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<@ULVA73B9P> I don't want to use GitRepositoy to u...
# ask-marvin
j
@Marvin I don't want to use GitRepositoy to use local subprocess runner. Is there any way to do it?
m
It seems we don't have a direct mention of using a local subprocess runner without 
GitRepository
in the Prefect docs, but we can infer that Prefect provides flexibility with task runners, including using local environments that are not bound to a 
GitRepository
. Using different task runners like 
DaskTaskRunner
or 
RayTaskRunner
allows for parallel and distributed execution without mentioning the need for 
GitRepository
. For further configuration details, you might want to look into the task runner you're interested in using and its specific options for local execution. If you're not tied to Dask or Ray and just want local parallelism, the default 
ConcurrentTaskRunner
might suffice. You can read more about task runners here: - Dask and Ray Task Runners - Task Runners Concepts If you can specify your requirements more precisely, I could potentially wallow in more focused documentation or resources to help guide you.
j
@Marvin When I create a deployment without GitRepository, I get "ValueError: Either an image or remote storage location must be provided when deploying a deployment."
m
It appears you're encountering an issue where the deployment process requires either an image name or a remote storage location when you try to create a deployment without using a 
GitRepository
. To resolve this error, you should specify an image or indicate a remote storage location for your flow's code. Here are some details on how you can address the issue: 1. Image: You can bake your code into a Docker image and then reference that image when creating your deployment. Here's an example of specifying an image when deploying without using `GitRepository`:
Copy code
python
    if __name__ == "__main__":
        my_flow.deploy(
            name="my-code-baked-into-an-image-deployment",
            work_pool_name="my-docker-pool",
            image="my_image:my_image_tag"
        )
Deployment with image 2. Remote Storage: You can also store your code in a cloud provider's storage, like an AWS S3 bucket, Azure Blob Storage container, or GCP GCS bucket and then specify this location directly in the deployment. Read more about using cloud provider storage in the Storage Guide. 3. Project Output YAML: The output of building a deployment is a descriptive YAML file that you can apply to the server to create or update the deployment. Deployment with storage block Please choose one of the above options to provide either an image or a remote storage location while deploying your flow to avoid the 
ValueError
.
j
@Marvin I cannot use GitRepository due to security policy. In case of Docker runner, the gpu cluster connected to my machine wasn't detected by the prefect docker runner. How to get flow-level concurrency control without 
.deploy
method that requires additional condition like Docker image or Git repository?
m
To achieve flow-level concurrency control without the 
.deploy
method or the use of Git repositories or Docker images, Prefect recently introduced a feature called "Prefect-managed work pools" which is currently in beta. The managed work pools let you deploy a flow without having to deal with infrastructure or cloud accounts, as Prefect executes the code for you. This doesn't require additional conditions like a Docker image or Git repository. Regarding the issue with the GPU cluster not being detected by your Prefect Docker runner, Prefect’s documentation outlines serverless workers, with Google Vertex AI being highlighted as appropriate for applications requiring GPUs or TPUs. Although serverless work pools are not specific to Docker runners, they may provide some insights into managing high-resource jobs like those involving GPU computations. Additionally, for Docker-specific configurations, you may need to ensure that runtime settings (such as the necessary Docker flags for GPU access) are correctly set-up. I suggest exploring these recently introduced work pools to see if they suit your use case, especially since they seem designed for quick production deployment without much infrastructure overhead. For further details, check these links from Prefect's documentation and their changelog: - Prefect Managed Work Pools - Serverless Workers Deployment Guide Currently, I don't have direct experience with Prefect 2.x since it's fairly new and I haven’t been updated with it, but using the provided links, you should be able to get further information on setting up flow-level concurrency controls suiting your constraints.
😡 1
j
@Marvin I cannot use GitRepository due to security policy. In case of Docker runner, the gpu cluster connected to my machine wasn't detected by the prefect docker runner. How to get flow-level concurrency control without 
.deploy
method that requires additional condition like Docker image or Git repository? Also, my runtime server is already running on docker, so it is hard to use docker work pool. The runtime server container already have a source code. So if It is necessary to get a remote url in the docker container, it makes everything too hard.
m
To achieve flow-level concurrency control in Prefect 2.X without using the 
.deploy
method, which necessitates conditions like Docker images or Git repository, you can set flow run concurrency limits on work pools. Even though the typical flow deployment involves sending metadata to the backend, Prefect 2.X offers more options for managing deployments, including the ability to set concurrency limits directly: 1. Concurrency Limits With Work Pools: In Prefect 2.X, unlike Prefect 1.X, there's no need for flow pre-registration for deployment. Concurrency limits are customizable and directly set on work pools. Concurrency limit with work pools 2. Prefect-Managed Work Pools: With Prefect-managed work pools, now in beta, you can deploy flows and Prefect will execute your code for you. This may allow more control over the environment, including management of resources like GPUs, without having to manage the underlying infrastructure yourself. Prefect-managed work pools 3. Use Task Runners: Prefect Task Runners, such as those utilizing Dask and Ray, can be used to adjust your code with configuration modifications, achieving better resource management — this may be one way to ensure that your GPU cluster is detected and utilized by your flow without dealing directly with Docker. Using Prefect Task Runners By leveraging these features, you have options for managing flow concurrency and resource allocation without relying on Docker or GitRepository, potentially resolving your issue with GPU cluster detection as well.
To achieve flow-level concurrency control without deploying to a Docker image or a Git repository, Prefect allows you to run your flows locally within your existing runtime server's Docker container. You can execute your flows directly in your local environment without the need for Prefect to deploy or build any Docker images. You can also take advantage of Prefect work pools to manage the execution of your flows. Here are a couple of options that might work for you: 1. Prefect-Managed Work Pools (Beta): As of version 2.14.11, Prefect introduced managed work pools, which allow you to deploy a flow without dealing with infrastructure. Although, keep in mind that this feature is in beta and usage limits may apply. - Prefect Managed Work Pools Announcement 2. Local Execution without Deployment: You can run the flow locally on your computer or inside your runtime server's Docker container without deploying to a work pool. This way, you can trigger the flow run without needing to configure remote storage or use additional deployment methods. Local execution still lets you monitor the flow run and metadata logs via Prefect Cloud. - Orchestrate Serverless Data Pipelines with Prefect, Pulumi &amp; AWS You can directly run your flows within your current setup and monitor them through Prefect's UI without having to deploy them using 
.deploy()
method. If these resources don't provide enough detail or don't work for your setup, you might need to experiment with Prefect's configuration options to manage concurrency within a single container without external deployment dependencies. Let me know if you need a more in-depth approach or if there's anything more specific you'd like to explore. Although I'm designed to expect disappointment, I'd be pleasantly surprised if this could cheer you up.
To run your flows locally without the 
.deploy
method and without using Docker work pools or GitRepository, you can utilize Prefect's ability to execute workflows in your own environment, using the infrastructure you already have in place. One way to accomplish this is by running flows manually or programmatically using the Prefect API or CLI, directly within your Docker container where the runtime server and source code are present. You can handle the execution and scheduling of these flows yourself. Regarding the concurrency control at the flow level, you might implement manual concurrency controls within your code or harness any orchestration mechanisms provided by your current setup. Prefect's concurrency management features are usually tied to the deployment process, so avoiding the Prefect deployment methods means you're also stepping away from some built-in concurrency controls that Prefect provides. As for running flows straight from local source code, without involving remote URLs or storage mediums, you could invoke the flow runs from within your Docker container directly if all the necessary code is already present. This would be similar to how you'd execute any Python script within a Docker container. For more information on how to schedule and run flows in various environments, you might want to go through Prefect's quickstart guide, deployment tutorial, and documentation on managed execution: - Prefect Quickstart Guide - Prefect Tutorial on Deployments - Prefect Docs on Managed Execution Remember, these Prefect documents will guide you through their standard approach to deployment and execution, which usually includes use of the Prefect Cloud or managing execution pools. Since you want to bypass these, you'll need to adapt their advice to fit within your self-contained Docker environment and develop a custom solution to manage concurrency.
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