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<@ULVA73B9P> prefect deployment job variables add ...
# ask-marvin
m
@Marvin prefect deployment job variables add env vars example
m
To add environment variables to a Prefect deployment, you can define these variables in the 
prefect.yaml
file using the 
job_variables
section. This approach is applicable to both Prefect 2.x and 3.x versions. Here's a basic example: 1. Define Job Variables in `prefect.yaml`: You can add your environment variables directly within the 
job_variables
section. Here's how:
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yaml
deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
  job_variables:
    env:
      EXECUTION_ENVIRONMENT: staging
      MY_NOT_SO_SECRET_CONFIG: plumbus
  schedule: null
After adding these settings, deploy your flow using:
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shell
prefect deploy -n demo-deployment
2. Using Existing Environment Variables: If you want to use environment variables already set in your local environment, you can reference them in your 
prefect.yaml
using templating:
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yaml
deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
  job_variables:
    env:
      EXECUTION_ENVIRONMENT: "{{ $EXECUTION_ENVIRONMENT }}"
      MY_NOT_SO_SECRET_CONFIG: "{{ $MY_NOT_SO_SECRET_CONFIG }}"
  schedule: null
Ensure these environment variables are exported locally before running `prefect deploy`:
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shell
export EXECUTION_ENVIRONMENT=staging
export MY_NOT_SO_SECRET_CONFIG=plumbus
For more information, you can refer to the Prefect documentation for your specific version: - Prefect 2.x Documentation - Prefect 3.x Documentation
m
@Marvin dasktaskrunner example with kubernetes dask cluster that has 3 worker nodes
m
dasktaskrunner example with kubernetes dask cluster that has 3 worker nodes dasktaskrunner example with kubernetes dask cluster that has 3 worker nodes To use a 
DaskTaskRunner
with a Kubernetes Dask cluster having 3 worker nodes, you can configure it to connect to an existing Kubernetes Dask cluster or create one using a Dask cluster manager. Below is a general example of how to set this up: 1. Create or Refer to an Existing Dask Cluster: You have two options: connect to an existing Dask cluster deployed on Kubernetes, or create a temporary cluster using a Dask cluster manager. 2. Connect to Existing Dask Cluster: If your Dask cluster is already running on Kubernetes, you need to know the scheduler's address. Here’s how you can connect using Prefect:
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python
   from prefect import flow
   from prefect_dask import DaskTaskRunner

   @flow(task_runner=DaskTaskRunner(address="tcp://<scheduler-address>:8786"))
   def my_flow():
       # Your flow logic here
       pass

   if __name__ == "__main__":
       my_flow()
3. Creating a Temporary Dask Cluster: If you prefer to create a cluster for each flow run, you can use Dask’s Kubernetes integration. This requires configuring a Dask Kubernetes cluster:
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python
   from prefect import flow
   from prefect_dask import DaskTaskRunner

   @flow(task_runner=DaskTaskRunner(
       cluster_class="dask_kubernetes.KubeCluster",
       cluster_kwargs={
           "worker_image": "my-dask-image",
           "n_workers": 3
       }
   ))
   def my_flow():
       # Your flow logic here
       pass

   if __name__ == "__main__":
       my_flow()
References: - Prefect 2.x Task Runners - Prefect 3.x Prefect-Dask Integration This setup assumes you have the necessary Kubernetes resources set up and a Docker image (
my-dask-image
) suitable for Dask workers. Adjust the 
n_workers
and 
worker_image
as per your infrastructure and requirements.
dasktaskrunner example with kubernetes dask cluster that has 3 worker nodes To execute a flow using a 
DaskTaskRunner
with a Kubernetes Dask cluster comprising 3 worker nodes, you need a setup that leverages Dask's Kubernetes capabilities. Below is a more detailed example to guide you through configuring this with Prefect and Dask: 1. Prerequisites: Ensure that you have a Kubernetes cluster running and have the necessary permissions to deploy resources. You'll also need the 
dask-kubernetes
Python package installed, which provides the interface to interact with Kubernetes for Dask. 2. Set Up Dask Kubernetes Cluster: Use Dask's 
KubeCluster
to create a Dask cluster on Kubernetes. You can specify the number of workers in the cluster configuration. 3. Implement Prefect with `DaskTaskRunner`: Here's how you can set it up:
Copy code
python
   from prefect import flow
   from prefect_dask import DaskTaskRunner
   from dask_kubernetes import KubeCluster, make_pod_spec

   @flow(task_runner=DaskTaskRunner(
       cluster_class="dask_kubernetes.KubeCluster",
       cluster_kwargs={
           "pod_template": make_pod_spec(image="your-dask-image:latest"),
           "namespace": "default",
           "n_workers": 3
       }
   ))
   def my_flow():
       # Define your flow logic here
       pass

   if __name__ == "__main__":
       my_flow()
4. Details to Note: - `make_pod_spec`: This function is used to define the pod template for Dask workers. Ensure that the 
image
you use contains all the necessary dependencies. - `n_workers`: Specifies the total number of worker nodes. In this case, it is set to 3 as requested. - Kubernetes Namespace: Ensure you have the correct namespace (
default
in this example) where your resources will be deployed. 5. Execution: - Deploy this flow with your Kubernetes setup and ensure that Prefect is running in an environment that can access this Kubernetes cluster. This example provides a framework to use Prefect with Dask running on Kubernetes. You'll need to adapt it to your specific setup, especially addressing the Kubernetes configuration and Docker images. References: - Prefect Dask Integration - Dask Kubernetes Documentation To execute a flow using a 
DaskTaskRunner
with a Kubernetes Dask cluster comprising 3 worker nodes, you need a setup that leverages Dask's Kubernetes capabilities. Below is a more detailed example to guide you through configuring this with Prefect and Dask: 1. Prerequisites: Ensure that you have a Kubernetes cluster running and have the necessary permissions to deploy resources. You'll also need the 
dask-kubernetes
Python package installed, which provides the interface to interact with Kubernetes for Dask. 2. Set Up Dask Kubernetes Cluster: Use Dask's 
KubeCluster
to create a Dask cluster on Kubernetes. You can specify the number of workers in the cluster configuration. 3. Implement Prefect with `DaskTaskRunner`: Here's how you can set it up:
Copy code
python
   from prefect import flow
   from prefect_dask import DaskTaskRunner
   from dask_kubernetes import KubeCluster, make_pod_spec

   @flow(task_runner=DaskTaskRunner(
       cluster_class="dask_kubernetes.KubeCluster",
       cluster_kwargs={
           "pod_template": make_pod_spec(image="your-dask-image:latest"),
           "namespace": "default",
           "n_workers": 3
       }
   ))
   def my_flow():
       # Define your flow logic here
       pass

   if __name__ == "__main__":
       my_flow()
4. Details to Note: - `make_pod_spec`: This function is used to define the pod template for Dask workers. Ensure that the 
image
you use contains all the necessary dependencies. - `n_workers`: Specifies the total number of worker nodes. In this case, it is set to 3 as requested.
- Kubernetes Namespace: Ensure you have the correct namespace (
default
in this example) where your resources will be deployed. 5. Execution: - Deploy this flow with your Kubernetes setup and ensure that Prefect is running in an environment that can access this Kubernetes cluster. This example provides a framework to use Prefect with Dask running on Kubernetes. You'll need to adapt it to your specific setup, especially addressing the Kubernetes configuration and Docker images. References: - Prefect Dask Integration - Dask Kubernetes Documentation
To execute a flow using a 
DaskTaskRunner
with a Kubernetes Dask cluster comprising 3 worker nodes, you need a setup that leverages Dask's Kubernetes capabilities. Below is a more detailed example to guide you through configuring this with Prefect and Dask: 1. Prerequisites: Ensure that you have a Kubernetes cluster running and have the necessary permissions to deploy resources. You'll also need the 
dask-kubernetes
Python package installed, which provides the interface to interact with Kubernetes for Dask. 2. Set Up Dask Kubernetes Cluster: Use Dask's 
KubeCluster
to create a Dask cluster on Kubernetes. You can specify the number of workers in the cluster configuration. 3. Implement Prefect with `DaskTaskRunner`: Here's how you can set it up:
Copy code
python
   from prefect import flow
   from prefect_dask import DaskTaskRunner
   from dask_kubernetes import KubeCluster, make_pod_spec

   @flow(task_runner=DaskTaskRunner(
       cluster_class="dask_kubernetes.KubeCluster",
       cluster_kwargs={
           "pod_template": make_pod_spec(image="your-dask-image:latest"),
           "namespace": "default",
           "n_workers": 3
       }
   ))
   def my_flow():
       # Define your flow logic here
       pass

   if __name__ == "__main__":
       my_flow()
4. Details to Note: - `make_pod_spec`: This function is used to define the pod template for Dask workers. Ensure that the 
image
you use contains all the necessary dependencies. - `n_workers`: Specifies the total number of worker nodes. In this case, it is set to 3 as requested. - Kubernetes Namespace: Ensure you have the correct namespace (
default
in this example) where your resources will be deployed. 5. Execution: - Deploy this flow with your Kubernetes setup and ensure that Prefect is running in an environment that can access this Kubernetes cluster. This example provides a framework to use Prefect with Dask running on Kubernetes. You'll need to adapt it to your specific setup, especially addressing the Kubernetes configuration and Docker images. References: - Prefect Dask Integration - Dask Kubernetes Documentation To execute a flow using a 
DaskTaskRunner
with a Kubernetes Dask cluster comprising 3 worker nodes, you need a setup that leverages Dask's Kubernetes capabilities. Below is a more detailed example to guide you through configuring this with Prefect and Dask: 1. Prerequisites: Ensure that you have a Kubernetes cluster running and have the necessary permissions to deploy resources. You'll also need the 
dask-kubernetes
Python package installed, which provides the interface to interact with Kubernetes for Dask. 2. Set Up Dask Kubernetes Cluster: Use Dask's 
KubeCluster
to create a Dask cluster on Kubernetes. You can specify the number of workers in the cluster configuration. 3. Implement Prefect with `DaskTaskRunner`: Here's how you can set it up:
Copy code
python
   from prefect import flow
   from prefect_dask import DaskTaskRunner
   from dask_kubernetes import KubeCluster, make_pod_spec

   @flow(task_runner=DaskTaskRunner(
       cluster_class="dask_kubernetes.KubeCluster",
       cluster_kwargs={
           "pod_template": make_pod_spec(image="your-dask-image:latest"),
           "namespace": "default",
           "n_workers": 3
       }
   ))
   def my_flow():
       # Define your flow logic here
       pass

   if __name__ == "__main__":
       my_flow()
4. Details to Note: - `make_pod_spec`: This function is used to define the pod template for Dask workers. Ensure that the 
image
you use contains all the necessary dependencies. - `n_workers`: Specifies the total number of worker nodes. In this case, it is set to 3 as requested.
- Kubernetes Namespace: Ensure you have the correct namespace (
default
in this example) where your resources will be deployed. 5. Execution: - Deploy this flow with your Kubernetes setup and ensure that Prefect is running in an environment that can access this Kubernetes cluster. This example provides a framework to use Prefect with Dask running on Kubernetes. You'll need to adapt it to your specific setup, especially addressing the Kubernetes configuration and Docker images. References: - Prefect Dask Integration - Dask Kubernetes Documentation
To use a 
DaskTaskRunner
with a Kubernetes Dask cluster consisting of 3 worker nodes, you can set it up as follows. This example assumes you're using Prefect with the Dask and Kubernetes integration: 1. Set Up a Kubernetes Cluster for Dask: - Ensure that you have a Kubernetes cluster up and running. - Use Dask's Kubernetes integration to manage your Dask workers within this cluster. 2. Example with Prefect's `DaskTaskRunner`: The following example shows how to create and use a Kubernetes Dask cluster with 3 workers using the 
DaskTaskRunner
.
Copy code
python
from prefect import flow
from prefect_dask import DaskTaskRunner
from dask_kubernetes import KubeCluster, make_pod_spec

@flow(task_runner=DaskTaskRunner(
    cluster_class="dask_kubernetes.KubeCluster",
    cluster_kwargs={
        "pod_template": make_pod_spec(image="your-dask-image"),
        "namespace": "default",
        "n_workers": 3
    }
))
def my_flow():
    # Define your flow logic here
    pass

if __name__ == "__main__":
    my_flow()
3. Important Components: - `make_pod_spec`: This function is used to specify the pod specifications. Make sure the 
image
you refer to has all the necessary dependencies installed. - `n_workers`: Set this to 3 to specify the number of worker nodes within the Dask cluster. - `DaskTaskRunner`: Specify using 
cluster_class
and 
cluster_kwargs
to integrate with Kubernetes. 4. Running Your Flow: - Ensure your script runs in an environment that has access to your Kubernetes cluster, and your kubeconfig is properly set up. - Deploy the flow with your Kubernetes cluster running. This setup allows Prefect to leverage Dask's parallel computing capabilities within a Kubernetes environment, using 3 workers to process tasks in parallel. References: - Prefect 2.x Task Runners Documentation - Prefect 3.x Prefect-Dask Integration - Dask Kubernetes Documentation
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