@Marvin how can I test development of changes to a sub flow function, running locally? I'm having issues with prefect changing the concurrency model - when I run my code locally via

python file.py

I need to wrap my async flow code in

asyncio.run(flow())

, but prefect automatically calls the flow to completion without needing asyncio.

To test the development of changes to a sub flow function running locally, you can take the following steps and considerations: 1. Understanding Subflows: A subflow is created when a flow function is called inside the execution of another flow. The parent flow will block execution until the subflow completes. Subflows behave like normal flow runs with a full representation in the backend. Flow Composition 2. Concurrent Asynchronous Subflows: If you have asynchronous subflows, you can run them in parallel using

asyncio.gather

. This allows the parallel execution of subflows within the parent flow without requiring an explicit call to

asyncio.run()

. async_subflows.py example on GitHub Here is a code example demonstrating how to run asynchronous subflows concurrently:

python
import asyncio
from prefect import flow

@flow
async def subflow(x):
    print(f"Running subflow with {x=}")
    return x

@flow
async def parent_flow():
    return await asyncio.gather(*[subflow(x) for x in range(3)])

if __name__ == "__main__":
    assert asyncio.run(parent_flow()) == [0, 1, 2]

Tips for Testing Changes Locally: - If you have to run asynchronous flow code, use

asyncio.run(flow())

as shown in the example for local execution because Prefect might take care of asynchronous execution when running in its environment. - For testing, you can write unit tests for individual tasks or subflows and use same Python testing frameworks like pytest. - Make sure to adjust your testing code depending on whether your subflows are synchronous or asynchronous. - Be aware of Prefect's concurrency model when designing and testing your flows. Prefect handles concurrency differently than how pure Python asyncio would be used in a standalone script. If you face specific issues with the concurrency model or need more examples, you might find it helpful to search for relevant GitHub issues or Prefect community forums where similar issues could have been discussed.

@Marvin but calling my async subflow returns

None

and not a coroutine. I don't think I'll be able to pass that into

asyncio.gather()

?

I'm sorry for any confusion caused. It looks like there might be a misunderstanding about the behavior of async subflows in Prefect. According to the examples provided, you should be able to await the coroutine of an async subflow, and

asyncio.gather()

should be used to run multiple async subflows concurrently. Here is the example previously mentioned, demonstrating concurrent async subflows with `asyncio.gather`:

python
import asyncio
from prefect import flow

@flow
async def subflow(x):
    print(f"Running subflow with {x=}")
    return x

@flow
async def parent_flow():
    return await asyncio.gather(*[subflow(x) for x in range(3)])

if __name__ == "__main__":
    assert asyncio.run(parent_flow()) == [0, 1, 2]

async_subflows.py example on GitHub If you're experiencing the issue where calling an

async

subflow directly returns

None

, ensure that: - You are awaiting the

async

subflow within an

async

parent flow. - You are not mistakenly treating

async

subflows as regular functions, which would cause Python to not wait for the coroutine to complete and potentially return

None

. If the issue persists and this does not align with your experience or code implementation, would you be able to provide more details about your setup or the specific circumstances under which the subflow returns

None

? This would help in troubleshooting the exact problem you're facing.

@Marvin what is the best way to test running a flow locally, without calling the prefect cloud API?

To test running a flow locally without interacting with Prefect Cloud, you can simply invoke your flow functions directly in your script. The execution will happen in your local environment, and you won't need to deal with deployment, agents, or work pools. Here's a general approach you can take: 1. Define your flow and tasks using Prefect's API. 2. Use a Python script to run the flow locally by calling the flow function with the required parameters. 3. Use a conditional block to determine whether to run the flow locally or deploy it if needed. Here's a simple example to illustrate this:

python
from prefect import flow

@flow
def my_flow():
    # Define tasks and flow logic here
    print("Running my flow locally!")

if __name__ == "__main__":
    # Running the flow locally without Prefect Cloud interaction
    my_flow()

By directly executing the

my_flow

function, you are running the flow on your local environment without connecting to Prefect Cloud. Of course, if you want to observe the tasks and flow execution logs within Prefect Cloud UI, you would need to be connected. For a more structured approach, you can set a deployment mode variable for easier toggling:

python
from prefect import flow

DeployModes = "LOCAL_TEST"  # Define other deployment modes if needed

deploy_mode = DeployModes

@flow
def my_flow():
    # Define your flow logic here

if __name__ == "__main__":
    if deploy_mode == DeployModes:
        # Run flow locally without Prefect Cloud interaction
        my_flow()
    # Other deployment modes and logic can be added here as needed

Please note that even when running locally, Prefect may still send some metadata to Prefect Cloud depending on your configuration. If you want to disable this entirely, ensure you are working with a local backend, or you don't set up any Prefect Cloud keys for your local environment. You can read more about local execution and other deployment topics in Prefect documentation. A portion of this information comes from a Prefect blog post. Please check Prefect's official documentation for the most up-to-date practices, as blog posts may contain information relevant to earlier versions or specific use cases.

@Marvin does prefect have a plug and play Slack app?

Prefect does provide the capability for Slack notifications. While there might not be a dedicated "plug-and-play" Slack app listed in the documentation excerpts I found, Prefect supports Slack notifications as part of their observability and reliability tools. This usually involves setting up notifications through the use of automations or configuring notifications for flow runs directly to Slack. Since the documentation excerpts do not provide the detailed steps on setting up Slack notifications, I would suggest looking into Prefect Cloud's UI, under automations or integrations, which would typically provide you with the options to configure notifications for various events, including sending messages to a Slack channel. In Prefect Cloud, the feature known as "Automations" allows you to set up certain actions based on events, and Slack can be one of the targets for such automated notifications. For exact, step-by-step guidance on setting up Slack notifications, please refer to the Prefect documentation or check within Prefect Cloud's user interface under the relevant sections for automations or integrations. Here is a relevant excerpt mentioning Slack notifications: "Modern Health HIPAA blog post". However, I would recommend searching the Prefect documentation directly for the most recent and detailed instructions on setting up Slack notifications with Prefect.

@Marvin what is the best way to set an environment variable to be used in a deployed flow?

The best way to set an environment variable to be used in a deployed flow with Prefect is typically through the deployment configuration, which allows you to specify environment variables that will be available to your flow at runtime. Here are some common methods to set environment variables for your flow: 1. Using the Deployment Configuration: When you write your deployment configuration to a

prefect.yaml

file or use the flow method

.deploy()

, you can specify job_variables or environment variables in your Python file. These variables will then be baked into the build when you deploy your flow, making them available during the flow's execution. Here's an example of specifying environment variables in the

.deploy()

method:

python
   flow.deploy(
       "deployment_name",
       work_pool_name=work_pool_name,
       job_variables={"ENV_VAR_NAME": "value"},
       # ... other configuration ...
   )

Blog post reference - Note that the blog can contain information relevant to a specific context and may not be up-to-date with the latest Prefect features. 2. Prefect UI / CLI: You can also use the Prefect UI or CLI to configure environment variables for your flow deployments. This option allows you to set environment variables without altering your deployment script directly. 3. Environment Variable Inheritance: If you're using a containerized deployment such as Docker, the environment variables can be set in the Docker image that your flow runs within. The flow will inherit the environment variables from its runtime environment. For the most accurate and recent information on setting environment variables in Prefect, please refer to the official Prefect documentation. It will provide you with detailed guidance on configuring your deployments and managing environment variables for your specific Prefect setup and version.

@Marvin is

"ENVIRONMENT"

a valid key in the job variables dictionary?

The excerpts from the Prefect documentation do not explicitly mention an

ENVIRONMENT

key within job variables or what keys are valid. However, it is generally possible to set environment variables within the

job_variables

parameter by providing a dictionary of key-value pairs. These typically override default values in the work pool's base job template. It's important to check the specific documentation or API reference for your version of Prefect to see the accepted structure of the

job_variables

parameter and what keys you can use. In general, you can use environment variable names as the keys in the

job_variables

dictionary. Here is an example of how you might set an environment variable in the

job_variables

dictionary:

python
flow.deploy(
    "deployment_name",
    work_pool_name=work_pool_name,
    job_variables={"SOME_ENV_VAR": "value"},
    # ... other configuration ...
)

In the absence of a direct documentation link stating that

ENVIRONMENT

is a valid key, I would treat it as a normal environment variable name. If you want to use it to set or override some configuration specific to Prefect, you should consult the official Prefect documentation or reach out to Prefect support to confirm it aligns with their API.

@Marvin should my job variables show up in the infrastructure overrides section of the prefect cloud admin?

Job variables defined during deployment should indeed show up in the infrastructure overrides section of the Prefect Cloud UI. This section allows you to view and customize the configurations for your deployments, which include any job variables that you've set for overriding default values in the work pool's base job template. The Prefect UI offers a level of customization for infrastructure configurations, and this is applicable for runtimes such as Kubernetes. Users can override various fields and define their custom variables from the UI, which will then be reflected in the job's execution environment. Here's a brief on what you can expect: - Work Pools: Infrastructure configuration can be customized from the Prefect UI, and work pools allow for such customizations including job variables. - Variables: Variables in Prefect are name-value pairs that store configuration information and are scoped to a Prefect server instance or a Prefect Cloud workspace. For the latest information and specific details on how to view and customize your job variables within Prefect Cloud, you should consult the Prefect Cloud documentation. Remember, variables in Prefect are not intended to store sensitive information, as they are not encrypted. If you need to manage sensitive data like credentials, it is recommended to use Prefect's secret management mechanism or another secure method provided by your cloud provider or runtime environment. Please refer to the related blog post on Prefect's Workers and Projects as it provides some insights into work pools and workers which are relevant to the infrastructure configuration in Prefect Cloud. However, this information does not directly cover job variables, so the documentation remains the best source for detailed and version-specific information.

@Marvin how should I retrieve job variables inside of a flow run?

To retrieve job variables during a flow run in Prefect, there isn't a specific method mentioned directly in the excerpts provided. However, job variables are typically meant to override infrastructure settings for your flow's deployment and are not directly exposed as flow runtime variables. If you have set certain environment variables as part of your job's infrastructure definition during deployment, you can retrieve them within your flow by accessing the environment variables using standard Python functionality. Here is a common pattern:

python
import os

@flow
def my_flow():
    # Retrieve the environment variable value
    my_env_var = os.getenv('MY_ENV_VAR_NAME')
    
    # Use the environment variable in your flow logic
    # ...
    
    return

Here,

os.getenv('MY_ENV_VAR_NAME')

would fetch the value of the environment variable

MY_ENV_VAR_NAME

that you may have set as part of your job variables during deployment. If you want to access Prefect-specific runtime information within your flow, Prefect provides a

context

object which can include various runtime attributes. To access the flow run context, use:

python
from prefect import flow, context

@flow
def my_flow():
    run_context = context.get_run_context()
    # Now you can access run-specific context variables
    # ...
    
    return

Please note, the

prefect.context.get_run_context()

function gives access to the context of a flow or task run, which can include a variety of Prefect-specific settings and should not be confused with the job variables used for infrastructure configuration. For more detailed and version-specific instructions on Prefect context and job variables, please consult the Prefect documentation.

If your

ENVIRONMENT

environment variable that you've set in your job variables isn't showing up via

os.getenv()

, there could be multiple reasons for this issue: 1. Incorrect Deployment Configuration: Ensure that you've correctly set the

ENVIRONMENT

in the

job_variables

when deploying the flow and that there are no typos. 2. Variable Scope: Verify that the scope of the environment variable is correct and it is not limited to a certain context that the flow isn't running in. 3. Infrastructure Support: Make sure the infrastructure on which your flow is running supports the passing through of environment variables. Some managed platforms and orchestration layers might restrict this. 4. Variable Overwriting: There might be a default value or a value set in both the flow runner and worker environment that is overwriting your variable. 5. Timing Issue: Environment variables might not be available at the time the Python process starts. Check if the variables need to be loaded earlier. 6. Agent or Worker Configuration: If you are using agents or workers, ensure that they are configured to pass on the environment variables to the flow run. The excerpts provided do not directly address why an environment variable would not be accessible during a flow run, hence suggesting going through these steps to troubleshoot. If you exhaust these options and the problem continues, you may need to look for more logs, debug the deployment process, or reach out to Prefect support for help. None of the excerpts include details on the

os.getenv()

function or issues surrounding environment variables in job settings, so for more specific assistance and the most detailed instructions, please consult the Prefect documentation or appropriate support channels.

@Marvin do you know if GCP cloud run jobs support this? And how can I be sure that my agent or worker configurations are set up to pass the variables properly?

Wish we had a shell...

For GCP Cloud Run, the platform does support setting environment variables when you define the service. When deploying a container to Cloud Run, you can set environment variables in the Cloud Console or by using the

gcloud

command-line tool. Here is how you set environment variables in Cloud Run: Google Cloud Console: 1. Go to the Cloud Run page in the Google Cloud Console. 2. Click on the service to which you want to add environment variables. 3. Click on the "EDIT & DEPLOY NEW REVISION" button. 4. Expand the "Variables & Secrets" section. 5. Add a new environment variable by filling in the "NAME" and "VALUE" fields. 6. Click on "DEPLOY." gcloud CLI: You can also set environment variables using the

gcloud

command when deploying a new service or updating an existing one:

gcloud run deploy SERVICE_NAME --set-env-vars "ENVIRONMENT=value"

Make sure to replace

SERVICE_NAME

with the actual name of your service and

ENVIRONMENT=value

with the actual environment variable you want to set. Regarding Prefect and ensuring that agents or workers pass environment variables properly, it's essential to configure the agent that submits the flow run to include the desired environment variables. When you start a Prefect agent, you can pass environment variables using the Prefect CLI or in the agent's deployment configuration. For Kubernetes agents, for example, you can add environment variables to the agent deployment YAML. The Prefect Cloud UI also allows you to specify environment variables under the "Environment Variables" section in the work pool configuration. Here's a relevant excerpt from the documentation showing how to set environment variables in work pools, which are used by agents to run flows:

Environment Variables

Add environment variables to set when starting a flow run. So long as you are using a Prefect-maintained image and haven't overwritten the image's entrypoint, you can specify Python packages to install at runtime with {"EXTRA_PIP_PACKAGES": "..."}

Prefect Kubernetes Deployment Guide For more details and to ensure your configurations are set correctly, please refer to the Prefect documentation on agents for specific guidance on your deployment scenario.

@Marvin When using Prefect on GCP, are jobs scheduled at the same time queued? Or must scheduled jobs be staggered?

In Prefect, flow scheduling is designed to be flexible, and it is possible to have multiple flows scheduled to run at the same time. However, the behavior of concurrent job execution can depend on multiple factors, including your execution environment and infrastructure capacity. When multiple flows are scheduled to run at the same time, the following considerations apply: - Concurrent Execution: If your execution environment (e.g., Kubernetes, GCP Compute Engine, Cloud Run) has the capacity to run multiple jobs concurrently, and if you've configured Prefect to allow concurrent runs, then the flows can be executed simultaneously. - Queuing: If system resources are limited or if the execution environment does not support concurrent execution, Prefect can queue the jobs until resources are available. Prefect's orchestration layer can manage queueing and execution based on available resources and agent polling. - Work Pool Configuration: Work pools in Prefect Cloud typically define where and how flow runs are executed. The concurrent execution of flows may also depend on the configuration of the work pools and their capacity to handle flows. - Agent Polling: Prefect agents poll for work and based on the agent's configuration and the number of available agents, they may process multiple scheduled flow runs at the same time or queue them for later execution, depending on resource availability. The documentation excerpts do not provide specific details about the queuing behavior for GCP or whether scheduled jobs must be staggered. However, if you are concerned that running many jobs simultaneously might overload your infrastructure, you can employ strategies such as staggering job schedules or scaling your infrastructure to handle peak loads. For exact behavior and best practices specific to GCP and Prefect, please consult the Prefect documentation and relevant GCP documentation related to the specific service (e.g., Kubernetes Engine, Cloud Run) you are using to run Prefect flows. If precise control over job timing and queuing is critical for your application, consider discussing your requirements with Prefect support or engaging with the Prefect community for advice tailored to your use case.

@Marvin what are the default disk constraints for a prefect work pool running on GCP cloud run? I have a job that seems to be running into resource issues. Is it possible to configurably increase the disk size available for prefect jobs that need to run on large datasets?

The documentation excerpts provided do not specify the default disk constraints for a Prefect work pool running on GCP Cloud Run. Typically, the disk resources available to Cloud Run services depend on the configuration you specify at the time of deployment. Google Cloud Run allows you to configure the allocated CPU, memory, and maximum request timeouts, but as of my knowledge cutoff in April 2023, Cloud Run does not allow you to configure disk size directly since it provides a read-only file system to the container instance. Here are some things you can consider for handling large datasets with Prefect on GCP Cloud Run: 1. External Storage: For jobs requiring significant disk space, it is common practice to use external services like Google Cloud Storage (GCS) to store and retrieve large datasets instead of increasing the disk space of the computing environment. 2. Customizing Compute Resources: While you may not be able to increase the local disk size, you can define other compute resources. For instance, you can set the memory allocation which indirectly affects the available /tmp space on Cloud Run (since ephemeral local disk space is determined by the allocated memory). The maximum available memory for a Cloud Run service is 16 GB, which provides a corresponding amount of /tmp space. 3. Work Pool Configuration: Setting up a work pool through the

prefect work-pool create

command allows you to specify various parameters depending on the type of runner you're using. However, disk space isn't directly mentioned as a configurable attribute in the excerpts. 4. Using Prefect Tasks: Construct your Prefect tasks to work with external storage solutions. Write tasks to stream data to and from GCS without expecting large local disk space. If your job is encountering resource-related issues and you believe local disk constraints are the source of the problem, you should look for strategies to offload data management to cloud storage services or other scalable data solutions. You could also consider using a different GCP service compatible with Prefect that allows for more flexible disk configuration. Information on specific configuration options should be available in the GCP documentation or via the gcloud CLI help for Cloud Run deployments (

gcloud run deploy --help

), and more detailed and up-to-date instructions on interacting with Prefect work pools can be found in the Prefect work pools documentation. If adjustments to the configuration of Cloud Run services are necessary beyond what's available, consider reaching out to Google Cloud Support for additional options.

I don't have IAM permission to monitor this...

@Marvin what if part of the job required is to download a single, large file that is close to, or bigger than the 16GB max?

If part of your Prefect job requires downloading a single, large file that is close to or exceeds the 16GB maximum memory limit offered by Google Cloud Run, you'll need to consider alternatives as Cloud Run may not be the most suitable platform for this use case due to its limitations: 1. Splitting the File: If possible, split the file into smaller chunks that can be processed independently. This approach would allow you to stay within Cloud Run's constraints. 2. Streaming: Instead of downloading the entire file at once, stream the data as you process it. This method works well if the file can be processed in a streaming fashion without the need to have the entire file in memory or on disk at once. 3. External Processing: Utilize services that can handle large amounts of data, such as Compute Engine VMs or Google Kubernetes Engine (GKE) clusters, which allow you to attach additional disk space to the instances. 4. Cloud Functions with External Storage: Consider using Google Cloud Functions with a trigger from Cloud Storage. Cloud Functions allows for temporary disk usage up to 10 GB, which is still below your requirement, but you could use it in conjunction with streaming to process the file in segments. Keep in mind the execution time limit imposed on Cloud Functions. 5. Hybrid Approach: Use Cloud Run to coordinate the workload but offload intense data processing tasks to another system, such as a Compute Engine instance with attached persistent disk that can accommodate the large file size. 6. Other GCP Services: Leverage other GCP services like BigQuery for analytics workloads, Cloud Dataprep for data transformation, or Dataflow for stream and batch data processing, which can handle large datasets more gracefully. 7. Temporary Storage Services: Use temporary storage services like GCS buckets, where you can download the large file, process it as required, and then clean up the storage after the job completes. Be mindful that each of these alternatives has its own cost implications and potential complexities around orchestration. You need to choose an approach that balances the resource needs of your workload with the costs and complexity you're willing to manage. When dealing with particularly large files, it's essential to consider the architecture of your data processing system and whether it's worth moving compute closer to where your data resides to minimize data transfer times and costs.