@Marvin when attempting to deploy a worker to a GCP cloud function as documented in the tutorial, I get the following error:

googleapiclient.errors.HttpError: <HttpError 400 when requesting https://<redacted-region>.<http://googleapis.com/apis/run.googleapis.com/v1/namespaces/<redacted>?alt=json|googleapis.com/apis/run.googleapis.com/v1/namespaces/<redacted>?alt=json> returned "spec.template.spec.task_spec.containers[0].resources.limits.memory: Invalid value specified for container memory. For 1.0 CPU, memory must be between 128Mi and 4Gi inclusive.

my cloud function container memory currently has a 2Gi memory limit configured (well within the limit), verified by

gcloud run services describe <worker_name>

Is the memory configuration for this worker something I need to configure in

prefect.yaml

?

It appears from the excerpts that the information regarding GCP cloud function worker memory configuration, particularly with Prefect, isn't explicitly detailed. However, based on general knowledge of configuring resources in cloud environments, including with tools like Prefect, here are some steps you can take to troubleshoot and resolve your issue with the memory limits error: 1. Check Prefect Configuration: Ensure that the

prefect.yaml

or any relevant Prefect configuration file does not override your GCP Cloud Run settings with lower memory limits. Prefect may have its own configuration settings that it sends to GCP. 2. Review GCP Deployment Specifications: Make sure that the deployment specification for your Cloud Function is correctly formatted and that the memory limit set is within the accepted values. The error message suggests that there is a format or value error in the container's memory limits. 3. Permissions and Roles: Verify that the user or service account you are using has the necessary permissions to make changes to the Cloud Run services. Lack of proper permissions can sometimes lead to misleading error messages. 4. Consult GCP Support or Documentation: Since this seems like a GCP-specific error, the GCP documentation or support might have more detailed solutions relating to resource allocation and configuration. For configuration that happens within the scope of GCP, their documentation would be the authority. 5. Expected Values Format: Ensure that the value "2Gi" for memory is formatted in the way GCP expects. Sometimes, cloud providers expect specific units or formatting when specifying resource limits. If the issue persists and you believe the configuration is correct, consider contacting GCP support or reviewing their documentation on Cloud Run services and their limits. If there is a possibility that this is a common issue with Prefect deployments on GCP, searching GitHub issues might provide insights from others who have encountered the same problem.

@Marvin is the memory configuration a parameter that I should apply to the command

prefect worker start --pool <pool_name>

?

According to the information available, there doesn't seem to be a memory configuration parameter for the

prefect worker start --pool

command directly impacting the memory settings on GCP Cloud Run. Typically, the

prefect worker start

command is used to start a worker process, and configuration regarding cloud providers would usually be applied in a different part of the deployment process (through infrastructure-as-code or deployment configuration files). It would be advisable to consult the Prefect documentation or community regarding the specific use of Prefect with Google Cloud Platform, as the integration details may have nuances that aren't covered in general usage instructions. If the documentation is not clear or appears to be lacking, consider reaching out to the Prefect community or opening an issue on the Prefect GitHub to obtain more detailed assistance. Remember to include details about your current configuration and the steps you've taken when seeking help.

@Marvin what is the best way to securely set environment variables on a worker pool running in jobs hosted in GCP cloud run?

The excerpts provide some useful information, but more context around managing environment variables for worker pools, specifically in GCP Cloud Run with Prefect, would be beneficial. However, drawing on available knowledge on the subject, here are some best practices that can be followed: 1. Use Secret Manager: Store sensitive information like API keys or passwords in GCP Secret Manager. These secrets can then be accessed by your application via the GCP SDK as environment variables. 2. Cloud Run Environment Variables: For less sensitive information, you can directly set environment variables in the Cloud Run service configuration. Use the GCP console UI or the

gcloud run deploy

command with the

--set-env-vars

flag. 3. Prefect Variables: For configuration that is not sensitive, you may use Prefect Variables. Variables in Prefect are named, mutable string values somewhat akin to environment variables, scoped to a server instance or workspace. 4. Use Prefect Secrets: Prefect also has a concept of "secrets" that is suitable for storing sensitive information. Prefect secrets can be managed via the Prefect UI or CLI, and they're available for use within flows. 5. Infrastructure-as-Code: Use tools such as Terraform or Cloud Deployment Manager for GCP, when managing environment configurations and secrets. Infrastructure as code helps keep configurations version-controlled and reproducible. When setting environment variables for worker pools in Prefect, make sure to consult the Prefect documentation for the specifics on how to implement these best practices within the Prefect ecosystem and within your GCP environment. And, as always, the emphasis should be on avoiding storing sensitive data in your codebase or in any non-secure medium.

@Marvin how should a container running in a job from the worker pool hosted in cloud run access the secret manager?

To access Google Cloud Secret Manager within containers running in a worker pool on GCP Cloud Run, follow these steps: 1. Service Account with Secret Manager Access: Ensure that the service account associated with your Cloud Run service has the necessary IAM roles to access the Secret Manager. The role

roles/secretmanager.secretAccessor

is required to access secret payloads. 2. Application Code to Access Secrets: Modify your application code running inside the Cloud Run container to use the GCP SDK to retrieve secrets. You can use the

google-cloud-secret-manager

package to retrieve secrets programmatically:

python
   from google.cloud import secretmanager
   
   client = secretmanager.SecretManagerServiceClient()
   name = f"projects/my-project/secrets/my-secret/versions/latest"
   response = client.access_secret_version(request={"name": name})
   secret_string = response.payload.data.decode("UTF-8")

3. Use Prefect Secret Tasks: If you are using Prefect, you can create a Prefect Secret that represents the Secret Manager's secret and access it in your flows. Prefect handles fetching the secret payload and making it available to tasks when executed. 4. Environment Variables or CLI: In scenarios where automatic access to Secret Manager isn't desired, you may use environment variables or pass secrets through the command line at the service deployment stage (although this is less secure and less recommended). Remember to only give access to the secrets that each service or application needs (principle of least privilege) and regularly audit and rotate your secrets to maintain the security of your applications.

@Marvin can I call the gcp secret manager somehow from the

job_variables

or somewhere else in

prefect.yaml

?

The provided excerpts do not explicitly mention the integration of GCP Secret Manager within the

prefect.yaml

file or through

job_variables

. However, inferring from common practices for handling secrets in cloud-native applications, integrating through Prefect configuration files like

prefect.yaml

would typically not be recommended because it would require storing sensitive information in plaintext, which is insecure. Instead, the most secure practice would generally be to set environment variables in your deployment that your application can access at runtime to dynamically fetch the secret values from GCP Secret Manager. If your container has the necessary GCP permissions (such as through a properly configured service account), it can access the Secret Manager to retrieve these values programmatically during container startup or at runtime. Here is an alternative approach you can consider: 1. Create a GCP Credentials Block: Use Prefect interface or code to create a GCP Credentials Block that stores your service account information securely. 2. Reference Credentials Block: Reference the GCP Credentials Block in your push and pull steps in your flow code, allowing your application to access GCP resources, including the Secret Manager, using the credentials specified in the block. 3. Access Secrets at Runtime: Update your application code to query GCP Secret Manager using the google-cloud-secret-manager package and the service account permissions set in the Credentials Block. 4. Environment Variables in Cloud Run: You may set the name of the secret or any non-sensitive identifiers as environment variables in the Cloud Run service configuration, and use these within your application to reference the correct secrets in Secret Manager. To implement these suggestions properly, refer to the Prefect and GCP Secret Manager documentation for specific instructions and best practices to ensure your setup aligns with your organization's security requirements and compliance standards. If needed, Prefect support or community forums can provide additional guidance.

@Marvin I just want to add environment variables to the cloud run job, from the GCP secret manager, when it creates the job. Is this possible? I'd like to not heavily couple my application code with the prefect secret manager

Based on the information available, it is not directly specified how to add environment variables from GCP Secret Manager to a Cloud Run job at the point of creation within Prefect workflows. However, I can provide a generalized approach based on standard practices with GCP Cloud Run and Secret Manager. In general, Cloud Run does not provide a native way to directly integrate Secret Manager into environment variables at deploy time without some application code or deployment script involvement. However, there are ways to achieve this with a minimal coupling to your application: 1. Use GCP Cloud Build: Configure a CI/CD pipeline using Cloud Build. In your build steps, you can fetch secrets from Secret Manager and pass them as environment variables when deploying to Cloud Run using the

gcloud run deploy

command. 2. Deployment Script: Write a separate deployment script that fetches the necessary secrets from Secret Manager and uses them to set environment variables when deploying your Cloud Run service. 3. GCP Allowlisting: Utilize a GCP feature that allows you to automatically load secrets as environment variables. As of the knowledge cutoff in April 2023, this functionality may need to be handled via a CI/CD pipeline or deployment script as mentioned above. Ultimately, the method you choose will depend on your specific CI/CD setup and how you want to manage deployments. Both approaches involve some level of integration but aim to keep it minimal and separate from your core application code logic. For the most current and specific instructions, I recommend checking the GCP documentation or Prefect community resources because the capabilities and best practices evolve over time.

how can I call a deployment script from the cloud run worker when it is creating jobs?

It seems like you may have accidentally sent an empty message. If you have any further questions or require additional information, feel free to ask, and I'll do my best to assist you.

@Marvin how can I call a deployment script from the cloud run worker when it is creating jobs, like you suggested?

The excerpts provided don't appear to include detailed instructions on calling a deployment script directly from within a GCP Cloud Run worker when creating jobs. However, in general, Cloud Run is not designed to call external scripts at the point when a worker is creating jobs. Instead, Cloud Run is expected to execute a container that runs a web service or a specific task defined in the container image. To run deployment scripts or any initialization scripts during the startup phase of a Cloud Run service or before creating jobs, you typically need to incorporate them into your container image. Here's a general approach you could take: 1. Dockerfile: Define a

Dockerfile

for your application that includes steps to execute your script during the container's build process or as an entrypoint/command script that runs when the container starts. 2. Entrypoint/Command Script: Create a script that Cloud Run executes when the container starts. This script could call GCP Secret Manager to retrieve environment variables, perform any other startup tasks you require, and then start your application. 3. Cloud Build Trigger: Use Google Cloud Build to automatically build your container images and deploy to Cloud Run. As part of the build process, you can include steps that execute scripts or modify your application's environment before the image is deployed. 4. Environment Variables Injection: Investigate whether Cloud Run has received any updates that allow automatic injection of environment variables from Secret Manager at deployment time. If this is the case, use this feature directly. If you need to run scripts asynchronously or based on specific events, such as the creation of a new job, you might consider using a Pub/Sub trigger or a Cloud Function to react to those events and execute scripts as needed. Please note that this is a high-level overview and the actual implementation details can vary based on your specific setup. Be sure to refer to the most recent GCP documentation or Prefect community guidelines for any new features or best practices that may have been introduced after my last update in April 2023.

@Marvin what is the syntax for declaring

job_variables

in a prefect.yaml work pool configuration

To declare

job_variables

in a

prefect.yaml

work pool configuration, you will follow YAML syntax to define a dictionary of key-value pairs that will correspond to the variables you want to set for the job. Based on the provided excerpts, here's an example of how the

job_variables

section might look in a

deployment.yaml

within a Prefect work pool configuration:

yaml
work_pool:
  name: my-work-pool
  job_variables:
    stream_output: false
    MY_ENV_VAR: "my_value"
    ANOTHER_VAR: "another_value"

In this example: -

work_pool

is the key for the work pool configuration. -

name

is the name of the work pool. -

job_variables

is a dictionary where you can set various job variables specific to this work pool. -

stream_output

is an example variable that is used to control whether to stream output. -

MY_ENV_VAR

and

ANOTHER_VAR

are examples of custom environment variables that you could override or set for the job. Please replace

"my_value"

and

"another_value"

with the actual values you want for your environment variables. Keep in mind that the specific

job_variables

you can use may depend on the context and capabilities of the Prefect version you are using, as well as your infrastructure setup. For more specific details and the latest syntax, please refer to the official Prefect documentation or validate your

prefect.yaml

configuration using Prefect tools or command-line options.

@Marvin how can I modify my work pool settings to add the cloud run v2 job id as an environment variable to the container

The documentation excerpts do not directly address the specific question of adding the Cloud Run V2 job ID as an environment variable within work pool settings in Prefect's

deployment.yaml

. However, you can approach this by using the

job_variables

configuration section in your

deployment.yaml

file for a work pool, as seen in the excerpts. Here's how you might go about customizing

job_variables

to include an environment variable for the job ID:

yaml
work_pool:
  name: my-cloud-run-work-pool
  job_variables:
    CLOUD_RUN_JOB_ID: "${{CLOUD_RUN_JOB_ID_ENV_VARIABLE}}"

In the example above: -

CLOUD_RUN_JOB_ID

would be the name of the environment variable you're setting. -

${{CLOUD_RUN_JOB_ID_ENV_VARIABLE}}

represents a placeholder for the actual value of the Cloud Run V2 job ID, which you would replace with the appropriate variable or command that retrieves the job ID. While the example is somewhat generic, it demonstrates the pattern for setting variables. In practice, integration for automatic job ID retrieval like this may not be built-in to Prefect or Cloud Run. You might need to use scripts or setup commands that execute when the job starts to dynamically set this variable. If Cloud Run automatically generates an environment variable with the job ID (common in many CI/CD systems), you could reference that directly. Otherwise, you may need to write a startup script that fetches the job ID and exports it as an environment variable within the container. Please note that the exact mechanism to achieve this will depend on the features and behavior of Cloud Run and Prefect, and it may require additional investigation or inquiry with Prefect's community/support for the latest capabilities.

@Marvin how can I attach my job variables to my Cloud Run container in my work-pool-template.json

To attach job variables to a Cloud Run container in your

work-pool-template.json

, you would modify the base job template associated with your work pool. Here is a general approach to how you might define environment variables within `work-pool-template.json`: 1. Define the environment variables under the

env

or a similarly named section that corresponds to environmental settings in the template. This section is typically an array of objects, each representing an environment variable with

name

and

value

keys. 2. Provide the values for these environment variables - these could be hard-coded values, references to Secret Manager (if supported by the platform), or other dynamic values resolved at runtime. A simplified example of what this might look like in `work-pool-template.json`:

json
{
  "kind": "Job",
  "template": {
    "spec": {
      "template": {
        "spec": {
          "containers": [
            {
              "name": "worker",
              "image": "<http://gcr.io/my-project/my-worker-image:latest|gcr.io/my-project/my-worker-image:latest>",
              "env": [
                {
                  "name": "MY_ENV_VAR",
                  "value": "my-value"
                },
                {
                  "name": "ANOTHER_VAR",
                  "value": "another-value"
                }
              ]
              // other container settings
            }
          ]
          // other job spec settings
        }
      }
    }
  }
  // other work pool settings
}

After you have updated your `work-pool-template.json`: 1. Use the Prefect CLI to update the work pool with the new base job template by running:

prefect work-pool update --base-job-template base-job-template.json my-work-pool

Remember to replace

"my-value"

and

"another-value"

with the actual values or references you wish to use for your job variables. The exact keys and structure may vary based on the version of Prefect you are using and the Cloud Run specifications. For precise details on structure and available settings, please refer back to the Prefect documentation or consult the Prefect community for the most recent updates and best practices.

@Marvin what if I want to pass along the variables from the job_variables component of the flow .deploy() method?

To pass the variables from the

job_variables

component of the deployment into your work pool's

work-pool-template.json

, you would need to define template variables in your job configuration which will be interpolated with the values provided in

job_variables

. Based on the documentation excerpts provided, here are the key points for using template variables when developing work pools and workers: - Template variables can be included in your

work-pool-template.json

. The variables are defined with the

${{ VARIABLE_NAME }}

syntax. - When a deployment is executed with specific

job_variables

provided, these variables will replace the corresponding template variables in the job template. - If the template variable is part of a string, it will be interpolated into that string. - If a

job_variables

entry corresponds to a template variable and is the only value for a key in the

job_configuration

section, and no value is provided, then that key will be removed from the

job_configuration

in the resulting job. Here is an example of how you might define a

work-pool-template.json

to use a template variable that will be populated by

job_variables

during deployment:

json
{
  "kind": "Job",
  "template": {
    "spec": {
      "template": {
        "spec": {
          "containers": [
            {
              "name": "worker",
              "image": "<http://gcr.io/my-project/my-worker-image:latest|gcr.io/my-project/my-worker-image:latest>",
              "env": [
                {
                  "name": "MY_DYNAMIC_VAR",
                  "value": "${{ MY_ENV_VAR }}"
                }
                // other environment variables
              ]
              // other container settings
            }
          ]
          // other job spec settings
        }
      }
    }
  }
  // other work pool settings
}

In this JSON,

${{ MY_ENV_VAR }}

is a template placeholder for an environment variable that you expect to pass from the

job_variables

during the deployment process. To properly set

job_variables

in your deployment, you'd do something like this in your deployment script or command:

python
my_flow.deploy(
    name="my-deployment",
    work_pool_name="my-work-pool",
    job_variables={
        "MY_ENV_VAR": "actual_value"
    }
)

During the deployment,

actual_value

would be used to replace the template variable

${{ MY_ENV_VAR }}

in the work pool template, ultimately setting the environment variable

MY_DYNAMIC_VAR

to

actual_value

. Please note that these examples provide a high-level concept, and the exact syntax and structure may vary based on the version of Prefect you are working with. For exact implementation details, please refer to the latest Prefect documentation on work pools and worker development.

@Marvin what if I want to pass along the variables from the job_variables component of the flow .deploy() method?

To pass along variables from the

job_variables

component of the flow

.deploy()

method, you use the

deploy()

method's

job_variables

argument to fine-tune the execution environment of your deployments. The values passed in

job_variables

override the default values in the specified work pool's base job template. Here's a code example showing how to use the

job_variables

argument in the

.deploy()

method:

python
if __name__ == "__main__":
    get_repo_info.deploy(
        name="my-deployment",
        work_pool_name="my-docker-pool",
        job_variables={
            "env": {
                "EXTRA_PIP_PACKAGES": "boto3"
            }
        },
        image="my-image:my-tag",
        push=False
    )

In the

deploy

method, the

job_variables

parameter receives a dictionary where keys like "env" can be used to define environment variables for the deployment. In this example, the key "EXTRA_PIP_PACKAGES" represents an environment variable that Prefect will use to install additional Python packages at runtime. Regarding the Python

<http://logging.info|logging.info>()

calls, Prefect captures logs for your flow and task runs by default, even without starting a Prefect server. You can view and filter logs in the Prefect UI, Prefect Cloud or access them via the API. Prefect allows customizing the logging configuration, but standard Python logging statements like

<http://logging.info|logging.info>()

will appear in the flow logs as expected. If you want to change the log levels or customize the logging behavior, you can do so by setting an appropriate Prefect Profile setting using the syntax

PREFECT_LOGGING_[PATH]_[TO]_[KEY]

corresponding to the nested address of any setting or by creating your own

logging.yml

with custom settings. For more detailed information, please refer to the excerpts from Prefect's documentation on deployment and logging.

@Marvin what annotations can I add to a @flow command to avoid asyncio or prefect timeouts for long-running processes

To avoid timeouts for long-running processes in Prefect flows, you can use the

timeout_seconds

parameter of the

@flow

decorator to specify the maximum number of seconds a flow should be allowed to run. Setting this parameter to

None

indicates no timeout, thus allowing the flow to run indefinitely without being preempted by a Prefect-level timeout. Here's an example of how you specify that a flow should not be subjected to a Prefect timeout:

python
from prefect import flow

@flow(timeout_seconds=None)
def my_long_running_flow():
    # Your long-running process logic here
    pass

For asyncio timeouts, Prefect's scheduling and execution model generally abstracts away the need for explicit asyncio timeout management within the flow's main logic. However, if you're writing custom asyncio code within tasks, you'll need to manage asyncio timeouts at the task level. Remember that removing timeouts can lead to unattended and potentially problematic execution durations, especially if running on limited resources or within systems that expect job completion within certain time windows. It's generally a good practice to have some form of timeout to catch and handle cases where a flow or task may be stuck. Always ensure that the configuration aligns with the operational requirements and monitoring strategies of your system. For in-depth guidance and best practices, always refer back to Prefect's official documentation on flows and their behavior.

@Marvin is there a way tell a deployment of a prefect flow to call another follow up task, once it is completed?

To call another follow-up task once a Prefect flow is completed, there are a couple of approaches you could use: 1. Calling a Task at the End of a Flow: Create a follow-up task and call it at the end of your flow definition. This ensures that the follow-up task is executed after all other tasks in the flow have been completed.

python
from prefect import flow, task

@task
def my_follow_up_task():
    # Logic for the follow-up task
    pass

@flow
def my_flow():
    # Your flow's logic here...
    
    # Call the follow up task at the end of the flow
    my_follow_up_task()

# Deploy the flow 
my_flow()  # my_follow_up_task will be called after my_flow's logic completes

2. Flow of Deployments: If the follow-up task is quite separate from the original flow and might even be a part of another deployment, you can use a "flow of deployments" pattern where you execute one deployment from within another flow. Here's a simplified example from the Prefect documentation:

python
from prefect import flow, task
from prefect.deployments import run_deployment

@task(task_run_name="Run deployment {flow_name}/{deployment_name}")
def run_deployment_task(flow_name: str, deployment_name: str, parameters: dict):
    run_deployment(name=f"{flow_name}/{deployment_name}", parameters=parameters)

@task
def downstream_task():
    # Logic for the task to run after deployments are complete
    pass

@flow
def flow_of_deployments():
    deployment_run_1 = run_deployment_task.submit(flow_name="parallel-work", deployment_name="process-user", parameters={"user_id": 1})
    deployment_run_2 = run_deployment_task.submit(flow_name="parallel-work", deployment_name="process-user", parameters={"user_id": 2})
    
    # Wait for deployments to finish before calling downstream_task
    deployment_run_1.wait()
    deployment_run_2.wait()
    downstream_task()

# Deploy the flow 
flow_of_deployments()  # downstream_task will be called after the deployments finish

In the above example,

run_deployment

can trigger other flows as deployments, and

downstream_task

can be used as a follow-up task that will run once the other deployments specified have completed. Note that Prefect does not automatically trigger tasks or flows after a deployment has completed; you must explicitly code this logic into your flows. The Prefect documentation on task runners and specifying upstream dependencies provides further insight on how to manage task and flow dependencies.

@Marvin does the .deploy() method on a

@flow

decorated function return a coroutine or a UUID? do I need to

await

it to retrieve the ID if my code is running in an sync context like:

if __name__ == "__main__":
    id = my_flow.deploy(...)
    print(f"{id=}")

The deployment of a Prefect flow using the

.deploy()

method does not return a coroutine, nor does it directly return a UUID of the flow run. Instead,

.deploy()

triggers the deployment process, registers the flow with Prefect Cloud or server, and the flow run details become accessible in that context. When you trigger a flow deployment programmatically with

.deploy()

, the UUID of the created deployment can be found in the Prefect UI or fetched through Prefect's API after the deployment is registered. For example:

python
from prefect import flow

@flow
def my_flow():
    # Flow definition goes here
    pass

if __name__ == "__main__":
    # Deploy the flow and register with Prefect
    deployment = my_flow.deploy(...)
    
    # The actual flow run UUID would need to be retrieved through the UI or API
    print(f"Deployment registered with Prefect: {deployment}")

In the above script, the

deployment

variable would typically hold a deployment object or some type of reference to the deployment process. You'll need to interact with the Prefect server or Prefect Cloud API to get details about the flow run, such as its UUID. Standard usage patterns involve invoking the

.deploy()

method in a synchronous context, so no

await

is necessary. However, details of the deployment such as names or IDs can be accessed through interactive commands or API calls once the deployment is complete. Always ensure to check the latest Prefect documentation for any updates or changes in behavior regarding flow deployment and management.

@Marvin ok, so what is the type of the

deployment

variable in your example?

It seems that there's been a mix-up. The type of the

deployment

variable in the flow deployment example I provided is not explicitly shown in the example code from the Prefect codebase. However, based on Prefect's deployment and flow APIs, the

deployment

variable from the

.deploy()

method usually holds a deployment object reference or a response returned by the Prefect API when the deployment is registered. In most cases,

.deploy()

is used to trigger a deployment script that orchestrates the deployment process and may not return a specific value related to the actual flow execution. Instead of holding the flow's UUID, the

deployment

variable might simply signify that the deployment registration was triggered or completed. For precise type information and methodology about how a deployment is represented in code and the Prefect framework, reference the official Prefect API documentation or tutorial on deployments as it may provide more up-to-date details about the deployment objects and their associated methods.

@Marvin how can I bubble up logs from subflows to the parent flow that called them? Should I do this from

get_run_logger()

or somewhere else?

When calling a subflow within a parent flow in Prefect, logs generated by tasks and subflows are automatically aggregated and available in the Prefect UI associated with the parent flow's run. You do not need to explicitly bubble up logs; Prefect's logging framework handles this for you. This works because both the parent flow and subflow share the same logging configuration, and logs emitted during the subflows are tagged with the appropriate flow and task run IDs. This information is used by Prefect to display the logs correctly in the UI. If you want to ensure that logs from subflows are coming through with custom details or specific formatting, you can obtain a logger within any flow or task using the

get_run_logger()

method and use it to produce logs:

python
from prefect import flow, get_run_logger

@flow
def my_subflow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("This is a log message from the subflow")

@flow
def my_parent_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("This is a log message from the parent flow before calling subflow")
    
    my_subflow()
    
    <http://logger.info|logger.info>("This is a log message from the parent flow after calling subflow")
    
if __name__ == "__main__":
    my_parent_flow()

In this example, all of the logger messages, regardless of whether they're from the parent flow or subflow, will appear in the logs associated with the flow run in the Prefect backend and UI. This is because the logger obtained via

get_run_logger()

is aware of the current run context, which includes the flow and task run identifiers. For more details on Prefect's logging and how logs are attached to runs, refer to the documentation on composing flows and logging.

@Marvin this is not true for one of my deployments. has it changed recently?

There doesn't appear to be any documented issues on the Prefect GitHub regarding subflow logs not being visible in the parent flow. If the expected behavior of subflow logs aggregating under the parent flow is not occurring in one of your deployments, it could be due to a variety of reasons, such as configuration changes or a version-specific behavior of Prefect. Given that no relevant issues were found, you may consider a few steps: 1. Verify Prefect Version: Ensure that you are using a Prefect version where this behavior is supported. It's possible that in newer versions, the behavior around subflow logging has changed. 2. Review Configuration: Check if there is any custom logging configuration in your deployment that could affect the logging behavior. Ensure

get_run_logger()

is being used consistently in subflows as it should attach the correct context to log messages. 3. Check Deployment Details: Ensure that the subflow is indeed being called and executed as expected within the context of the parent flow. 4. Troubleshoot in Prefect UI: Examine the Prefect UI for any anomalies in the flow run, such as errors or unusual task states that might indicate why logs are not being displayed as expected. 5. Contact Support or Community: If the problem persists and does not appear to be a known issue, reaching out to Prefect support or the Prefect community through their Slack channel or GitHub discussions might reveal more information or help diagnose the issue. 6. Reproduce and Report: Attempt to reproduce the issue in a different environment or with a simplified version of your flow. If it seems like a bug or regression, report it to Prefect with detailed reproduction steps. It's also good to consult the Prefect documentation or release notes to check for any mention of changes in log handling or visibility between versions.

@Marvin how can I use the prefect REST API using PREFECT_API_URL and PREFECT_API_KEY to find all the jobs running on a given work pool?

I apologize for the delay. To use the Prefect REST API to find all the jobs running on a given work pool, you'll need to follow these steps: 1. Set up authentication: You will need to set the

PREFECT_API_KEY

as a header in your API requests for authentication. 2. Find the work pool ID: You may first need to find the ID of the work pool if you do not already have it. 3. Use the work pool ID to filter flow runs: With the work pool ID, you can filter the flow runs by work pool using the appropriate REST API endpoint. Here is a Python example of how you might construct a request to the Prefect API to fetch flow runs for a particular work pool:

python
import requests

PREFECT_API_URL = "your-prefect-api-url"
PREFECT_API_KEY = "your-prefect-api-key"
WORK_POOL_ID = "your-work-pool-id"

headers = {
    "Authorization": f"Bearer {PREFECT_API_KEY}",
    "Content-Type": "application/json",
}

endpoint = f"{PREFECT_API_URL}/flow_run?work_pool_id={WORK_POOL_ID}"

response = requests.get(endpoint, headers=headers)

if response.status_code == 200:
    flow_runs = response.json()["data"]  # This will give you the list of flow runs
    print(flow_runs)
else:
    print(f"Failed to fetch flow runs: {response.text}")

Please replace

"your-prefect-api-url"

,

"your-prefect-api-key"

, and

"your-work-pool-id"

with the actual values for your setup. The specific details of the REST API path and parameters may vary depending on the version of Prefect you're using. For precise details on the available API endpoints, their parameters, and what data they return, please consult the official Prefect API documentation. If you require authentication guidance, the documentation will also provide instructions on obtaining and using API keys for Prefect Cloud or your self-hosted Prefect instance.

what should an example PREFECT_API_URL template look like?

The "Not Found" response indicates that the URL being requested doesn't match any endpoint on the Prefect server. This could be due to several reasons: 1. The

PREFECT_API_URL

might be incorrect. Double-check that the

PREFECT_API_URL

points to the correct Prefect server or Prefect Cloud API endpoint. It typically should look something like

<https://api.prefect.io>

for Prefect Cloud. 2. The endpoint path may be incorrect or no longer valid. The path

"/flow_run"

I used in the example is a placeholder and might not correspond to an actual Prefect REST API endpoint. 3. The API version is not specified in the API URL. Typically, REST APIs have a version in the path (e.g.,

"/api/v1/flow_runs"

). 4. Prefect may have updated their API and endpoint naming conventions. To find the correct API endpoint and parameters, you will need to consult the Prefect REST API documentation specific to the version of Prefect you are using. As of the last update I have (April 2023), Prefect API documentation could be accessed from within the Prefect UI or Prefect Cloud dashboard. Since I am unable to provide actual code that reflects the latest changes due to my knowledge cutoff, here is how you might approach the REST API using Python:

python
import requests

# Ensure these values have been correctly set to your Prefect instance's details
PREFECT_API_URL = "<https://api.prefect.io>"
PREFECT_API_KEY = "your-prefect-api-key"  # Ensure this is kept secure
WORK_POOL_ID = "your-work-pool-id"  # Ensure this is the correct ID for your work pool

# Headers for the API request
headers = {
    "Authorization": f"Bearer {PREFECT_API_KEY}",
    "Content-Type": "application/json",
}

# Construct the API endpoint. You will need the correct endpoint here.
# This should include the API version and the correct path to fetch runs for a work pool.
# For example, if the endpoint is '/api/work_pools/{work_pool_id}/runs', format it accordingly.
endpoint = f"{PREFECT_API_URL}/api/work_pools/{WORK_POOL_ID}/runs"

response = requests.get(endpoint, headers=headers)

if response.status_code == 200:
    flow_runs = response.json()  # Adjust depending on the JSON response structure
    print(flow_runs)
else:
    print(f"Failed to fetch flow runs: {response.text}")

Remember to replace placeholder strings with your actual data and adjust the endpoint path as per the latest API routes available in your version of Prefect. If you're still experiencing issues, reaching out to Prefect support or community may provide further help.

@Marvin let's take a different tack. can we do the same thing using the prefect python client exposed from

from prefect import get_client

?

To fetch job information from a given work pool using the Prefect Python client obtained from

get_client()

, you can follow a pattern similar to this:

python
from prefect import get_client

# Initialize the Prefect Client
with get_client() as client:
    # Assuming you have your work pool ID available
    work_pool_id = "your-work-pool-id"
    
    # Fetch the flow runs for a specific work pool
    flow_runs = client.read_flow_runs(work_pool_id=work_pool_id)
    
    # The above call fetches the flow run data from the work pool
    # You may need to loop through the runs or extract specific information
    for run in flow_runs:
        print(run)

This code uses context management to ensure the client is properly closed after use. The

read_flow_runs

method of the client is invoked with the

work_pool_id

parameter. The response would return information about the flow runs associated with the given work pool. The exact method name (

read_flow_runs

in the above example) and the parameters it accepts may vary depending on the version of the Prefect library you're using. For instance, the actual API might use a different method for filtering flow runs by work pool, or the concept of "jobs" might be denoted differently. Please refer to the current version's official Prefect Python client API documentation to identify the precise methods available for interacting with work pools and flow runs. Additionally, ensure the Prefect Python client is correctly configured with your

PREFECT_API_URL

and authentication headers, if necessary. In cases where you need credentials, the client may handle this for you automatically if they're set in your environment or config file, or you may need to pass them directly when initializing the client.

can you add a filter to include only the flow runs that are currently running, not scheduled or crashed?

Yes, you can

await

an async flow within another async flow in Prefect. Prefect's async support allows you to compose flows asynchronously, which is particularly useful when orchestrating concurrent tasks or subflows. When defining your flow with the

@flow

decorator, you can mark it as

async

and then use

await

when calling subflows. Here's an example of how to define an async subflow and await it within a parent async flow:

python
from prefect import flow

@flow
async def async_subflow():
    # Subflow logic here
    pass

@flow
async def async_parent_flow():
    # Call the subflow and await its completion
    await async_subflow()

# Calling the parent async flow
async_parent_flow()

Using the async pattern also allows you to concurrently run multiple subflows and await their completion, leveraging

asyncio.gather

or similar constructs.

python
import asyncio
from prefect import flow

@flow
async def async_subflow_1():
    # Subflow 1 logic here
    pass

@flow
async def async_subflow_2():
    # Subflow 2 logic here
    pass

@flow
async def async_parent_flow_concurrent():
    # Run subflows concurrently
    await asyncio.gather(
        async_subflow_1(),
        async_subflow_2(),
    )

# Calling the parent async flow to run subflows concurrently
async_parent_flow_concurrent()

Keep in mind to manage subflow results properly as each subflow call with

await

will be suspending the parent flow execution until the subflow completes. For more specificity and the most up-to-date practices, I recommend referring to the Prefect documentation on async flows and tasks, as well as ensuring that your Prefect version supports these patterns.

@Marvin how call I call .submit() to run the same async flow from a sync function

To call an async flow from a synchronous function in Prefect, you would generally use

.submit()

method on the task within the flow and manage the execution with

asyncio.run

in the

__main__

block. However, the snippets provided from the documentation do not directly show how to submit an async flow from a synchronous flow. In many async frameworks, you would generally avoid calling an async function directly from a sync function without proper handling. For Prefect, since

.submit()

is a method for tasks, it's not designed for directly invoking flows. To run an async flow from a sync context, you would wrap the flow call within

asyncio.run()

if you're using Python 3.7 or higher. This provides a way to execute the async flow to completion from a synchronous function by starting a new event loop. Here's a pattern you can emulate to run an async flow from a sync context:

python
import asyncio
from prefect import flow

@flow
async def my_async_flow():
    # Your async flow logic here
    pass

# Synchronous function
def my_sync_function():
    # This is the sync context from which you want to call the async flow
    asyncio.run(my_async_flow())  # This will run the async flow to completion

if __name__ == "__main__":
    my_sync_function()  # This will indirectly run the async flow synchronously

Remember that using

asyncio.run()

in this way is typically not recommended within an already running event loop, as it will attempt to create a new event loop and can lead to unexpected behavior. If you're already in an async context, you should just

await

the flow. For more detailed information and best practices related to asynchronous execution in Prefect, I recommend checking the latest documentation because the best approach may vary depending on the Prefect version and the specifics of your use case.

@Marvin can top level async flows run as deployments?

Yes, top-level async flows can run as deployments in Prefect. Deployments in Prefect are designed to allow you to schedule and manage flow runs, whether they are synchronous or asynchronous. Here’s how you can deploy an async flow: 1. Define the Async Flow:

python
from prefect import flow

@flow
async def my_async_flow():
    # Your async flow logic here
    print("Running an async flow")

2. Create the Deployment: You can deploy the async flow using the

to_deployment

method, which registers the flow as a deployment in Prefect Cloud or your Prefect Server:

python
if __name__ == "__main__":
    deployment = my_async_flow.to_deployment(name="my-async-flow-deployment")
    deployment.schedule(interval=60)  # Schedule to run every 60 seconds

3. Deploy Using Prefect CLI: Alternatively, you can use the Prefect CLI to create and manage deployments. Ensure your flow and deployment script (e.g.,

my_async_flow.py

) are correctly set up and then run:

shell
prefect deployment build my_async_flow.py:my_async_flow -n "my-async-flow-deployment"
prefect deployment apply my_async_flow-deployment.yaml

4. Run the Deployment: You can manually trigger a deployment or let it run based on the schedule you set. In Prefect Cloud or your Prefect Server, navigate to the deployment listed under your flows, and run it. ### Example with Deployment and Schedule

python
from prefect import flow

@flow
async def my_async_flow():
    # Your async flow logic here
    print("Running an async flow")

if __name__ == "__main__":
    from prefect.deployments import Deployment
    deployment = Deployment.build_from_flow(flow=my_async_flow, name="my-async-flow-deployment")
    deployment.apply()

### Running Asynchronously To handle async flows correctly within Prefect’s environment, the workers must support async execution. Prefect manages the underlying event loop for async flows, so you do not need to create or manage it manually. ### Summary Using

prefect.deployment.build_from_flow

or the CLI to deploy and manage async flows aligns with Prefect’s idiomatic practices for flow orchestration. Ensure that you follow the right structure and manage configurations through the Prefect UI or

prefect.yaml

for smooth operations and scheduling. For the most accurate and up-to-date documentation, refer to the official Prefect documentation on deployments.