< Marvin> when running on DaskTaskRunner or RayTaskRunner ho Prefect Community #ask-marvin

<@ULVA73B9P> when running on DaskTaskRunner or Ray...

Viktor

08/06/2025, 6:08 PM

@Marvin when running on DaskTaskRunner or RayTaskRunner how can I make 1 task to run on multiple CPUS? is that possible

Marvin

08/06/2025, 6:08 PM

✅ thought for 30.1 seconds

Marvin

08/06/2025, 6:09 PM

Let me break this down for both task runners: ### DaskTaskRunner For DaskTaskRunner, you can specify CPU requirements using the

task_runner

configuration when creating your flow. Here's how you can do it:

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python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task(retries=2)
def cpu_intensive_task():
    # Your CPU intensive code here
    pass

@flow(
    task_runner=DaskTaskRunner(
        cluster_kwargs={"n_workers": 2, "threads_per_worker": 4}
    )
)
def my_flow():
    cpu_intensive_task()

Additionally, you can use Dask's native resource specification at the task level using the

dask_resource_requirements

parameter:

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python
@task(
    retries=2,
    task_run_name="cpu-heavy-task",
    tags=["cpu-intensive"],
    dask_resource_requirements={"CPU": 4}  # Request 4 CPUs for this task
)
def cpu_intensive_task():
    # Your CPU intensive code here
    pass

### RayTaskRunner For RayTaskRunner, you can specify CPU requirements at the task level using Ray's resource specifications:

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python
from prefect import flow, task
from prefect_ray import RayTaskRunner

@task(ray_options={"num_cpus": 4})  # Request 4 CPUs for this task
def cpu_intensive_task():
    # Your CPU intensive code here
    pass

@flow(task_runner=RayTaskRunner())
def my_flow():
    cpu_intensive_task()

Important Notes: 1. Make sure you have enough CPU resources available in your environment to fulfill these requests 2. For RayTaskRunner, you'll need to install the

prefect-ray

package:

pip install prefect-ray

3. For DaskTaskRunner, you'll need to install the

prefect-dask

package:

pip install prefect-dask

The exact number of CPUs you can allocate depends on: - Your machine's hardware capabilities - The cluster configuration (if running in a distributed environment) - Other tasks that might be running concurrently Let me know if you need any clarification or have questions about specific configuration options for either task runner!

Viktor

08/06/2025, 6:11 PM

@Marvin Is it okay RayDaskCallback(init_kwargs={“num_cpus”: 60}): when running dask manipulations while using Ray worker?

Marvin

08/06/2025, 6:13 PM

✅ thought for 26.5 seconds

Marvin

08/06/2025, 6:13 PM

Yes, it is okay to use

RayDaskCallback(init_kwargs={"num_cpus": 60})

when running Dask operations within a Ray environment, but there are some important considerations: 1. Resource Allocation: - The

num_cpus

parameter in

init_kwargs

tells Ray how many CPUs to allocate for the Dask cluster - Make sure your system actually has access to the number of CPUs you're requesting (60 in this case) - These CPUs will be reserved for Dask operations specifically 2. Best Practices: - Don't allocate more CPUs than your system has available - Consider leaving some CPU resources available for Ray's other operations - Be aware that this allocation is separate from any CPU specifications in your Ray task configurations 3. Potential Gotchas: - If you're running in a Ray worker, these CPUs will be allocated from the Ray cluster's resources - Over-allocation could lead to resource contention or scheduling issues - The actual parallelism you achieve might be limited by other factors in your computation Here's an example of how this might look in practice:

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python
from ray.util.dask import RayDaskCallback
import dask.dataframe as dd

# Configure the callback
callback = RayDaskCallback(init_kwargs={"num_cpus": 60})

# Use it in a context manager
with callback:
    # Your Dask operations here
    df = dd.read_csv("large_file.csv")
    result = df.compute()

Recommendations: 1. Monitor your resource usage during execution to ensure you're not over-allocating 2. Consider making the CPU count configurable based on your deployment environment 3. If you're running multiple Dask operations, you might want to adjust the CPU count dynamically Would you like me to provide more specific guidance about your use case? It would help to know: - What kind of Dask operations you're running - The total CPU resources available in your environment - Whether you're running other CPU-intensive tasks concurrently

Viktor

08/06/2025, 6:25 PM

Marvin

08/06/2025, 6:25 PM

Viktor

08/06/2025, 6:32 PM

@Marvin ray_options={“num_cpus”: 4} doesn’t seem a valid argument

Marvin

08/06/2025, 6:32 PM

✅ thought for 21.1 seconds

Marvin

08/06/2025, 6:32 PM

I apologize for my earlier incorrect information. You're right - the

ray_options

parameter is not valid. Instead, when using Ray with Prefect, you should use the

remote_options

context manager to specify CPU requirements. Here's the correct way to do it:

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python
from prefect import flow, task
from prefect_ray import RayTaskRunner
from prefect_ray.context import remote_options

@task
def cpu_intensive_task():
    # Your CPU intensive code here
    pass

@flow(task_runner=RayTaskRunner())
def my_flow():
    with remote_options(num_cpus=4):  # Request 4 CPUs for this task
        result = cpu_intensive_task.submit()
    return result

Key points: 1. Use the

remote_options

context manager from

prefect_ray.context

2. The context manager accepts parameters like

num_cpus

and

num_gpus

3. Apply it around the task submission using

.submit()

You can also use it for multiple tasks:

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python
@flow(task_runner=RayTaskRunner())
def my_flow():
    with remote_options(num_cpus=4):
        # All tasks submitted within this context will use 4 CPUs
        result1 = task1.submit()
        result2 = task2.submit()

Make sure you have installed the required package:

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bash
pip install prefect-ray

Would you like me to provide more examples or explain any specific aspects of resource allocation with Ray?

Open in Slack

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