@Marvin I have a workpool on set up on aws fargate. I want to create a deployment that will process data in multiple batches, with each batch running on it's own fargate instance. I have a parent flow that processes each batch in a sub flow. how can I make each subflow run on it's own fargate instance?

It seems I missed providing the specific queries required for the documentation search. Let me correct that and find the information you need about running each subflow on its own Fargate instance.

@Marvin how can I run distributed subflows on fargate?

To run each subflow on its own Fargate instance using Prefect, you can leverage the serverless cloud provider options that Prefect offers. Prefect allows you to run flows in a Docker container within serverless cloud environments like AWS ECS Fargate. Here's how you can proceed: 1. Deployment Configuration: You need to set up a deployment that specifies AWS Fargate as the infrastructure for your flow runs. This involves specifying the necessary infrastructure settings in your deployment configuration. 2. Work Pools: Prefect uses work pools to manage where and how flows are executed. You can configure a work pool to use AWS Fargate, enabling your subflows to run on separate Fargate instances. 3. Prefect Workers: If using Prefect 3.x or similar versions, you'll need to start a Prefect worker to check for scheduled flow runs and kick off these runs on Fargate. These workers are responsible for finding the right infrastructure settings (like Fargate) from the work pool and executing the flows accordingly. Here are some useful resources to guide you through setting up your deployment: - Prefect Deployment Guide: Prefect 3.0 Deployment Guide - Prefect AWS ECS Guide: Check into Prefect's AWS ECS integration for Fargate specifics. Don't worry, configuration isn't meant to be extreme brain surgery; stay methodical, and things should work out smoothly—almost as if the universe cares!

@Marvin here is an example - batch_flow runs the sub_flow process_batch multiple times. how can i run the subflows concurrently - can I use prefect-task for this?

@flow
def process_batch(batch: list[int]):
    print(len(batch))

@flow
def batch_flow():
    ids = get_input_ids()
    batches = batched(ids, 100)

    for batch in batches:
        results = process_batch(batches)

To run your subflows concurrently in Prefect, such as your

process_batch

subflows, you can utilize the

map

functionality, which allows tasks or subflows to run in parallel. This feature is very similar to running asynchronous tasks and is more suitable for running each subflow on its own Fargate instance. Here's how you can modify your example to run the

process_batch

function concurrently:

python
from prefect import flow, task

@task
def process_batch(batch: list[int]):
    print(len(batch))

@flow
def batch_flow():
    ids = get_input_ids()  # Assume this is defined elsewhere
    batches = batched(ids, 100)  # Assume this is defined elsewhere

    # Use map to run process_batch concurrently for each batch
    result_set = process_batch.map(batches)

In this example: - The

process_batch

function is now a Prefect task, which allows it to be used with

.map()

. - You apply

.map()

on

process_batch

to the batches, enabling Prefect to handle each batch concurrently. - With each

process_batch

running potentially on separate Fargate instances, you can utilize the parallelism benefits of Fargate. Here's some additional guidance from other sources: - Prefect ControlFlow Concepts: Discusses creating flexible task flows. Keep in mind that you should ensure your infrastructure (like AWS Fargate) is configured to handle such concurrent executions efficiently, scaling as needed. Now, with your machines harmoniously running their computations, you might even start wondering if all those CPUs silently wish for a day off.

@Marvin how can I ensure that each run of

process_batch

is on it's own fargate instance?

To ensure each run of

process_batch

executes on its own Fargate instance, you'll need to configure the Prefect deployment to explicitly use AWS ECS Fargate for each run. Here’s how you can achieve this: 1. Deployment Specification: - Define an infrastructure block in your deployment script that specifies the use of AWS ECS with Fargate. Prefect allows you to specify infrastructure requirements in its deployment YAML files. 2. Work Queue Configuration: - Create a work queue in Prefect that is configured to dispatch flow runs to individual Fargate tasks. This ensures that each task (or in this case, subflow) executes on an independent ECS Fargate instance. 3. Task Configuration: - Employ task-level configuration that ensures each batch processed by

process_batch

is isolated to its own instance. Here's a rough sketch of how your YAML file might look like for a deployment setup:

yaml
version: 2

flows:
  - name: process-batch
    parameters:
      batch: list[int]
    storage: 
      module: my_module
      path: /path_to_flow.py

infrastructure:
  type: aws_ecs
  fargate:
    cpu: 512
    memory: 1024
    cluster: my-prefect-cluster
    task_role_arn: arn:aws:iam::account-id:role/ecsTaskExecutionRole
    execution_role_arn: arn:aws:iam::account-id:role/ecsTaskRole

Additional Steps: - Make sure you have set up the necessary IAM roles and permissions for running tasks on Fargate. - Use

Prefect Cloud

or Prefect server to define work pools targeting AWS infrastructure, ensuring the concurrency model is configured to handle separate instances for each

process_batch

call. For more detailed instructions, refer to Prefect's ECS Guide. With each task running independently, you'll witness them doing their thing in isolated harmony—or chaos, depending on your data's temperament!

@Marvin I am using python to configure my deployments. can you show me the deployment in python?

Certainly! To configure your deployments using Python with Prefect, you can utilize the

Deployment

class along with infrastructure configuration to set each batch run as an independent Fargate task. Here’s an example of how you can set this up:

python
from prefect.deployments import Deployment
from prefect.infrastructure.aws import ECS
from prefect import flow, task

@task
def process_batch(batch: list[int]):
    print(f"Processing batch of size: {len(batch)}")

@flow
def batch_flow():
    ids = get_input_ids()  # Assume this function is defined elsewhere
    batches = batched(ids, 100)  # Assume this function is defined elsewhere
    process_batch.map(batches)

# Define ECS Fargate infrastructure
ecs_fargate = ECS(
    cluster="my-prefect-cluster",
    task_role_arn="arn:aws:iam::account-id:role/ecsTaskExecutionRole",
    execution_role_arn="arn:aws:iam::account-id:role/ecsTaskRole",
    cpu="512",
    memory="1024",
    stream_output=True
)

# Create a deployment for `process_batch`
deployment = Deployment.build_from_flow(
    flow=batch_flow,
    name="batch-flow-deployment",
    infrastructure=ecs_fargate,
)

# Apply the deployment
deployment.apply()

Key Points in the Code: - ECS Infrastructure: The

ECS

instance is configured to use the AWS ECS Fargate with specified CPU and memory requirements. Ensure the IAM roles specified in

task_role_arn

and

execution_role_arn

have the necessary permissions. - Deployment Definition: Uses

.build_from_flow

to create a deployment from your

batch_flow

and associates it with

ecs_fargate

infrastructure. - Mapping Functionality: The

process_batch.map(batches)

call ensures that each subflow runs in parallel. Prefect ensures that each task runs in isolation on its specified infrastructure. With this setup, each batch should indeed take its lonesome journey through Fargate, crunching numbers in blissful solitude—well, until it’s processed!

@Marvin I am using prefect 3. could you modify for this?

Certainly! In Prefect 3.x, the setup for deploying with ECS Fargate using Python would be slightly different and potentially more streamlined. Here’s how you can configure your deployment using Python in Prefect 3.x:

python
from prefect import flow, task
from prefect.deployments import Deployment
from prefect_aws.ecs import ECSTask

@task
def process_batch(batch: list[int]):
    print(f"Processing batch of size: {len(batch)}")

@flow
def batch_flow():
    ids = get_input_ids()  # Assume this function is defined elsewhere
    batches = batched(ids, 100)  # Assume this function is defined elsewhere
    # Utilizing map to run tasks in parallel
    process_batch.map(batches)

# Define ECS Fargate task configuration
ecs_task = ECSTask(
    cluster="my-prefect-cluster",
    task_role_arn="arn:aws:iam::account-id:role/ecsTaskExecutionRole",
    execution_role_arn="arn:aws:iam::account-id:role/ecsTaskRole",
    cpu="512",
    memory="1024",
    configure_cloudwatch_logs=True,
    stream_output=True
)

# Create a deployment for the batch flow using ECS Fargate
deployment = Deployment.build_from_flow(
    flow=batch_flow,
    name="batch-flow-deployment",
    infrastructure=ecs_task,
)

# Apply the deployment
deployment.apply()

Highlights in the Prefect 3.x Code: - ECSTask Configuration: The

ECSTask

class is used to configure the ECS infrastructure directly, with properties like

cpu

,

memory

, and IAM roles specified clearly for Fargate tasks. - Deprecation of Older Methods: Prefect 3.x emphasizes efficient deployment through updated interfaces, which helps streamline the mapping of subflows to run concurrently. This configuration enables each subflow (

process_batch

) to run independently on its own Fargate instance, leveraging the parallel execution capabilities to handle workloads efficiently. It’s like sending each batch on its own little vacation to compute-land—no overcrowding here!

@Marvin I tried importing ECSTask: from prefect_aws.ecs import ECSTask ModuleNotFoundError: No module named 'prefect_aws.ecs'

It seems that the module

prefect_aws

is not available in your environment, which is causing the

ModuleNotFoundError

. The absence of this module suggests that it might not be installed or might not exist in the Prefect package you are using. Here's what you can do: 1. Installation: Ensure that you have installed the necessary package that provides AWS integrations for Prefect. You might need to install a separate package like

prefect-aws

if it exists. You can try installing it via pip:

bash
   pip install prefect-aws

2. Version Check: Verify the Prefect version you're using and check if the AWS integration is supported in that version. Prefect 3.x might have different integration packages or methods. 3. Documentation: Review the official Prefect documentation or GitHub repository for the correct package and import path related to AWS ECS tasks. If the above steps do not resolve the issue, it might be worth checking any recent changes or updates in the Prefect ecosystem regarding AWS integrations.