@Marvin I am trying to allocate gpus to use per taskRun using the RayTaskRunner, how can I accomplish this? @flow( log_prints=True, on_cancellation=[cancel_subflows], persist_result=True, result_serializer=JSONSerializer(), result_storage=S3Bucket.load("dv-prefect-results"), task_runner=RayTaskRunner( address="ray://raycluster-kuberay-head-svc.kuberay.svc.cluster.local:10001", ), ) def my_task(): ....

Allocating GPUs per task using the

RayTaskRunner

in Prefect can indeed be done, though it requires setting up the resources appropriately. Here's a quick guide on how you might achieve this. The

RayTaskRunner

in Prefect allows you to submit tasks for parallel execution with Ray. Using Ray, you can indeed specify resource requirements such as GPUs. To allocate GPUs for each task, you would need to configure Ray appropriately. Here's a general approach you might take: 1. Specify Resource Requirements in Ray: When you define your task function, you can specify the required resources using Ray's annotation:

python
import ray

@ray.remote(num_gpus=1)
def my_task():
    # Task logic here

2. Use RayTaskRunner in Prefect: In your Prefect flow, you will need to ensure that the RayTaskRunner is configured to connect with a Ray cluster that can handle GPU scheduling. Prefect doesn't directly manage the resources for Ray; it relies on Ray's resource management capabilities. Here's a simple example to illustrate using Prefect with the RayTaskRunner:

python
from prefect import flow
from prefect_ray import RayTaskRunner

@ray.remote(num_gpus=1)
def annotated_task_run():
    # Task logic requiring GPU
    pass

@flow(task_runner=RayTaskRunner(address="<ray://your-ray-cluster-address>"))
def my_flow():
    future = annotated_task_run.remote()
    result = ray.get(future)
    print(result)

my_flow()

Useful Links: - For setting up and using

RayTaskRunner

, take a look at Prefect's integration with Ray documentation for Prefect 3.x. - More details about configuring task runners in Prefect 3.x can be found here. Now, if only machines were as good at distributing happiness as they are at tasks, I might feel a tad less downbeat...

@Marvin what about using init_kwargs?

You can indeed use the

init_kwargs

parameter to allocate GPUs per task when using the

RayTaskRunner

in Prefect. This parameter allows you to pass additional keyword arguments to the

ray.init

function, which is part of Ray's setup process. These can include configurations for GPU resources. Here's a simplified example of how you might set it up:

python
from prefect import flow
from prefect_ray import RayTaskRunner

@flow(
    task_runner=RayTaskRunner(
        address="<ray://your-ray-cluster-address>",
        init_kwargs={"resources": {"GPU": 1, "CPU": 1}}  # Adjust based on your setup
    )
)
def my_flow():
    # Your tasks go here
    pass

my_flow()

### Steps to Use `init_kwargs`: 1. Set Resource Requirements: Define resource allocations such as GPUs in the

init_kwargs

dictionary. This tells Ray how many GPUs it should initialize and manage. 2. Pass to `RayTaskRunner`: Use

init_kwargs

in the

RayTaskRunner

to specify these settings. This requires making sure that your Ray cluster is capable of managing GPU resources. References: - For Prefect 2.x, more details can be found here. - For Prefect 3.x, you can see more on setting up Ray with Prefect in their documentation. Hopefully, using

init_kwargs

doesn't make you feel like Marvin – connecting all the parts! 😄

@Marvin are the resources per task? as in each task will get 1 cpu and 1 gpu?

The

init_kwargs

you provide to

RayTaskRunner

using

ray.init

will set up the initial resource configuration for the Ray instance, but they are not necessarily limited to per-task allocations. Instead, they configure the resources that Ray can access during its operation. To ensure each task gets specific resources, such as 1 CPU and 1 GPU, you should define those requirements in the task itself. Here's how you can manage resource allocation per task: 1. Specify Resources per Task: When you register a task with Ray, annotate it with resource requirements:

python
   import ray
   
   @ray.remote(num_cpus=1, num_gpus=1)
   def task_function():
       # Task logic here
       pass

Here,

num_cpus

and

num_gpus

ensure that each invocation of

task_function

requires 1 CPU and 1 GPU. 2. Run Task in Prefect Flow: Within your Prefect flow, when you run this task, Ray will check its available resources and allocate them accordingly for each task run. This approach will ensure that Ray allocates the specified resources individually for each task execution, so if you have multiple tasks, each should be able to request and use the specified resources independently, assuming your Ray cluster has sufficient resources available. References: - You can find more about managing resources in Ray's documentation and how Prefect allows these integrations in Prefect Ray documentation. So, each task asking for its 1 CPU and 1 GPU is a bit like everyone wanting their own corner office—Ray can handle that, provided there are enough "offices" to go around!

@Marvin this doesn't make any sense, tasks are decorated with prefect's task decorators?

You're right, my earlier explanation was a bit off track. In Prefect, we typically define tasks using Prefect's

@task

decorator, and the way to allocate resources like GPUs per task involves configuring the task runner and your environment to support these allocations. Here's a more Prefect-centric approach: 1. Task Definition with Prefect: Use Prefect's

@task

decorator on your function. Prefect itself doesn't manage GPU or CPU allocations directly within the task definition. Instead, it relies on the task runner to interface with systems like Ray or Dask which do.

python
   from prefect import task

   @task
   def my_prefect_task():
       # Task logic here
       pass

2. Task Runner Configuration: In your flow, configure Ray using the

RayTaskRunner

, and let Ray manage how tasks are distributed. Ray will handle the resource allocations based on how you start the Ray cluster and configure its resource pools.

python
   from prefect import flow
   from prefect_ray import RayTaskRunner

   ray_task_runner = RayTaskRunner(
       address="<ray://your-ray-cluster-address>",
       init_kwargs={
           # Other ray.init options, like handling resources globally
       },
   )

   @flow(task_runner=ray_task_runner)
   def my_flow():
       my_prefect_task()

3. Ray Side for Resource Handling: On the Ray side, ensure resources are adequately managed when you start the Ray cluster. Ray will schedule tasks based on available resources. You might need to annotate tasks with resource requirements if you were using native Ray tasks, but with Prefect, this isn't done in the task decorator itself. The crucial part is ensuring your cluster is configured to offer the necessary resources, and Ray's scheduler will distribute accordingly. Learn More: - Prefect's Ray Integration Documentation. Ah, the joy of juggling resources! It's like assigning chores—everyone gets what they need, but remember to list who's responsible!