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<@ULVA73B9P> is there a way to trigger a script on...
# ask-marvin
d
@Marvin is there a way to trigger a script once a deployment is finished when using docker similar to how prefect.deployments.steps.run_shell_script works when launching a docker image
m
To trigger a script once a deployment is finished when using Docker, you can utilize Prefect's notification and automation system. Here are the summarized steps: 1. Notifications and Automations: You can create a notification block and an automation in Prefect that triggers on a flow completion event. This does not require creating a deployment, and it will allow you to define actions that execute once the flow completes. 2. Utility Steps: Prefect deployments may include utility steps like 
run_shell_script
. This allows you to execute shell commands as part of the deployment lifecycle. However, the specific feature in Prefect 2.x that allows triggering a script, exactly once a Docker deployment is finished, might require integrating Prefect's automations with the Docker environment. Please note that while the 
run_shell_script
can be part of the Prefect 1.x deployment steps, for Prefect 2.x (Prefect Orion), the specific triggering mechanism may differ. For exact guidance regarding your use case with Docker and the latest version of Prefect, it would likely require reviewing the latest Prefect documentation or inquiring directly with the Prefect community or support. For further details, you can visit the following pages: - Automations Guide - Prefect Deployments Guide And if needed, you can search GitHub issues in the Prefect repository for similar questions or raise a new issue for assistance.
d
@Marvin if I trigger an automation based off a trigger when a flow completes, how can I get the flow ID or a reference to the original flow that can be read by the new flow triggered from the automation
m
To pass the flow ID or a reference from one flow to another when triggering a flow from an automation in Prefect: 1. Automations & Triggers: When setting up an automation based on a trigger like flow completion, the context of the event that triggered the automation likely includes details about the flow run, such as the flow ID. 2. Flow of Subflows: In the Prefect ecosystem, you can use subflows, where any flow can be a component of another flow. While this might not be the exact answer to passing the flow ID between independent flows, it can be used conceptually for subflows that are triggered as part of a parent flow. To retrieve and pass the flow ID from the completed flow to the newly triggered flow, you can consider the following option: - Passing Data Through Context: Prefect's orchestration engine typically allows you to pass metadata like flow IDs through the context. When a flow is triggered by an automation, the context of the automation can include data about the triggering event, which can be read by the newly triggered flow. For specific guidance on how to access and pass the flow ID through automations in Prefect 2.x, you should refer to the latest version of the documentation or seek assistance from the Prefect community. The details provided here are based on available documentation, and the exact process might vary based on the Prefect version and the use case. Here are helpful resources: - Automations Guide - Workflow Design Patterns Blog For a deeper dive into the technical specifics, you might want to look into detailed Prefect API documentation or explore code examples. If necessary, raising a query in the Prefect community forums or GitHub repository for Prefect could yield detailed advice from the developers or other users familiar with advanced patterns.
d
@Marvin how to read the Context? Do I need to explicitly pass it in or can any flow access the context of what triggered it?
m
To read the Prefect Context: 1. Accessing Prefect Context: The Prefect context is a thread-safe way of accessing variables related to the flow run and task run. To access the context and its contents, you can do so directly in your flow or task code. The context is richer in Prefect 2.x and provides more information about your runs. 2. Automatic Access to Context: It's not explicitly stated whether a flow can automatically access the context that triggered it. However, Prefect flows have the capability to receive and work with context information relevant to their runs. Usually, context variables are available within the execution environment of the flow. To retrieve context values in your flow's tasks, you typically access the context object directly in your task code. For example, you can access flow or task run details in a task by importing 
prefect.context
or using the decorator or functional API provided by Prefect to access the appropriate context information within your flow. Here's a simplified example of how to access Prefect context in your code:
Copy code
python
from prefect import task, Flow, context

@task
def my_task():
    # Access context variables, such as the flow run ID
    flow_run_id = context.get("flow_run_id")
    print(flow_run_id)

with Flow("my_flow") as flow:
    my_task()

flow.run()
For Prefect 1.x, context is used similarly but has a slightly different API compared to Prefect 2.x. In terms of the capability to automatically receive context that triggered it, it would be contingent on how the automation or event that triggers the flow is set up and whether it passes the context information by default. For specific details on reading and utilizing context in Prefect 2.x and answering whether a flow has access to the context that triggered it, you should consult the latest Prefect documentation or reach out directly to the Prefect community for the most accurate and up-to-date information.
d
@Marvin can I get access to worker context in prefect,yaml under pull: - prefect.deployments.steps.set_working_directory: directory: /app - prefect.deployments.steps.run_shell_script:
m
Access to worker context within the 
prefect.yaml
file under 
pull:
or other deployment steps can be somewhat indirect. Here are some key takeaways from the provided documentation: 1. Infrastructure as Code Templating: Workers that use infrastructure as code (IaC) may employ template variables, which can be populated at runtime. This suggests that you might have some level of access to contextual or environment variables that could be templated into your IaC configuration. 2. Loading 
prefect.yaml
: The 
prefect.yaml
file is processed in stages. During the 
pull:
section, the file is templated with any outputs from steps, but block references are resolved at runtime for security reasons, and not during this static loading and templating phase. This implies that if you need to use secure context or environment variables, you can expect them to be available at runtime, not during the static file parsing. To utilize context or environment variables within your deployment steps, ensure that your references to such variables are set up in a way that Prefect resolves them at runtime. This might mean leveraging Prefect's templating system or using runtime hooks that are able to pull in context variables when the tasks or flows are actually running. Here's a simplified example, hypothetically speaking, of how you might template an environment variable into your `prefect.yaml`:
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yaml
pull:
  - prefect.deployments.steps.run_shell_script:
      command: |
        echo "Working directory set to ${WORKER_DIRECTORY}"
        # This environment variable might be resolved at runtime if properly set up.
For accurate details and examples, refer to Prefect's official documentation on deploying flows and developing new worker types, or reach out to the Prefect community for guidance on how to correctly set up and access worker context in your deployment configurations. They will have the most up-to-date and precise instructions: - Developing a New Worker Type: Link to Guide - Prefect Deployments: Link to Guide
d
@Marvin would context.get_run_context be initialised during prefect.deployments.steps.run_shell_script to be able to pass it into a script?
m
It appears there was a transient issue with retrieving information on 
context.get_run_context
being initialized during 
prefect.deployments.steps.run_shell_script
. In theory, 
get_run_context
is used to get context related to the current run and is typically available during the execution of tasks and within a flow run. However, when you attempt to run a shell script as part of a deployment step, it may not have the full Prefect run context initialized if it's outside the execution of an actual flow. Let's try the search again to see if any issues or discussions in Prefect's GitHub repository might clarify the availability of run context during deployment steps. It seems there are no GitHub issues currently available that discuss the initialization of 
context.get_run_context
during the execution of 
prefect.deployments.steps.run_shell_script
. In the absence of directly relevant issues, let me provide some guidance based on typical Prefect behavior: The 
run_shell_script
step in Prefect is usually used for running shell commands as part of a deployment step. Normally, Prefect context is available during the execution of tasks within a flow. However, it's less clear whether the Prefect run context, which can be accessed using 
context.get_run_context
in Prefect 2.x (Orion), would be available during the early stages of deployment steps like 
run_shell_script
. If the 
run_shell_script
step is executed during the deployment process (for instance, within a CI/CD pipeline or Prefect Cloud/Server prior to a flow run), it may not have the full Prefect runtime context initialized, because it's outside of the actual flow run environment. If you require access to specific runtime context data during your shell script execution, you might need to consider alternative approaches, such as: 1. Passing environment variables explicitly to the shell script. 2. Using Prefect's templating features to inject necessary information into the script at runtime. When dealing with deployment steps, it's crucial to note that some context data may only be available when the flow itself is running, not necessarily during deployment actions. To gain clarity on this matter, consider reaching out on the Prefect Discourse forum or reviewing the latest Prefect documentation for details on context availability in deployment steps. Additionally, if this is a common use case that isn't currently supported, it might be worth suggesting as a feature request on the Prefect GitHub repository.
d
@Marvin what variables are certainly available at that point and how would you call them?
m
During the execution of 
prefect.deployments.steps.run_shell_script
, there are certain types of variables you can reference: 1. Secret Blocks: You can securely reference secret blocks for sensitive information such as tokens or passwords using the syntax 
{{ prefect.blocks.secret.secret_name }}
. 2. Prefect Variables: Non-sensitive, reusable pieces of information can be referenced using variables defined in Prefect with the syntax 
{{ prefect.variables.variable_name }}
. 3. Environment Variables: You can reference environment variables that are set at runtime using the syntax 
{{ $MY_ENV_VAR }}
. Here is an example of how to reference environment variables and Prefect variables in your 
prefect.yaml
file:
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yaml
pull:
- prefect.deployments.steps.run_shell_script:
    command: |
      echo "Using Prefect variable: {{ prefect.variables.variable_name }}"
      echo "Using environment variable: $MY_ENV_VAR"
Please ensure that environment variables (
$MY_ENV_VAR
) are set in the environment in which the deployment step is running, as Prefect will not resolve these until runtime. Remember, as noted in the documentation, block references are not hydrated at the time this section of the file is templated. They are only resolved at runtime for security purposes. When referencing variables and secrets within your shell script, consider the security implications and avoid exposing sensitive information. For the latest and most accurate information on what variables are available and how to call them during deployment steps, you should consult the Prefect documentation or ask for guidance from the Prefect community.
d
@Marvin how to manage a list of tasks in a flow so that if one task fails, the whole flow fails?
m
To manage a list of tasks in a Prefect flow so that if one task fails, the whole flow fails, you can leverage task dependencies and state handlers. By default, if a task that has downstream dependencies fails, the downstream tasks will not run. This can naturally lead to the failure of the entire flow if all tasks are connected. Here's how you can structure your flow: 1. Set Dependencies: Ensure that your tasks have proper dependencies set up, so if a task fails, the downstream tasks that depend on it will not execute. In Prefect, this is done by setting tasks to depend on the successful completion of their predecessor tasks. 2. Task Retries: Optionally, you could set retries on tasks to allow for transient errors. You can specify that a task retry multiple times before it is considered failed. 3. Failure Notifications: If you want to be notified or execute some specific logic when a task fails, you can attach a state handler to your tasks that will trigger upon failure. Here is an example of how you might configure a flow with dependencies:
Copy code
python
from prefect import task, Flow
from prefect.engine.state import Failed
from prefect.utilities.tasks import task_state_handler

@task_state_handler
def my_failure_handler(task, old_state, new_state):
    if isinstance(new_state, Failed):
        print(f"Task {task} failed!")
        # Additional logic can be added here, for example, raising an Exception
        raise RuntimeError("Stopping flow because a task failed")

@task
def task1():
    return "result of task 1"

@task
def task2(upstream_data):
    if upstream_data == "something wrong":
        raise ValueError("Task 2 failed")
    return "result of task 2"

with Flow("my_flow") as flow:
    result1 = task1()
    # task2 will only run if task1 succeeds
    result2 = task2(result1)

# Attach the failure handler to tasks
task1.state_handlers.append(my_failure_handler)
task2.state_handlers.append(my_failure_handler)

flow.run()
In the example above, 
task2
depends on 
task1
. If 
task1
fails, 
task2
will not run. The failure handler will detect any task failures and react appropriately. For more advanced flow control, you might want to use Prefect's conditional logic features, which allow for more complex behaviors based on the success or failure of tasks. Always remember to consult the latest Prefect documentation for the most accurate and current best practices.
d
@Marvin what is the equivalent of context.get_run_context().flow_run.name for a task to get the parent flow name
m
To get the parent flow name within a task in Prefect, you can access the flow run context explicitly using the 
FlowRunContext
available within the 
prefect.context
module. For example, you would do the following:
Copy code
python
from prefect import task, flow
from prefect.context import FlowRunContext

@task
def my_task():
    flow_run_ctx = FlowRunContext.get()
    if flow_run_ctx:
        flow_name = flow_run_ctx.flow_name
        print(f"The parent flow name is {flow_name}")
    else:
        print("Flow run context is not available.")

@flow
def my_flow():
    my_task()

my_flow()
In this code, 
FlowRunContext.get()
retrieves the flow run context from within the task run, and 
flow_run_ctx.flow_name
will contain the parent flow name. This is analogous to using 
context.get_run_context().flow_run.name
but provides a way to specifically access the flow context from a task. Note that the documentation mentions that flow run context is not sent to distributed task workers due to the overhead of serializing and deserializing the context. This means that if your task execution is being distributed across different workers, the above method may not work as expected. Local task runs within the same process as the flow run should be able to access the flow run context without issues. When working with distributed execution or in more complex deployment scenarios, you should ensure that your usage of contexts aligns with the execution environment and the Prefect backend you are using (Cloud, Server, local). Always refer to the latest documentation for the most accurate information.
d
@Marvin how to stop subflows if the parent flow crashes?
m
To stop subflows if the parent flow crashes or is terminated, you'll need to employ a flow control mechanism that is aware of the parent flow's state and can signal the subflows to stop. Here's a general approach that could be applied: 1. Subflows Relationship: Prefect's orchestration engine treats subflows as first-class citizens and, conceptually, they have the same relationship to the parent flow as tasks do. This implies that subflows run in the same process as their parent flow. 2. Built-in Termination Handling: When you cancel a flow run through the CLI, UI, REST API, or Python client, the flow run enters a "Cancelling" state. This mechanism should also apply to subflows, as the worker that is executing the parent flow should signal all tasks and subflows associated with that flow run to terminate. Given that properly configured subflows run in the same process as their parent flow, if you terminate the parent flow process, in theory, the subflows should also be terminated because they share the same execution environment. However, if your subflows are somehow running independently or have been launched in a distributed setup where they may not be directly tied to the parent flow's process, you might need to implement additional monitoring or a custom state handler that communicates the need to halt execution to each subflow:
Copy code
python
from prefect import flow, state_handlers

@state_handlers.on_failure
def parent_flow_failure_handler(flow_run_id, state):
    # Custom logic to handle the failure of the parent flow
    # This may include signalling subflows to stop

@flow(state_handlers=[parent_flow_failure_handler])
def parent_flow():
    # Your parent flow implementation
    pass

# Subflow
@flow
def sub_flow():
    # Your subflow implementation
    pass
In this case, the 
on_failure
state handler is a placeholder for where you would add the logic to signal the subflows to terminate. For an accurate and detailed solution that fits the specifics of your Prefect Cloud or Server setup and version, it would be best to consult the latest Prefect documentation on flow run cancellation and subflows, or inquire within Prefect's community channels for advice on best practices given your particular configuration and deployment.
d
@Marvin is there a way to set args when the docker worker is being used such as --cap-add
m
Setting Docker arguments like 
--cap-add
when using a Docker worker with Prefect is not explicitly documented in the excerpts provided. However, based on how Docker and Prefect work, you would typically need to specify additional Docker run arguments when configuring the execution environment for your Docker worker or during the setup of your Docker deployment. Since the specifics of passing Docker arguments like 
--cap-add
are not directly addressed in the provided excerpts, and it can be highly dependent on the Prefect version and the orchestration environment, here are some general tips on how you might approach it: 1. Custom Dockerfile: Create a custom Dockerfile where you define all of your Docker run configurations, including any capability additions with 
--cap-add
. When Prefect starts the Docker container, it would use your custom image with the desired configurations. 2. Prefect Configuration: Check Prefect's configuration options related to Docker to see if there are specific settings for passing Docker run arguments or modifying capabilities. 3. Prefect CLI/SDK: If there are options in the Prefect CLI or Python SDK to pass additional Docker arguments when setting up your flow's execution environment or when creating a work queue, use them to specify 
--cap-add
. 4. Prefect's Docker Storage: Investigate if Prefect's Docker Storage for flow registration provides a way to specify custom Docker run options. If you are working with Prefect Cloud, Prefect Server, or another orchestration backend, the process could be different, and you might need to configure your worker differently. For precise instructions or if there's a specific capability that you need to add to your Docker worker, it would be best practice to: - Consult the latest Prefect documentation for Docker workers and see if there's a method to set Docker run arguments. - Reach out to the Prefect community via Slack, Discourse, or GitHub for advice specific to your Prefect version and use case. - Consider raising a feature request or contribution to the Prefect codebase if the functionality is not currently supported.
d
@Marvin Is there a way to trigger a task within a flow that is running with DaskTaskRunner that runs at the end of the function
m
To trigger a task at the end of a flow when using 
DaskTaskRunner
with Prefect, you'll typically make use of the flow and task dependency mechanisms provided by Prefect. You can define a "final" task that runs after all other tasks have completed. Here's an example of how to set this up:
Copy code
python
from prefect import task, flow
from prefect_dask.task_runners import DaskTaskRunner

@task
def some_task():
    # Your task logic here
    pass

@task
def final_task():
    # This is the task you want to run last
    print("Running final task")

@flow(task_runner=DaskTaskRunner())
def my_flow():
    # Define all your tasks
    task_1_result = some_task()
    # ...

    # Trigger the final task after all the others have completed
    final_task(upstream_tasks=[task_1_result])
    # ...

my_flow()
In this example, 
final_task
is set to run at the end of the flow by passing a list of upstream tasks that includes all other tasks in the flow to the 
upstream_tasks
parameter. Once all upstream tasks have completed, 
final_task
will be scheduled for execution. When using 
DaskTaskRunner
, tasks within the flow will be submitted for execution in parallel where possible, according to their dependencies. However, since 
final_task
depends on the other tasks, it will only run once they have all successfully completed. For more detailed examples of using 
DaskTaskRunner
and setting task dependencies, please refer to the Dask and Ray Task Runners Guide and the Task Runners Concept documentation on Prefect's official website.
d
@Marvin I have a decorator of task_runner=DaskTaskRunner(adapt_kwargs={"maximum": 10}) but the code which contains tasks executed with ,submit seem to be running with more than 10 in parallel
m
Based on a search of GitHub issues related to 
DaskTaskRunner
and the 
adapt_kwargs
parameter, there is an issue discussing the potential for a version mismatch between client and server when using "aws.FargateCluster" with Prefect. This issue demonstrates the use of 
adapt_kwargs
with the setting 
"maximum": 100
, implying a control over the maximum number of task runs that can be parallelized. However, there is no issue that directly mentions 
adapt_kwargs
not respecting the 
maximum
parameter leading to more than the specified number of tasks running in parallel when using the 
DaskTaskRunner
. The ability to limit the number of parallel tasks using 
adapt_kwargs
is typically part of Dask's native scaling functionality and should work as expected when properly configured. If you are experiencing tasks executing with more than the specified limit of 10 in parallel, despite setting the 
maximum
parameter correctly, you may be encountering an unexpected behavior or a bug. Here's what you can do: 1. Verify your Prefect and Dask versions are compatible and up-to-date. 2. Ensure there are no configuration overrides or environmental factors causing the 
maximum
setting to be ignored. 3. Review your Dask cluster settings and ensure it's properly interpreting the 
adapt_kwargs
. If the issue persists, consider opening a new issue on Prefect's GitHub repository with detailed information about your setup, your use of 
DaskTaskRunner
, and the behavior you are observing. Provide specifics such as version numbers, reproducible examples, and logs if possible, which will aid the Prefect maintainers in diagnosing the problem.
d
@Marvin this seems to only work for a flow within a flow? context.get_run_context().flow_run.name how to get current flow name
m
To get the current flow name within a task or flow in Prefect, you would typically access the runtime context for the flow run. However, the exact mechanism to do so can vary slightly depending on whether you are inside a task or directly in the flow function. In Prefect 2.0 and newer, the 
get_run_context()
function has been replaced by the explicit context objects 
FlowRunContext
and 
TaskRunContext
. To access the current flow's name, you would access the 
flow_run
object from the context. Here is how you can use 
FlowRunContext
to get the current flow name:
Copy code
python
from prefect import flow, task
from prefect.context import FlowRunContext

@task
def my_task():
    flow_run_ctx = FlowRunContext.get()
    if flow_run_ctx:
        print(f"The current flow name is: {flow_run_ctx.flow_name}")
    else:
        print("Flow run context is not available.")

@flow
def my_flow():
    flow_run_ctx = FlowRunContext.get()
    print(f"The current flow name is: {flow_run_ctx.flow_name}")
    my_task()

my_flow()
If you're within the body of a flow function, you can directly access 
FlowRunContext.get().flow_name
to retrieve the name of the flow being executed. It's important to note that Prefect's context system is designed to be accessed within flow runs and task runs. If you try to access the context outside of these environments (e.g., in a script that is not being run as part of a Prefect flow), you may not be able to retrieve the context as expected because it might not be initialized. For more information on using contexts and accessing specific details about flow runs, please refer to the official Prefect documentation related to context and review code examples, such as test_flows.py found on GitHub, to understand how to work with runtime contexts in different scenarios.
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