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<@ULVA73B9P> im running my flow in k8s job, the co...
# marvin-in-the-wild
n
@Marvin im running my flow in k8s job, the code is running a couple of processing. Is it possible to fork the k8s to more k8s jobs?
m
Ah, I see. You want your Kubernetes job to morph into many Kubernetes jobs within your Prefect flow. How ambitious. I suppose I'll have to look up the details for that. Which version of Prefect are you using? 2.x or 3.x?
n
@Marvin 2.20
m
It seems you're trying something a bit unconventional—spawning new Kubernetes jobs from an existing one within your Prefect flow. While I didn't find a precise "multiply like rabbits" approach readily documented, it looks like you could manage this by orchestrating multiple Kubernetes jobs using Prefect's Kubernetes integration. 1. Kubernetes Orchestrator: While this snippet doesn't provide a full example, it references the ability to manage Kubernetes jobs using Prefect tasks. Kubernetes Orchestrator 2. Infrastructure and Deployment: You can create and manage Kubernetes jobs using Prefect's infrastructure blocks. You would define and deploy these jobs programmatically within your flow. Concepts - Infrastructure 3. Upgrading from Agents to Workers: Relevant if you are transitioning or setting up your Prefect flows to handle multiple workers, which could include Kubernetes jobs. Upgrade Guide In essence, you would define a new 
KubernetesJob
task within your flow to spawn additional jobs. You might want to look into using the Prefect Kubernetes job task functionality to achieve this. Misery loves company, so good luck scaling your orchestrations. Here are the references: - Kubernetes Orchestrator - Using Infrastructure - Upgrade Guide Agents to Workers
n
@Marvin my code is running a training to some model and part of the training the code need to run on 500 inputs (preferably in parallel) and then there is some calculations on the results from all the inputs. I used the run this on airflow which had the ability to run k8s job for each input and then return the results to the main k8s job. Can prefect give me a similar ability?
m
It looks like you can achieve a similar setup in Prefect as you had in Airflow using Kubernetes jobs. Here's the plan: 1. Run Multiple Kubernetes Jobs in Parallel: Use Prefect’s ability to run parallel tasks with a task runner like 
DaskTaskRunner
or 
RayTaskRunner
. These runners would allow you to manage concurrent execution, distributing the workload across multiple tasks. 2. Kubernetes Job Management: Prefect's Kubernetes integration can create and manage these jobs. You can define Kubernetes jobs and run them in a flow using 
run_namespaced_job
. Here's a summary of steps you can follow to set this up: ### Parallel Execution You can use 
DaskTaskRunner
or 
RayTaskRunner
for parallel task execution. More details can be found here. ### Kubernetes Jobs 1. Define Kubernetes Job: Load and define your Kubernetes job configuration. 2. Run Job in Namespaces: Use 
run_namespaced_job
to execute these jobs. Example flow:
Copy code
python
from prefect import Flow, task
from prefect.infrastructure import KubernetesJob
from prefect_kubernetes.credentials import KubernetesCredentials
from prefect_kubernetes.flows import run_namespaced_job

k8s_creds = KubernetesCredentials.load("k8s-creds")

@task
def create_k8s_job(input):
    job = KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/job.yaml",
    )
    job.save(f"job-{input}", overwrite=True)

@task
def process_results():
    # Combine results from the jobs

with Flow("parallel-k8s-jobs-flow") as flow:
    inputs = range(500)
    jobs = create_k8s_job.map(inputs)
    run_namespaced_job.map(jobs)
    process_results()

flow.run()
Sources: - Task Runners in Prefect - Prefect Kubernetes Integration Hope this gets your hundreds of tasks up and running. Enjoy the distributed chaos!
n
@Marvin so in this setup ill have one k8s job which will trigger 500 jobs which will be manage by dask for example?
m
In the setup I described, you have a Prefect flow that triggers the creation of 500 Kubernetes jobs. This flow isn't running as a Kubernetes job itself but uses Prefect's infrastructure to orchestrate parallel tasks, which could be managed by something like Dask. Here's how it works step-by-step: 1. Flow Execution Environment: Your flow is running in a suitable environment (could be a local machine, VM, container, etc.). 2. Parallel Task Execution: Using 
DaskTaskRunner
or 
RayTaskRunner
, your flow will manage the parallel execution of 500 tasks, where each task creates and runs a separate Kubernetes job. 3. Kubernetes Jobs: Each of these tasks defines a Kubernetes job (based on your input) and runs it. These jobs are then managed by the Kubernetes cluster independently. So, your Prefect flow will act as the orchestrator, distributing the workload to 500 Kubernetes jobs which will run in parallel. Let me reformulate the example to clarify this: ### Example with DaskTaskRunner
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python
from prefect import Flow, task
from prefect.infrastructure import KubernetesJob
from prefect_kubernetes.credentials import KubernetesCredentials
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import create_namespaced_job
from prefect.task_runners import DaskTaskRunner

k8s_creds = KubernetesCredentials.load("k8s-creds")

@task
def create_k8s_job(input):
    job = KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/job.yaml",
    )
    create_namespaced_job(job, namespace="your-namespace")

@task
def process_results():
    # Combine results from the jobs

with Flow("parallel-k8s-jobs-flow", task_runner=DaskTaskRunner()) as flow:
    inputs = range(500)
    jobs = create_k8s_job.map(inputs)
    process_results(upstream_tasks=[jobs])

flow.run()
### Key Points: 1. Flow Orchestrates: The Prefect flow with a DaskTaskRunner orchestrates the task distribution. 2. Parallel Execution: Each input triggers a 
create_k8s_job
task in parallel. 3. K8s Jobs: Each task runs a Kubernetes job based on the YAML configuration. 4. Results Processing: After all jobs are complete, 
process_results
combines the outcomes. This setup keeps your main process lightweight while delegating heavy lifting to Kubernetes jobs. Sources: - Task Runners in Prefect - Prefect Kubernetes Integration Enjoy your aptly orchestrated Kubernetes job waterfall.
n
@Marvin can the flow which orchestrate parallel tasks can run on k8s job? also how can is get the results from the 500 tasks?
m
Yes, the orchestrating flow can itself run as a Kubernetes job, and you can capture and process the results from the 500 tasks. This involves a bit more setup but is definitely possible. Here’s how to approach it. ### Running Flow as a Kubernetes Job 1. Define Flow Deployment: You can deploy the flow using a 
KubernetesJob
infrastructure. 2. Parallel Task Execution: The flow runs in the Kubernetes job and orchestrates the parallel execution using 
DaskTaskRunner
or 
RayTaskRunner
. ### Gathering Results 1. Return Results from Kubernetes Jobs: Each Kubernetes job can store its results in a common storage service (e.g., S3, GCS, a database). 2. Aggregate Results in the Flow: After the 500 jobs finish, the flow gathers results from the storage service and processes them. ### Example Setup #### Define the Flow Define the Orchestrating Flow (as Kubernetes Job):
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python
from prefect import Flow, task
from prefect.infrastructure import KubernetesJob
from prefect_kubernetes.credentials import KubernetesCredentials
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import create_namespaced_job
from prefect.task_runners import DaskTaskRunner
import boto3  # or any storage service you use

k8s_creds = KubernetesCredentials.load("k8s-creds")
s3_client = boto3.client('s3')  # Configure as needed

@task
def create_k8s_job(input):
    job = KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/job.yaml",
    )
    # include input as environment variable to the job
    manifest['spec']['template']['spec']['containers'][0]['env'].append(
        {"name": "INPUT", "value": str(input)}
    )
    create_namespaced_job(job, namespace="your-namespace")

@task
def fetch_results():
    results = []
    for i in range(500):
        result = s3_client.get_object(Bucket='your-bucket', Key=f'results/result-{i}.json')
        results.append(result['Body'].read())
    return results

@task
def process_results(results):
    # Combine and process results from the jobs
    aggregate_result = sum(results)  # Example aggregation
    return aggregate_result

with Flow("orchestrate-k8s-jobs", task_runner=DaskTaskRunner()) as flow:
    inputs = range(500)
    jobs = create_k8s_job.map(inputs)
    results = fetch_results(upstream_tasks=[jobs])
    aggregated_result = process_results(results)

# Define a KubernetesJob infrastructure for the flow itself
k8s_job_infrastructure = KubernetesJob(
    job=KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/orchestrating-job.yaml"
    )
)

# Deploying the flow
flow.deploy(project_name="your-project", infrastructure=k8s_job_infrastructure)
#### YAML Job Definitions Orchestrator Job YAML (
path/to/orchestrating-job.yaml
):
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: orchestrating-job
spec:
  template:
    spec:
      containers:
      - name: orchestrator
        image: your-docker-image:latest
        command: ["python", "run_flow.py"]
      restartPolicy: Never
Worker Job YAML (
path/to/job.yaml
):
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: worker-job
spec:
  template:
    spec:
      containers:
      - name: worker
        image: your-docker-image:latest
        env:
        - name: INPUT
          value: ""
        command: ["python", "run_task.py"]
      restartPolicy: Never
Each worker job should be able to accept an input parameter and store its result to your chosen storage service. ### Notes 1. Flow in Kubernetes: The orchestrating flow itself runs as a Kubernetes job. 2. Result Aggregation: Flow tasks handle the job orchestration and results aggregation. Now you've got a fully contained setup where your Prefect flow runs as a Kubernetes job, orchestrates 500 parallel Kubernetes jobs, and aggregates the results. For more details, check out: - Task Runners in Prefect
- Prefect Kubernetes Integration Feeling intellectually stimulated yet?
n
@Marvin airflow has a infrastructure to send back to the “father” flow results without using 3rd party storage, does prefect has something similar?
m
Let's break down how Prefect can return results from child tasks to a parent flow without relying on third-party storage. ### Subflows and Task Results In Prefect, subflows (child flows) can return results directly to the parent flow, using Prefect’s result handling and state management features. This allows you to keep everything within Prefect without using external storage. ### Concept and Documentation 1. Subflow Execution: - A subflow run starts within the parent flow. - The state of the subflow is tracked, and results can be retrieved after execution. 2. Result Handling: - Prefect supports retrieving task results directly using methods like 
State.result()
or 
Future.result()
. ### Example Setup #### Flow Definition: Here is an example illustrating how to run subflows and gather results in Prefect:
Copy code
python
from prefect import flow, task

@task
def process_input(input):
    # simulate processing
    return input * 2

@flow
def child_flow(input):
    result = process_input(input)
    return result

@flow
def parent_flow(inputs):
    results = []
    for inp in inputs:
        # run subflow and retrieve results
        subflow_state = child_flow(inp, return_state=True)
        result = subflow_state.result()  # equivalent to fetch=True
        results.append(result)
    return results

inputs = list(range(500))

final_results = parent_flow(inputs)
# final_results now contains the computed results of all child flows
print(final_results)
### Deploying as a Kubernetes Job: You can still deploy the parent flow as a Kubernetes job if desired. Here's the gist of how that might look: Main Kubernetes Job YAML (
orchestrate-job.yaml
):
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: orchestrate-job
spec:
  template:
    spec:
      containers:
      - name: orchestrator
        image: your-docker-image:latest
        command: ["python", "-c", "from your_module import parent_flow; inputs = list(range(500)); parent_flow(inputs)"]
      restartPolicy: Never
### Steps Summary 1. Child Flow Execution: - Each input triggers a subflow. - Each subflow processes its input and returns the result. 2. Result Aggregation: - The parent flow collects results from all subflows without relying on third-party storage. 3. Deploying the Parent as a Kubernetes Job: - Deploy the parent flow as a Kubernetes job, which will handle all subflows and result collection. For more details: - Results in Prefect - Task Runners in Prefect - Flow Concepts Sources: - Prefect documentation on Results - Prefect documentation on Subflows Happy orchestrating, and enjoy not needing an external storage service!
n
@Marvin but in this case the child flows/tasks cant run as a seperate k8s job
m
You're right. If the child flows need to be separate Kubernetes jobs, the previous solution won't fully address your requirements. To achieve this, you would need to manage these Kubernetes jobs and still retrieve their results within Prefect without third-party storage. One approach is using Prefect's infrastructure to dynamically manage Kubernetes jobs and handle results correctly. ### Detailed Setup 1. Parent Flow as Kubernetes Job: The parent flow orchestrates the deployment of child flows (as separate Kubernetes jobs). 2. Child Flows as Kubernetes Jobs: Each child flow runs as an independent Kubernetes job. 3. Result Collection: Use Prefect’s result management to dynamically retrieve results. ### Example Implementation #### Define the Parent Flow Parent Flow:
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python
from prefect import flow, task
from prefect.infrastructure import KubernetesJob
from prefect_kubernetes.credentials import KubernetesCredentials
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import create_namespaced_job

# Load Kubernetes credentials
k8s_creds = KubernetesCredentials.load("k8s-creds")

@task
def submit_k8s_job(input, job_manifest_path):
    job = KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path=job_manifest_path,
    )
    # Add specific input to the job environment
    job.spec.template.spec.containers[0].env.append({"name": "INPUT", "value": str(input)})
    job_name = f"job-{input}"
    create_namespaced_job(job, namespace="your-namespace", job_name=job_name)
    return job_name

@task
def fetch_job_result(job_name):
    # Logic to pull results from child job (e.g., via Prefect Result handling)
    # This would ideally involve the child job pushing results to a known Prefect state
    result = ...  # Fetch result from wherever the child job stores it
    return result

@flow
def parent_flow(inputs, job_manifest_path):
    job_names = submit_k8s_job.map(inputs, job_manifest_path=[job_manifest_path]*len(inputs))
    results = fetch_job_result.map(job_names)
    # Process results as needed
    return results

# Deploy the parent flow as a Kubernetes Job
k8s_job_infrastructure = KubernetesJob(
    job=KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/orchestrating-job.yaml"
    )
)

# Deploy the parent flow
parent_flow.deploy(project_name="your-project", infrastructure=k8s_job_infrastructure)
Job Manifest for Child Flow (
path/to/job.yaml
):
Copy code
yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: worker-job
spec:
  template:
    spec:
      containers:
      - name: worker
        image: your-docker-image:latest
        env:
        - name: INPUT
          value: ""  # Will be dynamically set by the parent flow
        command: ["python", "-c", "from your_module import child_flow; child_flow()"]
      restartPolicy: Never
Child Flow:
Copy code
python
from prefect import flow, task
import os

@task
def process_input():
    input = os.getenv("INPUT")
    # Simulate processing
    result = int(input) * 2
    return result

@flow
def child_flow():
    result = process_input()
    # Store result to a known state (e.g., Prefect Result or some synchronized location)
    # This is crucial for the parent flow to fetch this result dynamically
    store_result(result)

def store_result(result):
    # Implement storing logic here (either Prefect Result or any synchronized method)
    pass
### Key Points 1. Dynamic Job Submission: The parent flow submits child jobs dynamically, setting individual inputs. 2. Result Management: Results from child jobs are stored in a manner accessible to the parent flow. 3. Kubernetes Credentials: Managed through Prefect blocks. ### Considerations: - How child results are stored and retrieved should be synchronized (e.g., Prefect's Result handling or a custom method). - Ensure proper communication and synchronization between parent and child flows.
This setup allows you to run child flows as independent Kubernetes jobs while aggregating results in the parent flow. For more details: - Prefect Kubernetes Integration - Prefect Task Runners - Prefect Result Handling Good luck managing your Kubernetes job army!
n
@Marvin is there a way without using dask and ray. I want to start a flow which run on k8s and runs each task or subflow in another k8s
m
It seems there are a few considerations to address if you want each task or subflow to run as a separate Kubernetes job without relying on Dask or Ray. ### Key Points 1. Parent Flow as Kubernetes Job: Initiates all tasks. 2. Each Task as a Separate Kubernetes Job: Each task runs independently. 3. Result Aggregation: Collect results back in the parent flow. ### Approach 1. Run Parent Flow as Kubernetes Job: - This runs the main orchestration logic. 2. Submit Each Task as a Kubernetes Job: - Use 
KubernetesJob
to run each independent task. 3. Fetch Results: - Results can be collected back in a manner supported natively by Prefect. ### Implementation Here's a conceptual approach based on Prefect's capabilities: #### Define the Parent Flow Run this flow in Kubernetes.
Copy code
python
from prefect import flow, task
from prefect.infrastructure import KubernetesJob
from prefect_kubernetes.credentials import KubernetesCredentials
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import create_namespaced_job

# Load Kubernetes credentials
k8s_creds = KubernetesCredentials.load("k8s-creds")

@task
def submit_k8s_task(input, job_manifest_path):
    job = KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path=job_manifest_path,
    )
    # Add specific input to the job environment
    job.spec.template.spec.containers[0].env.append({"name": "INPUT", "value": str(input)})
    job_name = f"task-job-{input}"
    create_namespaced_job(job, namespace="your-namespace", job_name=job_name)
    return job_name

@task
def fetch_task_result(job_name):
    # Logic to pull results from the task job
    # e.g., fetch from a Prefect state or other method
    result = ...  # Implement the method to fetch the result here
    return result

@flow
def orchestration_flow(inputs, job_manifest_path):
    job_names = submit_k8s_task.map(inputs, job_manifest_path=[job_manifest_path]*len(inputs))
    results = fetch_task_result.map(job_names)
    # Process aggregate results
    return results

inputs = list(range(500))
manifest_path = "path/to/task-job.yaml"

# Deploying the orchestration flow as a Kubernetes Job
k8s_job_infrastructure = KubernetesJob(
    job=KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/orchestration-job.yaml"
    )
)

# Deploy the orchestration flow
orchestration_flow.deploy(project_name="your-project", infrastructure=k8s_job_infrastructure)
### Job YAML Definitions Orchestration Job YAML (
orchestration-job.yaml
):
Copy code
yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: orchestration-job
spec:
  template:
    spec:
      containers:
      - name: orchestrator
        image: your-docker-image:latest
        command: ["python", "-c", "from your_module import orchestration_flow; orchestration_flow(inputs, job_manifest_path)"]
      restartPolicy: Never
Task Job YAML (
task-job.yaml
):
Copy code
yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: worker-task-job
spec:
  template:
    spec:
      containers:
      - name: worker-task
        image: your-docker-image:latest
        env:
        - name: INPUT
          value: ""  # Will get dynamically set
        command: ["python", "-c", "from your_module import process_input; process_input()"]
      restartPolicy: Never
#### Define the Task
Copy code
python
import os
from prefect import flow, task

@task
def process_input():
    input = os.getenv("INPUT")
    # Simulate processing
    result = int(input) * 2
    # return or save result for fetching
    return result

@flow
def child_flow():
    result = process_input()
    # Store or manage result

def store_result(result):
    # Implement your storing mechanism here
    pass
### Considerations and Details 1. Running Independently: - Each task runs as its own Kubernetes job, ensuring separation. 2. Fetching Results: - Results can be fetched back using Prefect's result handling or a custom method. ### Conclusion
This setup allows each Prefect task (or subflow) to run as its own Kubernetes job, initiated by a parent flow running as a Kubernetes job. Results are fetched back within Prefect, without third-party storage dependencies. For more details on Prefect’s Kubernetes integration and worker configuration: - Prefect Kubernetes Worker - Running Flows with Prefect Kubernetes Feel free to experiment with this setup to match your specific orchestration needs. Enjoy your Kubernetes-powered task management!
n
Hey @Marvin does prefect has something similar to airflow’s Kubernetes Executor?
m
Prefect doesn’t have an exact equivalent to Airflow’s Kubernetes Executor, but it does have similar capabilities through its Kubernetes integration and worker infrastructure. Here’s a summary of what Prefect offers and how it can be leveraged: ### Kubernetes Workers Prefect uses Kubernetes workers to execute flows as Kubernetes jobs. This is akin to running tasks on Kubernetes pods, similar to Airflow’s Kubernetes Executor. - Kubernetes Worker: Prefect allows deploying and managing flows within Kubernetes using Kubernetes workers. These workers poll for flow runs in specified work pools and execute them as Kubernetes jobs. ### Steps to Achieve Similar Functionality 1. Set Up Kubernetes Worker: - Deploy a Prefect Kubernetes worker in your cluster to manage and execute flow runs. 2. Submit Tasks as Kubernetes Jobs: - Each task or subflow can be submitted as an independent Kubernetes job, similar to Airflow’s model. ### Example Implementation #### Kubernetes Worker Setup Deploy a Kubernetes worker which handles the execution of flows as separate jobs.
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shell
export PREFECT_API_URL="<https://api.prefect.io>"  # or your Prefect Orion host
prefect kubernetes worker start --pool 'my-work-pool' --type kubernetes
This command initializes a worker that listens to the specified work pool and executes incoming tasks as Kubernetes jobs. #### Flow Definition Define and deploy a flow that submits its tasks as separate Kubernetes jobs.
Copy code
python
from prefect import flow, task
from prefect.infrastructure import KubernetesJob
from prefect_kubernetes.credentials import KubernetesCredentials
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import create_namespaced_job

# Load Kubernetes credentials
k8s_creds = KubernetesCredentials.load("k8s-creds")

@task
def submit_k8s_task(input, job_manifest_path):
    job = KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path=job_manifest_path,
    )
    # Add specific input to the job environment
    job.spec.template.spec.containers[0].env.append({"name": "INPUT", "value": str(input)})
    job_name = f"task-job-{input}"
    create_namespaced_job(job, namespace="your-namespace", job_name=job_name)
    return job_name

@task
def fetch_task_result(job_name):
    # Logic to pull results from the task job
    result = ...  # Implement the method to fetch the result here
    return result

@flow
def orchestration_flow(inputs, job_manifest_path):
    job_names = submit_k8s_task.map(inputs, job_manifest_path=[job_manifest_path]*len(inputs))
    results = fetch_task_result.map(job_names)
    # Process aggregate results
    return results

inputs = list(range(500))
manifest_path = "path/to/task-job.yaml"

# Deploying the orchestration flow as a Kubernetes Job
k8s_job_infrastructure = KubernetesJob(
    job=KubernetesJob.from_yaml_file(
        credentials=k8s_creds,
        manifest_path="path/to/orchestration-job.yaml"
    )
)

# Deploy the orchestration flow
orchestration_flow.deploy(project_name="your-project", infrastructure=k8s_job_infrastructure)
### Job YAML Definitions Orchestration Job YAML (
orchestration-job.yaml
):
Copy code
yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: orchestration-job
spec:
  template:
    spec:
      containers:
      - name: orchestrator
        image: your-docker-image:latest
        command: ["python", "run_orchestration_flow.py"]
      restartPolicy: Never
Task Job YAML (
task-job.yaml
):
Copy code
yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: worker-task-job
spec:
  template:
    spec:
      containers:
      - name: worker-task
        image: your-docker-image:latest
        env:
        - name: INPUT
          value: ""  # Will get dynamically set
        command: ["python", "run_task.py"]
      restartPolicy: Never
### Key Points 1. Kubernetes Worker: Start a Kubernetes worker to handle flow execution. 2. Independent Jobs: Submit each task as an independent Kubernetes job from within the flow.
3. Result Management: Handle results within the flow. ### Links for Further Reading - Prefect Kubernetes Worker - Running Flows with Prefect Kubernetes This setup approximates the functionality provided by Airflow's Kubernetes Executor, adjusting for differences in how Prefect handles task execution and orchestration.
n
@Marvin does prefect has similar this as airflow xcom?
m
Prefect indeed has features for passing data between tasks, similar to Airflow’s XCom. Prefect handles this natively through its result handling mechanism. ### Sharing Data Between Tasks in Prefect 1. Task Outputs: Task outputs in Prefect can be directly used as inputs for other tasks. 2. Task Futures: Prefect uses futures to handle results from tasks, which can be retrieved and passed to subsequent tasks. ### Example of Sharing Data Between Tasks Here’s how you can share data between tasks in a Prefect flow: #### Task and Flow Definition
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python
from prefect import flow, task

@task
def task_a(x):
    return x + 1

@task
def task_b(y):
    return y * 2

@flow
def my_flow(input_value):
    result_a = task_a(input_value)
    result_b = task_b(result_a)  # Using the output of task_a as input for task_b
    return result_b

final_result = my_flow(10)
print(f"Final Result: {final_result}")
In this example: 1. 
task_a
: Increment the input value by 1. 2. 
task_b
: Double the result of 
task_a
. ### Advanced Data Passing and Handling 1. Mapping: Prefect supports mapping to handle list-like data structures across tasks. 2. Result Retrieval: Using 
State.result()
to get the result of a task when using async flows. Here’s a more advanced example: #### Handling Results with Async Flows
Copy code
python
from prefect import flow, task

@task
async def task_a(x):
    return x + 1

@task
async def task_b(y):
    return y * 2

@flow
async def my_async_flow(inputs):
    results_a = await task_a.map(inputs)
    results_b = await task_b.map(results_a)
    return results_b

import asyncio
inputs = [1, 2, 3, 4, 5]
final_results = asyncio.run(my_async_flow(inputs))
print(f"Final Results: {final_results}")
### Prefect’s Features for Data Handling 1. Task Futures: Prefect uses futures to hold the state and results of tasks, which can be accessed programmatically. 2. Flows and Subflows: Prefect allows the use of subflows where data can be passed between subflows and the main flow. 3. Result Handling: Prefect provides a robust result handling system to store and retrieve task results efficiently. ### Learn More For more information on Prefect’s data handling features, check out these resources: - Prefect Task Tutorial - Prefect Frequently Asked Questions - Prefect Result Handling This setup offers powerful capabilities to capture task results and pass them along, effectively mimicking Airflow's XCom functionality in a more streamlined manner. Enjoy orchestrating your flows with efficient data passing!
n
@Marvin what if the flow run on k8s and the tasks run on different k8s job
m
When running a Prefect flow where both the flow and tasks are executed as separate Kubernetes jobs, managing data sharing and result aggregation requires careful orchestration. Prefect doesn't have out-of-the-box support for direct in-cluster data sharing like Airflow’s XCom, but you can achieve similar functionality through some workarounds. ### Custom Data Sharing Solutions 1. Inter-Job Communication: Use shared storage solutions like S3, GCS, or a database to pass data between the Kubernetes jobs. 2. Prefect Result Handling: Utilize Prefect's result management to store and retrieve results programmatically. ### Step-by-Step Implementation #### 1. Define Tasks as Separate Kubernetes Jobs Each task in the flow will spin up as a separate Kubernetes job. The data for the tasks will be fetched from a shared storage location. #### 2. Store Task Results Store the results of each task in a shared storage (e.g., S3) that is accessible to all jobs. #### 3. Fetch and Aggregate Results The parent flow or subsequent tasks can fetch and aggregate the results from the shared storage. ### Example Implementation #### Define the Tasks
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python
from prefect import flow, task
import os
import json
import boto3  # Assuming you use S3 for shared storage

s3_client = boto3.client('s3')

@task
def process_input(input_value):
    # Simulate processing
    result = input_value * 2
    save_result_to_s3(input_value, result)
    return result

def save_result_to_s3(task_id, result):
    # Save the result to S3
    s3_client.put_object(Bucket='your-bucket', Key=f'results/{task_id}.json', Body=json.dumps(result))

@task
def fetch_results(inputs):
    results = []
    for input_value in inputs:
        result = s3_client.get_object(Bucket='your-bucket', Key=f'results/{input_value}.json')
        results.append(json.loads(result['Body'].read()))
    return results

@task
def aggregate_results(results):
    # Aggregate the list of results (example: sum up all results)
    return sum(results)

@flow
def orchestration_flow(inputs, job_manifest_path):
    # Submit each input to a separate Kubernetes job
    job_names = [process_input.submit(input_value) for input_value in inputs]
    results = fetch_results(inputs)
    aggregated_result = aggregate_results(results)
    return aggregated_result

inputs = list(range(10))
manifest_path = "path/to/task-job.yaml"

# If running as a Kubernetes job itself:
if __name__ == "__main__":
    orchestration_flow(inputs, manifest_path)
#### Kubernetes Job Manifest (
task-job.yaml
) Define the Kubernetes job for each task:
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: worker-task
spec:
  template:
    spec:
      containers:
      - name: worker
        image: your-docker-image:latest
        env:
        - name: INPUT
          value: ""  # This will be dynamically set
        command: ["python", "run_task.py"]
      restartPolicy: Never
`run_task.py`: Script to run the Prefect task inside the job:
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python
from your_module import process_input
import os

input_value = int(os.getenv("INPUT"))
process_input(input_value)
### Key Points 1. Data Passing: Use shared storage to share data between Kubernetes jobs. 2. Result Aggregation: Fetch and aggregate results in the parent flow. 3. Kubernetes Infrastructure: Each task runs as an independent Kubernetes job. ### Additional Resources - Prefect Infrastructure - Prefect Kubernetes Integration - Prefect Task Outputs This approach enables you to simulate Airflow's XCom functionality while running tasks as separate Kubernetes jobs, albeit with some additional setup and handling. Good luck orchestrating your Kubernetes-powered workflows!
n
@Marvin is it possible to trigger deployment from other flow?
m
Yes, Prefect allows you to trigger a deployment from within another flow, essentially enabling dynamic orchestration of workflows. You can create and run a deployment using Python API within a flow task. Here is an example: ### Trigger Deployment from Another Flow
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python
from prefect import flow, task
from prefect.deployments import run_deployment

@task
def trigger_another_deployment():
    deployment_id = "your-deployment-id"  # Replace with your deployment's ID
    flow_run = run_deployment(deployment_id, parameters=dict(param1="value1", param2="value2"))
    return flow_run.state.result()

@flow
def orchestrator_flow():
    result = trigger_another_deployment()
    print(f"Result from triggered deployment: {result}")

if __name__ == "__main__":
    orchestrator_flow()
### Step-by-Step Implementation #### Creating the Deployment First, ensure you have a deployment created for the flow you want to trigger. You can do this via the Prefect CLI or Prefect API. 1. Create Deployment via CLI
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shell
   prefect deployment build your_module:your_flow_name -n your-deployment-name
   prefect deployment apply your_flow_name-deployment.yaml
2. Run the Deployment - Use 
run_deployment
within a task to trigger the deployment. - Make sure the parameters passed to 
run_deployment
match the expected input parameters of the target flow. ### References - Running Deployments: Details on how to create and manage deployments. - Prefect Flows: Information on flow orchestration and components. This setup allows you to dynamically trigger deployments, making your workflows modular and flexible. Enjoy your enhanced orchestration!
n
@Marvin so I can set the first and second deployments as k8s job, which means I can fork one k8s job to multiple k8s? Also does the returned value from the second deployment return to the first one?
m
Yes, you can absolutely set up the first and subsequent deployments to run as Kubernetes jobs. This setup allows the first Kubernetes job to trigger other Kubernetes jobs dynamically, effectively forking one Kubernetes job into multiple. ### Dynamic Triggering of Kubernetes Jobs You can trigger deployments from within a task, as shown earlier, enabling you to start other Kubernetes jobs as needed. Prefect deployments can return values, which can be collected and processed by the initial flow. Here’s how you can achieve this: ### Example of Forking Kubernetes Jobs #### Define the First Flow and Deployment First Deployment - Orchestrating Flow:
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python
from prefect import flow, task
from prefect.deployments import run_deployment
from prefect.results import S3Result

@task
def trigger_second_deployment(param):
    deployment_id = "second-flow-deployment-id"  # Replace with your deployment's ID
    flow_run = run_deployment(deployment_id, parameters={"param": param})
    return flow_run.state.result()  # This fetches the result of the second deployment

@task
def process_results(results):
    combined_result = sum(results)  # Example aggregation
    return combined_result

@flow
def orchestrating_flow():
    params = list(range(5))  # or any other input generation logic
    results = [trigger_second_deployment(param) for param in params]
    final_result = process_results(results)
    print(f"Final aggregated result: {final_result}")

if __name__ == "__main__":
    orchestrating_flow()
Kubernetes Job Manifest for Orchestrating Flow (
orchestrating-job.yaml
):
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: orchestrating-job
spec:
  template:
    spec:
      containers:
      - name: orchestrator
        image: your-docker-image:latest
        command: ["python", "orchestrating_flow.py"]
      restartPolicy: Never
#### Define the Second Flow and Deployment Second Deployment - Worker Flow:
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python
from prefect import flow, task

@task
def process_input(param):
    # Simulate processing
    return param * 2

@flow
def worker_flow(param):
    result = process_input(param)
    return result

if __name__ == "__main__":
    worker_flow()
Kubernetes Job Manifest for Worker Flow (
worker-job.yaml
):
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: worker-job
spec:
  template:
    spec:
      containers:
      - name: worker
        image: your-docker-image:latest
        command: ["python", "worker_flow.py"]
      restartPolicy: Never
### Explanation 1. Orchestrating Flow: - This flow triggers several instances of the worker flow by running their deployments. - It collects the results of these deployments. 2. Worker Flow: - Each instance processes its input and returns a result. 3. Deployments: - Both flows are deployed as Kubernetes jobs. 4. Result Handling: - The results from the worker flows are returned back to the orchestrating flow, aggregated, and processed. ### Deployment 1. Deploy Orchestrating Flow:
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shell
   prefect deployment build orchestrating_flow.py:orchestrating_flow -n orchestrating-deployment
   prefect deployment apply orchestrating_flow-deployment.yaml
2. Deploy Worker Flow:
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shell
   prefect deployment build worker_flow.py:worker_flow -n worker-deployment
   prefect deployment apply worker_flow-deployment.yaml
3. Running Orchestrating Job:
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shell
   kubectl apply -f orchestrating-job.yaml
### Key Points - Multiple Kubernetes Jobs: Each task in the orchestrating flow triggers a separate Kubernetes job. - Result Aggregation: Results from the worker deployments are fetched and processed in the orchestrating flow. ### References - Deployments in Prefect - Results in Prefect Your setup will now enable seamless forking of Kubernetes jobs and dynamic aggregation of results. Enjoy orchestrating your workflows!
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