is there a way to do it? Or should i just move this attribute accessing inside my train_model task?

message has been deleted

While we are at it, just asked Prefect to visualize my pipeline, and its beautiful 😢 😲 Thanks for the good work 🙂

Does anyone know how to optimize a mapping task with a 5000+ list of values. The task stalls ands all the memory on the dask worker

Hi All, I am new to Prefect and our team just started using it. I am working on a POC to integrate our Prefect Cloud to GCP Kubernetes Engine, Can you direct me to appropriate documentation or any suggestions to help me get started?

Hey guys, I think I’m going insane. I’m trying to set up a little introduction flow (code at the bottom), and I keep getting an error when trying to run this of:

AttributeError: Context object has no attribute dict

. Can anyone spot something wrong with the flow (like structurally or conceptually)?

from prefect import Flow, task
from prefect.environments.storage import Local

@task
def extract():
    return [1, 2, 3]


@task
def always_process_and_pass_downstream(element: int) -> int:
    return element * 2


@task
def load(elements: List[int]) -> None:
    total = sum(elements)


@task
def process_and_finish(element: int) -> None:
    avg = sum(element) / len(element)
    pass


with Flow("test client register flow 1", storage=Local()) as flow:
    extracted = extract()
    processed = always_process_and_pass_downstream(element=extracted, mapped=True)
    process_and_finish(element=processed)
    load(elements=processed)

result = flow.run()

Thanks!

Hi all. Bit confused on how to use prefect with dask dataframes Does the following stub code make sense:

from prefect import Flow, task

@task
def read_parquet(src_path)
    return df # returns dask df


@task
def retrieve_img(x):
    return img

@task
def model(img):
    return result

@task
def write_db(result):
    # write to db

@task
def write_parquet(dst_path, df):
    # write to disk

with Flow('Test') as flow:
    df = read_parquet(path)
    images = retrieve_img.map(df.iterrows())
    results = model.map(images)
    write_db.map(results)
    write_parquet(df.join(dd.from_sequence(results)))

essentially each row of my input dask dataframe needs to be processed and the results appended to the dataframe and saved in a database.

Hello again - I would like to parallelize a

task, make_node

in a

LOOP

. The current implementation is a

task

that sits inside a

Flow

wrapped up in a

looped

task

. I tried some quick stabs at the

Dask Executor

. Errors arose. Is there a better pattern to achieve parallelizing

make_node

? Thanks

Hi, When using tags for dask resources, I observe a strange behavior while using mapping. Let's consider the following flow:

map_fn = FunctionTask(lambda x: x + 1, tags=["dask-resource:GPU=1"])

with Flow('test') as flow:
    list = [1]
    map_fn.map(list)

While running such a flow on a dask cluster with 1 scheduler, and 1 worker (with

--resources GPU=1

), it turns out that the flow is blocked because there are 2 tasks that "reserved" the gpu resources (see attached pics from the dask dashboard). It looks like the map itself reserved 1 GPU, and then the map_fn map on element from numbers cannot be processed because dask wait for the resource to be released. Is that the expected behavior of the mapping with dask resources?

Question: is it possible to use the @task decorator on a class method?

does shelltask support timeout?

Hey, Prefect looks great! One important thing for me would be a UI for monitoring Flow execution and scheduling. When do you plan to release your UI and can you recommend any intermediate solutions?

The docs say "Prefect’s UI is not yet available to the public" but telling by this issue (https://github.com/PrefectHQ/prefect/issues/1404) you don't plan to release an open-source tool for managing multiple workflows. What could be a good starting point for building a homegrown solution?

Hi :)) Question: how does one scale tasks vertically? I have a pipeline that maps some initial values to multiple tasks. I want the entire pipeline to be performed on each initial value before moving to the next value (or that each worker will process the entire pipeline before moving to the next one)

after my flow completes successfully

[2020-02-04 05:17:43,587] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded

I try and terminate the dask distrbuted cluster cleanly, but always get :

2020-02-04 05:17:44,467 INFO stopping Dask cluster

distributed.scheduler - INFO - Scheduler closing...

distributed.scheduler - INFO - Scheduler closing all comms

distributed.scheduler - INFO - Remove worker <Worker '<tcp://100.96.120.2:37227>', name: <tcp://100.96.120.2:37227>, memory: 0, processing: 0>

distributed.core - INFO - Removing comms to <tcp://100.96.120.2:37227>

distributed.batched - INFO - Batched Comm Closed: in <closed TCP>: Stream is closed

distributed.scheduler - INFO - Remove worker <Worker '<tcp://100.96.120.2:43873>', name: <tcp://100.96.120.2:43873>, memory: 0, processing: 0>

followed by:

distributed.scheduler - INFO - Lost all workers

2020-02-04 05:17:45,604 WARNING Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f3856212470>: Failed to establish a new connection: [Errno 111] Connection refused',)': /api/v1/namespaces/logflow/pods?labelSelector=app%3Ddask%2Ccomponent%3Dworker%<http://2Cdask.org|2Cdask.org>%2Fcluster-name%3Ddask-root-b81ddee0-5%2Cuser%3Droot

2020-02-04 05:17:45,604 WARNING Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f384f59eb38>: Failed to establish a new connection: [Errno 111] Connection refused',)': /api/v1/namespaces/logflow/pods?labelSelector=app%3Ddask%2Ccomponent%3Dworker%<http://2Cdask.org|2Cdask.org>%2Fcluster-name%3Ddask-root-b81ddee0-5%2Cuser%3Droot

2020-02-04 05:17:45,605 WARNING Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f384f59e0b8>: Failed to establish a new connection: [Errno 111] Connection refused',)': /api/v1/namespaces/logflow/pods?labelSelector=app%3Ddask%2Ccomponent%3Dworker%<http://2Cdask.org|2Cdask.org>%2Fcluster-name%3Ddask-root-b81ddee0-5%2Cuser%3Droot

Traceback (most recent call last):

File "/usr/local/lib/python3.6/dist-packages/urllib3/connection.py", line 157, in _new_conn

(self._dns_host, self.port), self.timeout, **extra_kw

File "/usr/local/lib/python3.6/dist-packages/urllib3/util/connection.py", line 84, in create_connection

raise err

File "/usr/local/lib/python3.6/dist-packages/urllib3/util/connection.py", line 74, in create_connection

sock.connect(sa)

ConnectionRefusedError: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 672, in urlopen

chunked=chunked,

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 376, in _make_request

self._validate_conn(conn)

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 994, in _validate_conn

conn.connect()

File "/usr/local/lib/python3.6/dist-packages/urllib3/connection.py", line 300, in connect

conn = self._new_conn()

File "/usr/local/lib/python3.6/dist-packages/urllib3/connection.py", line 169, in _new_conn

self, "Failed to establish a new connection: %s" % e

urllib3.exceptions.NewConnectionError: <urllib3.connection.VerifiedHTTPSConnection object at 0x7f3874be2fd0>: Failed to establish a new connection: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

File "/usr/lib/python3.6/weakref.py", line 624, in _exitfunc

f()

File "/usr/lib/python3.6/weakref.py", line 548, in __call__

return info.func(*info.args, **(info.kwargs or {}))

File "/usr/local/lib/python3.6/dist-packages/dask_kubernetes/core.py", line 623, in _cleanup_resources

pods = core_api.list_namespaced_pod(namespace, label_selector=format_labels(labels))

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/apis/core_v1_api.py", line 12372, in list_namespaced_pod

(data) = self.list_namespaced_pod_with_http_info(namespace, **kwargs)

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/apis/core_v1_api.py", line 12472, in list_namespaced_pod_with_http_info

collection_formats=collection_formats)

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/api_client.py", line 334, in call_api

_return_http_data_only, collection_formats, _preload_content, _request_timeout)

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/api_client.py", line 168, in __call_api

_request_timeout=_request_timeout)

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/api_client.py", line 355, in request

headers=headers)

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/rest.py", line 231, in GET

query_params=query_params)

File "/usr/local/lib/python3.6/dist-packages/kubernetes/client/rest.py", line 205, in request

headers=headers)

File "/usr/local/lib/python3.6/dist-packages/urllib3/request.py", line 76, in request

method, url, fields=fields, headers=headers, **urlopen_kw

File "/usr/local/lib/python3.6/dist-packages/urllib3/request.py", line 97, in request_encode_url

return self.urlopen(method, url, **extra_kw)

File "/usr/local/lib/python3.6/dist-packages/urllib3/poolmanager.py", line 330, in urlopen

response = conn.urlopen(method, u.request_uri, **kw)

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 760, in urlopen

**response_kw

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 760, in urlopen

**response_kw

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 760, in urlopen

**response_kw

File "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py", line 720, in urlopen

method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]

File "/usr/local/lib/python3.6/dist-packages/urllib3/util/retry.py", line 436, in increment

raise MaxRetryError(_pool, url, error or ResponseError(cause))

urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='localhost', port=443): Max retries exceeded with url: /api/v1/namespaces/logflow/pods?labelSelector=app%3Ddask%2Ccomponent%3Dworker%<http://2Cdask.org|2Cdask.org>%2Fcluster-name%3Ddask-root-b81ddee0-5%2Cuser%3Droot (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f3874be2fd0>: Failed to establish a new connection: [Errno 111] Connection refused',))

is there something specific I need to do to properly tear down the dask cluster?

Hi all! Trying to construct some simple pipeline. One of tasks return dict, that I want to unpack in another one:

with Flow("Data collection") as flow:
    params = task(params)
    another_task(**params)

but python raise an error

TypeError: FunctionTask object argument after ** must be a mapping, not FunctionTask

Is there any way to unpack dict from task?

Hi - I am getting lost in the documentation regarding deployment. What is the right path to deploy a flow to Fargate? Agent or Task? Thanks

Is there anything written up on using core alone with dask-kubernetes?

Ok, another question. Would you mind describing at a high level how I would, on a schedule: reach out to an SFTP server, list the files (dynamic workflows!), copy them somewhere, and send a slack notification? I think the dynamic part has me the most confused right now, the rest is just stringing tasks together from the library

Hey - is there a way in the task

map

to run a cross product. For example, task_one with inputs [a, b] and [c, d] will run {a,c] [a,d] [b, c] [b,d]

It seems I've hit a limitation. I'm working on my sftp thing I mentioned earlier and figured I'd use namedtuples for the parameters. It seems that's not possible?

Also is there a way to pass streams across task boundaries? I'd like to stream data from sftp to GCS if possible, that way it doesn't need to be all downloaded to disk first

New question: can you run tasks inside of other tasks, or can you only call tasks from the flow itself?

I've been working through the tutorial and ran into a Dask KilledWorker error and was hoping someone might be able to point me to a solution or give me some pointers on how to debug this. I can start dask-scheduler and 2 dask-workers to successfully run the "Deployment: Dask" example. https://docs.prefect.io/core/tutorials/dask-cluster.html I can run the "Advanced Features" example through the DaskExecutor section where the DaskExecutor starts up the dask-cluster and process the URLs in 20% of the single threaded mode. https://docs.prefect.io/core/tutorials/advanced-mapping.html#outline If I add the scheduler address to the executor parameters

executor = DaskExecutor(address="<tcp://192.168.86.23:8786>")
flow.run(executor=executor)

I get the KilledWorker error. ERROR - prefect.Flow: x-files | Unexpected error occured in FlowRunner: KilledWorker('retrieve_url-a032a3d8-fa89-4131-ad4c-4f3c5fbd282c', <Worker 'tcp://192.168.86.23:58923', name: tcp://192.168.86.23:58923, memory: 0, processing: 1>) By commenting things out I'm down to the offending line being

html = requests.get(url)

in the retrieve_url function. Any thoughts?

This runs with line 8 commented out, but raises the KilledWorker when it is run.

I am interested in using Prefect core, but have a few questions about it: 1. Do I need to start each flow via

python flow_x.py

? 2. If I update the flow, I then need to stop the existing process running said flow, and restart it with the updated version. Is that correct? Is there a way to refresh this automatically? (Seems like this is part of prefect cloud, but wanted to check) 3. What if I have a branch in a flow where several mapped tasks share some common dependency that has costly start-up/tear-down? (E.G. Several tasks in the flow can run, then start a shared EMR cluster, use the EMR cluster, then when all are complete, tear the cluster down)

So I'm using the DaskExecutor with a task that has a map() over a list of files. I would expect this to be done in parallel but it runs serially. How do I allow a flow to run in parallel?

Any reason not to spin up a whole dask cluster per flow you're running?

Also can I have flows within flows