I have a flow that appears to be running and then it fails with this message, however, I don't think it is failing.

No heartbeat detected from the remote task; marking the run as failed.

Hi folks, I think I am facing a prefect bug when I enable

PREFECT_FLOWS_CHECKPOINTING

and I use a

pd.DataFrame

as a parameter - I am using prefect

v0.13.3

export PREFECT_FLOWS_CHECKPOINTING=true


In [1]: import pandas as pd
In [2]: from prefect import Parameter, task, Flow
In [3]: @task
   ...: def simple(df):
   ...:     return df
   ...: 
In [4]: with Flow("test") as flow:
   ...:     df = Parameter(name="df")
   ...:     simple(df)
   ...: flow.run(df=pd.DataFrame())
[2020-08-20 14:20:38] ERROR - prefect.TaskRunner | Unexpected error: TypeError('Object of type DataFrame is not JSON serializable')
Traceback (most recent call last):
  File "~/.pyenv/versions/3.7.7/envs/infima/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
    new_state = method(self, state, *args, **kwargs)
  File "~/.pyenv/versions/3.7.7/envs/infima/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 838, in get_task_run_state
    result = self.result.write(value, **formatting_kwargs,)
  File "~/.pyenv/versions/3.7.7/envs/infima/lib/python3.7/site-packages/prefect/engine/results/prefect_result.py", line 60, in write
    new.location = self.serializer.serialize(new.value).decode("utf-8")
  File "~/.pyenv/versions/3.7.7/envs/infima/lib/python3.7/site-packages/prefect/engine/serializers.py", line 100, in serialize
    return json.dumps(value).encode()
  File "~/.pyenv/versions/3.7.7/lib/python3.7/json/__init__.py", line 231, in dumps
    return _default_encoder.encode(obj)
  File "~/.pyenv/versions/3.7.7/lib/python3.7/json/encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "~/.pyenv/versions/3.7.7/lib/python3.7/json/encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
  File "~/.pyenv/versions/3.7.7/lib/python3.7/json/encoder.py", line 179, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type DataFrame is not JSON serializable
Out[4]: <Failed: "Some reference tasks failed.">

when I disable

PREFECT_FLOWS_CHECKPOINTING

flow runs successfully

export PREFECT_FLOWS_CHECKPOINTING=false

In [1]: import pandas as pd
In [2]: from prefect import Parameter, task, Flow
In [3]: @task
   ...: def simple(df):
   ...:     return df
   ...: 
In [4]: with Flow("test") as flow:
   ...:     df = Parameter(name="df")
   ...:     simple(df)
   ...: flow.run(df=pd.DataFrame())
Out[4]: <Success: "All reference tasks succeeded.">

Hi all, are there any examples where a task can be paused awaiting some sort of manual approval?

Using prefect cloud, have a very simply flow running. Sometimes my flows just don't run and are in a scheduled state, the agent is running it is waiting for flow runs. It seems to work fine the first couple times, any ideas?

Follow ups from yesterday’s question, since I still can’t seem to make it work. • Is there anything special needed to make a

PrefectResult

work for a task, presuming I’m using Docker storage for the flow? • Does adding a

PrefectResult

to a task somehow transparently change the labels of a Flow?

Hi everyone, hope your Thursday is going well. I have a flow that stuck on in a “Scheduled” state on Prefect Cloud. There are no other flows scheduled or running currently and the agent is alive and logging “Waiting for flow runs”. How can I debug why this flow isn’t being submitted? The flow run id is 80230641-deec-466f-b12c-cea1ef8704ae

Hello :) I'm wondering if someone can explain to me how does prefect core execute mapping with the DaskExecuter. In the Docs, it's said that Prefect automatically creates a copy of the task for each element of its input data. However, looking at the code I'm not understanding where that is being done, and when those new tasks are being submitted to Dask.

Hey folks! I'm working on a personal project and I'm hoping to use Prefect to automate some of the workflows 🙂

Hi all, when I registered a flow, the flow-run will be created automatically depends on the given schedule with python code. I wonder are there any ways to mark a flow-run "skipped" when the task found the connection to RDB is temporary dropped(in our use case, we need "skipped" instead of "failed"). That is, I want to change the flow-run mark in Task layer. Is that possible? I've found a way is "`client.set_flow_run_state(flow_run_id="ce7ce40b-a21b-4273-b39f-6ebea071a76e", state=skip_state)`", but the flow-run is created by scheduler, I could not find the flow-run id automatically. Thanks!!

Hi all. Is it possible to migrate from 0.11.5 to 0.13.3? I get the following error, so I assume it’s not possible?

graphql_1   |                                                                                  
graphql_1   | Running Alembic migrations...                                                    
graphql_1   | INFO  [alembic.runtime.migration] Context impl PostgresqlImpl.
graphql_1   | INFO  [alembic.runtime.migration] Will assume transactional DDL.
graphql_1   |                                                                                  
graphql_1   | Could not upgrade the database!
graphql_1   | Error: Can't locate revision identified by '7e65dadba625'

We just got this error in our logs? Anybody knows what this means?

Failed to set task state with error: ClientError([{'path': ['set_task_run_states'], 'message': 'State update failed for task run ID 0c1f4327-19fe-4522-8f8c-ad4c2fb7dc45: provided a running state but associated flow run c4d445b2-8d4e-4863-9ba5-b89b5addeba3 is not in a running state.', 'extensions': {'code': 'INTERNAL_SERVER_ERROR'}}])
Traceback (most recent call last):
  File "/opt/app/.venv/lib/python3.8/site-packages/prefect/engine/cloud/task_runner.py", line 112, in call_runner_target_handlers
    state = self.client.set_task_run_state(
  File "/opt/app/.venv/lib/python3.8/site-packages/prefect/client/client.py", line 1295, in set_task_run_state
    result = self.graphql(
  File "/opt/app/.venv/lib/python3.8/site-packages/prefect/client/client.py", line 287, in graphql
    raise ClientError(result["errors"])
prefect.utilities.exceptions.ClientError: [{'path': ['set_task_run_states'], 'message': 'State update failed for task run ID 0c1f4327-19fe-4522-8f8c-ad4c2fb7dc45: provided a running state but associated flow run c4d445b2-8d4e-4863-9ba5-b89b5addeba3 is not in a running state.', 'extensions': {'code': 'INTERNAL_SERVER_ERROR'}}]

Hello everyone! I'm completely new to prefect (in fact I discover it couple days ago) and I wonder if my use case fits in what prefect can deliver. I need to run some kind of reporting pipeline working similarly to ETL schema. I need to extract ~1M of records from postgresql than for each of this record I need to query redis database, run some processing and save results to db. I manage to POC it as prefect flow but I have some questions. Does prefect support some concurrency for tasks similar to MapReduce? I can imagine that my data extracted from psql by extraction task can be splited and processed separately on multiple agents.

If we start to use Prefect

Results

like

S3Result

do I need to use them for all tasks to be able to restart failed flows? Not totally clear to me

If I want my flow to run a series of tasks in an exact order ignoring data dependencies, is there an easier way to do that other than manually adding an edge between all the tasks?

Does the

late_work_seconds

setting shown in the [prefect server config](https://github.com/PrefectHQ/server/blob/master/src/prefect_server/config.toml) actually do anything? As far as I can tell there's nothing referencing

config.services.sla

in the open-source server code, but this would be really useful to have access to

Good day ! noticed that while executing a flow, the delay in transitioning from an upstream task to a downstream task is about 10 seconds. Is there some way to lessen this delay? Deployed local prefect server. Server resources are not used at all. a local agent is used, with an executor on the dask cluster.

Hi folks, moving my issue post to the prefect-community channel. I am trying the S3 flow storage option - using a kubernetes agent and a DaskKubernetesEnvironment execution environment. I was facing aws s3 authentication issues but thanks to Josh - I got them resolved. I am now facing a new issue which is being raised by the prefect-job pod before the flow ever gets to a running state ... Here is the error I am getting:

[5:57 PM] $ kubectl logs pod/prefect-job-e74114b4-26n6v 
[2020-08-21 21:55:08] INFO - prefect.S3 | Downloading simple-flow-8/2020-08-21t21-54-33-484226-00-00 from infima-etl-flows
Traceback (most recent call last):
  File "/usr/local/bin/prefect", line 8, in <module>
    sys.exit(cli())
  File "/usr/local/lib/python3.8/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/usr/local/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/usr/local/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/usr/local/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/usr/local/lib/python3.8/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/prefect/cli/execute.py", line 80, in cloud_flow
    raise exc
  File "/usr/local/lib/python3.8/site-packages/prefect/cli/execute.py", line 69, in cloud_flow
    flow = storage.get_flow(storage.flows[flow_data.name])
  File "/usr/local/lib/python3.8/site-packages/prefect/environments/storage/s3.py", line 101, in get_flow
    return cloudpickle.loads(output)
TypeError: an integer is required (got type bytes)

I am wondering if anyone else faced something similar

Hi Community! Is there any established best practices for developing a library of prefect flows? One of the struggles I have is instituting standards for a CI process for a library of flows. Part of the challenge is that code for flows represents a delayed execution of tasks, so it is difficult to test flows without deploying them to a specific environment

Is there a way to run functions that are decorated with @task outside of prefect? I'm trying to rapidly develop individual task stages but I'd prefer to not have two functions defined. Currently, calling a function decorated with @task in a jupyter notebook throws a context error.

Next newbie question: What's the best way to apply a task over a list? The task also takes additional static arguments that don't change for each element. I tried to use map but then it tried to access indices of the static args

Is it possible to use the imperative API to have child tasks that live in multiple files? Ex: File 1 makes a flow that calls a task in File 2. This works so long as the task in File 2 doesn't call another task within File 2. If I try to do that, I get a

ValueError: Could not infer an active Flow context.

Hey everyone, I apologize in advance for how much I wrote here... I realize that I'm attempting to use Prefect in a manner that it's not originally designed for, so I have a longer explanation of why I'm doing this below. While most users are coming from AirFlow, I'm trying to transition from FireWorks (https://github.com/materialsproject/fireworks). So here's my main question: Can I setup Prefect with many Agents that make a single task request and then terminate? In addition, when an agent is started and no tasks are ready for execution, the Agent will terminate. If this is possible, what would you estimate for the overhead per task? I would expect the main overhead comes from Agent connection to the Prefect Cloud/Server. This may sound like an inefficient use of Prefect, but it is intentional. Fireworks is designed with this setup in mind and is thus limited to 6/tasks per second -- this is acceptable because the average task submitted via FireWorks is on the hour timescale. I'm a materials chemistry researcher at UNC, where I must submit tasks as individual SLURM jobs, each with their own time and memory restrictions. These tasks (DFT energy calculations) range drastically in their required resources (one could be <1GB while another >200GB memory), launch in parallel using mpirun, and require their own isolated directory. I could use a single Executor like Dask, which supports queues like SLURM, but this would cause a number of problems for me. Dask holds onto worker resources indefinitely, which research-cluster admins don't want. If there are no tasks ready to execute, the cluster's resources should be released. Dask (as far as I'm aware) does not allow for setting time/memory limits on a per-task basis. And I'm unsure how Dask will handle tasks that execute via mpirun and also implement isolated directories per task. Fireworks was made by the materials chemistry community specifically for this. In the setup, you constantly submit SLURM jobs. Once a SLURM job makes it through the queue, the job itself simply starts an Agent, runs a single task, and then closes. This submission architecture is something I would like to replicate with Prefect. Fireworks has a number of limitations that I think Prefect can fix - such as their WorkFlow classes and meta-database (MongoDB) - so I'm looking into switching over. Again, sorry for the long write! Thanks for reading through, and let me know if you think a multi-Agent approach is possible with Prefect. -Jack

Hi folks! How can i start prefect server? Only using prefect start ? I noticed that there is docker-compose.yml in package path. And which is right way to stop server? So, for example in airflow, you stored only docker images and orchestrateting them using docker/k8s. In this case , your system is clean. But for starting prefect server you need to install python, prefect and some nessesary packages that "pollute"system.

Hi guys I have an issue with a flow. In the transform function if certain data is not created I want to retry the fetch function after some time? How can I do it?

Hey, I’m using a

LocalEnvironment

/

DaskCluster

to run my flow on a local cluster. Where does prefect report the LocalCluster provisioning logs? I’m interested in what port the UI is served (port 8787 is already taken on the server)

How can I raise an error within a task that executes a new task?

Is there any way to setup an account wide flow error alerting in Prefect Cloud? I know this can be done by flow but it's a bit involved to do this for every flow.

Hi, I am having problems running the local DaskExecutor. When running the standard local executor all works fine, but when running a flow with

executor=DaskExecutor()

i get

RuntimeError:

An attempt has been made to start a new process before the

current process has finished its bootstrapping phase.

. Anyone having the same issue?

I'm on the developer tier, why is it my flows are stuck in scheduled for a long time sometimes? I don't have anything else taking up a concurrency slot

Good morning! I had a quick question on registering flows. I'm trying to figure out the best way for our team to deploy our Prefect workflows in production. From what I can tell, I've seen two ways: 1 - flow.register() in Python 2- export flow to JSON and import using graphql For option 1, we have a unique situation where we can't run Python command in our production environment. For option 2, some of the docker storage info changes from development to production which means we have to edit the JSON files. So, hopefully a simple question - are there other/better ways to package a workflow for deployment that I might be missing? I can run Python commands in development, but just not in production.