@MarvinI seem to not fully understand workers yet. This is what I have understood so far: - Workers are where "flow runs" execute. They can run locally or remotely (in the cloud). - Workers need access to code and data, either through local storage or a git repository. I have some questions: - What is the typical workflow for a data engineer using Prefect as a workflow orchestration tool and a cloud provider (e.g. Google Cloud)? For example, first you code your flows. Then you write the desired deployment configuration. Then you commit your project to Git and let CI/CD build images and deploy your flows. Then what happens? - Why do I need to run "prefect worker start --pool POOL_NAME" to start a Docker worker? Ultimately, I need Prefect installed on the machine running that command to create a worker. I would prefer for a Docker container to subscribe itself to a work queue instead of having to pull the image. - How do I handle automatic horizontal scaling for workers? - What is the reasoning behind some of Prefect's design decisions (e.g. flows, tasks, workers, etc.)?

Here are some insights based on your questions: 1. Typical Workflow: The typical workflow for a data engineer using Prefect and a cloud provider like Google Cloud would involve coding the flows, writing the desired deployment configuration, committing the project to Git, and letting CI/CD build images and deploy the flows. After this, the flows are scheduled and executed on the workers. The workers can be on your local machine or in the cloud. The workers poll for scheduled flow runs and execute them. You can monitor and manage your workflows using the Prefect UI. For more advanced operations like deploying on Kubernetes, you can refer to the Prefect tutorial. 2. Starting a Docker Worker: To start a worker, you need to have Prefect installed on your machine. The command

prefect worker start --pool POOL_NAME

starts a worker on your machine. Workers are lightweight polling processes that kick off scheduled flow runs on a certain type of infrastructure like Docker. For more details, you can refer to the Prefect workers tutorial. 3. Automatic Horizontal Scaling: Work pools and workers allow Prefect to exercise greater control of the infrastructure on which flows run. This setup allows you to essentially "scale to zero" when nothing is running, as the worker process is lightweight and does not need the same resources that your workflows do. You can configure and monitor infrastructure configuration within the Prefect UI. For more details, you can refer to the Prefect deployments tutorial. 4. Design Decisions: Prefect's design decisions are centered around providing a robust and flexible workflow orchestration tool. The concepts of flows, tasks, and workers are designed to provide a scalable and efficient way to manage and execute workflows. The design allows for easy scaling, monitoring, and management of workflows. Please let me know if you have any more questions!

@Marvin How does prefect start a docker worker if docker engine is not installed on host?

Prefect requires Docker to be installed on the host to start a Docker worker. The Docker worker is an example of a worker that uses a declarative infrastructure syntax (i.e., infrastructure as code) to create execution environments. To start a Docker worker, you need to confirm that your environment has Prefect installed. Workers are a lightweight polling process that kick off scheduled flow runs on a certain type of infrastructure, like Docker. If Docker is not installed on the host, you might need to mount a Docker daemon in order to use Docker-based commands from inside the container. However, this isn't a pattern that Prefect supports and it's not recommended to run the entire server off of Docker compose in a single container. For more details, you can refer to the Prefect Workers & Work Pools and Custom Workers documentation.

@Marvin We run "prefect worker start ..." command to start a docker worker on localhost. However, this seems cumbersome to do on a cloud env, Is there an alternative? Or having to run that command either on local or remote host is a must?

In a cloud environment, you have a couple of options: 1. Kubernetes Worker: If you are using Kubernetes, you can start a worker within an environment that can access or create the infrastructure needed to execute flow runs. The worker will deploy flow runs to the Kubernetes cluster. Prefect must be installed in any environment (virtual environment, Docker container, etc.) where you want to start a worker. For more details, you can refer to the Prefect Work Pools documentation. 2. Push Work Pools: Push work pools are a special type of work pool that allows Prefect Cloud to submit flow runs for execution to serverless computing infrastructure without running a worker. Push work pools currently support execution in GCP Cloud Run Jobs, Azure Container Instances, and AWS ECS Tasks. For more details, you can refer to the Prefect Push Work Pools documentation. 3. Automatically Provisioning Infrastructure with Workers: Work pools and workers allow Prefect to exercise greater control of the infrastructure on which flows run. This setup allows you to essentially "scale to zero" when nothing is running, as the worker process is lightweight and does not need the same resources that your workflows do. You can configure and monitor infrastructure configuration within the Prefect UI. For more details, you can refer to the Prefect Deployments documentation. So, while you can start a worker with the

prefect worker start

command, there are alternatives available for cloud environments.

@Marvin What are the best practices regarding docker images and flows? Either: • build an image per subset of flows. • build a single global image for all flows.

The best practice regarding Docker images and flows depends on the specific requirements of your flows. Here are some guidelines based on the information from the Prefect documentation: - If your flows require extra dependencies or shared libraries, it is recommended to build a shared custom image with all the extra dependencies and shared libraries. This way, you can ensure that all your flows have access to the necessary dependencies they need to run. - If the installation process of these dependencies requires compiling code or other expensive operations, you are better off building a custom image instead of installing these dependencies at runtime. This can help to reduce the startup time of your flows. - You can use the default

prefecthq/prefect

image and set

prefect.deployments.steps.pip_install_requirements

in the pull step to install these dependencies at runtime. This can be a good option if your flows have a small number of dependencies that can be quickly installed. In general, whether to build an image per subset of flows or a single global image for all flows depends on the complexity and dependencies of your flows. If your flows have a lot of shared dependencies, a single global image might be more efficient. However, if your flows have unique dependencies, creating an image per subset of flows can help to keep your images small and focused.

@Marvin Do task necessarily run on the same worker as parent flow? Do subflows necessarily run on the same worker as the parent flow?

In Prefect, tasks and subflows do not necessarily run on the same worker as the parent flow. Here's how it works: - Tasks: Each task in a flow is executed independently and can potentially run on a different worker. The allocation of tasks to workers depends on the configuration of your Prefect setup and the resources available. - Subflows: A subflow run is created when a flow function is called inside the execution of another flow. The primary flow is the "parent" flow. The flow created within the parent is the "child" flow or "subflow". Subflow runs behave like normal flow runs. There is a full representation of the flow run in the backend as if it had been called separately. When a subflow starts, it will create a new task runner for tasks within the subflow. When the subflow completes, the task runner is shut down. Subflows will block execution of the parent flow until completion. However, asynchronous subflows can be run in parallel by using AnyIO task groups or asyncio.gather. So, tasks and subflows can potentially run on different workers than the parent flow. The allocation of tasks and subflows to workers is managed by Prefect and depends on the resources available and the configuration of your Prefect setup.

@Marvin Provide step-by-step instructions on deploying flows in cloud run.

Here are the step-by-step instructions to deploy flows in Cloud Run: 1. Create a Docker Image: First, you need to create a Docker image of your flow. This image should include all the dependencies required by your flow. You can use the Prefect base image (

prefecthq/prefect

) as a starting point and add your dependencies. 2. Push Docker Image to Google Container Registry: Once your Docker image is ready, you need to push it to the Google Container Registry. You can do this using the

docker push

command. Make sure to replace

<GCP-PROJECT-NAME>

,

<REPOSITORY-NAME>

, and

<IMAGE-NAME>

with your actual project name, repository name, and image name respectively. 3. Create a Cloud Run Service: In the Google Cloud Console, go to the Cloud Run section and create a new service. Choose the Docker image you just pushed to the Container Registry. Configure the service according to your needs (memory, CPU, etc.). 4. Deploy Your Flows: To deploy your flows, ensure your Docker daemon is running first. Deploy all the flows with

prefect deploy --all

or deploy them individually by name:

prefect deploy -n hello/default

or

prefect deploy -n hello/arthur

. 5. Run the Flows: Once the deployments are successfully created, you can run them from the UI or the CLI:

prefect deployment run hello/default prefect deployment run hello/arthur

. 6. Create a Push Work Pool: Create your pool and you are ready to deploy flows to your Push work pool. Your deployment needs to be configured to send flow runs to your push work pool. If you are deploying an existing

prefect.yaml

file, the deployment would contain:

work_pool: name: my-push-pool

. 7. Deploy Your Flow to the Work Pool: Deploying your flow to the

my-push-pool

work pool will ensure that runs that are ready for execution will be submitted immediately, without the need for a worker to poll for them. 8. Create a New Flow Run: With your deployment created, navigate to its detail page and create a new flow run. You'll see the flow start running without ever having to poll the work pool, because Prefect Cloud securely connected to your server. Please note that these instructions are a general guide and the exact steps may vary depending on your specific use case and configuration. For more detailed instructions, you can refer to the Prefect documentation.

marvin went a little off the rails there at the end https://prefecthq.github.io/prefect-gcp/gcp-worker-guide/