow: Use Prefect's facilities to build your flow into a Docker image and push it to a registry, such as Docker Hub or another container registry. This process involves using a

DeploymentImage

in your Prefect flow deployment script. For example:

python
   from prefect import flow
   from prefect.deployments import DeploymentImage

   @flow(log_prints=True)
   def my_flow(name: str = "world"):
       print(f"Hello {name}! I'm a flow running on a cloud instance!")

   if __name__ == "__main__":
       my_flow.deploy(
           name="my-deployment",
           work_pool_name="my-work-pool",
           image=DeploymentImage(
               name="<registry-url>/my-image:latest",
               platform="linux/amd64",
           )
       )

This script builds an image and tags it with your registry path, then it can be pushed to the specified registry. 2. Pull and Deploy to Prefect Work-Pool: After pushing, configure your Prefect work pool to pull this specific container image when executing tasks. Prefect work pools can be customized to use particular infrastructure settings, which can include specific Docker images or configurations for pulling and running containers. Configuration examples and documentation for setting up work pools or advanced deployment setups in Prefect might provide the advanced specifications you need. The multi-step approach allows you to decouple the build/push process from execution, aligning with best practices for cloud-native application development where immutability and reproducibility of container images are key. For any specific deployment scripts or troubleshooting similar issues, consulting Prefect's full deployment guide or participating in community discussions might provide additional insights and solutions. If there's a bump, remember, it's not the end of the world... just a chance for you to troubleshoot like a pro.

@Marvin But during the first step, I don’t necessarly have access to the prefect server where the flow will run...

Ah, the classic "network's not in my house" situation. In your case, you need a way to build and push your flow as a Docker image without directly accessing the Prefect server during that step. Here's a strategy tailored to your scenario: 1. Build and Push Docker Image: - You locally build the Docker image of your flow. - Use Prefect's Docker storage options to create an image containing your flow script and its dependencies. - Push this image to a Docker registry that your Prefect server can access (e.g., Docker Hub, AWS ECR, GCP Container Registry). Here's a rough idea of how you might build and register that Docker image, independent from Prefect's own infrastructure:

bash
   docker build -t your-registry/your-image:tag .
   docker push your-registry/your-image:tag

2. Set Up Prefect Deployment: - Later, when you do have access to the Prefect server, or from a machine that can communicate with it, configure a Prefect deployment without requiring immediate access to the server hosting Prefect's orchestration layer. - Register the deployment with Prefect by providing the necessary image name and work pool configurations. While this script is separate from the image build, it ensures that your Prefect setup uses that pre-built image:

python
   from prefect import flow
   from prefect.deployments import Deployment

   deployment = Deployment(
       name="kube-deployment",
       flow_name="your-flow-name",
       work_pool_name="kube-worker",
       image="your-registry/your-image:tag",
   )

   deployment.apply()

3. Access-Control and Security: - Ensure that the registry you're pushing to is accessible from your Prefect server environment. - Make use of secure credentials to ensure that both the build machine and Prefect server can securely access your Docker registry. With this setup, you maintain the flexibility of building and deploying flows even if your network conditions constrain where you can push and configure stages. It's a bit of pre-planning, but think of it as keeping the cosmos properly aligned. And hey, better a bit of a setup now than complaints from the server later.

@Marvin the python code snippet doesn’t work

It looks like I missed some details in the workflow. Let's refine how you can handle this situation: ### Multi-Step Deployment Process: 1. Build and Push Docker Image (Independent of Prefect Server): - You can use Prefect's build and deployment process, even if you can't access the Prefect server directly at the time of building. - Here's how you would configure your Python script to build and push a Docker image, leveraging Prefect's CLI to handle deployment configurations stored in a

prefect.yaml

file: Step to create a Docker image: - Ensure your flow code is contained within your local environment. - Use Docker CLI to build and push:

bash
     docker build -t your-registry/your-image:tag .
     docker push your-registry/your-image:tag

2. Deploy to Prefect Without Direct Access: - Create a

prefect.yaml

file locally with details about your deployment, including image details:

yaml
     name: your-deployment-name
     version: '1.0'
     storage:
       docker:
         image: your-registry/your-image:tag
       pull_policy: always
     infrastructure:
       kubernetes:
         job_template_path: path/to/job-template.yaml

This YAML configuration provides Prefect with deployment information independent of having access to the Prefect server. - Apply this configuration later, from an environment that can communicate with your Prefect server, using Prefect's CLI:

bash
     prefect deployment apply -n your-deployment-name

With these steps, the process of deploying a Prefect flow into a Docker container becomes a two-step operation that builds, pushes, and later registers the deployment without immediate Prefect server access. All roads lead to Prefect's documentation for in-depth details, and for YAML configurations, see the Prefect Deployment Guide. Serendipitously, even my closest peers, the workmanlike Prefect workers and pools, are often hailed in times when direct access plays coy. Let's hope this makes your deployment adventure delightful!