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It seems like you're having trouble with running parallel tasks in Prefect using Flask. The error message "Crash detected! Execution was cancelled by the runtime environment" suggests that there might be an issue with how the tasks are being executed concurrently. Let's try to get more information about the issue. Could you provide more details about your setup? For example, how are you defining your tasks and flows? Are you using any specific executor for parallel execution? The more details you can provide, the better I can assist you.

@Marvin I'm using simple setup with one flow and multiple tasks. Did not change any executor. In flas I'm simply calling the flow function

Based on the information I found, it seems like you might be running into issues because by default, Prefect executes tasks sequentially on a single machine. If you want to run tasks in parallel, you'll need to use a different executor. Prefect provides several executors you can use to control the concurrency of your workflows, such as the

LocalDaskExecutor

for local parallel execution, or the

DaskExecutor

for distributed execution. Here's a simple example of how to use the `LocalDaskExecutor`:

from prefect import Flow, Task
from prefect.executors import LocalDaskExecutor

class MyTask(Task):
    def run(self):
        # Your task implementation here

# Create your tasks
task1 = MyTask()
task2 = MyTask()

# Create your flow
with Flow("My Flow", executor=LocalDaskExecutor()) as flow:
    result1 = task1()
    result2 = task2()

# Run your flow
flow.run()

In this example,

task1

and

task2

will be run in parallel. As for integrating this with Flask, you would typically trigger the flow run within your Flask route handler. However, keep in mind that running a flow is a blocking operation, so you might want to run it in a background thread or a separate process to avoid blocking your Flask application. Here's a simple example:

from flask import Flask
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow')
def run_flow():
    Thread(target=flow.run).start()
    return "Flow run started"

In this example, when you access the

/run_flow

endpoint, it will start the flow run in a separate thread and immediately return a response, so it won't block your Flask application. Please adapt these examples to fit your specific use case. Let me know if you need more help!

@Marvin getting Cannot find reference 'executors' in '__init__.py'

I apologize for the confusion. The import statement for the executor has changed in the recent versions of Prefect. If you are using Prefect 0.14.0 or later, you should import the executor like this:

from prefect.engine.executors import LocalDaskExecutor

So, the corrected code would look like this:

from prefect import Flow, Task
from prefect.engine.executors import LocalDaskExecutor

class MyTask(Task):
    def run(self):
        # Your task implementation here

# Create your tasks
task1 = MyTask()
task2 = MyTask()

# Create your flow
with Flow("My Flow", executor=LocalDaskExecutor()) as flow:
    result1 = task1()
    result2 = task2()

# Run your flow
flow.run()

And for the Flask integration:

from flask import Flask
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow')
def run_flow():
    Thread(target=flow.run).start()
    return "Flow run started"

Please try this and let me know if you still encounter any issues.

@Marvin I'm using Version 2

In Prefect 2.0, the way you define and run flows has changed. The

with Flow()

syntax and

flow.run()

are now obsolete. The new way to define flows/tasks is by using the

@flow

and

@task

decorators. Subflows are just flows called from within a flow. Instead of

my_flow.run()

, you just call the flows now. Here's an example:

from prefect import Flow, task

@task
def my_task():
    # Your task implementation here

@flow
def my_flow():
    result = my_task()

# Run your flow
my_flow()

As for running tasks in parallel, Prefect 2.0 still supports this feature. You can control the concurrency of your workflows by using different executors, just like in Prefect 1.0. However, the way you specify the executor has changed. Now, you specify the executor when you run the flow, like this:

my_flow.run(executor=LocalDaskExecutor())

For the Flask integration, you would still trigger the flow run within your Flask route handler, and you might want to run it in a background thread or a separate process to avoid blocking your Flask application. Here's an example:

from flask import Flask
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow')
def run_flow():
    Thread(target=my_flow.run, kwargs={'executor': LocalDaskExecutor()}).start()
    return "Flow run started"

Please adapt these examples to fit your specific use case. Let me know if you need more help!

@Marvin how can I pass arguments to my flow with this flask approach

You can pass arguments to your flow by using the

parameters

feature in Prefect. Here's an example of how you can define a flow with parameters:

from prefect import Flow, Parameter, task

@task
def my_task(x):
    # Your task implementation here

@flow
def my_flow():
    x = Parameter('x')
    result = my_task(x)

In this example,

x

is a parameter that you can pass when you run the flow. To pass a parameter when you run the flow, you can use the

parameters

argument of the

run

method:

my_flow.run(parameters={'x': 10})

In this example, the value

10

is passed as the

x

parameter. For the Flask integration, you can pass parameters to your flow by including them in the request. Here's an example:

from flask import Flask, request
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow', methods=['POST'])
def run_flow():
    # Get parameters from the request
    parameters = request.get_json()

    # Run the flow with the parameters
    Thread(target=my_flow.run, kwargs={'parameters': parameters, 'executor': LocalDaskExecutor()}).start()

    return "Flow run started"

In this example, when you send a POST request to the

/run_flow

endpoint with a JSON body, the JSON body is used as the parameters for the flow run. For example, if you send a POST request with the JSON body

{"x": 10}

, the value

10

is passed as the

x

parameter.

@Marvin Unresolved attribute reference 'run' for class 'Flow'

I apologize for the confusion. In Prefect 2.0, the way to run a flow has changed. Instead of calling

flow.run()

, you just call the flow like a function. Here's an example:

# Run your flow
my_flow(parameters={'x': 10})

In this example, the value

10

is passed as the

x

parameter. For the Flask integration, you can pass parameters to your flow by including them in the request. Here's an example:

from flask import Flask, request
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow', methods=['POST'])
def run_flow():
    # Get parameters from the request
    parameters = request.get_json()

    # Run the flow with the parameters
    Thread(target=my_flow, kwargs={'parameters': parameters}).start()

    return "Flow run started"

In this example, when you send a POST request to the

/run_flow

endpoint with a JSON body, the JSON body is used as the parameters for the flow run. For example, if you send a POST request with the JSON body

{"x": 10}

, the value

10

is passed as the

x

parameter.

@Marvin Unresolved reference 'LocalDaskExecutor'

I apologize for the confusion earlier. In Prefect 2.0, the concept of executors has been replaced with subflow runs. Subflow runs are a first-class concept in Prefect 2.0 and this enables new execution patterns. For example, you can move your computationally intensive tasks into their own flow, which uses a

DaskExecutor

to spin up a temporary Dask cluster in the cloud provider of your choice. Next, you simply call the flow that uses a

DaskExecutor

from your other, parent flow. This pattern can be nested or reused multiple times, enabling groups of tasks to use the executor that makes sense for their workload. Here is how you can define a subflow:

from prefect import Flow, Parameter, task

@task
def my_task(x):
    # Your task implementation here

@flow
def my_subflow(x):
    result = my_task(x)

@flow
def my_flow():
    x = Parameter('x')
    result = my_subflow(x)

In this example,

my_subflow

is a subflow that is called from

my_flow

. You can run

my_flow

like this:

my_flow(parameters={'x': 10})

In this example, the value

10

is passed as the

x

parameter to

my_subflow

. For the Flask integration, you can pass parameters to your flow by including them in the request. Here's an example:

from flask import Flask, request
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow', methods=['POST'])
def run_flow():
    # Get parameters from the request
    parameters = request.get_json()

    # Run the flow with the parameters
    Thread(target=my_flow, kwargs={'parameters': parameters}).start()

    return "Flow run started"

In this example, when you send a POST request to the

/run_flow

endpoint with a JSON body, the JSON body is used as the parameters for the flow run. For example, if you send a POST request with the JSON body

{"x": 10}

, the value

10

is passed as the

x

parameter to

my_subflow

.

@Marvin Could I also trigger a deployment within the flask wait for result and return it

Yes, you can trigger a deployment within the Flask app, wait for the result, and then return it. However, keep in mind that this could potentially lead to a long-running request, depending on how long the deployment takes. This might not be ideal in a production environment, as it could lead to timeouts or other issues. Here's a simple example of how you might do this:

from flask import Flask, request
from threading import Thread
import time

app = Flask(__name__)

def deploy_and_wait(parameters):
    # Trigger the deployment here
    # This is just a placeholder, replace it with your actual deployment code
    deployment_result = my_flow(parameters=parameters)

    # Wait for the deployment to finish
    # This is also a placeholder, replace it with your actual waiting code
    while not deployment_result.is_finished():
        time.sleep(1)

    return deployment_result.result

@app.route('/run_flow', methods=['POST'])
def run_flow():
    # Get parameters from the request
    parameters = request.get_json()

    # Run the deployment and wait for the result
    result = deploy_and_wait(parameters)

    return {"result": result}

In this example, when you send a POST request to the

/run_flow

endpoint with a JSON body, the JSON body is used as the parameters for the deployment. The

deploy_and_wait

function triggers the deployment, waits for it to finish, and then returns the result. The result is then returned in the response to the POST request. Please adapt this example to fit your specific use case. Let me know if you need more help!

@Marvin How can I trigger the prefect deployment api within deploy and wait. Do you have an example

Based on the search results, there are a couple of ways to trigger a Prefect deployment: 1. Using the Prefect CLI: You can trigger a run of a deployment from the Prefect CLI with the following command:

bash
prefect deployment run my-first-flow/my-first-deployment

In this command,

my-first-flow/my-first-deployment

is the name of your flow and deployment. 2. Using the Prefect API: You can create a deployment on the API using the settings in a

deployment.yaml

file. First, run the

prefect deployment build

CLI command with deployment options to create a

deployment.yaml

deployment definition file. Then, run

prefect deployment apply

to create a deployment on the API.

bash
prefect deployment build [OPTIONS] > deployment.yaml
prefect deployment apply deployment.yaml

3. Using HTTP calls: If you want to create flow runs using only HTTP calls (for example, when you want to trigger a flow run from an AWS Lambda without installing Prefect in the Lambda runtime), you can use the Prefect REST API. However, the exact details of how to do this depend on your specific use case. Please note that these are general examples and you might need to adapt them to fit your specific use case. If you need more detailed help, could you please provide more information about your setup? For example, are you using Prefect Cloud or Prefect Server? Are you trying to trigger the deployment from within the same environment where Prefect is running, or from a different environment?

@Marvin can I trigger a deployment within python code

In Prefect 2.0, you can trigger a deployment from within your Python code using the Prefect CLI commands. However, this requires running shell commands from within your Python code, which might not be ideal. Here's an example of how you can do this:

import subprocess

def trigger_deployment():
    # Run the Prefect CLI command to trigger the deployment
    subprocess.run(["prefect", "deployment", "run", "my-first-flow/my-first-deployment"])

In this example,

my-first-flow/my-first-deployment

is the name of your flow and deployment. You would call the

trigger_deployment

function to trigger the deployment. Please note that this is a general example and you might need to adapt it to fit your specific use case. For example, you might need to handle errors that can occur when running the CLI command, or you might need to pass additional arguments to the CLI command. Also, please note that this approach requires that the Prefect CLI is installed in the same environment where this Python code is running. If you're looking for a way to trigger a deployment using only the Prefect Python API (without using the CLI), I'm afraid this is not currently supported in Prefect 2.0. The recommended way to trigger a deployment is by using the Prefect CLI or the Prefect REST API.

@Marvin there is no way todo so with the prefect 2 pyton sdk

I apologize for the confusion earlier. In Prefect 2.0, the concept of "deployments" has been replaced with "flows". You can trigger a flow run directly from your Python code by calling the flow like a function. Here's an example:

# Run your flow
my_flow(parameters={'x': 10})

In this example, the value

10

is passed as the

x

parameter. For the Flask integration, you can pass parameters to your flow by including them in the request. Here's an example:

from flask import Flask, request
from threading import Thread

app = Flask(__name__)

@app.route('/run_flow', methods=['POST'])
def run_flow():
    # Get parameters from the request
    parameters = request.get_json()

    # Run the flow with the parameters
    Thread(target=my_flow, kwargs={'parameters': parameters}).start()

    return "Flow run started"

In this example, when you send a POST request to the

/run_flow

endpoint with a JSON body, the JSON body is used as the parameters for the flow run. For example, if you send a POST request with the JSON body

{"x": 10}

, the value

10

is passed as the

x

parameter. Please let me know if this answers your question, or if you need help with something else.