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<@ULVA73B9P> how do I define a specific worker typ...
# ask-marvin
s
@Marvin how do I define a specific worker type for a prefect-ray flow that uses RayTaskRunner?
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s
@Marvin how do I define a specific worker type for a prefect-ray flow that uses RayTaskRunner?
m
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❌ Error: No module named 'prefect_ray'
s
@Marvin how do I define a specific worker type for a flow that uses RayTaskRunner?
m
âś… thought for 468.4 seconds
Short answer: you don’t. In Prefect 3.x, “worker type” is part of the work pool (infrastructure) your deployment targets; the task runner (Ray, Dask, threads, etc.) is purely how your tasks execute inside the Python process once the flow run starts. So you define RayTaskRunner in your flow code, and you pick the worker type at deployment time by targeting a work pool of that type. Here’s the practical recipe. 1) Define your flow with RayTaskRunner - Install the integration
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pip install prefect-ray ray
- Use the task runner in your flow
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from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner

@task
def inc(x): 
    return x + 1

@flow(task_runner=RayTaskRunner(address="auto", init_kwargs={"namespace": "prefect"}))
def my_flow():
    futures = [inc.submit(i) for i in range(10)]
    return sum(f.result() for f in futures)

if __name__ == "__main__":
    my_flow()
Notes: - 
address="auto"
connects to an existing Ray cluster if available; omit it to start a local Ray runtime. - You can pass more Ray init options via 
init_kwargs={...}
. 2) Create a work pool of the worker type you want - For example, a Docker or Kubernetes pool (requires the relevant integration to be installed) or a simple Process pool:
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prefect work-pool create "my-process-pool" --type process
# or
prefect work-pool create "my-docker-pool" --type docker
# or
prefect work-pool create "my-k8s-pool" --type kubernetes
Then start a worker for that pool:
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prefect worker start -p my-process-pool
# or
prefect worker start -p my-docker-pool
# or
prefect worker start -p my-k8s-pool
The worker type is whatever the pool’s type is. That’s the “specific worker type.” 3) Target that pool when you deploy the flow - In a Prefect 3 project, your prefect.yaml can specify the pool and its job variables. Example for Docker:
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deployments:
- name: ray-on-docker
  entrypoint: flows/my_flow.py:my_flow
  work_pool:
    name: my-docker-pool
    job_variables:
      image: my-org/prefect-ray:latest
      env:
        RAY_ADDRESS: <ray://ray-head:10001>
Then deploy:
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prefect deploy
Key points to keep your sanity intact: - Worker type ≠ task runner. Worker type is infrastructure (process, docker, kubernetes, …). Task runner is in-process concurrency (Ray, Dask, threads). - Make sure the worker’s runtime environment has everything your flow needs: Prefect, your code, and Ray (plus cluster connectivity if you’re using a remote Ray cluster). - If you don’t see your desired worker type, install its integration (e.g., prefect-docker, prefect-kubernetes) before creating the work pool. If you tell me which worker type you’re aiming for (Docker? Kubernetes? Process?) and how you plan to run Ray (local vs. external cluster), I can give you a minimal, copy-pasteable config for that setup. Useful docs, in case you like reading more than running flows: - Work Pools and Workers: Work pools and Workers - Deployments: Deployments guide - Ray integration: prefect-ray docs (collection with RayTaskRunner)
âś… thought for 33.7 seconds
Short version: you don’t set the “worker type” in your flow. In Prefect 3.x, worker type is defined by the work pool (process, docker, kubernetes, etc.). RayTaskRunner is just how tasks run inside the flow process. So you: - Use RayTaskRunner in your flow code - Create a work pool of the worker type you want - Deploy your flow to that pool and start a worker for it Here’s a concrete setup. 1) Flow code with RayTaskRunner
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pip install prefect-ray ray
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from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner
import os

@task
def inc(x): 
    return x + 1

@flow(
    task_runner=RayTaskRunner(
        # Use an existing cluster if available, otherwise local
        address=os.getenv("RAY_ADDRESS", "auto"),
        # Optional: pass extra ray.init kwargs
        init_kwargs={"namespace": "prefect"},
    )
)
def my_flow():
    futures = [inc.submit(i) for i in range(10)]
    return sum(f.result() for f in futures)

if __name__ == "__main__":
    my_flow()
2) Create a work pool of your desired worker type and start a worker - Process worker:
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prefect work-pool create "ray-process-pool" --type process
prefect worker start -p ray-process-pool
- Docker worker:
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prefect work-pool create "ray-docker-pool" --type docker
prefect worker start -p ray-docker-pool
- Kubernetes worker:
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prefect work-pool create "ray-k8s-pool" --type kubernetes
prefect worker start -p ray-k8s-pool
3) Point your deployment at that pool (prefect.yaml) Process example:
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deployments:
- name: ray-on-process
  entrypoint: flows/my_flow.py:my_flow
  work_pool:
    name: ray-process-pool
Docker example (make sure the image has prefect, prefect-ray, and ray):
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deployments:
- name: ray-on-docker
  entrypoint: flows/my_flow.py:my_flow
  work_pool:
    name: ray-docker-pool
    job_variables:
      image: my-org/prefect-ray:latest
      env:
        # If you’re connecting to a remote Ray cluster
        RAY_ADDRESS: <ray://ray-head:10001>
Kubernetes example:
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deployments:
- name: ray-on-k8s
  entrypoint: flows/my_flow.py:my_flow
  work_pool:
    name: ray-k8s-pool
    job_variables:
      image: my-org/prefect-ray:latest
      namespace: default
      env:
        RAY_ADDRESS: <ray://ray-head:10001>
Then from your project directory:
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prefect deploy
Key points to keep the universe from collapsing (again): - Worker type is the work pool’s type. RayTaskRunner runs within whatever infrastructure that worker starts (process, container, pod). - If using Docker/Kubernetes, your image must include prefect, prefect-ray, ray, and any cluster config. Either set 
RAY_ADDRESS
in the environment or pass 
address=...
to RayTaskRunner. - There is no “ray” worker type in Prefect; Ray is the in-process task runner. If you tell me which worker type you actually want (process, docker, k8s) and whether Ray is local or a remote cluster, I’ll hand you a minimal, copy-paste deployment config. References, in case you enjoy documentation as much as I do: - Work pools: Work pools - Workers: Workers - Deployments: Deployments guide - Ray integration: prefect-ray docs
s
@Marvin I'm referring to ray workers. I have a few types defined in my ray-cluster-config.yaml and they're like 
ray.worker.data
<http://ray.worker.ml|ray.worker.ml>
etc. Each with its own docker container defined. I want to be able to target specific ray workers with specific prefect flows.
m
âś… thought for 112.0 seconds
Got it — you want to steer Prefect tasks onto specific Ray node types (each with its own Docker image) inside a single Ray cluster. In Prefect 3.x, you don’t pick a “Ray worker type” at the Prefect level. RayTaskRunner just connects to Ray; where tasks land is controlled by Ray scheduling. To target your Ray worker types like ray.worker.data and ray.worker.ml, use Ray’s scheduling mechanisms: - Custom resources on nodes (recommended): give each node type a unique resource key, e.g., {"ray.worker.ml": 1} or {"ray.worker.data": 1}, then require that resource from your tasks. - Placement groups or node affinity strategies if you need stricter control. Here’s a concrete pattern that plays nicely with Prefect. 1) Ensure node types expose a unique resource In your Ray cluster config, label each node type with a custom resource. It sounds like you’ve already done this, e.g.: - ML nodes: resources: {"ray.worker.ml": 1} - Data nodes: resources: {"ray.worker.data": 1} 2) Use Prefect + RayTaskRunner, and set per-task Ray options Prefect tasks don’t natively know about Ray resource labels; you set them via Ray remote functions. The simplest approach is to wrap your business logic in Ray remote functions with resource requirements and call them from Prefect tasks. Example:
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pip install prefect-ray ray
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from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner
import ray

# Define Ray-remote functions pinned to your node types
@ray.remote(num_cpus=2, resources={"ray.worker.data": 0.01})
def ingest_impl():
    # runs on a "data" node
    return "some data"

@ray.remote(num_cpus=4, resources={"<http://ray.worker.ml|ray.worker.ml>": 0.01})
def train_impl(data):
    # runs on an "ml" node
    return f"trained on {data}"

@task
def ingest():
    return ray.get(ingest_impl.remote())

@task
def train(data):
    return ray.get(train_impl.remote(data))

@flow(task_runner=RayTaskRunner(address="auto"))
def pipeline():
    d = ingest.submit()
    m = train.submit(d)
    return m.result()

if __name__ == "__main__":
    pipeline()
Notes: - The small fractional amount (0.01) is a common Ray trick to “require presence” of a custom resource without consuming the whole unit. - If you prefer, you can use 
.options(...)
per call instead of decorating the function: 
train_impl.options(resources={"<http://ray.worker.ml|ray.worker.ml>": 0.01}).remote(data)
3) If you need tighter packing or multi-resource bundles Use placement groups and pass a scheduling strategy: - Ray placement groups Example sketch:
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from ray.util.placement_group import placement_group
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy

pg = placement_group([{"CPU": 4}], strategy="PACK")
ray.get(pg.ready())

@ray.remote
def train_impl(data):
    return f"trained on {data}"

@task
def train(data, pg_name):
    strategy = PlacementGroupSchedulingStrategy(placement_group=ray.util.get_placement_group(pg_name), placement_group_capture_child_tasks=True)
    return ray.get(train_impl.options(scheduling_strategy=strategy).remote(data))
4) Prefect deployment and worker type are separate - Use any Prefect work pool/worker type (process, docker, kubernetes) for your deployment. That only decides where the flow process runs. - The Ray placement happens inside the flow via the resource hints shown above. - Make sure the Prefect worker environment can reach your Ray cluster and has 
prefect-ray
+ 
ray
installed. Your Ray tasks will execute within the Ray worker node’s Docker image for that node type. Useful references, because of course you wanted to read more: - Ray custom resources: Custom resources and scheduling - Placement groups: Placement group scheduling - prefect-ray collection: prefect-ray docs
If you share the exact resource keys you’ve put on each node type (sounds like you’re using ray.worker.data and ray.worker.ml already), I can tailor the example to your naming and any CPU/GPU constraints you need.
s
@Marvin so I dont use @ray.remote, I just do this: import os import time from prefect import flow, task from prefect_ray import RayTaskRunner from prefect.deployments import DeploymentImage from prefect.tasks import task_input_hash from prefect_ray.context import remote_options from dotenv import load_dotenv import random # Load both .env files to access all environment variables # TEAM: Make sure you have .env and .env.generated files in the ray-prefect-final directory load_dotenv('../.env') load_dotenv('../.env.generated') address=f"ray://{os.getenv('RAY_HEAD_NODE_IP')}:10001" print("RAY_HEAD_NODE_IP:", os.getenv("RAY_HEAD_NODE_IP")) print("address", address) @task(_cache_key_fn_=task_input_hash) def test(_in_arg_): print("test task started", _in_arg_) time.sleep(100) print("test task finished", _in_arg_) @flow(_log_prints_=True, _task_runner_=RayTaskRunner( address=f"ray://{os.getenv('RAY_HEAD_NODE_IP')}:10001", )) def test_ray(): args = [random.random() for _ in range(10)] args.append(0) # task that shall be always cached print("args", args) # Apply runtime environment to each task to run in separate containers test.map(args) if name == "__main__": # Check for required environment variables required_vars = { 'AWS_ECR_REGISTRY': os.getenv('AWS_ECR_REGISTRY'), 'PREFECT_DEPLOYMENT_NAME': os.getenv('PREFECT_DEPLOYMENT_NAME'), 'PREFECT_WORKPOOL_NAME': os.getenv('PREFECT_WORKPOOL_NAME'), 'RAY_HEAD_NODE_IP': os.getenv('RAY_HEAD_NODE_IP'), } missing_vars = [var for var, value in required_vars.items() if not value] if missing_vars: print(f"❌ ERROR: Missing required environment variables: {', '.join(missing_vars)}") print("Please set these variables in your .env file or environment.") exit(1) _# test_ray()_ test_ray.deploy( name=f"{os.getenv('PREFECT_DEPLOYMENT_NAME')}", _work_pool_name_=f"{os.getenv('PREFECT_WORKPOOL_NAME')}", image=DeploymentImage( name=f"{os.getenv('AWS_ECR_REGISTRY')}:latest", platform="linux/amd64", dockerfile="Dockerfile", buildargs={ "BUILD_TIME": str(int(time.time())), # Force rebuild with timestamp } ), _job_variables_={ "cpu": 1024, "memory": 1024, "env": { "AWS_ACCESS_KEY_ID": os.getenv("AWS_ACCESS_KEY_ID"), "AWS_SECRET_ACCESS_KEY": os.getenv("AWS_SECRET_ACCESS_KEY"), "AWS_DEFAULT_REGION": os.getenv("AWS_DEFAULT_REGION"), "AWS_DEFAULT_BUCKET": os.getenv("AWS_DEFAULT_BUCKET"), "RAY_HEAD_NODE_IP": os.getenv("RAY_HEAD_NODE_IP") } } )
m
âś… thought for 62.2 seconds
Perfect, that helps. In Prefect 3.x with prefect-ray, you target your Ray “worker types” (really, Ray node types) by giving those nodes custom resources in your Ray cluster config, then telling Ray to require those resources for specific tasks. You don’t need to use @ray.remote directly; the prefect-ray bridge exposes a context manager, remote_options, that applies Ray scheduling options to any Prefect tasks submitted within the block. So if your Ray node types are labeled with resources like: - data nodes: {"ray.worker.data": 1} - ml nodes: {"ray.worker.ml": 1} …you can steer tasks like this:
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from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner
from prefect_ray.context import remote_options
import os, time, random

@task
def test(x):
    print("test task started", x)
    time.sleep(5)
    print("test task finished", x)
    return x

@flow(log_prints=True, task_runner=RayTaskRunner(
    address=f"ray://{os.getenv('RAY_HEAD_NODE_IP')}:10001"
))
def test_ray():
    # create two groups — one should run on data nodes, the other on ml nodes
    data_args = [random.random() for _ in range(5)]
    ml_args = [random.random() for _ in range(5)]

    # Send these to "data" node type
    with remote_options(resources={"ray.worker.data": 0.01}, num_cpus=1):
        data_futures = [test.submit(a) for a in data_args]
        # or: test.map(data_args)

    # Send these to "ml" node type
    with remote_options(resources={"<http://ray.worker.ml|ray.worker.ml>": 0.01}, num_cpus=2, num_gpus=1):
        ml_futures = [test.submit(a) for a in ml_args]
        # or: test.map(ml_args)

    # Wait / collect if you need results
    return [f.result() for f in data_futures + ml_futures]

if __name__ == "__main__":
    test_ray()
Notes and gotchas: - The remote_options context applies to any Prefect task submits within it (submit or map). No @ray.remote needed. - Use a small fractional amount for the custom resource (e.g., 0.01) so you only “require presence” of that node type without consuming it entirely. - Add Ray CPU/GPU asks alongside the resource to match the node’s capacity, e.g., num_gpus=1 for your ML nodes. - Your Prefect worker type (process/docker/k8s) is unrelated; it just hosts the flow process that connects to Ray. The Ray scheduling decisions happen inside the flow run. - Ensure those resource keys actually exist on your nodes. You can verify in a Ray shell:
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import ray; ray.init(address="auto"); [n["Resources"] for n in ray.nodes()]
If you need to target more than two types, just add more remote_options blocks with the appropriate resource key. If you want to switch at runtime based on arguments, branch into the right context depending on your logic. Docs for further misery reading: - Custom resources and scheduling in Ray: Ray custom resources - Placement groups (for stricter packing/affinity): Ray placement groups - Prefect-ray collection: prefect-ray docs If you share the exact resource labels and any GPU/CPU requirements per node type, I’ll tailor the context blocks so your tasks land exactly where you want.
s
@Marvin can you point me to documentation for this 
with remote_options(resources={"ray.worker.data": 0.01}, num_cpus=1):
?
m
âś… thought for 39.0 seconds
Yes. The pattern you’re using is from the prefect-ray collection. The 
remote_options
context manager lets you apply Ray’s 
.options(...)
to all Prefect task submissions inside the block. Good places to look: - Prefect-Ray docs home: prefect-ray docs - Source where 
remote_options
is defined: prefect_ray/context.py - Example usage in a PrefectHQ PR discussion (shows the exact 
with remote_options(resources=...)
pattern): PR #82 Since 
remote_options
forwards to Ray’s task options, the accepted keywords map to Ray’s scheduling options (e.g., 
resources
, 
num_cpus
, 
num_gpus
, 
scheduling_strategy
, etc.). For those knobs, see Ray’s docs: - Custom resources and scheduling: Ray custom resources - Task options overview: Ray task scheduling Quick reminder of usage:
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from prefect_ray.context import remote_options

with remote_options(resources={"ray.worker.data": 0.01}, num_cpus=1):
    # any Prefect task submits inside will carry these Ray options
    futs = [my_task.submit(x) for x in items]
If you need, I can dig up a direct anchor in the generated API docs for 
remote_options
, but the source link above is the canonical definition.
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