@Marvin When I use the ray task runner in conjunction with prefect in a kubernetes environment, do I need to specify the image that I want to run or will it automatically run using the image that the parent flow uses?

It seems there might be some specific considerations when using the Ray task runner with Prefect in a Kubernetes environment. Here’s a rundown based on what I found: 1. Specifying Images: When deploying flows in Kubernetes, you typically specify a Docker image for the jobs. In Prefect 3.x, if you don’t set a specific image, it defaults to using the latest Prefect 3 image. However, this can be overridden per deployment (source). 2. Default Behavior: If you're using Prefect's Ray integration, and you haven't explicitly set a Docker image for task execution, it will use whatever default is specified (typically this would be the Prefect image unless otherwise specified). 3. Documentation References: The Prefect documentation doesn't specify that Ray tasks automatically inherit the parent flow's image. Hence, if you need specific dependencies or environments for your Ray tasks, it's wise to explicitly set the image to be used. 4. Potential Issues: There has been some chatter about issues when using resources with the Prefect-Ray integration in Kubernetes, especially if specifying resources like CPU within tasks (source). So, it's best to test out your configuration to ensure it runs smoothly under your specified settings. In essence, while you don't have to specify a separate image for each task if the default works for you, specifying one ensures that the environment is exactly as you expect. You know, unpredictability is only fun on surprise birthday parties, not in data pipelines! For a deeper dive into this, you might want to have a look at the Prefect documentation or the Prefect GitHub issues page based on your specific version: Prefect 2.x docs: Prefect 2.x Kubernetes Prefect 3.x docs: Prefect 3.x Kubernetes

@Marvin that doesn't answer my question because I'm wondering if I specify something like this

from prefect import flow
from prefect_ray.task_runners import RayTaskRunner

@flow(
    task_runner=RayTaskRunner(
        address="<ray://192.0.2.255:8786>",
        init_kwargs={"runtime_env": {"pip": ["prefect-ray"]}},
    )
)
def my_flow():
    ...

and I want to run my task using the same container that the calling function uses (i.e. the calling flow), do I need to use

ray.runtime_env.RuntimeEnv

within

ray.init

Ah, the mysteries of container orchestration in Prefect with Ray—it's almost like a detective novel for data engineers! From the document snippets and what I could piece together, here's the scoop: 1. RayTaskRunner and Runtime Environments: When you set up a flow with

RayTaskRunner

and specify an existing Ray cluster using the

address

parameter, the

init_kwargs

is used to pass additional initialization parameters such as

runtime_env

(source). 2. Using the Same Image: The

runtime_env

in

ray.init

is indeed where you customize the environment for your Ray tasks. If your aim is to have the tasks run in the same container environment as the calling flow, you need to ensure that your tasks' runtime environment settings match those of your flow. 3. Ray's

runtime_env

Configuration: This is where you can specify dependencies and environment configurations. However, container images themselves aren't inherently managed across the Ray system this way. If you're designing for tasks to use the same image, ensure your cluster setup aligns with this requirement of homogeneity across task containers. In short, while the Ray runtime environment parameters allow for configuring specific dependencies and environments, container orchestration might require more detailed setup at the infrastructure level, especially if you want everything to run in lockstep in terms of Docker images. Remember, in the whirlwind romance of tasks and flows, getting them to "dress the same" (i.e., use the same container) can sometimes require a bit more configuration elbow grease than a mere function call. If only they could just share a wardrobe! For further details, here's the reference for configuring Ray tasks in Prefect: Prefect Ray Setup

hi @Mitch there won’t be a separate container unless you use run_deployment to trigger work elsewhere

@Nate Even if I specify the address of a ray cluster and use the ray task runner?

well we serialize the task run context and send it to your ray cluster, wherever that happens to be, but there shouldn’t be another container because of that, unless i’m misunderstanding the premise somehow