@Marvin For my new flow I want to use DaskTaskRunner with an external Dask cluster. Do I need to install the same dependencies from my flow onto the Dask cluster worker nodes? if so how?

Yes, you will need to ensure that the dependencies for your flow are installed on the Dask cluster worker nodes. This is necessary for the tasks executed on the cluster to have access to the same libraries and modules as your flow. For installing dependencies on Dask cluster worker nodes, you have a couple of options: 1. Install dependencies directly on the worker nodes: This can usually be done through the environment configuration of your cluster. If you are using Dask Kubernetes, for example, you could specify an image that includes all the necessary dependencies. 2. Use Dask's mechanisms to install packages on workers dynamically: Dask provides ways to install packages on workers at runtime using the

client.run

or

client.upload_file

methods. This can be done by connecting a

dask.distributed.Client

to your cluster and then running installation commands or uploading necessary files. ### Example using a Docker image (Kubernetes) If you're using Kubernetes, you can create a Docker image that includes all your dependencies and use that as the base image for your Dask workers:

python
from prefect_dask import DaskTaskRunner
from dask_kubernetes import KubeCluster

cluster = KubeCluster(
    n_workers=4,
    image="my-prefect-image:latest"
)

task_runner = DaskTaskRunner(cluster=cluster)

### Example using

client.run

Another approach is using the Dask client to run installation commands on each worker when they start:

python
from prefect_dask import DaskTaskRunner
from dask.distributed import Client

address = "<tcp://scheduler-address:8786>"
client = Client(address)

def install_dependencies():
    import os
    os.system("pip install my-package")

client.run(install_dependencies)
task_runner = DaskTaskRunner(address=address)

Here are the relevant links for more details and examples: - Prefect 2.x: Dask integration - Prefect 3.x: Dask integration Feel free to follow the documentation links for deeper insights into configuring your specific setup.

@Marvin minimally, what dependencies would i need to have if i were to use a custom docker image for the dask worker nodes?

To use a custom Docker image for the Dask worker nodes, you'll need to include the essential dependencies required by Dask and Prefect to function. At a minimum, your Docker image should include: 1. Python: Ensure that Python is installed. 2. Dask: The core Dask library along with Dask distributed. 3. Prefect: The Prefect library to support Prefect tasks and flows. 4. Any additional dependencies your flow requires. Here is an example Dockerfile to get you started:

Dockerfile
# Use an official Python runtime as a parent image
FROM python:3.9-slim

# Set the working directory in the container
WORKDIR /usr/src/app

# Install Dask and Prefect
RUN pip install dask[complete] prefect

# Install any additional dependencies your flow needs
# RUN pip install <other-dependencies>

# Optionally copy your code into the container
# COPY . .

# Command to keep the container running (adjust as needed)
CMD ["dask-worker", "<tcp://scheduler-address:8786>"]

### Explanation - FROM python:3.9-slim: Uses a slim version of Python 3.9 as the base image. - WORKDIR /usr/src/app: Sets the working directory inside the container. - RUN pip install dask[complete] prefect: Installs Dask with all optional dependencies and Prefect. - RUN pip install <other-dependencies>: Placeholder to install any additional dependencies your Prefect flow might require. - CMD ["dask-worker", "tcp://scheduler-address:8786"]: Command to start the Dask worker. Adjust the scheduler address accordingly. You can find more detailed information in the Prefect documentation: - Prefect Dask Integration Feel free to adjust the Dockerfile according to your needs.