Which syntax I can use to get current flow run's parameters value

Any idea what might be causing errors with artifact generation? I’m getting these errors in my task runs:

Error during execution of task: ClientError([{'path': ['create_task_run_artifact'], 'message': 'Task run <task_run_id> not found', 'extensions': {'code': 'INTERNAL_SERVER_ERROR'}}])

Hi - we’re experiencing a read_timeout issue with the

invokeLambda

task. I think we need to pass it some extra config through boto_kwargs. Has anyone came across needing to do this before?

Hello, I am running an Azure DevOps pipeline with service connection set up to access my AWS account. As a pipeline task, I run a sagemaker processing job that includes a Prefect flow.

- task: AWSShellScript@1
              inputs:
                # awsCredentials: 'xxxxx'
                regionName: 'xxx'
                scriptType: 'inline'
                inlineScript: python3 cli.py -remote S3 -p "['a','2020-09-01', '2020-09-02']"

I get this error:

[2022-02-03 21:29:07+0000] ERROR - prefect.S3Result | Unexpected error while reading from S3: TypeError('expected string or bytes-like object')
Traceback (most recent call last):
  File "/miniconda3/lib/python3.7/site-packages/prefect/engine/results/s3_result.py", line 142, in exists
    self.client.get_object(Bucket=self.bucket, Key=location.format(**kwargs))
  File "/miniconda3/lib/python3.7/site-packages/botocore/client.py", line 391, in _api_call
    return self._make_api_call(operation_name, kwargs)
  File "/miniconda3/lib/python3.7/site-packages/botocore/client.py", line 692, in _make_api_call
    api_params, operation_model, context=request_context)
  File "/miniconda3/lib/python3.7/site-packages/botocore/client.py", line 738, in _convert_to_request_dict
    api_params, operation_model, context)
  File "/miniconda3/lib/python3.7/site-packages/botocore/client.py", line 770, in _emit_api_params
    params=api_params, model=operation_model, context=context)
  File "/miniconda3/lib/python3.7/site-packages/botocore/hooks.py", line 357, in emit
    return self._emitter.emit(aliased_event_name, **kwargs)
  File "/miniconda3/lib/python3.7/site-packages/botocore/hooks.py", line 228, in emit
    return self._emit(event_name, kwargs)
  File "/miniconda3/lib/python3.7/site-packages/botocore/hooks.py", line 211, in _emit
    response = handler(**kwargs)
  File "/miniconda3/lib/python3.7/site-packages/botocore/handlers.py", line 238, in validate_bucket_name
    if not VALID_BUCKET.search(bucket) and not VALID_S3_ARN.search(bucket):
TypeError: expected string or bytes-like object

the same task runs good when I just run it on an Azure Devops agent:

- task: AWSShellScript@1
              inputs:
                # awsCredentials: 'xxxxx'
                regionName: 'xxx'
                scriptType: 'inline'
                inlineScript: python3 cli.py -local S3 -p "['a','2020-09-01', '2020-09-02']"

has anyone had this issue before?

I want to deploy prefect UI and Backend on azure app service using docker and docker compose. is it possible? If yes, can someone guide me how I'll be able to deploy ? I'm totally new on prefect. Thanks,

@Marvin I am trying this to dynamically create a flow def create_flow(deployment_name: str, command: str): """Dynamically create a flow with the given deployment name""" @flow(name=deployment_name) def automation_executor(): task_result = execute_shell_command(command) return task_result return automation_executor but I encounter error saying Flow function with name '*****"' not found in file*8

@Marvin how do you set an environment variable in a worker pool from the CLI? currently I’m running

prefect worker start --pool test

but then have to configure the environment variable in the UI to set the

PREFECT_API_URL

, I’d like to be able to do it in one line on the CLI.

Was hoping for a little help with a task caching issue...

def cache_results_within_flow_run(
    context: TaskRunContext, parameters: dict[str, Any]
) -> str:
    """Caches a task result within the context of the flow it is run in."""
    return f"{context.task_run.flow_run_id}:{context.task_run.task_key}"


@task(
    name="example",
    tags=["pipelines"],
    version=get_version(),
    retries=2,
    retry_delay_seconds=exponential_backoff(backoff_factor=60),
    retry_jitter_factor=0.5,
    on_failure=[alert_slack_on_task_failure],
    cache_key_fn=cache_results_within_flow_run,
)
def trademark_etl() -> None:
    """Task for running the earnings calls etl Prefect deployment."""
    deployment_name = "example-flow/example-deployment"

    run_prefect_deployment_check_successful(deployment_name=deployment_name)

We have been overhauling our orchestration and aren't seeing the expected behavior for caching. Most likely we are doing something incorrectly but not sure what. Our goal is to cache task results in the context of the flow they were run in, so that if the flow fails due to any of its tasks failing, we can retry the flow, and only the tasks that have not run successfully (in the flow being retried) will be run. I implemented a caching function that attempts to do this, however, this morning when one of our tasks failed and I went to retry the flow, each task started running as normal, without regard to having already completed in the same flow. Could it be that this is happening because we are not returning anything from our tasks?

@Marvin how to prevent flow from running two instances?

Hi! Quick question about "default" parameters. I have a flow that takes a date as a parameter. It runs on a schedule and should default to whatever is

date.today()

when the flow runs. Currently I solve this by defaulting the date-parameter to

None

and replacing it with

date.today()

inside the flow if this is the case. This works fine, but I would really like a way to populate the parameters tab in the UI with the actual parameters used in the flow. Is there a better way to do dynamic parameters like this?