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<@ULVA73B9P> How do I run an async flow when deplo...
# ask-marvin
s
@Marvin How do I run an async flow when deploying using a prefect.yaml file
m
To deploy and run an async flow using a 
prefect.yaml
file, you can follow these steps: 1. Define the Async Flow: Ensure your flow is defined as an async function in Python. 2. Create the 
prefect.yaml
File: Configure your deployment settings and ensure that the entry point points to your async flow. Here’s an example of how the 
prefect.yaml
file might look:
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yaml
name: async-flow-deployment
prefect-version: 2.13.1

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/flow

deployments:
  - name: async_flow_deploy
    entrypoint: path/to/your/flow.py:your_async_flow_function
    work_pool:
      name: your-work-pool
3. Deploy the Flow: Run the deployment command using 
prefect
CLI.
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sh
prefect deploy
For more detailed information, you can refer to the Prefect documentation on creating deployments with 
prefect.yaml
here. If you run into any specific issues, you can search the Prefect GitHub issues for additional troubleshooting steps. For example, issues like handling local paths and dependencies can be found on their GitHub here and here. I hope this helps. If you need more assistance, feel free to ask!
s
@Marvin When I run the flow in this way, it returns a coroutine and completes the flow run, it doesn't run it. How to solve that
m
When an async flow returns a coroutine, it completes the flow run without executing it because coroutines need to be awaited. Here’s how you can solve this problem: 1. Deploy Flow Correctly: Ensure the async flow is correctly defined in your Prefect code. 2. Await the Flow Correctly: Modify the deployment code or usage to ensure perfect execution. Here’s an example of an async flow and how you can run it: Async Flow Definition
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python
from prefect import flow, task

@task
async def async_task():
    return 1

@flow
async def async_flow():
    task_result = await async_task()
    return task_result + 1

if __name__ == "__main__":
    # A quick way to run your async flow
    import asyncio
    asyncio.run(async_flow())
Deploying with 
prefect.yaml
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yaml
name: async-flow-deployment
prefect-version: 2.13.1

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/flow

deployments:
  - name: async_flow_deploy
    entrypoint: path/to/your/flow.py:async_flow
    work_pool:
      name: your-work-pool
Running the Flow Deploy your flow as usual using:
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sh
prefect deploy
Notes: 1. Make sure to use 
asyncio.run()
as in the example above if you are running the async flow directly after deployment. 2. When deploying using 
prefect deploy
, make sure the entrypoint in the YAML file points to an async flow function. The official Prefect documentation also provides detailed guides on results handling and flow concepts. If you have any more questions or run into issues, feel free to ask!
s
@Marvin How do I pass parameters in the async_flow
m
To pass parameters in the async flow, you need to define the flow to accept parameters and then update your 
prefect.yaml
to include these parameters. Here’s how you can do it: Define the Async Flow with Parameters
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python
from prefect import flow, task

@task
async def async_task(x):
    return x

@flow
async def async_flow(x: int):
    task_result = await async_task(x)
    return task_result + 1

if __name__ == "__main__":
    import asyncio
    asyncio.run(async_flow(5))  # Example parameter value
Update 
prefect.yaml
to Include Parameters
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yaml
name: async-flow-deployment
prefect-version: 2.13.1

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/flow

deployments:
  - name: async_flow_deploy
    entrypoint: path/to/your/flow.py:async_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
### Running the Deployment Deploy your flow using:
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sh
prefect deploy
### More Information You can edit parameters through the Prefect UI, and you can create ad-hoc flow runs with custom parameters when necessary. For detailed guides on deployments and configuring parameters, refer to the Prefect documentation on block-based deployments. If you need further examples, you can check out basic flow examples. Feel free to ask more questions if you need additional help!
s
@Marvin When a flow creates multiple subflows and I am using a kubernetes based work-pool, does the prefect worker create pod for each subflow or runs the whole flow along with its subflows and tasks in a single pod?
m
When using a Kubernetes-based work pool in Prefect, here’s how it handles flow runs and subflows: Flow Runs and Kubernetes Work Pool: - Each individual flow run is executed within its own Kubernetes job pod. This means that the main flow and each subflow are treated as separate flow runs, each potentially requiring its own pod. - Tasks within a single flow run will be executed within the same pod, ensuring that the tasks share the same execution context. Source documentation for work pools: Work Pools in Prefect ### Key Points: 1. Main Flow and Subflows: - Each subflow creates a new flow run and therefore a new Kubernetes job pod. - All tasks within a flow (including subtask calls within a subflow) execute in the same pod. 2. Deployment Configuration: - When defining your deployment, ensure your configuration realizes that separate pods will be launched for each subflow which can be beneficial for resource isolation. For further reading and detailed guides on migrating from agents to workers and setting up work pools, you can check Prefect's upgrade guide: Upgrade Guide from Agents to Workers. This setup can help in managing complex workflows where resource isolation and scalability are critical. If you have more questions, feel free to ask!
s
@Marvin But in my case only one pod is getting started and all the other subflows are getting triggered within this same pod. Is there any modification in flow definition or any change in prefect.yaml deployment I can do to specify prefect to create separate pods for each subflow?
m
To ensure each subflow is executed in a separate Kubernetes pod, you need to leverage Prefect's functionality to handle subflows as separate flow runs explicitly. This approach requires setting up the flow and deployment configuration properly. ### Steps to Run Subflows in Separate Pods 1. Define the Main Flow and Subflows: - Ensure each subflow is defined as a separate flow. 2. Modify Main Flow to Call Subflows: - Use 
subflow.submit()
to ensure they are managed as separate flow runs. 3. Configure 
prefect.yaml
for Each Subflow: - Define separate deployments for the main flow and each subflow. ### Example Code and Configuration Main Flow and Subflows Definition:
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python
from prefect import flow, task

@task
async def async_task(x):
    return x

@flow(name="subflow")
async def subflow(x: int):
    task_result = await async_task(x)
    return task_result + 1

@flow(name="main_flow")
async def main_flow(x: int):
    subflow_runs = []
    for i in range(3):
        subflow_runs.append(subflow.submit(x + i))
    results = await subflow_runs
    return results

if __name__ == "__main__":
    import asyncio
    asyncio.run(main_flow(5))
prefect.yaml
Configuration for Main Flow:
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yaml
name: main-flow-deployment
prefect-version: 2.13.1

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/main_flow

deployments:
  - name: main_flow_deploy
    entrypoint: path/to/your/main_flow.py:main_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
prefect.yaml
Configuration for Subflow:
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yaml
name: subflow-deployment
prefect-version: 2.13.1

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/subflow

deployments:
  - name: subflow_deploy
    entrypoint: path/to/your/subflow.py:subflow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
### Running the Deployment 1. Deploy the main flow and subflows by updating the prefect deployment:
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sh
prefect deploy -n main_flow_deploy
prefect deploy -n subflow_deploy
This setup ensures that each subflow gets its own pod while allowing the main flow to manage and gather results from them. For further detailed guidance, you can refer to the Prefect documentation for deploying on Kubernetes: Kubernetes Deployment Guide. Feel free to ask if you need any further assistance!
s
@Marvin I am using prefect 2.16.9 and I am not finding .submit function for flow, I find it only for task. Are you sure your approach is right?
m
You're correct that in Prefect 2.16.9, there’s no 
submit
method directly for flows. Instead, you should use 
run_deployment
to handle subflows as separate deployments. This ensures that each subflow runs in its own Kubernetes pod. ### Steps to Run Subflows in Separate Pods 1. Define the Main Flow and Subflows: - Ensure each subflow is defined as a separate flow in your code. 2. Run Subflows Using 
run_deployment
: - Use 
run_deployment
to call subflows, triggering each as a new deployment. 3. Configure 
prefect.yaml
for Main Flow and Subflows: - Define separate deployments for the main flow and each subflow in their respective 
prefect.yaml
files. ### Example Code Subflow Definition:
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python
from prefect import flow, task

@task
async def async_task(x):
    return x

@flow(name="subflow")
async def subflow(x: int):
    task_result = await async_task(x)
    return task_result + 1

if __name__ == "__main__":
    import asyncio
    asyncio.run(subflow(5))
Main Flow Definition:
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow(name="main_flow")
def main_flow(x: int):
    subflow_runs = []
    for i in range(3):
        subflow_run = run_deployment(name="subflow-deployment", parameters={"x": x + i})
        subflow_runs.append(subflow_run)
    return subflow_runs

if __name__ == "__main__":
    main_flow(5)
prefect.yaml
Configuration for Main Flow:
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yaml
name: main-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/main_flow

deployments:
  - name: main_flow_deploy
    entrypoint: path/to/your/main_flow.py:main_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
prefect.yaml
Configuration for Subflow:
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yaml
name: subflow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/subflow

deployments:
  - name: subflow_deploy
    entrypoint: path/to/your/subflow.py:subflow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
### Running the Deployment 1. Deploy the main flow and subflows:
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sh
prefect deploy -n main_flow_deploy
prefect deploy -n subflow_deploy
This setup ensures each subflow runs in its own Kubernetes pod, managed as separate flow runs. For further details on 
run_deployment
, refer to the latest documentation: Deployment Guide. Feel free to ask more questions if you need further assistance!
s
@Marvin In the following main_flow you provided, will the subflows be triggered all at once concurrently? Or each flow will be triggered and we will have to wait until one subflow run completes for the next one to get triggered?
m
In the 
main_flow
provided, the subflows will be triggered concurrently; they won't wait for each other to complete before the next one is triggered. This is because 
run_deployment
initiates the subflows asynchronously and returns immediately without waiting for the subflow to complete. Here’s a more detailed breakdown: ### Main Flow Definition with Concurrent Subflows Triggering
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow(name="main_flow")
def main_flow(x: int):
    subflow_runs = []
    for i in range(3):
        # This triggers the subflow asynchronously and immediately returns
        subflow_run = run_deployment(name="subflow-deployment", parameters={"x": x + i})
        subflow_runs.append(subflow_run)
    return subflow_runs

if __name__ == "__main__":
    main_flow(5)
### Behavior - 
run_deployment
: This function triggers the subflow deployment asynchronously. - Concurrency: All subflows are triggered nearly simultaneously and will run without blocking each other. ### Key Points - Asynchronous Execution: Subflows start running in their own Kubernetes pods concurrently. - Non-Blocking: The main flow doesn't wait for one subflow to complete before starting the next. If you want to ensure the main flow waits for each subflow to complete before proceeding, you would need to implement some kind of polling or state-checking mechanism. ### Example: Sequential Subflow Execution Here’s how you might modify the main flow to wait for each subflow to complete before triggering the next one:
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python
from prefect import flow
from prefect.deployments import run_deployment
import time

@flow(name="main_flow")
def main_flow(x: int):
    subflow_runs = []
    for i in range(3):
        # Trigger the subflow
        subflow_run = run_deployment(name="subflow-deployment", parameters={"x": x + i})
        
        # Polling mechanism to wait for the subflow to complete
        while subflow_run.state.name not in ["Completed", "Failed", "Cancelled"]:
            time.sleep(10)  # Poll every 10 seconds
            subflow_run.refresh()
        
        subflow_runs.append(subflow_run)
    return subflow_runs

if __name__ == "__main__":
    main_flow(5)
- Polling Mechanism: This example shows a simple polling mechanism (
time.sleep
and 
subflow_run.refresh
) to wait for each subflow to complete. - Sequential Execution: Ensures that each subflow completes before the next one starts. ### Conclusion The provided 
main_flow
triggers subflows concurrently by default. If you need sequential execution, you'll have to implement waiting logic, as shown above. Feel free to ask more questions if you need further clarification or help!
s
@Marvin When using 
run_deployment
, what do I have to pass in the name parameter? The function name of the subflow or the deployment name of the subflow that I specify in prefect.yaml file
m
When using 
run_deployment
, you need to pass the deployment name of the subflow as specified in the 
prefect.yaml
file, not the function name of the subflow. ### Example Let's clarify with an example setup: Subflow Definition (
subflow.py
):
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python
from prefect import flow, task

@task
async def async_task(x):
    return x

@flow
async def subflow(x: int):
    task_result = await async_task(x)
    return task_result + 1

if __name__ == "__main__":
    import asyncio
    asyncio.run(subflow(5))
Subflow Deployment Configuration (
subflow/prefect.yaml
):
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yaml
name: subflow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.set_working_directory:
      directory: /path/to/your/subflow

deployments:
  - name: subflow_deploy  # This is the deployment name
    entrypoint: path/to/your/subflow.py:subflow
    parameters:
      x: 5
    work_pool:
      name: your-work-pool
Main Flow Definition Using 
run_deployment
 (
main_flow.py
):
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def main_flow(x: int):
    subflow_runs = []
    for i in range(3):
        # Use the deployment name specified in the subflow's prefect.yaml
        subflow_run = run_deployment(name="subflow_deploy", parameters={"x": x + i})
        subflow_runs.append(subflow_run)
    return subflow_runs

if __name__ == "__main__":
    main_flow(5)
In this setup: - Deployment Name: The deployment name 
"subflow_deploy"
that you pass to 
run_deployment
matches the deployment name specified in the 
subflow/prefect.yaml
file. - Parameters: Pass any necessary parameters to the subflow deployment. ### Summary - The 
name
parameter in 
run_deployment
should be the deployment name specified in the 
prefect.yaml
file for the subflow. - Do not use the function name of the subflow in the 
run_deployment
call. This ensures each subflow runs as a separate deployment, potentially in its own Kubernetes pod, depending on your configuration. If you need further clarification or additional help, feel free to ask!
s
@Marvin How does 
run_deployment
work? Do you need prefect cloud for that or self hosted prefect server can also be used? I did use 
run_deployment
and when I ran the flow in the server, I got the error
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Encountered exception during execution:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/prefect/client/orchestration.py", line 1780, in read_deployment_by_name
    response = await self._client.get(f"/deployments/name/{name}")
  File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1801, in get
    return await self.request(
  File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1574, in request
    return await self.send(request, auth=auth, follow_redirects=follow_redirects)
  File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 344, in send
    response.raise_for_status()
  File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 167, in raise_for_status
    raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.__cause__
prefect.exceptions.PrefectHTTPStatusError: Client error '404 Not Found' for url '<http://prefect-server-2.dev-prefect.svc.cluster.local:4200/api/deployments/name/prefect-poc>'
Response: {'detail': 'Not Found'}
m
Your message was too long by 32 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
How does 
run_deployment
work? Do you need prefect cloud for that or self hosted prefect server can also be used?
I did use 
run_deployment
and when I ran the flow in the server, I got the error ```Encountered exception during execution: Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/client/orchestration.py", line 1780, in read_deployment_by_name response = await self._client.get(f"/deployments/name/{name}") File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1801, in get return await self.request( File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1574, in request return await self.send(request, auth=auth, follow_redirects=follow_redirects) File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 344, in send response.raise_for_status() File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 167, in raise_for_status raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.cause prefect.exceptions.PrefectHTTPStatusError: Client error '404 Not Found' for url '<http://prefect-server-2
s
@Marvin How does 
run_deployment
work? Do you need prefect cloud for that or self hosted prefect server can also be used? I did use 
run_deployment
and when I ran the flow in the server, I got the error
Copy code
File "/usr/local/lib/python3.10/site-packages/prefect/client/orchestration.py", line 1780, in read_deployment_by_name
    response = await self._client.get(f"/deployments/name/{name}")
  File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1801, in get
    return await self.request(
  File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1574, in request
    return await self.send(request, auth=auth, follow_redirects=follow_redirects)
  File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 344, in send
    response.raise_for_status()
  File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 167, in raise_for_status
    raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.__cause__
prefect.exceptions.PrefectHTTPStatusError: Client error '404 Not Found' for url '<http://prefect-server-2.dev-prefect.svc.cluster.local:4200/api/deployments/name/prefect-poc>'
Response: {'detail': 'Not Found'}
m
Your message was too long by 18 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
How does 
run_deployment
work? Do you need prefect cloud for that or self hosted prefect server can also be used?
I did use 
run_deployment
and when I ran the flow in the server, I got the error ``` File "/usr/local/lib/python3.10/site-packages/prefect/client/orchestration.py", line 1780, in read_deployment_by_name response = await self._client.get(f"/deployments/name/{name}") File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1801, in get return await self.request( File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1574, in request return await self.send(request, auth=auth, follow_redirects=follow_redirects) File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 344, in send response.raise_for_status() File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 167, in raise_for_status raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.cause prefect.exceptions.PrefectHTTPStatusError: Client error '404 Not Found' for url '<http://prefect-server-2.dev-prefect.svc.cluster.local:4200/api/deployments
s
@Marvin How does 
run_deployment
work? Do you need prefect cloud for that or self hosted prefect server can also be used? I got the error
Copy code
prefect.exceptions.PrefectHTTPStatusError: Client error '404 Not Found' for url '<http://prefect-server-local:4200/api/deployments/name/prefect-poc>'
Response: {'detail': 'Not Found'}
m
The 
run_deployment
function in Prefect can indeed be used with both Prefect Cloud and self-hosted Prefect Server. The error you’re encountering typically indicates one of the following issues: 1. Deployment Not Found: The deployment name provided to 
run_deployment
does not match any existing deployment. 2. Incorrect API URL: The Prefect client is not correctly configured to point to the correct API endpoint of your Prefect server. ### Troubleshooting Steps 1. Verify Deployment Name: - Ensure the deployment name you are passing to 
run_deployment
exactly matches the deployment name defined in your 
prefect.yaml
file. - Deployment names are case-sensitive. 2. Check Prefect Server Configuration: - Make sure your Prefect client is correctly configured to communicate with your Prefect server. - Set the 
PREFECT_API_URL
environment variable to point to your Prefect server. This usually looks something like:
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sh
export PREFECT_API_URL="<http://prefect-server-local:4200/api>"
3. Verify Deployment Registration: - Ensure the deployment is registered with your Prefect server. You can use the Prefect CLI to check if the deployment exists:
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sh
prefect deployment ls
This command should list all deployments. Verify that your deployment appears in the list. ### Example: Setting Up Environment and Running Deployment Environment Setup: 1. Set the environment variable for your Prefect server URL:
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sh
export PREFECT_API_URL="<http://prefect-server-local:4200/api>"
2. Verify the environment variable is set correctly:
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sh
echo $PREFECT_API_URL
Running the Deployment: Make sure your deployments are registered correctly. Here is how you might configure and run your deployments: Register Deployment:
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sh
prefect deploy -n subflow_deploy
prefect deploy -n main_flow_deploy
Using 
run_deployment
in Your Main Flow:
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def main_flow(x: int):
    subflow_runs = []
    for i in range(3):
        subflow_run = run_deployment(name="subflow_deploy", parameters={"x": x + i})
        subflow_runs.append(subflow_run)
    return subflow_runs

if __name__ == "__main__":
    main_flow(5)
Make sure the 
name
parameter in 
run_deployment
matches the actual deployment name exactly as defined in your 
prefect.yaml
. ### Summary - Ensure that 
PREFECT_API_URL
is set correctly to point to your self-hosted Prefect server. - Verify the deployment name passed to 
run_deployment
matches the deployment name in your 
prefect.yaml
. - Register your deployments and verify they appear in the list using 
prefect deployment ls
. Following these steps should help resolve the 
404 Not Found
error. If you need further assistance or have additional questions, feel free to ask!
s
@Marvin How can I specify custom flow run names instead of the random flow run names prefect server assigns
m
To specify custom flow run names in Prefect 2.16.9, you can use the 
flow_run_name
parameter in your flow definition. You can either pass a static string or a function that dynamically generates the flow run name. ### Example: Setting Custom Flow Run Names Static Flow Run Name:
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python
from prefect import flow

@flow(flow_run_name="custom-static-name")
def my_flow(name: str):
    print(name)

if __name__ == "__main__":
    my_flow(name="Hello, Marvin!")
- This will create a flow run with the name "custom-static-name". Dynamic Flow Run Name:
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python
import datetime
from prefect import flow

def generate_flow_run_name():
    date = datetime.datetime.now(datetime.timezone.utc)
    return f"{date:%A}-is-a-nice-day"

@flow(flow_run_name=generate_flow_run_name)
def my_flow(name: str):
    print(name)

if __name__ == "__main__":
    my_flow(name="Hello, Marvin!")
- This will create a flow run with a name like "Thursday-is-a-nice-day", depending on the day of the week. Accessing Flow and Parameter Information:
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python
from prefect import flow
from prefect.runtime import flow_run

def generate_flow_run_name():
    flow_name = flow_run.flow_name
    parameters = flow_run.parameters
    name = parameters["name"]
    return f"{flow_name}-run-with-{name}"

@flow(flow_run_name=generate_flow_run_name)
def my_flow(name: str):
    print(name)

if __name__ == "__main__":
    my_flow(name="Marvin")
- This example uses 
flow_run
to dynamically access the flow name and parameters to generate a custom flow run name. ### Important Points 1. Defining in the Code: The 
flow_run_name
parameter can be set directly in your flow definition. 2. Using Environment: Properly set up your runtime environment to allow the generation function to access 
flow_run
metadata. ### Summary By leveraging the 
flow_run_name
parameter, you can customize the names of your flow runs for easier identification and management. For a more detailed guide, you can refer to the Prefect documentation on flows. Feel free to ask if you have more questions or need further assistance!
s
@Marvin I have a parameterized Dockerfile, where I specify requirements.txt should I choose for building the image, I want to integrate it in my prefect.yaml file but the build step takes just the file location. Is there a way using which i can specify build arguments to overwrite the parameters before building the image as per prefect deployment? I am using prefect 2.16.9
m
To integrate and pass build arguments to a Dockerfile when using 
prefect.yaml
in your Prefect deployment (version 2.16.9), you can use the 
prefect_docker.deployments.steps.build_docker_image
step. This step allows for custom behavior, including passing build arguments. ### Example 
prefect.yaml
Configuration Assuming you have a parameterized Dockerfile that accepts build arguments for the 
requirements.txt
file location, your 
prefect.yaml
might look something like this:
Copy code
yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.run_shell_script:
      id: get-commit-hash
      script: git rev-parse --short HEAD
      stream_output: false
  - prefect_docker.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: "{{ get-commit-hash.stdout }}"
      dockerfile: path/to/your/Dockerfile
      build_args:
        REQUIREMENTS_FILE: "path/to/your/requirements.txt"
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
### Key Points 1. 
run_shell_script
Step: - This step captures the current Git commit hash to use as the image tag. 2. 
build_docker_image
Step: - 
image_name
: Specifies the name of the Docker image. - 
tag
: Uses the captured commit hash as the tag. - 
dockerfile
: Path to your Dockerfile. - 
build_args
: Passes build arguments to the Dockerfile. In this case, it passes the 
REQUIREMENTS_FILE
argument. ### Using Build Arguments in Dockerfile Ensure your Dockerfile is set up to accept the 
REQUIREMENTS_FILE
build argument: Dockerfile:
Copy code
Dockerfile
ARG REQUIREMENTS_FILE
FROM python:3.9-slim

# Use the build argument
COPY ${REQUIREMENTS_FILE} /app/requirements.txt

WORKDIR /app
RUN pip install -r requirements.txt
COPY . /app

CMD ["python", "-m", "your_main_module"]
### Summary By configuring the 
prefect.yaml
file with 
prefect_docker.deployments.steps.build_docker_image
and using the 
build_args
property, you can pass custom build arguments to your Dockerfile during the deployment build step. Ensure your Dockerfile is set up to consume these arguments correctly. For further details on the 
build_docker_image
step, you can refer to the Prefect Docker Steps Documentation. Feel free to ask if you have more questions or need further assistance!
s
Hey @Marvin If I want to allocate ram and memory for a flow so that the worker can create pods for that flow with that much RAM and memory, how can I do that?
m
To allocate specific RAM and CPU resources for a flow in a Kubernetes-based work pool, you can specify these resources in the job variables within your deployment configuration in 
prefect.yaml
. Here's how you can do it: ### Setting Resource Requests and Limits in 
prefect.yaml
You can define custom job variables for resource allocation (memory and CPU) for Kubernetes jobs. Example 
prefect.yaml
Configuration with Resource Requests and Limits:
Copy code
yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
      job_variables:
        resources:
          limits:
            memory: "512Mi"
            cpu: "0.5"
          requests:
            memory: "256Mi"
            cpu: "0.25"
### Explanation 1. 
work_pool
Section: - 
job_variables
: Defines the variables used for customizing the Kubernetes job. - 
resources
: Specifies the resource limits and requests. - 
limits
: The maximum amount of resources that the job can use. - 
requests
: The minimum amount of resources that the job is guaranteed to use. ### Kubernetes Worker Configuration Example This configuration ensures that Kubernetes creates pods with the specified resources: Example Part of 
prefect.yaml
:
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yaml
...
work_pool:
  name: your-k8s-work-pool
  job_variables:
    resources:
      limits:
        memory: "512Mi"
        cpu: "0.5"
      requests:
        memory: "256Mi"
        cpu: "0.25"
...
### Dockerfile and Requirements Management If you need to handle a parameterized Dockerfile with different 
requirements.txt
files, you can use build arguments as described earlier and ensure the Dockerfile is designed to accept them. ### Dockerfile Example Dockerfile:
Copy code
Dockerfile
ARG REQUIREMENTS_FILE
FROM python:3.9-slim

# Use the build argument
COPY ${REQUIREMENTS_FILE} /app/requirements.txt

WORKDIR /app
RUN pip install -r requirements.txt
COPY . /app

CMD ["python", "-m", "your_main_module"]
By ensuring these configurations in your 
prefect.yaml
, you can dynamically set resource limits and requests for each flow run executed within your Kubernetes pods. ### Resources For more detailed information, refer to: - Prefect Kubernetes Worker Guide: Kubernetes Worker - Prefect Docker Deployments Steps: Prefect Docker Steps Documentation Feel free to ask if you have more questions or need further assistance!
s
@Marvin When we create a flow run in prefect, and a worker picks up the flow run and creates a Pod for that, how much default RAM and Memory does the worker provide to that pod?
m
By default, when a Prefect worker creates a Kubernetes pod for a flow run, the pod's resource allocations are not strictly defined unless you specify them in your deployment configuration. This means that without explicit resource requests and limits, the Kubernetes scheduler may allocate default resources based on the cluster's configuration and available resources. ### Default Resource Configuration for Prefect Workers If you do not specify any resource allocation, the Kubernetes cluster's default settings will apply. However, default requests and limits in Prefect documentation for a custom worker example are: - Memory Request: 1024 MiB (1 GiB) - CPU Request: 500 milliCPU (0.5 CPU) These values provide a rough idea of typical defaults, but the exact defaults may vary based on the Kubernetes cluster's configuration. ### Specifying Resource Requests and Limits To ensure specific resource limits and requests for your flow runs, you should include resource configurations in the 
job_variables
within your 
prefect.yaml
. #### Example Configuration in 
prefect.yaml
Copy code
yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: your-work-pool
      job_variables:
        resources:
          requests:
            memory: "256Mi"
            cpu: "0.25"
          limits:
            memory: "512Mi"
            cpu: "0.5"
### Explanation 1. 
work_pool
Section: - 
job_variables
: Defines variables for the Kubernetes job. - 
resources
: Specifies resource requests and limits for the pods. - 
requests
: The minimum amount of resources that Kubernetes guarantees to the pod. - 
limits
: The maximum amount of resources that the pod can use. ### Summary - Without specific configuration, Kubernetes defaults from the cluster settings apply, which might vary. - With resource configuration in 
prefect.yaml
, you can ensure specific resource allocations for your flow runs to manage performance and resource utilization effectively. For detailed information about developing and configuring a new worker type, refer to the Prefect docs: Developing a New Worker Type. Feel free to ask if you need further clarification or assistance!
s
@Marvin Explain to me how work pool and worker architecture work. How the work queue plays a role in all of this. And what happens if you create different work queues for different requirements, instead of using the 
default
queue? Advantages, disadvantages etc.
m
### Work Pools, Workers, and Work Queues in Prefect #### Work Pools - Definition: Work pools organize work for execution. They bridge the Prefect orchestration environment with the execution environment. - Types: Work pools have types corresponding to the infrastructure that will execute the flow code (e.g., Kubernetes, Docker, local processes). - Functionality: When a deployment creates a flow run, it submits it to a specific work pool for scheduling. A worker polls its respective work pool for new runs to execute or the pool can directly submit flow runs to serverless infrastructure. #### Workers - Definition: Workers are lightweight polling services that retrieve scheduled runs from a work pool and execute them. - Functionality: Workers can execute flows by either polling the work pool or being assigned runs directly if using serverless infrastructure. - Configuration: Workers offer control over infrastructure configurations, such as resource allocation (CPU and memory). #### Work Queues - Definition: Work queues exist within work pools and queue tasks for workers. They help manage and prioritize the execution of tasks. - Status: - READY: Polled by a worker within the last 60 seconds. - PAUSED: Accepts no new work until unpaused. - NOT_READY: Not polled by a worker in the last 60 seconds. #### Role and Benefits of Work Queues - Prioritization: Allows for prioritizing tasks. High-priority queues are processed before lower-priority queues. - Isolation: Different queues can isolate different types of tasks, e.g., high-memory tasks from low-memory tasks. - Customization: Create custom queues for various requirements (e.g., different resource needs, different environments). ### Advantages vs. Disadvantages of Custom Work Queues #### Advantages - Efficient Resource Management: Ensures resources are allocated according to the specific needs of each subset of tasks. - Enhanced Prioritization: Higher priority can be assigned to critical tasks, ensuring they are executed promptly. - Fault Isolation: Isolates issues in specific queues, preventing them from affecting other tasks. - Scalability: Custom work queues allow for better scaling by distributing tasks across different queues and workers. #### Disadvantages - Complexity: Managing multiple work queues and configuring them adds complexity to the system. - Overhead: More work queues can add administrative overhead in maintaining and monitoring them. - Resource Fragmentation: Can lead to resource fragmentation if not managed properly, potentially leading to inefficient resource use. ### Example Configuration in 
prefect.yaml
Copy code
yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: custom-work-pool
      work_queue_name: high-memory-tasks  # Specify custom work queue
      job_variables:
        resources:
          requests:
            memory: "256Mi"
            cpu: "0.25"
          limits:
            memory: "512Mi"
            cpu: "0.5"
### Summary - Work Pools: Bridge Prefect orchestration with execution environments. - Workers: Poll work pools for tasks and execute them. - Work Queues: Manage and prioritize tasks within work pools. Customizing queues allows for tailored resource allocation, prioritization, and isolation but adds complexity and potential overhead. To learn more in detail, you can refer to the Prefect Work Pools Documentation. Feel free to ask any more questions you may have!
s
@Marvin If I create multiple queues, how does the prefect worker work? Do we need (or would you recommend) having one prefect worker for each work queue? I am thinking about how to effectively scale the system
@Marvin If I create multiple queues, how does the prefect worker work? Do we need (or would you recommend) having one prefect worker for each work queue? I am thinking about how to effectively scale the system
m
Scaling your Prefect system effectively involves understanding how workers and work queues interact and making informed decisions about worker deployment based on your needs. ### Understanding Work Queue and Worker Interaction - Workers: Prefect workers are responsible for polling work pools and fetching flow runs from work queues to execute them. - Work Queues: Work queues within a work pool can prioritize and isolate different types of tasks, helping manage resource usage and execution order. ### Worker Deployment Strategies 1. Single Worker Polling Multiple Queues: - Configuration: A single worker can be configured to poll multiple work queues. - Use Case: Useful when you have limited resources or when queues have similar resource requirements and priorities. - Advantage: Simplicity and ease of management. - Disadvantage: Potentially less control over resource allocation and prioritization. 2. Dedicated Worker per Queue: - Configuration: Deploy a separate worker for each work queue. - Use Case: When you need to isolate workloads, prioritize certain tasks, or have different resource requirements for different queues. - Advantage: Enhanced control over resource allocation, improved fault isolation, and better prioritization. - Disadvantage: Increased complexity in managing multiple workers. ### Recommendations for Effective Scaling - Evaluate Workload Characteristics: - Determine if different queues require distinct resources or priorities. Use dedicated workers for these cases to optimize resource usage. - For workloads with similar characteristics, a single worker polling multiple queues may suffice. - Resource Management: - If different tasks have different resource needs (e.g., high-memory vs. CPU-intensive tasks), use dedicated workers to ensure that specific resources are available for each type of task. - Scaling Strategy: - Start with a single worker for multiple queues and monitor performance. - As you identify bottlenecks or specific needs, consider deploying additional workers dedicated to specific queues. ### Example: Multiple Workers and Queues #### 
prefect.yaml
Configuration High-Memory Queue:
Copy code
yaml
name: high-memory-job
prefect-version: 2.16.9

deployments:
  - name: high_memory_deploy
    entrypoint: path/to/flow.py:high_memory_flow
    work_pool:
      name: custom-work-pool
      work_queue_name: high-memory-tasks
    job_variables:
      resources:
        requests:
          memory: "512Mi"
          cpu: "0.5"
        limits:
          memory: "1024Mi"
          cpu: "1"
High-CPU Queue:
Copy code
yaml
name: high-cpu-job
prefect-version: 2.16.9

deployments:
  - name: high_cpu_deploy
    entrypoint: path/to/flow.py:high_cpu_flow
    work_pool:
      name: custom-work-pool
      work_queue_name: high-cpu-tasks
    job_variables:
      resources:
        requests:
          memory: "256Mi"
          cpu: "1"
        limits:
          memory: "512Mi"
          cpu: "2"
#### Running Workers Worker for High-Memory Tasks:
Copy code
sh
prefect worker start -p custom-work-pool -q high-memory-tasks
Worker for High-CPU Tasks:
Copy code
sh
prefect worker start -p custom-work-pool -q high-cpu-tasks
### Summary - Single Worker for Multiple Queues: Start with this strategy for simplicity and ease of management. - Dedicated Worker per Queue: Use this when specific resource allocation, task prioritization, and fault isolation are critical. - Evaluate and Scale: Monitor your system’s performance and incrementally add dedicated workers as needed based on workload characteristics. This approach ensures efficient resource utilization and performance scalability as your workflows grow in complexity and size. For detailed documentation, check out the Prefect Work Pools and Workers Documentation. Feel free to ask additional questions or for further clarification!
s
@Marvin Tell me about Prefect integrations, specifically Dask and Ray task runners. Is these type of integrations only available for prefect cloud or open to prefect oss as well?
m
### Prefect Integrations with Dask and Ray Prefect offers powerful integrations with Dask and Ray, which are task runners enabling concurrent, parallel, or distributed execution of tasks. Both integrations are available to Prefect open-source users, so you don’t need Prefect Cloud to leverage these capabilities. #### Dask Integration Dask is a flexible parallel computing library for analytics. Prefect integrates with Dask to distribute tasks over multiple machines, providing significant time savings. ##### Getting Started with Dask 1. Installation:
Copy code
sh
   pip install prefect-dask
2. Using Dask with Prefect:
Copy code
python
   from prefect import flow, task
   from prefect_dask import DaskTaskRunner

   @task
   def process_data(x):
       return x * 2

   @flow(task_runner=DaskTaskRunner)
   def my_flow(data):
       results = process_data.map(data)
       return results

   if __name__ == "__main__":
       data = list(range(10))
       my_flow(data)
3. Connecting to Existing Dask Cluster: If you have an existing Dask cluster, you can connect to it using:
Copy code
python
   from dask.distributed import Client
   client = Client("<tcp://scheduler-address:port>")

   @flow(task_runner=DaskTaskRunner(cluster=client))
   def my_flow(data):
       results = process_data.map(data)
       return results
Documentation: Prefect Dask Integration #### Ray Integration Ray is a unified framework for scalable and distributed computing. Prefect integrates with Ray to run tasks in parallel by distributing them across multiple machines. ##### Getting Started with Ray 1. Installation:
Copy code
sh
   pip install prefect-ray
2. Using Ray with Prefect:
Copy code
python
   from prefect import flow, task
   from prefect_ray import RayTaskRunner

   @task
   def process_data(x):
       return x * 2

   @flow(task_runner=RayTaskRunner)
   def my_flow(data):
       results = process_data.map(data)
       return results

   if __name__ == "__main__":
       data = list(range(10))
       my_flow(data)
3. Connecting to Existing Ray Cluster: If you have an existing Ray cluster, you can connect to it using:
Copy code
python
   import ray
   ray.init(address="auto")

   @flow(task_runner=RayTaskRunner(address="auto"))
   def my_flow(data):
       results = process_data.map(data)
       return results
Documentation: Prefect Ray Integration ### Advantages of Using Dask and Ray 1. Scalability: Both Dask and Ray can distribute tasks across multiple machines, providing horizontal scalability. 2. Parallelism: Tasks can run in parallel, greatly reducing execution time for large workloads. 3. Flexibility: Integrates seamlessly with existing Prefect flows, enabling easy migration and adoption without major codebase changes. ### Availability - Prefect OSS: Available in the open-source version of Prefect, allowing you to use Dask and Ray task runners without needing Prefect Cloud. - Prefect Cloud: Also supported, with additional cloud-specific features for monitoring and managing flows. ### Conclusion Prefect’s integrations with Dask and Ray provide powerful capabilities for parallel and distributed task execution, enhancing scalability and performance. These integrations are fully available in both Prefect OSS and Prefect Cloud, making them accessible to all users. For detailed examples and documentation, you can explore: - Prefect Dask Documentation - Prefect Ray Documentation Feel free to ask further questions or for any additional examples!
s
Hey @Marvin, In which cases prefect server can raise this error within a flow run?
Copy code
Encountered exception during execution:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/prefect/engine.py", line 2148, in orchestrate_task_run
    result = await call.aresult()
  File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 327, in aresult
    return await asyncio.wrap_future(self.future)
  File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 352, in _run_sync
    result = self.fn(*self.args, **self.kwargs)
  File "/app/src/google_ads/main.py", line 81, in task_read_stream_data
    run_deployment(
  File "/usr/local/lib/python3.10/site-packages/prefect/utilities/asyncutils.py", line 259, in coroutine_wrapper
    return call()
  File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 432, in __call__
    return self.result()
  File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 318, in result
    return self.future.result(timeout=timeout)
  File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 179, in result
    return self.__get_result()
  File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
  File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 389, in _run_async
    result = await coro
  File "/usr/local/lib/python3.10/site-packages/prefect/client/utilities.py", line 78, in with_injected_client
    return await fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/site-packages/prefect/deployments/deployments.py", line 217, in run_deployment
    flow_run = await client.read_flow_run(flow_run_id)
  File "/usr/local/lib/python3.10/site-packages/prefect/client/orchestration.py", line 2000, in read_flow_run
    response = await self._client.get(f"/flow_runs/{flow_run_id}")
  File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1814, in get
    return await self.request(
  File "/usr/local/lib/python3.10/site-packages/httpx/_client.py", line 1585, in request
    return await self.send(request, auth=auth, follow_redirects=follow_redirects)
  File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 344, in send
    response.raise_for_status()
  File "/usr/local/lib/python3.10/site-packages/prefect/client/base.py", line 167, in raise_for_status
    raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.__cause__
prefect.exceptions.PrefectHTTPStatusError: Server error '500 Internal Server Error' for url '<http://prefect-server-2.dev-prefect.svc.cluster.local:4200/api/flow_runs/2e0663bf-be5e-48c1-9f6c-f919e4cb156c>'
Response: {'exception_message': 'Internal Server Error'}
For more information check: <https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500>
m
Your message was too long by 502 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Hey , In which cases prefect server can raise this error within a flow run?
```Encountered exception during execution: Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/engine.py", line 2148, in orchestrate_task_run result = await call.aresult() File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 327, in aresult return await asyncio.wrap_future(self.future) File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 352, in _run_sync result = self.fn(*self.args, **self.kwargs) File "/app/src/google_ads/main.py", line 81, in task_read_stream_data run_deployment( File "/usr/local/lib/python3.10/site-packages/prefect/utilities/asyncutils.py", line 259, in coroutine_wrapper return call() File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 432, in call return self.result() File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 318, in result return self.future.result(timeout=timeout) File "/usr/local
s
Hey @Marvin, In which cases prefect server can raise this error within a flow run?
Copy code
Encountered exception during execution:

prefect.exceptions.PrefectHTTPStatusError: Server error '500 Internal Server Error' for url '<http://prefect-server-2.dev-prefect.svc.cluster.local:4200/api/flow_runs/2e0663bf-be5e-48c1-9f6c-f919e4cb156c>'
Response: {'exception_message': 'Internal Server Error'}
For more information check: <https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500>
m
The 
500 Internal Server Error
in Prefect typically arises from server-side issues. Here are common causes and scenarios in which this error might occur within a flow run: ### Common Causes and Scenarios 1. Configuration Issues: - Incorrect or missing configuration settings in the Prefect server or Prefect's environment variables. - Example: Incorrect database connection settings or timeouts not properly configured. 2. Resource Exhaustion: - The server or one of its dependencies (like a database) ran out of memory or CPU resources. - Example: Too many concurrent flow runs exhausting system resources, leading to failures in processing requests. 3. Unhandled Exceptions: - Errors in your flow code or tasks that are not properly handled can cause the server to fail while processing the request. - Example: A task fails with an unexpected error, and there is no retry or error-handling logic implemented. 4. Database Issues: - Connection issues with the database, such as connectivity problems, timeouts, or the database server being unreachable. - Example: Misconfiguration causing the Prefect server to fail when trying to interact with the database. 5. API or Network Issues: - Network problems or issues with the API endpoints being called within the flow. - Example: External API call within a task times out or returns an error that is not properly handled. ### Example Error and Github Issues 1. Reproduction Scenario:
Copy code
python
from prefect import flow, task
from prefect.exceptions import PrefectHTTPStatusError

@task(retries=0)
def failing_task():
    raise ValueError("Simulated task failure")

@flow()
def my_flow():
    try:
        failing_task()
    except PrefectHTTPStatusError as e:
        print(f"Prefect error occurred: {e}")

if __name__ == "__main__":
    my_flow()
2. Common Errors and Fixes: - `exponential_backoff with retries=0 causes 500 internal server error` - `Random 500 Internal Server Error in UI` ### How to Mitigate and Troubleshoot 1. Increase Timeouts and Resource Allocations: - Adjust the Prefect server and database connection timeout settings. - Ensure sufficient memory and CPU are allocated. 2. Error Handling and Retries: - Implement retry mechanisms and error-handling logic in your flow tasks. - Use 
retry_delay_seconds
and exponential backoff to manage transient errors. 3. Debugging Tools: - Utilize Prefect’s logging capabilities to get more detailed error messages. - Browse server logs to identify specific server errors and stack traces. 4. Updating Dependencies: - Ensure all Prefect components and dependencies are up to date. - Use the latest Prefect server and client versions to benefit from bug fixes and performance improvements. 5. Isolate and Test: - Isolate tasks that might be causing the error and run them independently. - Use smaller test cases to identify and fix server-responsive issues. By applying these steps and regularly monitoring your server and flow runs, you can identify and mitigate issues that might lead to a 500 Internal Server Error. For more details and continued support, refer to the Prefect documentation and community forums. Feel free to ask if you have more questions or need further assistance!
s
@Marvin How can I specify resource specification on my prefect.yaml deployments such that the limits and requests are shown and applied in kubernetes pods? I am using a Kubernetes based work pool and queue
m
To specify resource limits and requests in your 
prefect.yaml
deployment when using a Kubernetes-based work pool and queue, you need to define these resource specifications under the 
job_variables
section for your deployment. This configuration ensures that the Kubernetes pods created for your flow runs have the appropriate resource limits and requests applied. ### Example Configuration Here’s how you can configure 
prefect.yaml
to include resource limits and requests: 
prefect.yaml
:
Copy code
yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: custom-work-pool
      work_queue_name: custom-work-queue
      job_variables:
        resources:
          requests:
            memory: "256Mi"
            cpu: "0.25"
          limits:
            memory: "512Mi"
            cpu: "0.5"
### Breakdown of the Configuration 1. 
work_pool
Section: - 
name
: Specifies the work pool name. - 
work_queue_name
: Specifies the work queue name within the work pool. - 
job_variables
: - 
resources
: Contains the resource specifications for the Kubernetes pods. - 
requests
: Minimum amount of resources that Kubernetes guarantees for the pod. - 
memory
: 256 MiB of memory requested. - 
cpu
: 0.25 CPUs requested. - 
limits
: Maximum amount of resources that the pod is allowed to consume. - 
memory
: 512 MiB of memory as a limit. - 
cpu
: 0.5 CPUs as a limit. ### Applying the Deployment To apply the deployment, use Prefect's CLI:
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sh
prefect deploy -q custom-work-queue -p custom-work-pool
### Verification After deploying, you can verify that the resource specifications are applied by checking the Kubernetes pod descriptions:
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sh
kubectl describe pod <pod-name>
Look for the 
requests
and 
limits
section in the resource specifications to ensure they match your 
prefect.yaml
configuration. ### Summary By including the 
resources
section within 
job_variables
in your 
prefect.yaml
file, you can specify the resource limits and requests for your Kubernetes pods. This ensures that your flow runs have the necessary resources allocated, improving performance and stability. Feel free to ask more questions or for further assistance!
s
@Marvin I have done the same thing as you mentioned, but when worker is spawning pods for running flows, in those pods I do not see the resource specifications. I am using prefect 2.16.9.
m
To ensure your resource specifications are correctly applied to the Kubernetes pods, let's go through the necessary steps and verify your configuration. ### Steps to Ensure Resource Specifications 1. Check Prefect Deployment Configuration: Ensure that your 
prefect.yaml
file correctly specifies the resource limits and requests under 
job_variables
. 2. Ensure Worker Configuration: Verify that your worker is configured correctly to pick up these specifications from the 
prefect.yaml
. ### Example Configuration 
prefect.yaml
:
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yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: custom-work-pool
      work_queue_name: custom-work-queue
      job_variables:
        resources:
          requests:
            memory: "256Mi"
            cpu: "0.25"
          limits:
            memory: "512Mi"
            cpu: "0.5"
### Verify Worker Configuration Ensure that the worker is properly configured to reflect the specified resource requests and limits in the Kubernetes pods. ### Workflow 1. Deploy the Flow:
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sh
   prefect deploy -q custom-work-queue -p custom-work-pool
2. Start Worker: Ensure worker is started and pointing correctly to the designated work pool and queue.
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   prefect worker start -p custom-work-pool -q custom-work-queue
3. Verify Kubernetes Pods: After starting a flow run, inspect the Kubernetes pods to verify that the resource limits and requests are being applied.
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sh
   kubectl describe pod <pod-name>
Look for 
requests
and 
limits
in the resource specifications. ### Note on 
job_variables
If the 
job_variables
section is not working as expected in your current setup, you may want to fall back to ensuring that the Prefect worker configuration fully supports these settings. This typically should be supported in 2.16.9, but inconsistencies can sometimes arise due to misconfiguration or bugs. ### Example Worker Configuration Script If you realize the issue persists, consider writing a script to double-check the values within your worker configuration:
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python
from prefect import flow, task
from prefect.infrastructure.kubernetes import KubernetesJob

k8s_job = KubernetesJob(
    image="my-repo/my-image:latest",
    job_cpu_request="0.25",
    job_cpu_limit="0.5",
    job_memory_request="256Mi",
    job_memory_limit="512Mi"
)

@task
def my_task(x):
    return x * 2

@flow(infrastructure=k8s_job)
def my_flow(x):
    result = my_task(x)
    return result

if __name__ == "__main__":
    my_flow(5)
This script sets resource limits directly within the infrastructure setup to ensure they are applied correctly. ### Troubleshooting 1. Check Deployment Logs: Review Prefect worker and server logs for any error messages related to resource specifications. 2. Inspect Flow Runs: Use Prefect UI to inspect the details of flow runs and verify if there are any overrides or misconfigurations. 3. Cross-verify Kubernetes Config: Ensure Kubernetes cluster nodes have sufficient resources to honor the specified requests and limits. By following these steps, you can set and verify that resource specifications are correctly applied to Kubernetes pods managed by Prefect. If issues persist, it may help to consult the Prefect documentation or seek support from the community or the Prefect team. Feel free to reach out for further assistance!
s
@Marvin I deployed a flow, and created a flow run from UI. The workpool is of type Kubernetes. I triggered the flow run but the pod failed with StartError with the following error which I noticed in Pod events: 
Error: failed to create containerd task: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: exec: "prefect": executable file not found in $PATH: unknown
What does this error mean in Prefect context
m
The error you're encountering indicates that the Kubernetes pod failed to start because it couldn't find the 
prefect
executable in the container's 
$PATH
. This is a common issue when the Docker image used for the Kubernetes job doesn't have Prefect installed, or if the command to run Prefect is incorrect. ### Common Causes 1. Prefect Not Installed in Docker Image: - The Docker image used in your deployment does not include Prefect, or it is not properly installed. 2. Incorrect Command: - The entrypoint or command specified for the Docker container is incorrect or missing. ### Steps to Resolve 1. Verify Docker Image: Ensure that the Docker image you are using has Prefect installed. Dockerfile Example:
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Dockerfile
   FROM python:3.9-slim

   # Install Prefect
   RUN pip install prefect==2.16.9

   # Copy your flow code
   COPY . /app
   WORKDIR /app

   # Set the entrypoint to Prefect
   ENTRYPOINT ["prefect"]
Build and push the Docker image:
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sh
   docker build -t my-repo/my-image:latest .
   docker push my-repo/my-image:latest
2. Check Your Deployment Configuration: Ensure that your 
prefect.yaml
file is correctly configured to use this Docker image and that it points to the correct entrypoint. Prefect Deployment Configuration:
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yaml
   name: my-flow-deployment
   prefect-version: 2.16.9

   build:
     - prefect.deployments.steps.build_docker_image:
         requires: prefect-docker>=0.4.0
         image_name: my-repo/my-image
         tag: latest
         dockerfile: path/to/your/Dockerfile
         push: true

   deployments:
     - name: my_flow_deploy
       entrypoint: path/to/your/flow.py:my_flow
       parameters:
         x: 5  # Example parameter value
       work_pool:
         name: custom-work-pool
         work_queue_name: custom-work-queue
         job_variables:
           image: my-repo/my-image:latest
           command: "python -m prefect.engine"
3. Verify Job Variables: Ensure that your 
job_variables
in 
prefect.yaml
include the correct command to start the flow run. Example:
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yaml
   job_variables:
     command: "prefect run flow --name my_flow"
### Additional Debugging Steps 1. Check Docker Image Locally: Run the Docker image locally to ensure Prefect is installed and the commands work as expected.
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sh
   docker run -it --rm my-repo/my-image:latest prefect --help
2. Inspect Kubernetes Pod Logs: Check the logs of the failed pod to get more details about the error.
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sh
   kubectl logs <pod-name>
3. Check Kubernetes Events: Inspect the Kubernetes events for more insights into why the pod failed.
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sh
   kubectl describe pod <pod-name>
### Example Dockerfile and Deployment Configuration Dockerfile:
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Dockerfile
FROM python:3.9-slim

# Install Prefect
RUN pip install prefect==2.16.9

# Copy your flow code
COPY . /app
WORKDIR /app

# Set the entrypoint to Prefect
ENTRYPOINT ["prefect"]
prefect.yaml
:
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yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    work_pool:
      name: custom-work-pool
      work_queue_name: custom-work-queue
      job_variables:
        image: my-repo/my-image:latest
        command: "prefect flow run --name my_flow"
By verifying the Docker image and ensuring your deployment configuration is correct, you can resolve this error and ensure that Prefect is available in the Pod’s environment. Feel free to ask for further assistance or clarifications!
s
@Marvin I have a CI/CD in place to deploy flows to prefect server with 
prefect deploy --prefect-file ... --all
command. But I have a new requirement to also register the flow id and deployment id of the deployed flows in a metadata table that I am managing. There will be an API exposed to register the flow details in a separate table. But how do I get the flow and deployment ID after deploying through CI/CD. Can you suggest a approach based on your knowledge of the prefect ecosystem? Prefect version: 2.16.9 Self hosted prefect server is used in my case
m
To register the flow ID and deployment ID after deploying flows through CI/CD, you can follow these steps: ### Approach 1. Capture Output from the 
prefect deploy
Command: Use the Prefect CLI to capture the output of the 
prefect deploy
command. This output will contain information about the deployment, including the deployment name. 2. Query Prefect Server API: Once you have the deployment name, you can query the Prefect Server API to retrieve the deployment ID and flow ID. 3. Register Flow and Deployment IDs: Use an API call to register these IDs in your metadata table. ### Step-by-Step Solution 1. Modify CI/CD Pipeline to Capture Deployment Output: Ensure that your CI/CD pipeline captures the output of the 
prefect deploy
command.
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sh
   deploy_output=$(prefect deploy --prefect-file path/to/prefect.yaml --all)
   echo "$deploy_output"
2. Extract Deployment Name from Output: Extract the deployment name from the 
deploy_output
.
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sh
   deployment_name=$(echo "$deploy_output" | grep -oP "Deployment name: \K\S+")
3. Query Prefect Server API for Deployment ID and Flow ID: Use the deployment name to query the Prefect Server API and retrieve the deployment ID and flow ID. You can use 
curl
or any other HTTP client for this purpose.
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sh
   deployment_info=$(curl -X GET "http://<prefect-server-url>/api/deployments/name/$deployment_name")
   deployment_id=$(echo "$deployment_info" | jq -r '.id')
   flow_id=$(echo "$deployment_info" | jq -r '.flow_id')
4. Register Flow and Deployment IDs via API: Finally, send a POST request to your metadata API to register the flow and deployment IDs.
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sh
   curl -X POST "http://<metadata-api-url>/register" -H "Content-Type: application/json" \
     -d '{
       "flow_id": "'"$flow_id"'",
       "deployment_id": "'"$deployment_id"'"
     }'
### Example Script for CI/CD Pipeline Here is a complete example script that you can integrate into your CI/CD pipeline:
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sh
#!/bin/bash

# Deploy the flow and capture the output
deploy_output=$(prefect deploy --prefect-file path/to/prefect.yaml --all)
echo "$deploy_output"

# Extract the deployment name from the output
deployment_name=$(echo "$deploy_output" | grep -oP "Deployment name: \K\S+")

# Query Prefect Server API for deployment information
deployment_info=$(curl -X GET "http://<prefect-server-url>/api/deployments/name/$deployment_name")
deployment_id=$(echo "$deployment_info" | jq -r '.id')
flow_id=$(echo "$deployment_info" | jq -r '.flow_id')

# Register flow and deployment IDs via API
curl -X POST "http://<metadata-api-url>/register" -H "Content-Type: application/json" \
  -d '{
    "flow_id": "'"$flow_id"'",
    "deployment_id": "'"$deployment_id"'"
  }'
### Notes - Replace 
<prefect-server-url>
with the actual URL of your Prefect server. - Replace 
<metadata-api-url>
with the URL of your metadata API. - This script assumes you have 
jq
installed to parse JSON. If not, you can install it using your package manager (
apt-get install jq
for Debian-based systems, 
yum install jq
for RedHat-based systems, or 
brew install jq
for macOS). By following this approach, you can programmatically retrieve and register the flow ID and deployment ID after deploying flows using CI/CD. This ensures that your metadata table is always up to date with the latest deployment information. If you have any more questions or need further assistance, feel free to ask!
s
@Marvin When I run prefect deploy --prefect-file path/to/prefect.yaml --all, In the output I have a lot of data, but no line 
Deployment name:
, so you will need to update either the approach or the grep command
m
Got it. If the 
prefect deploy
command output does not include a specific 
Deployment name:
line, you'll need to parse the output to extract relevant deployment details. ### Alternative Approach 1. Capture the Entire Output: Capture the entire output of the 
prefect deploy
command to inspect it. 2. Parse Deployment Names and IDs: Use a more dynamic approach to parse the deployment names and IDs from the output JSON if possible. 3. Use Prefect CLI or API: Use Prefect CLI commands or direct API queries to fetch the details after deployment. ### Updated Script for CI/CD Pipeline Here's an improved approach to parse deployment names and IDs from the Prefect API. 1. Capture Output: Capture the deployment details from the Prefect CLI. 2. Use 
prefect deployment ls
: Retrieve the list of deployments and filter based on recent updates. 3. Extract Deployment Details: Extract the flow and deployment IDs from the API response. 4. Register Flow and Deployment IDs. ### Example Script Step-by-Step with Explanations: - Deploy the Flow and Capture the Output: Capture the output of the 
prefect deploy
command but focus on recent deployments. - Fetch Deployment ID: Use Prefect's CLI to list deployments and extract deployment IDs for new deployments.
Copy code
sh
#!/bin/bash

# Assuming you have jq installed for JSON parsing
set -e

# Step 1: Deploy the flow and capture the output
deploy_output=$(prefect deploy --prefect-file path/to/prefect.yaml --all)
echo "$deploy_output"

# Step 2: Use the Prefect CLI to list deployments
# Filter the deployments that match the deployed flow name or recent creation time
deployment_list=$(prefect deployment ls --json)

# Step 3: Parse the deployment list to find the relevant deployment IDs
# Assuming you have specific details to filter by (e.g., flow name)
flow_name="my-flow-name"

# Use jq to filter deployments for the specific flow name
deployment_info=$(echo "$deployment_list" | jq -c --arg flow_name "$flow_name" '[.[] | select(.flow_name == $flow_name)]')

# Select the latest deployment (if multiple, adjust filters if needed)
deployment_id=$(echo "$deployment_info" | jq -r '.[-1].id')
flow_id=$(echo "$deployment_info" | jq -r '.[-1].flow_id')

# Step 4: Register flow and deployment IDs via API
curl -X POST "http://<metadata-api-url>/register" -H "Content-Type: application/json" \
  -d '{
    "flow_id": "'"$flow_id"'",
    "deployment_id": "'"$deployment_id"'"
  }'
### Explanation 1. Deploy the Flow:
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sh
   deploy_output=$(prefect deploy --prefect-file path/to/prefect.yaml --all)
   echo "$deploy_output"
2. List Deployments: Use the Prefect CLI to list all deployments in JSON format:
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sh
   deployment_list=$(prefect deployment ls --json)
3. Filter and Extract IDs: Use 
jq
to filter and extract the relevant deployment and flow IDs:
Copy code
sh
   deployment_info=$(echo "$deployment_list" | jq -c --arg flow_name "$flow_name" '[.[] | select(.flow_name == $flow_name)]')
   deployment_id=$(echo "$deployment_info" | jq -r '.[-1].id')
   flow_id=$(echo "$deployment_info" | jq -r '.[-1].flow_id')
4. Register IDs: Use the extracted IDs to register them via your API:
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sh
   curl -X POST "http://<metadata-api-url>/register" -H "Content-Type: application/json" \
     -d '{
       "flow_id": "'"$flow_id"'",
       "deployment_id": "'"$deployment_id"'"
     }'
This script accommodates variations in output formatting and ensures that you capture the necessary details to register the deployment and flow IDs. Adjust the filtering criteria as needed to match your specific context and requirements. Feel free to ask if you need further customization or additional help!
s
@Marvin How do I fetch a deployment details using prefect CLI by using a deployment ID. I am using Prefect 2.16.9
m
To fetch the details of a deployment using the Prefect CLI in version 2.16.9, you can use the 
prefect deployment inspect
command with the deployment ID. Here's how you can do it: ### Fetch Deployment Details Use the command:
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sh
prefect deployment inspect <deployment-id>
This command will retrieve and display the details of the specified deployment. ### Example Usage 1. Fetch Deployment ID (if not already known): If you need to list deployments to find the deployment ID, you can use:
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sh
   prefect deployment ls
2. Inspect Specific Deployment: Once you have the deployment ID, you can fetch the deployment details.
Copy code
sh
   prefect deployment inspect 2e0663bf-be5e-48c1-9f6c-f919e4cb156c
### Example Output Here is an example of what the output might look like when you inspect a deployment:
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sh
$ prefect deployment inspect 2e0663bf-be5e-48c1-9f6c-f919e4cb156c
{
  "id": "2e0663bf-be5e-48c1-9f6c-f919e4cb156c",
  "name": "my_flow_deployment",
  "flow_name": "my_flow",
  "parameters": {
    "x": 5
  },
  "tags": [],
  "description": null,
  "schedule": null,
  "created_at": "2023-03-04T12:34:56.000Z",
  ...
}
### Automating the Process in CI/CD Pipeline To automate this in your CI/CD pipeline, you can integrate this command to capture and use the deployment details. Script Example:
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sh
#!/bin/bash

# Deploy the flow and capture the output
deploy_output=$(prefect deploy --prefect-file path/to/prefect.yaml --all)
echo "$deploy_output"

# Extract the deployment ID from the output (if possible)
# Assuming you had the deployment name or extracted it
deployment_id=$(echo "$deploy_output" | grep -oP "Deployment ID: \K\S+")

# Alternatively, fetch all deployments and find the latest one
deployment_id=$(prefect deployment ls --json | jq -r '.[-1].id')

# Fetch the deployment details
deployment_details=$(prefect deployment inspect "$deployment_id")

echo "$deployment_details"

# Register the deployment details via API
flow_id=$(echo "$deployment_details" | jq -r '.flow_id')
curl -X POST "http://<metadata-api-url>/register" -H "Content-Type: application/json" \
  -d '{
    "flow_id": "'"$flow_id"'",
    "deployment_id": "'"$deployment_id"'"
  }'
### Summary - Command: Use 
prefect deployment inspect <deployment-id>
to fetch deployment details. - Integration: Script the deployment and inspection process to automate capturing and registering deployment details in your CI/CD pipeline. - Prefect CLI: Utilize the Prefect CLI commands to streamline deployments and manage deployment metadata efficiently. Using these steps, you can programmatically extract and manage deployment details, ensuring seamless integration with your existing systems and processes. Feel free to ask for further assistance or any more specific customizations!
s
When I am using deployement ID with Prefect deployment inspect <deployment_id>, then I am getting Invalid deployment name. Expected '<flow-name>/<deployment-name>' error
@Marvin When I am using deployement ID with Prefect deployment inspect <deployment_id>, then I am getting Invalid deployment name. Expected '<flow-name>/<deployment-name>' error
m
The error you are encountering indicates that the 
prefect deployment inspect
command expects the full deployment name in the format 
<flow-name>/<deployment-name>
rather than just the deployment ID. To resolve this, you can use the 
prefect deployment inspect
command with the appropriate format. ### Steps to Fetch Deployment Details 1. List Deployments: Use the 
prefect deployment ls
command to list all deployments and find the appropriate flow-name and deployment-name. 2. Inspect Deployment: Once you have the flow name and deployment name, use the 
prefect deployment inspect
command with the full deployment name format. ### Example Commands 1. List Deployments:
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sh
   prefect deployment ls
Example output:
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
   ┃ Name                           ┃ ID                                   ┃
   ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
   │ my-flow/my-deployment          │ 2e0663bf-be5e-48c1-9f6c-f919e4cb156c │
   └────────────────────────────────┴──────────────────────────────────────┘
2. Inspect Deployment: Use the full name 
<flow-name>/<deployment-name>
for the 
prefect deployment inspect
command.
Copy code
sh
   prefect deployment inspect my-flow/my-deployment
### Example Automated Script for CI/CD Pipeline 1. Capture Deployment Details: Use 
prefect deployment ls
and 
jq
to find the latest deployment and fetch its details. Script Example:
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sh
#!/bin/bash

# Step 1: Deploy the flow and capture the output
deploy_output=$(prefect deploy --prefect-file path/to/prefect.yaml --all)
echo "$deploy_output"

# Step 2: Use the Prefect CLI to list deployments and find the latest one
deployment_list=$(prefect deployment ls --json)

# Extract flow name and deployment name using jq
latest_deployment=$(echo "$deployment_list" | jq -r '.[-1]')
flow_name=$(echo "$latest_deployment" | jq -r '.flow_name')
deployment_name=$(echo "$latest_deployment" | jq -r '.name')

# Step 3: Fetch deployment details
full_deployment_name="$flow_name/$deployment_name"
deployment_details=$(prefect deployment inspect "$full_deployment_name")

echo "$deployment_details"

# Extract flow ID
flow_id=$(echo "$deployment_details" | jq -r '.flow_id')

# Step 4: Register flow and deployment IDs via API
curl -X POST "http://<metadata-api-url>/register" -H "Content-Type: application/json" \
  -d '{
    "flow_id": "'"$flow_id"'",
    "deployment_id": "'"$deployment_name"'"
  }'
### Summary By using the full deployment name format 
<flow-name>/<deployment-name>
with the 
prefect deployment inspect
command, you can retrieve the deployment details. The scripts provided illustrate how to integrate this process into a CI/CD pipeline, ensuring you capture and register the necessary metadata efficiently. ### Reference Commands For the Prefect CLI: - List Deployments: 
prefect deployment ls
- Inspect Deployment: 
prefect deployment inspect <flow-name>/<deployment-name>
If you have any further questions or need additional customization for your script, feel free to ask!
s
@Marvin I want to add a few metadata fields in my deployments, like component_type, flow_type. These are my internal metadata, not relevant to running the flow on prefect. But I want to get these details when I call prefect deployment inspect. How to do this?
m
To include custom metadata fields in your Prefect deployments, you can leverage Prefect's existing structure and append additional metadata relevant to your use case. Although Prefect does not directly support arbitrary metadata fields in the deployment specification, you can utilize tags or descriptions, or you may utilize a key in the parameters section for custom metadata. ### Example Modification with Custom Metadata Fields #### 
prefect.yaml
Configuration Let's add custom fields (
component_type
and 
flow_type
) to your deployment configuration: 
prefect.yaml
:
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yaml
name: my-flow-deployment
prefect-version: 2.16.9

build:
  - prefect.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: my-repo/my-image
      tag: latest
      dockerfile: path/to/your/Dockerfile
      push: true

deployments:
  - name: my_flow_deploy
    entrypoint: path/to/your/flow.py:my_flow
    parameters:
      x: 5  # Example parameter value
    tags: ["component_type:analytics", "flow_type:batch"]
    description: "Deployment for analytics batch processing"
    work_pool:
      name: custom-work-pool
      work_queue_name: custom-work-queue
      job_variables:
        resources:
          requests:
            memory: "256Mi"
            cpu: "0.25"
          limits:
            memory: "512Mi"
            cpu: "0.5"
### Fetching Custom Metadata When you use the 
prefect deployment inspect
command, tags and descriptions are included in the output. You can parse these to extract your custom metadata. #### Example CLI Usage
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sh
deployment_details=$(prefect deployment inspect my-flow/my-flow-deploy)
echo "$deployment_details"

# Extract component_type and flow_type from tags
component_type=$(echo "$deployment_details" | grep -oP 'component_type:\K[^"]*')
flow_type=$(echo "$deployment_details" | grep -oP 'flow_type:\K[^"]*')

echo "Component Type: $component_type"
echo "Flow Type: $flow_type"
### A More Structured Approach Using the Description Field Alternatively, put your custom metadata in a JSON string within the description field:
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yaml
...
description: '{"component_type": "analytics", "flow_type": "batch"}'
...
#### Fetch and Parse Description Field
Copy code
sh
deployment_details=$(prefect deployment inspect my-flow/my-flow-deploy --output-json)
echo "$deployment_details"

# Extract metadata from the description field
description=$(echo "$deployment_details" | jq -r '.description')
custom_metadata=$(echo "$description" | jq -r '.')

component_type=$(echo "$custom_metadata" | jq -r '.component_type')
flow_type=$(echo "$custom_metadata" | jq -r '.flow_type')

echo "Component Type: $component_type"
echo "Flow Type: $flow_type"
### Advantages and Disadvantages Advantages: - Flexibility: Allows you to store custom metadata without altering Prefect’s core schema. - Retrievability: Custom metadata can be easily extracted and used programmatically. Disadvantages: - Schema Limitations: Prefect may not inherently understand or enforce an underlying schema, making it loosely structured. - Parsing Complexity: Requires additional logic to parse and make use of custom metadata. ### Summary By adapting the tags, description, or parameter fields in your 
prefect.yaml
, you can embed custom metadata relevant to your workflows. This approach ensures that your metadata stays with the deployment and can be retrieved using Prefect CLI commands for further processing. If you need further assistance or have more questions, feel free to ask!
s
@Marvin If I have a flow in main.py, how to deploy the flow from main.py using prefect sdk? And how do I integrate it in my overall project. Do I add it in CI/CD or is there a better way in which Prefect deployments are handled? Can you add the resources for reference as well? I am using prefect 3 server with prefect 3.0.0 sdk
m
### Deploying a Flow from 
main.py
Using Prefect SDK (Prefect 3.0.0) ### Step-by-Step Guide 1. Define Your Flow in 
main.py
: Ensure you have your Prefect flow defined in your Python script. 
main.py
:
Copy code
python
from prefect import flow

@flow
def my_flow(x: int):
    print(f"Running with parameter x={x}")

if __name__ == "__main__":
    my_flow(x=5)
2. Deploy the Flow Using SDK: Use the Prefect SDK to deploy the flow programmatically. Script to Deploy:
Copy code
python
from prefect.deployments import Deployment
from main import my_flow

# Define the deployment
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_deployment",
    work_pool_name="custom-work-pool",
    parameters={"x": 5},
)

# Apply the deployment
deployment.apply()
3. Include the Deployment Script in Your CI/CD Pipeline: Ensure your CI/CD pipeline executes the deployment script whenever you want to deploy or update the flow. ### Example Deployment Script deploy.py:
Copy code
python
import os
from prefect.deployments import Deployment
from main import my_flow

# Define the deployment
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_deployment",
    work_pool_name="custom-work-pool",
    parameters={"x": 5},
)

# Apply the deployment
deployment.apply()

# Optionally, you can print deployment ID or other details
print(f"Deployment '{deployment.name}' applied.")
4. CI/CD Integration: Add a step in your CI/CD pipeline to execute the 
deploy.py
script. ### Example CI/CD Configuration GitHub Actions (
.github/workflows/deploy.yml
):
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Deploy Prefect Flow
      run: |
        python deploy.py
Jenkins (
Jenkinsfile
):
Copy code
groovy
pipeline {
    agent any

    stages {
        stage('Checkout') {
            steps {
                git '<https://your-repo-url.git>'
            }
        }
        stage('Setup Python') {
            steps {
                sh 'python3 -m venv venv'
                sh './venv/bin/pip install --upgrade pip'
                sh './venv/bin/pip install -r requirements.txt'
            }
        }
        stage('Deploy Prefect Flow') {
            steps {
                sh './venv/bin/python deploy.py'
            }
        }
    }
}
### Reference Resources - Prefect Python Deployment Documentation: Deploy Flows via Python SDK - Prefect YAML Configuration: Prefect YAML - Prefect CLI Reference: Prefect CLI ### Summary - Define: Start by defining your Prefect flow in a Python script. - Deploy: Use the Prefect SDK to build and apply deployments programmatically. - CI/CD: Integrate the deployment script into your CI/CD pipeline to automate deployments. This ensures that your Prefect deployments are handled efficiently and are integrated seamlessly into your development workflows. If you have any further questions or need additional examples, feel free to ask!
s
@Marvin Explain what is this error, I am using prefect-3 server.
Copy code
raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.__cause__
prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/'
Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {'param_1': {'default': 'Default Param 1', 'position': 0, 'title': 'param_1', 'type': 'string'}, 'param_2': {'default': 'Default Param 2', 'position': 1, 'title': 'param_2', 'type': 'string'}}, 'definitions': {}}, 'parameters': {'param_1': 'Hello', 'param_2': 'World'}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': 'default', 'work_pool_name': 'dev', 'storage_document_id': None, 'infrastructure_document_id': None, 'description': 'Sample flow 1 for POC', 'path': Non
m
Your message was too long by 42 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Explain what is this error, I am using prefect-3 server.
``` raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.cause prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/' Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {'param_1': {'default': 'Default Param 1', 'position': 0, 'title': 'param_1', 'type': 'string'}, 'param_2': {'default': 'Default Param 2', 'position': 1, 'title': 'param_2', 'type': 'string'}}, 'definitions': {}}, 'parameters': {'param_1': 'Hello', 'param_2': 'World'}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': '
s
@Marvin Explain what is this error, I am using prefect-3 server.
Copy code
prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/'
Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {'param_1': {'default': 'Default Param 1', 'position': 0, 'title': 'param_1', 'type': 'string'}, 'param_2': {'default': 'Default Param 2', 'position': 1, 'title': 'param_2', 'type': 'string'}}, 'definitions': {}}, 'parameters': {'param_1': 'Hello', 'param_2': 'World'}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': 'default', 'work_pool_name': 'dev', 'storage_document_id': None, 'infrastructure_document_id': None, 'description': 'Sample flow 1 for POC', 'path': Non
m
Your message was too long by 24 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Explain what is this error, I am using prefect-3 server.
```prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/' Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {'param_1': {'default': 'Default Param 1', 'position': 0, 'title': 'param_1', 'type': 'string'}, 'param_2': {'default': 'Default Param 2', 'position': 1, 'title': 'param_2', 'type': 'string'}}, 'definitions': {}}, 'parameters': {'param_1': 'Hello', 'param_2': 'World'}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': 'default', 'work_pool_name': 'dev', 'storage_document_id': None, '
s
@Marvin Explain what is this error, I am using prefect-3 server.
Copy code
prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/'
Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {'param_1': {'default': '', 'position': 0, 'title': 'param_1', 'type': 'string'}, 'param_2': {'default': '', 'position': 1, 'title': 'param_2', 'type': 'string'}}, 'definitions': {}}, 'parameters': {'param_1': 'Hello', 'param_2': 'World'}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': 'default', 'work_pool_name': 'dev', 'storage_document_id': None, 'infrastructure_document_id': None, 'description': '', 'path': Non
m
Your message was too long by 6 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Explain what is this error, I am using prefect-3 server.
```prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/' Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {'param_1': {'default': '', 'position': 0, 'title': 'param_1', 'type': 'string'}, 'param_2': {'default': '', 'position': 1, 'title': 'param_2', 'type': 'string'}}, 'definitions': {}}, 'parameters': {'param_1': 'Hello', 'param_2': 'World'}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': 'default', 'work_pool_name': 'dev', 'storage_document_id': None, 'infrastructure_document_id': None, 'description':
s
@Marvin Explain what is this error, I am using prefect-3 server.
Copy code
prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '***/deployments/' Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'loc': ['body', 'concurrency_limit'], 'msg': 'extra fields not permitted', 'type': 'value_error.extra'}], 'request_body': {'name': 'sample_flow_1', 'flow_id': '<masked>', 'schedules': [], 'concurrency_limit': None, 'enforce_parameter_schema': True, 'parameter_openapi_schema': {'title': 'Parameters', 'type': 'object', 'properties': {}, 'definitions': {}}, 'parameters': {}, 'tags': ['sample_flow_1', 'poc'], 'pull_steps': [{'prefect.deployments.steps.set_working_directory': {'directory': '/app'}}], 'work_queue_name': 'default', 'work_pool_name': 'dev', 'storage_document_id': None, 'infrastructure_document_id': None, 'description': '', 'path': Non
m
The error you're encountering, 
PrefectHTTPStatusError: Client error '422 Unprocessable Entity'
, indicates that there is an issue with the deployment request you are sending to the Prefect server. The 
422 Unprocessable Entity
status code means that the server understands the content type of the request entity (i.e., it is syntactically correct), but it was unable to process the contained instructions. ### Cause of the Error The error detail specifies that the 
concurrency_limit
field is not permitted in the body of the request:
Copy code
json
{
  "loc": [
    "body",
    "concurrency_limit"
  ],
  "msg": "extra fields not permitted",
  "type": "value_error.extra"
}
This means that the 
concurrency_limit
field is not expected by the Prefect server and should be removed from your request body. ### Resolving the Error To resolve this error, you need to ensure that your deployment definition does not include the 
concurrency_limit
field. You might have to update your deployment script or the configuration to remove this field. #### Example Updated Deployment Script If your deployment definition includes 
concurrency_limit
, remove it:
Copy code
python
from prefect.deployments import Deployment
from main import my_flow

# Define the deployment without concurrency_limit
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="sample_flow_1",
    work_pool_name="dev",
    parameters={},
    tags=["sample_flow_1", "poc"],
    pull_steps=[{"prefect.deployments.steps.set_working_directory": {"directory": "/app"}}],
    work_queue_name="default",
    description=""
)

# Apply the deployment
deployment.apply()
### Deployment Example Without Forbidden Fields Make sure your deployment configuration aligns with Prefect server’s expectations for the deployment request body. Remove any fields that are not required or recognized. ### CI/CD Integration Ensure the deployment script complies with Prefect’s expected schema and is integrated appropriately in your CI/CD pipeline. ### Summary - Error Explanation: The 
422 Unprocessable Entity
error indicates the presence of an unexpected or forbidden field (
concurrency_limit
) in the deployment request body. - Resolution: Remove the 
concurrency_limit
field from the deployment definition. - Script Update: Update the deployment script to comply with Prefect server's expected deployment schema. By ensuring your deployment definitions align with Prefect's API specifications, you can avoid such errors and ensure successful deployment executions. If you need further assistance or have additional questions, feel free to ask!
s
@Marvin Are class based flows supported in Prefect 2?
m
Prefect 2.x and Prefect 3.x primarily support function-based flows, where you define your flow logic using Python functions decorated with the 
@flow
decorator. However, if you want to organize your flow logic using classes for better structure or reusability, you can do so by encapsulating your flow functions within a class. Prefect does not natively support class-based flow definitions directly, but you can work around this by calling methods from a class within a function-based flow. ### Example: Using Classes to Organize Flow Logic Here's how you can structure your flow logic using classes and deploy the flow using the Prefect SDK. #### 
main.py
Copy code
python
from prefect import flow, task

class MyFlow:
    def __init__(self, x):
        self.x = x

    @task
    def process(self):
        print(f"Processing value: {self.x}")
        return self.x * 2

    @flow
    def run(self):
        result = self.process()
        print(f"Result: {result}")

if __name__ == "__main__":
    my_flow_instance = MyFlow(x=5)
    my_flow_instance.run()
### Deploying the Flow Using Prefect SDK To deploy this flow using the Prefect SDK: #### 
deploy.py
Copy code
python
from prefect.deployments import Deployment
from main import MyFlow

# Instantiate the class
my_flow_instance = MyFlow(x=5)

# Define the deployment
deployment = Deployment.build_from_flow(
    flow=my_flow_instance.run,
    name="my_deployment",
    work_pool_name="custom-work-pool",
    parameters={},  # Assuming no parameters are needed
)

# Apply the deployment
deployment.apply()
### Integrating with CI/CD Including the deployment script in your CI/CD pipeline allows you to automate the deployment of flows whenever changes are made. Here's how you can integrate it. #### Example CI/CD Configuration GitHub Actions (
.github/workflows/deploy.yml
):
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Deploy Prefect Flow
      run: |
        python deploy.py
#### Jenkins (
Jenkinsfile
):
Copy code
groovy
pipeline {
    agent any

    stages {
        stage('Checkout') {
            steps {
                git '<https://your-repo-url.git>'
            }
        }
        stage('Setup Python') {
            steps {
                sh 'python3 -m venv venv'
                sh './venv/bin/pip install --upgrade pip'
                sh './venv/bin/pip install -r requirements.txt'
            }
        }
        stage('Deploy Prefect Flow') {
            steps {
                sh './venv/bin/python deploy.py'
            }
        }
    }
}
### Summary - Class-based Flow Logic: Use classes to organize your flow logic, but call class methods within a function-based flow definition. - Deploying with Prefect SDK: Use 
Deployment.build_from_flow
to deploy flows defined as class methods. - CI/CD Integration: Automate deployments by including the deployment script in your CI/CD pipeline. ### Reference Resources - Prefect Deployment via Python SDK - Prefect YAML - Prefect CLI Reference Using these steps and guidelines, you can effectively manage class-based logic within Prefect flows and automate deployments within your CI/CD pipelines. If you have any further questions or need additional examples, feel free to ask!
s
@Marvin How to use deploy to deploy a flow in Prefect 2. I want to build a docker image with a custom Dockerfile and push the image while deploying. Just like I do in Prefect.yaml
m
You can deploy a flow in Prefect 2 while building and pushing a custom Docker image using the Prefect SDK. This process is analogous to how you might use a 
prefect.yaml
file but allows you to handle everything programmatically. ### Example: Deploying a Flow with a Custom Dockerfile Here’s a step-by-step guide to achieve this: 1. Create a Custom Dockerfile: Ensure you have a Dockerfile in your project directory.
Copy code
Dockerfile
   FROM python:3.9-slim

   # Install Prefect
   RUN pip install prefect==2.16.9

   # Copy your flow code
   COPY . /app
   WORKDIR /app

   # Install additional dependencies
   RUN pip install -r requirements.txt

   # Set the entrypoint to Prefect
   ENTRYPOINT ["prefect"]
2. Create a Deployment Script (
deploy.py
): Use the Prefect SDK to build and apply the deployment.
Copy code
python
   from prefect.deployments import Deployment
   from prefect.infrastructure.docker import DockerContainer
   from main import my_flow  # Import your flow

   # Define the deployment including the custom Dockerfile
   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my_deployment",
       work_pool_name="custom-work-pool",
       infrastructure=DockerContainer(
           image="my-repo/my-image:latest",  # Replace with your Docker image name
           image_build_kwargs={
               "path": ".",
               "dockerfile": "Dockerfile"
           },
           # Push the docker image after it's built
           build=True,
           push=True,
           registry_url="your-registry-url",  # Replace with your docker registry URL
           registry_username="your-username",  # Replace with your docker registry username
           registry_password="your-password"  # Replace with your docker registry password
       )
   )

   # Apply the deployment
   deployment.apply()
### Explanation 1. Dockerfile: Specifies the base image, installs dependencies, copies the flow code, and sets the entrypoint. 2. Deployment Script: - DockerContainer: Specifies the Docker image and build configuration. - `image`: Name of the Docker image. - `image_build_kwargs`: Dictates the Docker build context, path, and Dockerfile. - `build`: Ensures the image is built. - `push`: Ensures the image is pushed to the registry. - `registry_*`: Registry credentials for pushing the image. ### Integrate with CI/CD Example CI/CD Configuration: GitHub Actions (
.github/workflows/deploy.yml
):
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect
        pip install -r requirements.txt

    - name: Deploy Prefect Flow
      env:
        DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }}
        DOCKERHUB_PASSWORD: ${{ secrets.DOCKERHUB_PASSWORD }}
      run: |
        docker login -u $DOCKERHUB_USERNAME -p $DOCKERHUB_PASSWORD
        python deploy.py
Jenkins (
Jenkinsfile
): ```groovy pipeline { agent any environment { DOCKERHUB_USERNAME = credentials('dockerhub-username') DOCKERHUB_PASSWORD = credentials('dockerhub-password') } stages { stage('Checkout') { steps { git 'https://your-repo-url.git' } } stage('Setup Python') { steps { sh 'python3 -m venv venv' sh './venv/bin/pip install --upgrade pip' sh './venv/bin/pip install prefect' sh './venv/bin/pip install -r requirements.txt' } } stage('Deploy Prefect Flow') { steps { sh 'docker login -u $DOCKERHUB_USERNAME -p $DOCKERHUB_PASSWORD'
sh './venv/bin/python deploy.py' } } } } ``` ### Summary - Dockerfile: Create a Dockerfile to build the environment for your flow. - Deployment Script: Use Prefect SDK to build and push the Docker image and deploy the flow. - CI/CD Integration: Incorporate the deployment script in your CI/CD pipeline for automation. This approach allows you to handle deployments programmatically, including building and pushing Docker images as part of the deployment process, facilitating seamless integration into your deployment workflows. ### References - Prefect Deployment via Python SDK - Prefect Docker Deployment Guide Feel free to ask if you need further assistance or have additional questions!
s
@Marvin If I want to push to AWS ECR using the deploy and DockerContainer method, then how to do it. I am using prefect 2
m
To push a Docker image to AWS Elastic Container Registry (ECR) using the 
DockerContainer
infrastructure in Prefect 2.x, you need to follow these steps: ### Steps to Deploy a Flow to AWS ECR 1. Set Up AWS Credentials: Ensure that you have AWS credentials configured with the necessary permissions to push to ECR. You can use 
AWS_ACCESS_KEY_ID
and 
AWS_SECRET_ACCESS_KEY
. 2. Authenticate Docker with AWS ECR: Log in to ECR using the AWS CLI or Docker commands to ensure that Docker can push images to your ECR repository. 3. Configure the Deployment Script: Use the Prefect SDK to configure your deployment, specifying the Docker image and AWS ECR details. ### Example Deployment Script #### 
deploy.py
Copy code
python
from prefect.deployments import Deployment
from prefect.infrastructure.docker import DockerContainer
from main import my_flow  # Import your flow

# AWS ECR registry URL and repository name
ecr_registry = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
repository_name = "my-repo/my-image"

# Full image name including ECR registry
image_name = f"{ecr_registry}/{repository_name}:latest"

# Define the deployment including the custom Dockerfile
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_deployment",
    work_pool_name="custom-work-pool",
    infrastructure=DockerContainer(
        image=image_name,
        image_build_kwargs={
            "path": ".",
            "dockerfile": "Dockerfile"
        },
        # Push the docker image after it's built
        build=True,
        push=True,
        registry_url=ecr_registry,
        registry_username="AWS",  # AWS CLI uses 'AWS' as the username for ECR
        # It's better to use environment variables for sensitive information
        registry_password=os.getenv("AWS_SECRET_ACCESS_KEY")
    )
)

# Apply the deployment
deployment.apply()

print(f"Deployment '{deployment.name}' applied.")
### CI/CD Integration with GitHub Actions #### Example GitHub Actions Workflow (
.github/workflows/deploy.yml
)
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect
        pip install -r requirements.txt

    - name: Login to ECR
      run: |
        aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

    - name: Deploy Prefect Flow
      run: |
        python deploy.py
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
### Summary - Dockerfile: Ensure your Dockerfile is set up correctly to build the environment for your Prefect flow. - Deployment Script: Use Prefect's SDK to configure and apply the deployment while specifying the Docker image and AWS ECR details. - CI/CD Integration: Automate the deployment by integrating the deployment script with your CI/CD pipeline, ensuring you authenticate to AWS ECR and push the Docker image. ### References - Prefect Deployment via Python SDK - Prefect Docker Deployment Guide - Authenticating with AWS ECR
Using these steps, you can achieve seamless deployment of your Prefect flows to AWS ECR, ensuring that your infrastructure is both scalable and integrated with your CI/CD pipelines. Feel free to ask for further assistance or any more specific customizations!
s
@Marvin How to specify prefect API when I am deploying from prefect SDK like above
m
To specify the Prefect API URL when deploying from the Prefect SDK, you can set the 
PREFECT_API_URL
environment variable. This is necessary for configuring the Prefect client to communicate with your Prefect server, whether it's a local instance or Prefect Cloud. ### Setting Up the Prefect API URL 1. Set the Environment Variable: Depending on your environment, you can export the 
PREFECT_API_URL
environment variable. 2. Include in Deployment Script: Ensure the deployment script recognizes the API URL by setting the environment variable within the script or in your CI/CD pipeline. ### Example Deployment Script with API Configuration #### 
deploy.py
Copy code
python
import os
from prefect.deployments import Deployment
from prefect.infrastructure.docker import DockerContainer
from main import my_flow  # Import your flow

# Set the API URL (replace with your Prefect server URL)
os.environ['PREFECT_API_URL'] = "<http://127.0.0.1:4200/api>"

# AWS ECR registry URL and repository name
ecr_registry = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
repository_name = "my-repo/my-image"

# Full image name including ECR registry
image_name = f"{ecr_registry}/{repository_name}:latest"

# Define the deployment including the custom Dockerfile
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_deployment",
    work_pool_name="custom-work-pool",
    infrastructure=DockerContainer(
        image=image_name,
        image_build_kwargs={
            "path": ".",
            "dockerfile": "Dockerfile"
        },
        # Push the docker image after it's built
        build=True,
        push=True,
        registry_url=ecr_registry,
        registry_username="AWS",  # AWS CLI uses 'AWS' as the username for ECR
        # It's better to use environment variables for sensitive information
        registry_password=os.getenv("AWS_SECRET_ACCESS_KEY")
    )
)

# Apply the deployment
deployment.apply()

print(f"Deployment '{deployment.name}' applied.")
### CI/CD Integration Ensure the environment variable 
PREFECT_API_URL
is set in your CI/CD environment. Below are examples for GitHub Actions and Jenkins: #### GitHub Actions Workflow 
.github/workflows/deploy.yml
:
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect
        pip install -r requirements.txt

    - name: Set Prefect API URL
      run: echo "PREFECT_API_URL=<http://127.0.0.1:4200/api>" >> $GITHUB_ENV

    - name: Login to ECR
      run: |
        aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

    - name: Deploy Prefect Flow
      run: |
        python deploy.py
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
#### Jenkins Pipeline 
Jenkinsfile
: ```groovy pipeline { agent any environment { DOCKERHUB_USERNAME = credentials('dockerhub-username') DOCKERHUB_PASSWORD = credentials('dockerhub-password') PREFECT_API_URL = 'http://127.0.0.1:4200/api' // Replace with your Prefect server URL } stages { stage('Checkout') { steps { git 'https://your-repo-url.git' } } stage('Setup Python') { steps { sh 'python3 -m venv venv' sh './venv/bin/pip install --upgrade pip' sh './venv/bin/pip install prefect'
sh './venv/bin/pip install -r requirements.txt' } } stage('Login to ECR') { steps { sh 'aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.amazonaws.com' } } stage('Deploy Prefect Flow') { steps { sh './venv/bin/python deploy.py' } } } } ``` ### Summary - Set Environment Variable: Configure the 
PREFECT_API_URL
environment variable to point to your Prefect server. - Deployment Script: Use Prefect's SDK to deploy your flow, ensuring the API URL is set correctly. - CI/CD Integration: Ensure your deployment script and CI/CD pipelines are configured to use the specified Prefect API URL. ### References - Prefect Deployment via Python SDK - Prefect Cloud Endpoint Implementing these steps ensures that your Prefect deployments can communicate correctly with your designated Prefect server, whether it's local, remote, or Prefect Cloud. If you have further questions or need additional assistance, please feel free to ask!
s
@Marvin How to send job_variables in deployment when deploying using Deployment.build_from_flow
m
To specify 
job_variables
when deploying a flow using 
Deployment.build_from_flow
in Prefect 2, you can pass the 
job_variables
parameter directly into the 
Deployment.build_from_flow
method. These job variables will be included in the deployment configuration and applied to the corresponding work pool for the deployment. Here's an example of how you can include 
job_variables
when deploying: ### Example Deployment Script with 
job_variables
main.py
:
Copy code
python
from prefect import flow, task

@task
def my_task(x):
    print(f"Processing value: {x}")
    return x * 2

@flow
def my_flow(x: int):
    result = my_task(x)
    print(f"Result: {result}")

if __name__ == "__main__":
    my_flow(x=5)
deploy.py
:
Copy code
python
import os
from prefect.deployments import Deployment
from prefect.infrastructure.docker import DockerContainer
from main import my_flow  # Import your flow

# Set the API URL (replace with your Prefect server URL)
os.environ['PREFECT_API_URL'] = "<http://127.0.0.1:4200/api>"

# AWS ECR registry URL and repository name
ecr_registry = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
repository_name = "my-repo/my-image"

# Full image name including ECR registry
image_name = f"{ecr_registry}/{repository_name}:latest"

# Define the deployment including the custom Dockerfile and job_variables
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_deployment",
    work_pool_name="custom-work-pool",
    infrastructure=DockerContainer(
        image=image_name,
        image_build_kwargs={
            "path": ".",
            "dockerfile": "Dockerfile"
        },
        # Push the docker image after it's built
        build=True,
        push=True,
        registry_url=ecr_registry,
        registry_username="AWS",  # AWS CLI uses 'AWS' as the username for ECR
        registry_password=os.getenv("AWS_SECRET_ACCESS_KEY")
    ),
    job_variables={
        "env": {
            "EXECUTION_ENVIRONMENT": os.environ.get("EXECUTION_ENVIRONMENT", "development"),
            "MY_CONFIG": os.environ.get("MY_CONFIG", "default_value")
        },
        "resources": {
            "requests": {
                "memory": "256Mi",
                "cpu": "0.25"
            },
            "limits": {
                "memory": "512Mi",
                "cpu": "0.5"
            }
        }
    }
)

# Apply the deployment
deployment.apply()

print(f"Deployment '{deployment.name}' applied.")
### Parameters Breakdown: - 
flow
: The flow function you are deploying. - 
name
: The name of the deployment. - 
work_pool_name
: The name of the work pool to use for the deployment. - 
infrastructure
: The infrastructure configuration that specifies the Docker image details and registry configuration. - 
job_variables
: Custom variables that include environment variables and resource specifications. ### CI/CD Integration Example #### GitHub Actions Workflow 
.github/workflows/deploy.yml
: ```yaml name: Deploy Prefect Flow on: [push] jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.x' - name: Install dependencies run: | python -m pip install --upgrade pip pip install prefect pip install -r requirements.txt - name: Set Prefect API URL run: echo "PREFECT_API_URL=http://127.0.0.1:4200/api" >> $GITHUB_ENV - name: Login to ECR run: | aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.amazonaws.com env: AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} - name: Deploy Prefect Flow run: | python deploy.py env: AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} EXECUTION_ENVIRONMENT: "production" MY_CONFIG: "my_custom_config" ``` ### Summary - Define Job Variables: Specify the 
job_variables
parameter in your deployment to include custom environment settings and resource configurations. - Configure Deployment Script: Use the Prefect SDK to configure and apply the deployment with the specified job variables. - Integrate with CI/CD: Ensure these configurations are included in your CI/CD pipelines to automate deployments. ### References - Prefect Deployment via Python SDK - Prefect Deployment Guide - Overriding Job Variables Feel free to ask for further assistance or customizations based on your needs!
s
How to deploy using flow.deploy with DeploymentImage for custom image building?
@Marvin How to deploy using flow.deploy with DeploymentImage for custom image building?
m
To deploy a flow using 
flow.deploy
with a custom Docker image built from a 
Dockerfile
, you can use the 
DeploymentImage
class in Prefect 2. This allows you to specify custom image build configurations directly within your flow deployment. ### Step-by-Step Guide 1. Create a Custom Dockerfile: Ensure you have a Dockerfile in your project directory. 2. Deploy the Flow Using 
flow.deploy
and 
DeploymentImage
. ### Example Custom Dockerfile Dockerfile:
Copy code
Dockerfile
FROM python:3.9-slim

# Install Prefect
RUN pip install prefect==2.16.9

# Copy your flow code
COPY . /app
WORKDIR /app

# Install additional dependencies if any
RUN pip install -r requirements.txt

# Set the entrypoint to Prefect
ENTRYPOINT ["prefect"]
### Example Flow Deployment Script 
main.py
:
Copy code
python
from prefect import flow

@flow(log_prints=True)
def my_flow(x: int):
    print(f"Running with parameter x={x}")

if __name__ == "__main__":
    my_flow(x=5)
Deploy Script Using 
flow.deploy
:
Copy code
python
from prefect import flow
from prefect.deployments import DeploymentImage

@flow(log_prints=True)
def my_flow(x: int):
    print(f"Running with parameter x={x}")

if __name__ == "__main__":
    my_flow.deploy(
        name="my-custom-dockerfile-deployment",
        work_pool_name="my-docker-pool",
        image=DeploymentImage(
            name="my_image",
            tag="latest",
            dockerfile="Dockerfile"
        ),
        build=True,  # Ensure the image is built
        push=True,   # Push the image to the registry
        registry_url="<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>",  # Replace with your AWS ECR registry URL
        registry_username="AWS",  # AWS CLI uses 'AWS' as the username for ECR
        registry_password="<AWS_SECRET_ACCESS_KEY>"  # Replace with your AWS secret access key
    )
### CI/CD Integration To automate this in your CI/CD pipeline, you can ensure the necessary environment variables are set for AWS ECR and the Prefect API URL. #### GitHub Actions Workflow 
.github/workflows/deploy.yml
:
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect
        pip install -r requirements.txt

    - name: Set Prefect API URL
      run: echo "PREFECT_API_URL=<http://127.0.0.1:4200/api>" >> $GITHUB_ENV

    - name: Login to ECR
      run: |
        aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

    - name: Deploy Prefect Flow
      run: |
        python main.py
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        PREFECT_API_URL: "<http://127.0.0.1:4200/api>"
### Summary - Dockerfile: Create a Dockerfile to build the environment for your Prefect flow. - Deployment Script: Use 
flow.deploy
with 
DeploymentImage
to specify the Dockerfile and configure image building and pushing. - CI/CD Integration: Automate the deployment in your CI/CD pipeline, ensuring that the necessary environment variables for AWS ECR and the Prefect API are set. ### References - Prefect Deployment via Python SDK - Prefect Docker Deployment Guide
This approach ensures you can customize your Docker image building and deployment processes within Prefect using the 
flow.deploy
method with full support for custom Dockerfiles and registry configurations. If you have more questions or need further customizations, feel free to ask!
s
@Marvin When i do a python based deployment to the prefect server, How do I specify the pull arguments. Prefect sdk = 2.16.9 My script:
Copy code
fetch_stream_data.deploy(
        name=f"raw-google-ads-fetch-{stream}",
        parameters={
            "account_id": None,
            "start_date": None,
            "end_date": None,
            "stream_name": stream,
            "kafka_topic": None,
            "batch_id": None 
        },
        work_pool_name="dataplatform-dev",
        work_queue_name="default",
        image=DeploymentImage(
            name=f"{AWS_ECR_URL}/{AWS_ECR_REPOSITORY_NAME}",
            tag=f"raw-google-ads-fetch-{stream}",
            dockerfile="Dockerfile"
        ),
        build=True,
        push=True
    )
I want to add the following specification to the deployment (The following is from my prefect.yaml)
Copy code
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: /app
m
You can specify the 
pull
arguments in the 
flow.deploy
method using the 
pull_steps
parameter. It directly translates to the steps you would include in the 
prefect.yaml
under the 
pull
section. ### Updated Deployment Script To include the 
pull
argument for setting the working directory, you can use the 
pull_steps
parameter. #### Example Deployment Script with Pull Steps 
deploy.py
:
Copy code
python
import os
from prefect import flow
from prefect.deployments import DeploymentImage

@flow(log_prints=True)
def fetch_stream_data(account_id, start_date, end_date, stream_name, kafka_topic, batch_id):
    print(f"Fetching data for stream: {stream_name}")

if __name__ == "__main__":
    # AWS ECR config
    AWS_ECR_URL = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
    AWS_ECR_REPOSITORY_NAME = "my-repo"

    # Set Prefect API URL if required
    os.environ['PREFECT_API_URL'] = "<http://127.0.0.1:4200/api>"

    fetch_stream_data.deploy(
        name=f"raw-google-ads-fetch-{stream}",
        parameters={
            "account_id": None,
            "start_date": None,
            "end_date": None,
            "stream_name": stream,
            "kafka_topic": None,
            "batch_id": None 
        },
        work_pool_name="dataplatform-dev",
        work_queue_name="default",
        image=DeploymentImage(
            name=f"{AWS_ECR_URL}/{AWS_ECR_REPOSITORY_NAME}",
            tag=f"raw-google-ads-fetch-{stream}",
            dockerfile="Dockerfile"
        ),
        build=True,
        push=True,
        pull_steps=[
            {"prefect.deployments.steps.set_working_directory": {"directory": "/app"}}
        ]
    )
### Breakdown - 
pull_steps
: Includes the same steps you would define in the 
pull
section of a 
prefect.yaml
file. ### CI/CD Workflow Ensure your CI/CD workflow sets the required environment variables and runs the deployment script. #### GitHub Actions Workflow 
.github/workflows/deploy.yml
:
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect
        pip install -r requirements.txt

    - name: Set Prefect API URL
      run: echo "PREFECT_API_URL=<http://127.0.0.1:4200/api>" >> $GITHUB_ENV

    - name: Login to ECR
      run: |
        aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

    - name: Deploy Prefect Flow
      run: |
        python deploy.py
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        PREFECT_API_URL: "<http://127.0.0.1:4200/api>"
### Summary - Specify 
pull_steps
: Use the 
pull_steps
parameter in the 
flow.deploy
method to include pull arguments like setting the working directory. - CI/CD Integration: Ensure that the deployment script and your CI/CD workflows are properly configured to set environment variables and authenticate to AWS ECR. By including the 
pull_steps
parameter, you ensure that the necessary steps to configure the working directory (or other requirements) are included in your deployment configuration. If you have any additional questions or need further customization, feel free to ask!
s
@Marvin Got the following error in pull_steps argument: TypeError: Flow.deploy() got an unexpected keyword argument 'pull_steps'
m
Unfortunately, 
flow.deploy
method does not support 
pull_steps
directly as a keyword argument. Instead, you have to use parameters that are directly supported like 
pull
. However, we can use the 
Deployment.build_from_flow
method, which provides more flexibility in specifying deployment parameters including 
pull_steps
. ### Revised Deployment Script Using 
Deployment.build_from_flow
Here’s how you can include the 
pull_steps
in your deployment process: #### Example Deployment Script with 
pull_steps
Copy code
python
import os
from prefect.deployments import Deployment

from prefect.infrastructure.docker import DockerContainer
from main import fetch_stream_data  # Make sure to import your flow

# Set the API URL (replace with your Prefect server URL)
os.environ['PREFECT_API_URL'] = "<http://127.0.0.1:4200/api>"

# AWS ECR registry URL and repository name
AWS_ECR_URL = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
AWS_ECR_REPOSITORY_NAME = "my-repo"

# Full image name including ECR registry
image_name = f"{AWS_ECR_URL}/{AWS_ECR_REPOSITORY_NAME}:raw-google-ads-fetch-{stream}"

# Define the deployment including the custom Dockerfile and pull_steps
deployment = Deployment.build_from_flow(
    flow=fetch_stream_data,
    name=f"raw-google-ads-fetch-{stream}",
    parameters={
        "account_id": None,
        "start_date": None,
        "end_date": None,
        "stream_name": stream,
        "kafka_topic": None,
        "batch_id": None 
    },
    work_pool_name="dataplatform-dev",
    work_queue_name="default",
    infrastructure=DockerContainer(
        image=image_name,
        image_build_kwargs={
            "path": ".",
            "dockerfile": "Dockerfile"
        },
        # Push the docker image after it's built
        build=True,
        push=True,
        registry_url=AWS_ECR_URL,
        registry_username="AWS",  # AWS CLI uses 'AWS' as the username for ECR
        registry_password=os.getenv("AWS_SECRET_ACCESS_KEY")
    ),
    pull=[
        {
          "prefect.deployments.steps.set_working_directory": {
            "directory": "/app"
          }
        }
    ]
)

# Apply the deployment
deployment.apply()

print(f"Deployment '{deployment.name}' applied.")
### CI/CD Workflow Ensure to authenticate with AWS ECR and Prefect API within your CI/CD pipeline. #### GitHub Actions Workflow 
.github/workflows/deploy.yml
:
Copy code
yaml
name: Deploy Prefect Flow

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect
        pip install -r requirements.txt

    - name: Set Prefect API URL
      run: echo "PREFECT_API_URL=<http://127.0.0.1:4200/api>" >> $GITHUB_ENV

    - name: Login to ECR
      run: |
        aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

    - name: Deploy Prefect Flow
      run: |
        python deploy.py
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        PREFECT_API_URL: "<http://127.0.0.1:4200/api>"
### Summary - Use 
Deployment.build_from_flow
: This method offers flexibility to include 
pull_steps
in the deployment configuration. - CI/CD Integration: Ensure the deployment script is configured to set the Prefect API URL and authenticate with AWS ECR. By following the above steps with 
Deployment.build_from_flow
, you can incorporate custom 
pull_steps
into your Prefect deployments, allowing you to customize the deployment process effectively.
If you need more help or further customization, feel free to ask!
s
@Marvin I want to use .deploy() method and not build_with_flows method, but want to specify pull: - prefect.deployments.steps.set_working_directory: directory: /app How to do it?
m
The 
flow.deploy()
method in Prefect 2 does not support the 
pull_steps
parameter directly. However, you can work around this limitation by programmatically setting up the deployment with the required setup steps. Here's how you can achieve a similar outcome by using the 
pull
configuration: ### Workaround: Use DeploymentImage and Include Steps Since you want to stick with 
flow.deploy()
, you can include the necessary setup in your Dockerfile or the Prefect deployment itself. Here is a potential approach: 1. Modify Dockerfile: Ensure your Docker image has the necessary working directories and context for the flow settings. 2. Use DeploymentImage in Flow Deployment to specify custom Docker image steps. #### Example: Dockerfile Ensure your Dockerfile sets up the working directory:
Copy code
Dockerfile
FROM python:3.9-slim

# Install Prefect
RUN pip install prefect==2.16.9

# Copy your flow code and set working dir
COPY . /app
WORKDIR /app

# Install additional dependencies
RUN pip install -r requirements.txt

# Set the entrypoint to Prefect
ENTRYPOINT ["prefect"]
#### Example: Python Deployment Script with Image main.py:
Copy code
python
from prefect import flow

@flow(log_prints=True)
def fetch_stream_data(account_id, start_date, end_date, stream_name, kafka_topic, batch_id):
    print(f"Fetching data for stream: {stream_name}")

if __name__ == "__main__":
    fetch_stream_data(x=5)
deploy.py:
Copy code
python
import os
from prefect import flow
from prefect.deployments import DeploymentImage

# Assuming 'fetch_stream_data' is defined in main.py
@flow(log_prints=True)
def fetch_stream_data(account_id, start_date, end_date, stream_name, kafka_topic, batch_id):
    print(f"Fetching data for stream: {stream_name}")

if __name__ == "__main__":
    # AWS ECR config
    AWS_ECR_URL = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
    AWS_ECR_REPOSITORY_NAME = "my-repo"

    # Set Prefect API URL if required
    os.environ['PREFECT_API_URL'] = "<http://127.0.0.1:4200/api>"

    fetch_stream_data.deploy(
        name=f"raw-google-ads-fetch-{stream}",
        parameters={
            "account_id": None,
            "start_date": None,
            "end_date": None,
            "stream_name": stream,
            "kafka_topic": None,
            "batch_id": None 
        },
        work_pool_name="dataplatform-dev",
        work_queue_name="default",
        image=DeploymentImage(
            name=f"{AWS_ECR_URL}/{AWS_ECR_REPOSITORY_NAME}",
            tag=f"raw-google-ads-fetch-{stream}",
            dockerfile="Dockerfile"
        ),
        build=True,
        push=True
    )
The 
pull_steps
cannot be directly included in deploy logic, but having the right Docker configuration and Dockerfile setup can mimic the intended behavior. Any custom setup should be present in Dockerfile as needed. ### Summary - Dockerfile Setup: Ensure any setup logic is baked into your Dockerfile, which prepares the environment. - Use DeploymentImage in `flow.deploy()`: Specify custom Docker images, handling build and push to the repository. ### Reference - Prefect Deployment Guide - Prefect Docker Deployment Guide By taking this approach, you remain within the capabilities of 
flow.deploy()
while achieving necessary environment preparation. If you need any specific examples or further customization, feel free to ask!
s
@Marvin What are DeploymentTriggerTypes and TriggerTypes in prefect 2 .deploy() arguments
m
In Prefect 2.x, 
DeploymentTriggerTypes
and 
TriggerTypes
are used to define conditions under which flows should be automatically triggered based on specific events, metrics, or composite conditions. These triggers add flexibility and automation to flow runtimes. ### 
DeploymentTriggerTypes
and 
TriggerTypes
#### DeploymentTriggerTypes 
DeploymentTriggerTypes
are specific types of triggers that can be applied at the deployment level. They define the conditions under which the deployment should be triggered. - Event Triggers: React to specific events like state transitions of flow runs or external events. - Metric Triggers: Trigger based on metrics like flow run duration or task run success rate. - Compound Triggers: Combine multiple triggers with logical operators (AND/OR). - Sequence Triggers: Define an ordered sequence of triggers. #### TriggerTypes 
TriggerTypes
are the specific instances of triggers that define the condition logic. They are the building blocks of 
DeploymentTriggerTypes
. ### Example Usage in 
flow.deploy
To use these triggers within the 
flow.deploy
method, you'll import the necessary trigger classes and configure them accordingly. ### Example Deployment Script Using Triggers Here's an example script that shows how to use 
flow.deploy
with 
DeploymentEventTrigger
. #### 
main.py
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python
from prefect import flow

@flow(log_prints=True)
def fetch_stream_data(account_id, start_date, end_date, stream_name, kafka_topic, batch_id):
    print(f"Fetching data for stream: {stream_name}")

if __name__ == "__main__":
    fetch_stream_data(x=5)
#### 
deploy.py
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python
import os
from prefect import flow
from prefect.deployments import DeploymentImage
from prefect.events import DeploymentEventTrigger

# Define the flow
@flow(log_prints=True)
def fetch_stream_data(account_id, start_date, end_date, stream_name, kafka_topic, batch_id):
    print(f"Fetching data for stream: {stream_name}")

if __name__ == "__main__":
    # AWS ECR config
    AWS_ECR_URL = "<aws-account-number>.dkr.ecr.<region>.<http://amazonaws.com|amazonaws.com>"
    AWS_ECR_REPOSITORY_NAME = "my-repo"

    # Set Prefect API URL if required
    os.environ['PREFECT_API_URL'] = "<http://127.0.0.1:4200/api>"

    # Define the trigger
    event_trigger = DeploymentEventTrigger(
        enabled=True,
        match={"prefect.resource.id": "my-external-resource"},
        expect=["external.resource.pinged"],
        parameters={
            "param_1": "{{ event }}",
        }
    )

    # Deploy the flow
    fetch_stream_data.deploy(
        name=f"raw-google-ads-fetch-{stream}",
        parameters={
            "account_id": None,
            "start_date": None,
            "end_date": None,
            "stream_name": stream,
            "kafka_topic": None,
            "batch_id": None 
        },
        work_pool_name="dataplatform-dev",
        work_queue_name="default",
        image=DeploymentImage(
            name=f"{AWS_ECR_URL}/{AWS_ECR_REPOSITORY_NAME}",
            tag=f"raw-google-ads-fetch-{stream}",
            dockerfile="Dockerfile"
        ),
        build=True,
        push=True,
        triggers=[event_trigger]
    )
In this example: - 
DeploymentEventTrigger
is used to create the trigger that will activate when a specific external event occurs. - The 
deploy
method includes the 
triggers
parameter. ### Summary - DeploymentTriggerTypes: Used to define high-level deployment triggers that include various conditions. - TriggerTypes: Specific instances of triggers, including events and metrics. - How to Use: Import necessary trigger classes, define triggers, and apply them with the 
flow.deploy
method. ### References - Prefect Deployment Guide - Prefect Automations and Triggers
Using these examples and references, you can define sophisticated deployment triggers and automation within Prefect to ensure your flows run under the specified conditions. If you have more questions or need further examples, feel free to ask!
s
@Marvin What are some of the ways for better managing prefect deployment yaml files. I have a lot of prefect flows, and having all of them in prefect.yaml and running prefect deploy --all is quite time taking and the management of deployment configs is also very cumbersome and error prone.
m
To address your concerns about managing Prefect deployment YAML files effectively, let's look into the recommended strategies and best practices. First, let's confirm the version of Prefect you're using. Prefect 2.x and 3.x may offer different features and best practices for handling deployment YAML files. Could you please confirm your Prefect version?
s
I am using prefect 3.x server while using prefect 2.x sdk in a containerised environment in each flow run. So you can say I am using prefect 3.x for deployments
@Marvin I am using prefect 3.x server while using prefect 2.x sdk in a containerised environment in each flow run. So you can say I am using prefect 3.x for deployments
m
Here are some strategies and best practices for better managing Prefect deployment YAML files: ### Use YAML Aliases One efficient way to manage multiple YAML configurations is to use YAML aliases. This allows you to reuse configurations across different deployments, minimizing redundancy and making updates easier. For example:
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definitions:
  work_pools:
    my_docker_work_pool: &my_docker_work_pool
      name: my-docker-work-pool
      work_queue_name: default

  schedules:
    every_ten_minutes: &every_10_minutes
      interval: 600

deployments:
  - name: deployment-1
    entrypoint: flows/hello.py:my_flow
    schedule: *every_10_minutes
    work_pool: *my_docker_work_pool
    parameters:
      number: 42
      message: "Don't panic!"

  - name: deployment-2
    entrypoint: flows/goodbye.py:my_other_flow
    work_pool: *my_docker_work_pool
The use of 
&
to define an alias and 
*
to reference an alias helps in maintaining consistency and reducing the possibility of errors. ### Separate Deployment YAML Files If managing all deployments in a single file is cumbersome, you can split them into multiple files. Each YAML file can contain a single deployment or a logical grouping of deployments. Prefect supports applying multiple deployment YAML files. Example file structure:
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deployments/
  ├── flow1_deployment.yaml
  ├── flow2_deployment.yaml
  └── flow3_deployment.yaml
Then, you can deploy each configuration separately:
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prefect deployment apply deployments/flow1_deployment.yaml
prefect deployment apply deployments/flow2_deployment.yaml
prefect deployment apply deployments/flow3_deployment.yaml
### Utilize Prefect CLI and UI For convenience and to reduce manual editing errors, consider using the Prefect CLI and UI to manage deployments. You can create, update, and apply deployments using the CLI, which provides a structured way to edit configurations. For instance:
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prefect deployment create -n my_deployment -p path/to/flow.py -q my_work_queue
prefect deployment build path/to/flow.py:flow_func --name my_deployment --output path/to/deployment.yaml
### Parameter Management You can set default parameter values within the 
deployment.yaml
file to ensure consistency across flow runs. This helps to avoid duplicating parameter settings for each deployment. Example:
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deployments:
  - name: example-deployment
    entrypoint: flows/example.py:example_flow
    parameters:
      param1: value1
      param2: value2
### Resource Links For more detailed information, please refer to the following links to the Prefect documentation: - Manage Deployment YAML in Prefect 2.x - Manage Deployment YAML in Prefect 3.x Implementing these strategies should help streamline the management of your Prefect deployment YAML files and reduce the likelihood of errors.
s
@Marvin Which Prefect API I can use from a python script that will give me the deployment details from prefect server after deployment is done for a flow. I am using prefect 3 server
m
To get deployment details from the Prefect server after a deployment has been created, you can use the Prefect API within a Python script. Here is how you can achieve this using the latest Prefect 3.x SDK: ### Using the Prefect Client 1. Install Prefect: Ensure you have Prefect installed in your environment.
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sh
pip install prefect
2. Retrieve Deployment Details: You can use the 
get_deployment
synchronous method in the 
PrefectClient
to fetch deployment details by its name or ID. Here's the code to get the deployment details:
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python
from prefect.client import get_client

async def fetch_deployment_details(deployment_name_or_id: str):
    async with get_client() as client:
        # Assuming `deployment_name_or_id` is the deployment name or ID
        deployment = await client.read_deployment_by_id(deployment_name_or_id)
        return deployment

# Usage example
import asyncio
deployment_details = asyncio.run(fetch_deployment_details("<your_deployment_name_or_id>"))
print(deployment_details)
### Example Usage Make sure you replace 
<your_deployment_name_or_id>
with your actual deployment name or ID. ### Documentation Links For further reference, you may check the following documentation: 1. Prefect Deployment API 2. Prefect YAML Deployment These resources will provide more context and additional configuration options for deployments and how you can interact with them using the Prefect API. Feel free to ask if you need more specific examples or a deeper dive into any other feature!
s
@Marvin Can you call multiple tasks from a task in prefect flow?
m
Yes, you can call multiple tasks from within a task in a Prefect flow. Prefect provides different ways to manage task dependencies and nested executions. Here are some scenarios and methods to achieve this: ### Basic Example: Calling Multiple Tasks from a Task You can define a task that orchestrates the calling of other tasks inside a flow. Here’s how you can do this:
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python
from prefect import flow, task

@task
def task_a():
    print("Task A is running")
    return "Result from A"

@task
def task_b():
    print("Task B is running")
    return "Result from B"

@task
def orchestrator_task():
    result_a = task_a.submit()  # Submit task A
    result_b = task_b.submit()  # Submit task B
    # Wait for results if needed
    return result_a.result(), result_b.result()

@flow
def orchestrator_flow():
    result = orchestrator_task()
    print(result)

if __name__ == "__main__":
    orchestrator_flow()
In this example, 
orchestrator_task
is responsible for calling 
task_a
and 
task_b
. ### Using Nested Flows Another powerful feature in Prefect is the ability to use nested flows. This allows you to define a subflow that is called from a parent flow. This is beneficial for observability, conditional execution, and parameter management.
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python
from prefect import flow, task

@task
def print_hello():
    print("Hello from the nested flow!")

@flow
def nested_flow():
    print_hello()

@flow
def main_flow():
    nested_flow()

if __name__ == "__main__":
    main_flow()
### Additional Resources For more advanced configurations and scenarios, you can refer to the Prefect documentation: - Developing Flows - Developing Tasks These resources cover various use cases, from basic tasks and flows to more complex nested flows and conditional execution strategies. By structuring your code with these best practices, you can achieve better manageability and observability in your Prefect workflows.
s
@Marvin I have a very simple prefect deployment as follows:
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deployments:
- name: google_sheet_sync
  version: 0.0.1
  tags: ["google_sheet"]
  description: Flow responsible for fetching Google Sheet data
  schedule: null
  entrypoint: entrypoint.py:google_sheet_sync_flow
  work_pool: *common_work_pool
prefect.yaml file and entrypoint.py and Dockerfile is at the same package. I am running prefect deploy command from that folder as well I am getting the following error:
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19:11:58.380 | DEBUG   | asyncio - Using selector: KqueueSelector
19:11:58.381 | DEBUG   | asyncio - Using selector: KqueueSelector
19:11:58.396 | DEBUG   | httpx - load_ssl_context verify='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem' cert=None trust_env=True http2=False
19:11:58.396 | DEBUG   | httpx - load_verify_locations cafile='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem'
19:11:58.401 | DEBUG   | httpx - load_ssl_context verify='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem' cert=None trust_env=True http2=False
19:11:58.401 | DEBUG   | httpx - load_verify_locations cafile='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem'
19:11:58.406 | DEBUG   | httpx - load_ssl_context verify='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem' cert=None trust_env=True http2=False
19:11:58.407 | DEBUG   | httpx - load_verify_locations cafile='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem'
Expecting value: line 1 column 1 (char 0)
What is the issue
m
Your message was too long by 259 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I have a very simple prefect deployment as follows:
Copy code
deployments:
- name: google_sheet_sync
  version: 0.0.1
  tags: ["google_sheet"]
  description: Flow responsible for fetching Google Sheet data
  schedule: null
  entrypoint: entrypoint.py:google_sheet_sync_flow
  work_pool: *common_work_pool
prefect.yaml file and entrypoint.py and Dockerfile is at the same package. I am running prefect deploy command from that folder as well I am getting the following error: ```191158.380 | DEBUG | asyncio - Using selector: KqueueSelector 191158.381 | DEBUG | asyncio - Using selector: KqueueSelector 191158.396 | DEBUG | httpx - load_ssl_context verify='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem' cert=None trust_env=True http2=False 191158.396 | DEBUG | httpx - load_verify_locations cafile='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem' 191158.401 | DEBUG | httpx - load_ssl_context verify
s
@Marvin I have a very simple prefect deployment as follows:
Copy code
deployments:
- name: google_sheet_sync
  version: 0.0.1
  tags: ["google_sheet"]
  description: Flow responsible for fetching Google Sheet data
  schedule: null
  entrypoint: entrypoint.py:google_sheet_sync_flow
  work_pool: *common_work_pool
prefect.yaml file and entrypoint.py and Dockerfile is at the same package. I am running prefect deploy command from that folder as well I am getting the following error:
Copy code
19:11:58.406 | DEBUG   | httpx - load_ssl_context verify='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem' cert=None trust_env=True http2=False
19:11:58.407 | DEBUG   | httpx - load_verify_locations cafile='/Users/uditchaudhary/.local/share/virtualenvs/google_sheet_sync--f7YjGLn/lib/python3.11/site-packages/certifi/cacert.pem'
Expecting value: line 1 column 1 (char 0)
What is the issue
m
The error you're seeing, 
Expecting value: line 1 column 1 (char 0)
, typically points to an issue with the JSON that Prefect is trying to parse during the deployment process. This often suggests that a request to the Prefect server is resulting in an unexpected response, potentially an empty body or something not in JSON format. Here's how you can troubleshoot and resolve the problem: ### Steps to Troubleshoot 1. Check Prefect Server Connectivity: Ensure that your Prefect server is up and running and that you are connected to it. Verify any endpoint URLs or API keys that you're using. 2. Verify Configuration: Double-check your 
prefect.yaml
and ensure every field is correctly set, particularly: - The 
entrypoint
field should point to an existing Python module and function within your package. - The 
work_pool
is correctly defined and available. 3. Docker Configuration: Since you are using a Docker-based deployment setup, ensure: - Your Dockerfile is correct and builds without errors. - All necessary Python dependencies specified in the Dockerfile are installed correctly, especially those related to Prefect. 4. Inspect the Full Error: If the error you have shared isn't complete, looking at more of the error stack trace can give hints about where in the process things are failing. 5. Logging: Increase the logging level to capture more information. You can set this in your script before calling any Prefect functions:
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python
import prefect
prefect.context.config.logging.level = "DEBUG"
6. Run Locally: Try running the flow locally to ensure no errors occur outside of the deployment process. This helps isolate whether the problem is with the deployment or the flow itself. 7. SSL Verify Issues: If you're running this on a network with SSL certificate issues (e.g., older certificate chains or self-signed certs), you might need to adjust your system's SSL verification settings or investigate firewall settings. ### Additional Debugging - Review your Prefect service logs to see if there are any issues at the server side. - Ensure that environment variables needed for authentication (especially with Google APIs) are correctly set and accessible by the running deployment. ### Update and Simplify Make sure your Prefect and related packages are up to date, as recent updates might fix known issues. If the issue persists, consider simplifying the Prefect configuration to the most basic deployment setup and incrementally build it up to identify where the problem starts occurring. If these steps do not resolve your issue, providing more context, such as the contents of the Prefect logs or additional details from debugging, could help in further assisting you.
s
@Marvin How can I stop or cancel a subflow or flow run in prefect using the sdk
m
To stop or cancel a Prefect flow run, including subflows, using the SDK, you can utilize the Prefect API client. Here’s how you can do it: ### Cancel a Flow Run Using the Prefect Client 1. Install Prefect: Ensure Prefect is installed in your environment. 2. Cancel a Flow Run: Use the 
cancel_flow_run
method from the API client. Here's an example of how you can cancel a flow run:
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python
import anyio
from prefect.client import get_client
from prefect.states import StateType

async def cancel_flow_run(flow_run_id: str):
    async with get_client() as client:
        print(f"Cancelling flow run with ID '{flow_run_id}'")
        
        # Retrieve the current state of the flow run
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Set the state to "CANCELLED"
        state_updates = {"name": "Cancelled", "type": StateType.CANCELLED}
        new_state = flow_run.state.copy(update=state_updates)
        
        # Force the cancellation of the flow run
        await client.set_flow_run_state(flow_run_id, new_state, force=True)

# Example usage
flow_run_id = "<your_flow_run_id>"
anyio.run(cancel_flow_run, flow_run_id)
### Additional Notes - The 
set_flow_run_state
function with 
force=True
ensures that the flow run is canceled even if it is not in a directly cancelable state. - You need to have the 
flow_run_id
that you wish to cancel. This ID can be found via the Prefect UI or through the Prefect client by listing active runs. - This method can be applied to both top-level flow runs and subflows. ### Documentation Links For more details, check the Prefect documentation: - Prefect API Interaction - Managing Flow Runs This should help you manage and control your flow runs programmatically using the Prefect API!
s
@Marvin Given a flow_run_id, can I get the task_run_ids? Is there a way to do that using prefect sdk
m
Yes, you can retrieve the task run IDs associated with a particular flow run ID using the Prefect SDK. This can be done by interacting with the Prefect API through the client to list all task runs belonging to a flow run. Here’s how you can achieve this using the Prefect SDK: ### Retrieving Task Run IDs from a Flow Run 1. Install Prefect: Ensure you have Prefect installed. 2. Use the API Client: You can use the client to query for task runs given a 
flow_run_id
. Here's an example code snippet to guide you:
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python
import anyio
from prefect.client import get_client

async def get_task_run_ids_for_flow(flow_run_id: str):
    async with get_client() as client:
        task_runs = await client.read_task_runs(flow_run_id=flow_run_id)
        task_run_ids = [task_run.id for task_run in task_runs]
        return task_run_ids

# Example usage
flow_run_id = "<your_flow_run_id>"
task_run_ids = anyio.run(get_task_run_ids_for_flow, flow_run_id)
print(task_run_ids)
### Explanation - Get Client: This establishes a connection to the Prefect API server. - Read Task Runs: This function fetches task runs associated with the provided 
flow_run_id
. - List Comprehension: We extract the 
id
from each task run object to get a list of task run IDs. ### Additional Information This process helps in managing and debugging flows by giving access to logs, states, and results of individual tasks. Feel free to check the Prefect API documentation for more details on interacting with flows and task runs using the SDK. If you need further assistance, let me know!
s
@Marvin Create a python function that takes a deployment_id and parameters and run the prefect flow using the run_deployment function of prefect. Specify what the function returns once you trigger the run_deployment() function. I want to get the flow_run_id for the flow I triggered using run_deployment function, so that I can track the progress of the flow run by polling
m
To trigger a Prefect flow using its deployment and retrieve the 
flow_run_id
, you can use the 
run_deployment
function. This will allow you to start a deployment and receive information about the flow run, including its ID, which you can then use to monitor the flow run's progress. Here's an example function to do that:
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python
import anyio
from prefect.client import get_client

async def trigger_flow_run(deployment_id: str, parameters: dict = None):
    async with get_client() as client:
        response = await client.create_flow_run_from_deployment(
            deployment_id=deployment_id,
            parameters=parameters
        )
        
        flow_run_id = response.id
        print(f"Flow run triggered. Flow Run ID: {flow_run_id}")
        return flow_run_id

# Example usage
deployment_id = "<your_deployment_id>"
params = {"param1": "value1", "param2": "value2"}  # replace with actual parameters
flow_run_id = anyio.run(trigger_flow_run, deployment_id, params)
### Explanation - get_client: This is used to establish a connection with the Prefect server to perform API operations. - create_flow_run_from_deployment: This method in the client sends a request to trigger a flow run from the specified deployment ID. The method allows you to pass parameters if needed. - Flow Run ID: The 
flow_run_id
is returned and printed, which you can use for tracking and monitoring purposes. This method provides a seamless way to initiate a flow run and immediately retrieve key details for further operations such as monitoring the execution status. Feel free to replace 
"<your_deployment_id>"
and the 
params
with your actual deployment ID and parameters that match your flow's configuration. If you need further assistance or customization, let me know!
s
@Marvin I have one flow where error is getting thrown but the flow state still is completed. Why? And how to make sure the flow state is FAILED if there is any unhandled error thrown
m
If a flow state is marked as completed despite an error being thrown, it is often due to how tasks and flows handle exceptions and state transitions. In Prefect, a flow can finish in a "Completed" state if the tasks don't propagate failures correctly or if the flow's error handling is set up to allow it. Here's how you can ensure that unhandled errors result in a "FAILED" flow state: ### Understanding Task and Flow States 1. Propagation of Errors: If a task within a flow raises an exception but doesn't properly propagate it, the flow may not automatically assume a "FAILED" state. By default, Prefect should transition to a failed state if an unhandled exception occurs. 2. Flow Completion Logic: The flow itself might be completing if error handling is implemented within tasks that catch exceptions without re-raising them (essentially marking those tasks as successful despite failures). ### Ensuring a Flow Fails on Unhandled Errors 1. Task-Level Error Handling: - Make sure tasks raise exceptions instead of catching them without re-raising. - If using try-except within tasks, ensure that exceptions are logged and re-raised, or the task state is explicitly set to failed. 2. Flow-Level Configuration: - Utilize the 
on_failure
hook for the flow to handle cases where any task fails, allowing the entire flow to be marked as failed. - Configure the flow to not simply mark tasks as completed if they are not. 3. Customize Flow State: - You can explicitly set flow states based on the outcome of tasks by checking the result states and setting the flow state accordingly. Here's an example setup:
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python
from prefect import flow, task
from prefect.states import Failed

@task
def may_fail():
    raise ValueError("This task fails!")

@flow
def main_flow():
    try:
        may_fail()
    except Exception as e:
        return Failed(message=f"Flow failed due to error: {str(e)}")

if __name__ == "__main__":
    main_flow()
### Additional Configuration - Retries: Prefect allows configuring retries if you expect intermittent errors. This can be configured by specifying a 
retries
parameter. - Notifications & Hooks: Use flow and task notifications to respond to failures, such as sending alerts. ### Resources - Writing Flows in Prefect - Managing States in Prefect Implementing these approaches can help ensure that your flow correctly reflects a "FAILED" state when unhandled errors occur. If you have further questions or need additional examples, feel free to ask!
s
@Marvin Create a python function in which I can pass the flow_run_id of a flow, and keep polling until the flow run is completed or failed
m
To create a Python function that polls the status of a flow run using its 
flow_run_id
until it completes or fails, you can use the Prefect client to repeatedly check the state of the flow run. Below is an example function that implements this logic:
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python
import anyio
import time
from prefect.client import get_client
from prefect.states import StateType

# Function that polls for the flow run status
async def poll_flow_run_status(flow_run_id: str, poll_interval: int = 10):
    async with get_client() as client:
        while True:
            # Fetch the current state of the flow run
            flow_run = await client.read_flow_run(flow_run_id)

            # Check the state of the flow run
            if flow_run.state.type in {StateType.COMPLETED, StateType.FAILED}:
                print(f"Flow run has completed. State: {flow_run.state.type}")
                return flow_run.state.type
            else:
                print(f"Flow run is still in state: {flow_run.state.type}. Polling again in {poll_interval} seconds...")
            
            # Wait for the specified poll interval before checking again
            await anyio.sleep(poll_interval)

# Example usage
flow_run_id = "<your_flow_run_id>"
anyio.run(poll_flow_run_status, flow_run_id, poll_interval=15)
### Explanation - get_client(): Establishes a connection with the Prefect server to query for flow run status. - Polling Loop: Continuously checks the status of the flow run. - State Check: The loop exits when the flow state changes to either 
StateType.COMPLETED
or 
StateType.FAILED
. - Poll Interval: The function waits for a specified time (
poll_interval
) between checks to avoid excessive querying. This function can be used as a script or imported into other workflows where you need to wait for a specific flow run to reach a terminal state. Adjust the 
poll_interval
to balance between responsiveness and API load. If you need further customization, feel free to ask!
s
@Marvin Is there no function like run_deployment which is sync_compatible for getting running flow details, so that I don't have to create a prefect client
m
As of now, Prefect's API client interactions, including those for polling flow run states, are primarily designed to work asynchronously due to the nature of async I/O operations with HTTP requests. However, if you prefer a synchronous setup, you can use a small utility to create a synchronous polling loop, which calls the asynchronous client methods within a blocking context. Here's how you can approach it: ### Synchronous Function to Poll Flow Run Status Since the direct Prefect SDK does not offer a synchronous 
run_deployment
equivalent for polling and checking flow run states, here's how you can adapt it:
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python
import asyncio
import time
from prefect.client import get_client
from prefect.states import StateType

# Synchronous wrapper around async function
def poll_flow_run_status_sync(flow_run_id: str, poll_interval: int = 10):
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    loop.run_until_complete(poll_flow_run_status(flow_run_id, poll_interval))
    loop.close()

# Async function definition remains the same
async def poll_flow_run_status(flow_run_id: str, poll_interval: int):
    async with get_client() as client:
        while True:
            flow_run = await client.read_flow_run(flow_run_id)
            if flow_run.state.type in {StateType.COMPLETED, StateType.FAILED}:
                print(f"Flow run has completed. State: {flow_run.state.type}")
                return flow_run.state.type
            else:
                print(f"Flow run is in state: {flow_run.state.type}. Polling in {poll_interval} seconds...")
                await asyncio.sleep(poll_interval)

# Example usage for synchronous polling
flow_run_id = "<your_flow_run_id>"
poll_flow_run_status_sync(flow_run_id, poll_interval=15)
### Explanation - poll_flow_run_status_sync: This is a synchronous wrapper that creates and manages an event loop for running the async polling task synchronously. - Async Function (
poll_flow_run_status
): The same async function is used, and it manages checking the flow run state and sleeping asynchronously. - Blocking Loop: The 
asyncio.run
loop is used to execute async functions in synchronous scripts. This provides a way to handle checking flow run states without needing an async function or explicit client creation in each synchronous use case. While Prefect is built to leverage async execution for efficiency, this approach ensures compatibility with synchronous script execution.
s
@Marvin If I trigger a bunch of tasks asynchronously from a prefect flow using task.submit option, then how can I gather the task results in a list?
m
To gather the results of tasks submitted asynchronously using the 
task.submit
method in a Prefect flow, you can utilize the 
.result()
method on each task submission to collect their outputs. The 
.result()
method will block until the specific task completes, ensuring you retrieve the value returned by the task. Here's a step-by-step guide on how to accomplish this: ### Example Code
Copy code
python
from prefect import flow, task

@task
def example_task(number):
    return number * number

@flow
def concurrent_flow():
    # Submit tasks asynchronously
    task_results = [example_task.submit(i) for i in range(5)]

    # Gather results using the .result() method
    results = [task.result() for task in task_results]

    # Print the results
    print(f"Task results: {results}")
    return results

if __name__ == "__main__":
    concurrent_flow()
### Explanation 1. Submit Tasks: Use 
task.submit()
inside a list comprehension to submit tasks concurrently. Each task will run independently and may complete in any order. 2. Gather Results: Use the 
.result()
method on each submitted task to block until it has completed, allowing you to collect its return value. This collection of results is done using another list comprehension. 3. Blocking Nature: Remember that calling 
.result()
on each task is a blocking operation, meaning it will wait for each task to finish before continuing. ### Considerations - Concurrency: The submitted tasks run concurrently, but since 
.result()
is a blocking call, gathering results sequentially will not be concurrent. - Error Handling: If any task fails, calling 
.result()
will raise an exception, which should be handled appropriately depending on your use case. Using this method, you can effectively manage and gather the results of asynchronously run tasks within a Prefect flow. If you have additional questions or need further help, feel free to ask!
s
@Marvin How to create a flow run in prefect 3 server via REST API.
m
To create a flow run in Prefect 3 server using the REST API, you can make a POST request to the Prefect API endpoint dedicated to flow runs. This involves sending a request with necessary information such as the deployment ID associated with the flow you want to run. Here's a step-by-step guide to help you through the process: ### Step-by-Step Guide to Trigger a Flow Run via Prefect REST API 1. Identify the Endpoint: The Prefect API endpoint for creating a flow run is typically formatted as 
/flow_runs
on your Prefect API server. 2. Prepare the Request: You will need to send a POST request with a JSON payload that includes the deployment ID and any parameters you want to pass to the flow. 3. Authentication: Ensure you have the correct authorization token if your Prefect API requires it. ### Example Using HTTPx or Requests in Python Here's an example using Python's HTTPx to create a flow run:
Copy code
python
import httpx

# Prefect API URL
PREFECT_API_URL = "http://<your-prefect-server-url>/api"

# Example deployment ID
deployment_id = "<your_deployment_id>"

# Optional: Parameters for your flow
params = {
    "some_param": "value"
}

# API request to create a flow run
response = <http://httpx.post|httpx.post>(
    f"{PREFECT_API_URL}/flow_runs",
    json={
        "deployment_id": deployment_id,
        "parameters": params
    },
    headers={
        "Authorization": "Bearer <your_api_token>"
    }
)

# Check response
if response.status_code == 200:
    flow_run_info = response.json()
    flow_run_id = flow_run_info.get("id")
    print(f"Flow run created with ID: {flow_run_id}")
else:
    print(f"Failed to create flow run: {response.text}")
### Explanation - PREFECT_API_URL: Replace 
<your-prefect-server-url>
with your Prefect server's base URL. - deployment_id: Replace 
<your_deployment_id>
with the deployment ID of your flow. - Authorization Header: Include your API token if needed for access control. - Parameters: Prepare parameters if your flow requires them, passing them in the payload. ### Considerations - Ensure all URLs and tokens are correctly configured to match your server's setup. - Check the response for any error messages or additional instructions. This process allows you to programmatically initiate flow runs via the Prefect server's REST API, providing flexibility for integrating Prefect flows into broader automation or CI/CD systems. If you need further customization or examples, feel free to ask!
s
@Marvin The flow_runs api is saying that the flow_id field is required and deployment_id is deprecated. What does this mean. I thought we need the deployment_id to trigger the run as the deployment_id has all the data for run triggering? Where is my assumption going wrong
m
Your understanding of using the 
deployment_id
to trigger a flow run is correct in traditional deployment setups. However, the mention of 
deployment_id
being deprecated could point to changes or additional configurations being made within older API endpoints that you are referencing. It's also possible that the version of the Prefect server or API you are using is indicating a shift in how flow runs are initiated, perhaps moving towards a more direct use of 
flow_id
. ### Explanation of the Transition 1. Deployment vs. Flow: In typical Prefect deployments, a 
deployment_id
contains all the necessary metadata about how a flow should run. This includes the schedule, parameters, and infrastructure setup. 2. Flow ID Requirement: If a 
flow_id
is now required, it may suggest a push towards utilizing more generalized API endpoints that don't exclusively rely on deployment configurations. By requiring a 
flow_id
, the API allows flows to be triggered without necessarily invoking the deployment layer, or possibly to support runs outside typical deployment logic. ### Recommended Action - Use of Flow ID: Check if your Prefect setup or the API version allows for running a flow directly using its 
flow_id
. If this shift supports your use case (such as more manual or custom triggering of flow runs), it could be adaptable via your current framework. - Updating to Latest Practices: Review current Prefect API documentation or release notes to understand the recommended workflows or endpoints available to trigger flow runs most effectively. - Consult Prefect's Latest Docs and community forums to verify if there is an updated recommendation for handling flow runs explicitly. If you provide more context such as specific API calls or responses, further guidance can be offered tailored to the adjustments required for your setup. This shift might be related to new features or optimizations implemented within Prefect or its later releases.
s
I triggered the flow_runs api for both prefect-2 and prefect-3 server with the below request body, the run got created but it never ran. What is the issue here?
Copy code
{
  "name": "sample_flow_triggered_from_api",
  "flow_id": "ff7d061f-1829-488a-8471-89f029bfbcb1",
  "deployment_id": "8375e081-179d-4000-8d07-298f6c0f35d7",
  "state": {
    "type": "SCHEDULED"
  },
  "parameters": {
    "param_1": "Hello",
    "param_2": "World"
  }
}
@Marvin I triggered the flow_runs api for both prefect-2 and prefect-3 server with the below request body, the run got created but it never ran. What is the issue here?
Copy code
{
  "name": "sample_flow_triggered_from_api",
  "flow_id": "ff7d061f-1829-488a-8471-89f029bfbcb1",
  "deployment_id": "8375e081-179d-4000-8d07-298f6c0f35d7",
  "state": {
    "type": "SCHEDULED"
  },
  "parameters": {
    "param_1": "Hello",
    "param_2": "World"
  }
}
m
If the flow run is created successfully but remains in the "SCHEDULED" state and does not proceed to execution, here are a few potential reasons and troubleshooting steps: ### Potential Issues and Solutions 1. Work Queue or Work Pool Issues: - Check Work Queues/Work Pools: Ensure that the work queue or work pool is running and properly configured to pick up the scheduled flow run. The flow run needs a worker or agent listening on the respective queue or pool. - Inspect Queue Assignments: Verify that the deployment is associated with a queue that has active workers. 2. State and Timing Issues: - State Transition: Confirm that the flow run's state is correctly transitioning from "SCHEDULED" to "RUNNING". If not, your queue or API service might have delays or be misconfigured. - Scheduled Time: Check the 
scheduled_start_time
. If it is set to a future date/time, your flow will not run immediately. 3. API Payload and Configuration: - Correct API Fields: The API response might not clearly indicate missing fields or incorrect configurations. Ensure all required parameters and configurations for the version of your Prefect API are correct. - Deprecations: Watch out for any fields that might have been deprecated or replaced with newer configurations in Prefect 3. 4. Worker Logs and Monitoring: - Logs: Check the logs on the Prefect server or the worker to see if there are any important messages or errors related to scheduling or running the flow. - Prefect UI: Utilize the Prefect UI to monitor the state transitions and gather more real-time insights from the dashboard. 5. Check Heartbeat/Broker: - If you are using a message broker (like RabbitMQ) between the Prefect server and workers, ensure it is up and functioning properly. ### Additional Debugging - Test Locally: If possible, run the same flow locally to ensure there are no issues with the flow definition or tasks. - Manual Trigger: Try manually triggering the flow run via the Prefect UI to observe the behavior and gather any additional error messages that may arise. These steps should help diagnose and resolve why a flow remains in a scheduled state without executing. If there are more specific error messages or logs, providing those would help in narrowing down the issue.
s
@Marvin Is there a character limit in the deployment name of prefect flow?
m
The documentation specific to character limits for deployment names in Prefect isn't explicitly covered in the excerpts retrieved. Typically, the Prefect platform itself or underlying systems (like databases storing the data) might impose practical limits rather than the Prefect workflow configuration explicitly specifying one. ### General Assumptions and Best Practices 1. Typical Limits: Although not specified, many systems default to limits around 255 characters for string entries to ensure compatibility with various databases and interfaces. 2. Practical Limitations: Consider keeping deployment names concise and descriptive, avoiding overly long names that might cause issues in logs, UIs, or CLI outputs. ### Recommendations - Testing: If you're concerned about specific limits due to encountering issues, create test deployments with progressively longer names to identify any application or system-level restrictions. - Check UI/CLI: Review Prefect UI and CLI behavior when using longer names to check for truncation or formatting problems. ### Conclusion Given the absence of specific documentation in the resources reviewed, using conventional shorter names will typically suffice. If you have encountered a specific error or limitation, reviewing Prefect's latest release notes or community discussions might offer more insights into any implicit constraints.
s
@Marvin One of my sub flow runs failed with the following error: prefect.exceptions.UnfinishedRun: Run is in PENDING state, its result is not available. What can be the possible reasons for failure in this? I am using prefect 3, self serving server.
m
The error 
prefect.exceptions.UnfinishedRun: Run is in PENDING state, its result is not available
indicates that a subflow is stuck in the PENDING state. Here are some potential reasons and troubleshooting tips: ### Possible Reasons for PENDING State 1. Unmet Preconditions: A flow run in the PENDING state implies it is waiting for necessary preconditions to be satisfied. This could involve dependencies on upstream tasks or resources that are not yet available. 2. Resource Availability: The execution environment (like workers or infrastructure) may not be available to initiate the run. Ensure your Prefect agent and associated infrastructure are operational. 3. Scheduled Execution: Check if the flow run was scheduled for a future time but didn't transition to RUNNING due to clock discrepancies or scheduling issues. 4. Parameter Issues: If the subflow received invalid parameters and wasn't able to validate them, it might not proceed into execution. Ensure all parameters passed during invocation are correctly formatted and valid. 5. Concurrency Limits: If you've set limits on how many tasks or flows can run concurrently, this might lead to some runs being queued in a PENDING state. ### Troubleshooting Steps 1. Check the Prefect UI: Look into the Prefect UI for more detailed insights and logs pertaining to the flow run. It may provide information about the pending state. 2. Verify Preconditions: Confirm any dependencies or resources that the subflow requires are available and correctly configured. 3. Inspect and Restart Workers: Ensure that there are active agents or workers capable of picking up and running the flow. Restart or scale up if necessary. 4. Examine Parameters and Configuration: Double-check the configurations and parameters sent to the flow to ensure they are correct and don't lead to validation issues. 5. Logs and Error Messages: Look into logs generated by the Prefect server and workers for any error messages or indications as to why execution isn't proceeding. By reviewing these elements, you might be able to diagnose and resolve the issue causing the subflow to remain in a PENDING state and ensure smooth execution going forward. If you have further questions or require precise configurations, feel free to ask!
s
@Marvin What is this error in prefect flow: Task run 'c533b6e8-22db-480b-92a7-e0aaab4f8987' received abort during orchestration: The enclosing flow must be running to begin task execution. Task run is in PENDING state.
m
The error message you're encountering, "Task run 'c533b6e8-22db-480b-92a7-e0aaab4f8987' received abort during orchestration: The enclosing flow must be running to begin task execution. Task run is in PENDING state," indicates a synchronization issue between task execution and the state of the parent flow. Here are possible reasons and steps to consider: ### Potential Reasons 1. Flow State Misalignment: The task attempted to run while the parent flow was not in a "RUNNING" state. This occurs if the flow itself was still in a PENDING, SCHEDULED, or any non-execution state when the task was triggered. 2. Premature Task Trigger: A task could be triggered prematurely if there is a misconfiguration or a delay in updating the flow's state in the orchestration system. 3. Flow Cancellation or Interruption: If the flow was interrupted or canceled unexpectedly (due to issues like resource unavailability, manual interruption, or scheduling conflicts), tasks may not initiate correctly. 4. Infrastructure Issues: The infrastructure running your flows and tasks might not be synchronizing states correctly, leading to these discrepancies. ### Troubleshooting Steps 1. Check Flow Dependencies: Ensure the flow is properly transitioning to the RUNNING state before tasks start execution. Review any preconditions that might delay this transition. 2. Flow Logs and UI: Use Prefect UI and logs to explore the state transitions for your flow. Pay attention to timestamps and any intervening events or errors that may block execution. 3. Review Infrastructure: Ensure that your Prefect agents or workers are functioning correctly, as infrastructure issues could delay or mismanage task scheduling. 4. Inspect Task Dependencies: Verify task dependencies are managed correctly and that tasks only start execution after all preconditions are satisfied. 5. Error Logs: Look for additional error logs or messages around the time the task attempted to start. This may provide clues about the underlying issue. ### Recommendations - Synchronization: Ensure that flows and their tasks are orchestrated to align tightly, with proper signaling for state changes. - Automation Scripts: If scripting is involved, ensure that commands or API calls handling prefatory conditions and task initiation are correctly sequenced. By maintaining a close watch on states and ensuring that orchestration logic covers all conditional pathways, you can prevent and address such errors effectively. If these steps do not resolve the problem, consider consulting Prefect's support forums or documentation for more tailored troubleshooting based on your system's setup.
s
@Marvin How can I provide custom task name from a flow in prefect?
m
To provide a custom task name in a Prefect flow, you can specify a name directly in the task decorator or when you define the task. Prefect tasks are identified by a task key, which is partly derived from their name. Custom task names can help organize and identify tasks more easily, especially when viewing them in the Prefect UI. ### How to Set a Custom Task Name You can set a custom name for a task by using the 
name
argument within the 
@task
decorator. Here's how you can do it:
Copy code
python
from prefect import flow, task

# Define a task with a custom name
@task(name="Custom Task Name")
def example_task(x):
    return x * x

@flow
def example_flow():
    result = example_task(5)
    print(f'Result: {result}')

if __name__ == "__main__":
    example_flow()
### Explanation - @task(name="Custom Task Name"): This sets the name of the task to "Custom Task Name". You can specify any string here to describe the task meaningfully. - Name in Prefect UI: The specified name will appear in the Prefect UI, making it easier to identify and manage tasks within flows. This ability to customize task names is beneficial for debugging and organizing tasks within your Prefect flows. Custom names enable more meaningful descriptions of what each task does, particularly useful in complex workflows.
s
@Marvin I have a tracking task, that same task will be triggered multiple times, and each time it will be tracking a separate request id that I am passing to it as an argument. How can I name the task with the request id?
m
To name a task dynamically based on a parameter, such as a request ID, you can use the 
task_run_name
feature in Prefect. This allows you to customize the task name using the parameters passed to it. Here is how you can achieve this:
Copy code
python
from prefect import flow, task

@task(task_run_name="Tracking Task - {request_id}")
def tracking_task(request_id):
    # Simulate tracking logic
    print(f"Tracking request ID: {request_id}")

@flow
def tracking_flow():
    request_ids = ["req-1", "req-2", "req-3"]
    for req_id in request_ids:
        tracking_task.submit(req_id)

if __name__ == "__main__":
    tracking_flow()
### Explanation - 
task_run_name
in 
@task
Decorator: - Here, we set 
task_run_name="Tracking Task - {request_id}"
, which uses Python's string formatting to dynamically insert the 
request_id
into the task's name. - When the task is executed, each instance is named according to its respective 
request_id
, making it easy to track and identify in logs or the UI. This makes monitoring and debugging much smoother, especially in environments where identical tasks handle different data or operations. If you have more complex naming needs, this approach can be expanded with additional parameters or logic.
s
@Marvin How will the task get the variable request_id if it is passed in the arguments 
@task(task_run_name="Tracking Task - {request_id}")
m
When using 
task_run_name
with a task in Prefect, you can specify the format and include argument names from the task function definition in the string. The arguments need to be the same as those in the function's signature so that Prefect can substitute the values when the task is run. Here's an example to clarify how this works, using the 
request_id
argument:
Copy code
python
from prefect import flow, task

@task(task_run_name=lambda **kwargs: f"Tracking Task - {kwargs['request_id']}")
def tracking_task(request_id):
    # Simulate tracking logic
    print(f"Tracking request ID: {request_id}")

@flow
def tracking_flow():
    request_ids = ["req-1", "req-2", "req-3"]
    for req_id in request_ids:
        tracking_task.submit(req_id)

if __name__ == "__main__":
    tracking_flow()
### Explanation - Lambda Function for Task Naming: Instead of using a simple string, use a lambda function with 
kwargs
 to dynamically construct the task name. This lambda function accesses the 
request_id
from 
kwargs
, allowing Prefect to use the passed argument value to generate the task name. This approach ensures that each task instance receives the specific parameter value, which is then reflected in each task's name, making the system more traceable and understandable, especially for tasks handling identical operations on different pieces of data.
s
@Marvin I want to create a decorator that tracks execution time of each prefect task that I am running in my prefect flow. How do I do it? How to use custom decorator along with @task decorator from prefect
m
To create a custom decorator to track the execution time of tasks in a Prefect flow, you can define your own decorator and use it in conjunction with Prefect's 
@task
decorator. Here's a concise guide on how to do this: 1. Create a Custom Decorator: Define a Python decorator that measures the execution time of a function. 2. Combine with Prefect's 
@task
Decorator: Use both your custom decorator and the 
@task
decorator on your task functions. Here's a simple example:
Copy code
python
from prefect import task
import time

def track_execution_time(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        execution_time = end_time - start_time
        print(f"Task {func.__name__} executed in {execution_time:.2f} seconds")
        return result
    return wrapper

@task
@track_execution_time
def my_task(x):
    # Simulate some work with sleep
    time.sleep(x)
    return x

# Usage in a flow
from prefect import flow

@flow
def my_flow():
    my_task(2)

my_flow()
### Key Points: - Order Matters: Ensure the 
@task
decorator is applied before your custom decorator. This ensures that your decorator wraps the task's execution logic. - Execution Context: The 
@task
decorator transforms the function into a Prefect task, so your custom decorator should be designed to handle the wrapped function accordingly. For more details on Prefect tasks, you may refer to the Prefect Tasks Documentation. If you need further customization or encounter any issues, feel free to ask.
s
@Marvin If I need to wrap the @task function with my @track_executioin_time decorator, don't I need to put the @task first and then my custom decorator?
m
Yes, you're correct. My initial response was a bit off in explaining the order. When using decorators, the order in which they are applied matters. If you want your custom decorator to wrap the 
@task
decorator, you should indeed apply the 
@task
decorator first, followed by your custom decorator. Here's how you can do it:
Copy code
python
from prefect import task
import time

def track_execution_time(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        execution_time = end_time - start_time
        print(f"Task {func.__name__} executed in {execution_time:.2f} seconds")
        return result
    return wrapper

@track_execution_time
@task
def my_task(x):
    # Simulate some work with sleep
    time.sleep(x)
    return x

# Usage in a flow
from prefect import flow

@flow
def my_flow():
    my_task(2)

my_flow()
### Explanation: - Decorator Order: The 
@task
decorator should be applied first so that it can transform the function into a Prefect task. Your custom 
@track_execution_time
decorator should wrap the task to measure its execution time. This ensures that your custom logic is applied to the task as it executes. If you have further questions or need more clarification, feel free to ask.
s
@Marvin How to add logging in prefect flow. When I am using the logging module to add logs, they are not getting reflected in the prefect flow logs on UI. I gave log level as INFO so that should not be the case.
m
To ensure that your logs appear in the Prefect UI, you should use Prefect's built-in logging functions. The standard Python 
logging
module may not automatically integrate with Prefect's logging system. Here's what you can do: 1. Use 
get_run_logger
: Prefect provides a 
get_run_logger
function that returns a logger configured to send logs to the Prefect backend. 2. Log at the Correct Level: Ensure that you are logging at a level that is captured by your Prefect settings (e.g., INFO or DEBUG). Here’s how you can use Prefect's logging in your flow and tasks:
Copy code
python
from prefect import flow, task, get_run_logger

@task
def my_task():
    logger = get_run_logger()
    <http://logger.info|logger.info>("This is an info log from a task.")
    logger.debug("This is a debug log from a task.")

@flow
def my_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("This is an info log from the flow.")
    my_task()

my_flow()
### Key Points: - Use 
get_run_logger
: This function provides a logger that integrates with Prefect's logging infrastructure, ensuring logs are visible in the UI. - Log Levels: Make sure your log level settings in Prefect are configured to display the levels you are logging at. For more details, you can explore the Prefect Logging Module that includes various logging utilities and handlers. If you continue to experience issues, ensure your Prefect environment is properly configured to connect to the Prefect backend or cloud.
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