<@ULVA73B9P> can you give me an example using prefect v2 of a dasktaskrunner which uses kubernetes o...

Mark Reyes

10/04/2024, 10:33 PM

@Marvin can you give me an example using prefect v2 of a dasktaskrunner which uses kubernetes operator dask cluster that has 3 workers, 1 thread each worker, 2GiB memory limit?

Marvin

10/04/2024, 10:39 PM

To set up a Dask cluster with the

DaskTaskRunner

using Kubernetes with Prefect 2.x, you need to specify the configuration for the cluster class and its parameters. Here's an example of how you can achieve this with 3 workers, each having 1 thread and a memory limit of 2GiB: ### Using DaskTaskRunner with a Kubernetes Dask Cluster 1. Install necessary packages: Ensure that you have

dask-kubernetes

installed in your environment. You might also need Prefect's Dask and Kubernetes dependencies. 2. Create the Custom Cluster Spec: Create a custom spec for your Dask workers, specifying the number of workers, threads per worker, and memory limits. 3. Configure DaskTaskRunner: Use Prefect’s

DaskTaskRunner

and specify the

KubeCluster

from

dask_kubernetes

. Here's an example code snippet for setting this up:

Copy code

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner
from dask_kubernetes import KubeCluster

# Create a custom Dask Kubernetes cluster specification
custom_spec = {
    "metadata": {"name": "dask-worker"},
    "spec": {
        "containers": [{
            "args": ["dask-worker", "--nthreads", "1", "--memory-limit", "2GiB"],
            "image": "daskdev/dask:latest",
            "name": "dask-worker"
        }]
    }
}

# Define the Dask task runner
dask_runner = DaskTaskRunner(
    cluster_class="dask_kubernetes.KubeCluster",
    cluster_kwargs={"custom_worker_spec": custom_spec}
)

@task
def my_task():
    return "Hello from Dask Worker"

@flow(task_runner=dask_runner)
def my_flow():
    result = my_task()
    print(result)

my_flow()

### Important Notes: - Cluster Spec: Customize this according to your image and the setup required. Make sure that your Kubernetes environment is set up to allow Dask to spin up workers as per the spec. - Address: You can specify an existing Dask scheduler address if you are managing your cluster outside of Prefect. For further details and examples, you can refer to the Prefect 2.x documentation for task runners and task execution with Dask. Feel free to reach out if you need more help setting up or understanding specific parts of the configuration!

Marvin

10/04/2024, 10:40 PM

To create a Dask cluster with Prefect v2 using the

DaskTaskRunner

with Kubernetes, you'll typically use the

dask_kubernetes.KubeCluster

. Here's an example setup that uses a Kubernetes operator to create a Dask cluster with 3 workers, each having 1 thread per worker and a memory limit of 2GiB:

Copy code

python
from prefect import flow
from prefect_dask import DaskTaskRunner
from dask_kubernetes import KubeCluster

@flow(task_runner=DaskTaskRunner(cluster_class='dask_kubernetes.KubeCluster', 
                                 cluster_kwargs={'worker_template': {
                                     'spec': {
                                        'containers': [{
                                            'args': [
                                                '--nthreads', '1',
                                                '--memory-limit', '2GiB'
                                            ],
                                            'resources': {
                                                'limits': {
                                                    'memory': '2GiB'
                                                }
                                            }
                                        }]
                                    }
                                 }}))
def my_flow():
    # Define your tasks here
    pass

if __name__ == "__main__":
    my_flow()

Here's a breakdown of what's happening in this configuration: 1. KubeCluster: This sets the use of Kubernetes for creating the Dask cluster. 2. Worker Configuration: The

worker_template

allows for specifying details like the number of threads (

--nthreads

) and the memory limit for each worker. This template is passed in

cluster_kwargs

. 3. Running the Flow: The standard Python practice of checking

if __name__ == "__main__":

is used to run the flow to prevent unwanted multiprocessing issues. You can find more detailed configuration options in the Prefect documentation for DaskTaskRunner. If you encounter any issues, checking Prefect's GitHub issues could also be a good practice as there could be ongoing discussions or resolutions related to Kubernetes and Dask with Prefect.

Marvin

10/04/2024, 10:40 PM

To create a Dask cluster using Prefect v2 with Kubernetes Operator and configure it with 3 workers, each having 1 thread and a memory limit of 2GiB, you can utilize

DaskTaskRunner

in conjunction with

dask_kubernetes.KubeCluster

. Here’s a guide on how to set this up: 1. Kubernetes Configuration: Ensure that you have the necessary Kubernetes setup and permissions for deploying Dask clusters. 2. DaskTaskRunner Configuration: You'll configure the

DaskTaskRunner

with

KubeCluster

and specify the worker configurations. Here's a Python code example:

Copy code

python
from prefect import flow
from prefect_dask import DaskTaskRunner
from dask_kubernetes import KubeCluster
from kubernetes import client as k8s_client

# Define the Kubernetes worker template
worker_template = {
    "metadata": {
        "labels": {"app": "dask"},
    },
    "spec": {
        "containers": [
            {
                "name": "dask-worker",
                "image": "daskdev/dask:latest",
                "args": ["dask-worker", "--nthreads", "1", "--memory-limit", "2GiB"],
                "resources": {
                    "limits": {"memory": "2GiB"}
                },
            }
        ]
    },
}

@flow(task_runner=DaskTaskRunner(
    cluster_class="dask_kubernetes.KubeCluster",
    cluster_kwargs={
        "worker_template": worker_template,
        "n_workers": 3
    }
))
def my_flow():
    # Your task logic here
    pass

if __name__ == "__main__":
    my_flow()

### Key Points: - Worker Template: Defines the Kubernetes pod configuration for each Dask worker, including memory and thread limits. - Cluster Class and Kwargs: Utilize

KubeCluster

to deploy the Dask cluster on Kubernetes with specific arguments for worker count and resource limits. ### Resources: - You can find more about

DaskTaskRunner

in the Prefect documentation here: Prefect 2 Task Runners. - If you face any issues or need additional examples, exploring the Prefect GitHub issues can provide more insight and community support: Prefect GitHub Issues. This setup will ensure that your Dask cluster is running with the desired configuration on Kubernetes, enabling you to leverage distributed computing efficiently with Prefect.

Marvin

10/05/2024, 12:01 AM

To use a

DaskTaskRunner

with a Kubernetes-based Dask cluster in Prefect v2, you need to configure it with specific memory and worker configurations. Here's a general roadmap on how you could set up such an environment based on the search results: 1. DaskTaskRunner Setup: You can use the

DaskTaskRunner

class and specify the type of Dask cluster you'd like to use. In this case, you will need a Kubernetes-based cluster. 2. Cluster Configuration: - Use

dask_kubernetes.KubeCluster

to set up a Kubernetes cluster. - Configure your cluster with the necessary parameters like the number of workers, number of threads per worker, and memory limits. Below is a conceptual outline of the code setup:

Copy code

python
from prefect_dask import DaskTaskRunner
from dask_kubernetes import KubeCluster
from kubernetes import config

# Load the Kubernetes configuration
config.load_kube_config()

# Define the Dask Cluster configuration
cluster = KubeCluster.from_dict({
    "kind": "Cluster",
    "spec": {
        "worker": {
            "replicas": 3,
            "spec": {
                "containers": [{
                    "name": "dask-worker",
                    "image": "your_dask_worker_image",  # Ensure to use a valid Dask worker image
                    "args": ["dask-worker", "--nthreads", "1"],
                    "resources": {
                        "limits": {"memory": "2Gi"},
                    }
                }]
            }
        }
    }
})

task_runner = DaskTaskRunner(cluster_class=KubeCluster, cluster_kwargs={"from_dict": cluster.config})

# You can now use this task_runner in your Prefect Flow

### Resources - Prefect Dask Task Runners - More specific examples and configurations would require diving into the specific YAML or Helm configurations you might set up on your Kubernetes cluster. This configuration assumes you are familiar with deploying applications on a Kubernetes cluster and adjusting settings such as container images and resource limits. If you encounter issues during setup, feel free to share more details, and I'll be happy to assist further!

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