Hello guys I have a question regarding the use of a storage of type Docker: I have defined a flow and have set Docker as the storage type, specifying the image name among other parameters. I then build the image and register the flow, and indeed an image is generated with the respective flow inside. But now, I have other flows, and I want to save them to the same image that the other first flow is in. So, I want to increment the flows inside a given image, so that I can then deploy this single image with all flows to my production environment. However, everytime I register a new flow, it recreates the image, leaving only the most recent flow. Is there a way to get around this? Thank you.

Hey folks, question for you all. Is it possible to encapsulate a series of tasks within one task in order to facilitate the writing of flows? Let’s say I have the following tasks A -> B -> C I want to create one task called D in such a way that if I create a flow E -> D, what I am really generating is E -> A -> B -> C Cheers!

Hi, I am digging a bit on a bug and while doing a minimum example I found something strange. Am I using upstream_tasks on a map correctly? On the following example, “flow2” fails (this is how I am used to declare upstream tasks on a map) but “flow1" works:

from prefect import Flow, Task


class Generate(Task):
    def run(self):
        return list(range(10))


class GetConfig(Task):
    def run(self):
        return {'offset': 1000}


class Process(Task):
    def run(self, *, data):
        result = data + 1000
        <http://self.logger.info|self.logger.info>('Process of %d = %d', data,  result)
        return result


generate = Generate()
config = GetConfig()
process = Process()

with Flow('flow1') as flow1:
    dataset = generate()
    config_result = config()
    clean = process.map(data=dataset)
    clean.set_upstream(config_result)


print('Flow1 edges:', flow1.edges)
flow1.run()


with Flow('flow2') as flow2:
    dataset = generate()
    config_result = config()
    clean = process.map(data=dataset, upstream_tasks=[config_result])


print('Flow2 edges:', flow2.edges)
flow2.run()

The traceback is:

[2020-04-21 12:16:45,325] INFO - prefect.TaskRunner | Task 'Process': Starting task run...
[2020-04-21 12:16:45,325] ERROR - prefect.TaskRunner | Task 'Process': unexpected error while running task: KeyError(0)
Traceback (most recent call last):
  File "/Users/david/.virtualenvs/iguazu-venv/lib/python3.8/site-packages/prefect/engine/task_runner.py", line 256, in run
    state = self.run_mapped_task(
  File "/Users/david/.virtualenvs/iguazu-venv/lib/python3.8/site-packages/prefect/engine/task_runner.py", line 721, in run_mapped_task
    upstream_state.result[i],
KeyError: 0

Hello I’m trying a simple flow of doing linear regression in batches. The flow works when doing it sequentially but when I try with Dask backend it causes memory problems. What is confusing is that there is ample memory per worker. Can someone help me identify the problem? Am I doing something of a Dask anti-pattern somewhere?

Hello guys I'm having issues getting my Local Agent to execute my flow runs I first register two test flows like this:

# CREATE STORAGE

storage=storage.build()

# ADD FLOWS TO STORAGE
storage.add_flow(test_flow)
storage.add_flow(test_flow2)

storage=storage.build()

test_flow.storage=storage
test_flow2.storage=storage

# REGISTER FLOWS IN UI/DB
test_flow.register(build=False)
test_flow2.register(build=False)

And they indeed appear in the UI (image bellow) Then I start the agent in my cli (image bellow) But when I try to run one of these flows, they stay permanently schedulled (image bellow) What am I doing wrong? Ty

Hi all, is it possible to get the state of a running flow from the Flow object or FlowRunner?

I'm running the flow in a thread, and would like to grab the result of the first task before the Flow.run() completes.

Hi, I am trying to implement a geographical data processing worflow, working on datasets that do not fit into memory, and I dont find a proper documented way to perform parallel computation on each object in my datasets. What I would like to do is not return all objects, as the result of a task, to map a function on them. I would like to read data inside a task and dispatch objects (either using a queue or some list containing a reasonnable number of objects) for parallel processing, until I have read and processed all data (+ dumped results into a DB or file, that will be handled by a later task), and I dont find a way to do so with Prefect. It seems I need to return all objects, which I cant, in order to parallelize computation on them with the map function. I have tried using the executor inside a task to call its map function, which works fine and could suit my needs. But I am not sure if this is the right way to do so, and if it can be considered as an official feature you will keep supporting. According to you, what is the best solution to handle this issue ?

I’m getting the following error within my app container when attempting to do a remote request. This is part of a kubernetes cluster that also has apollo, hasura, postgres, ui, graphql, and scheduler running. My most recent steps (about 30 minutes ago) were the following: • delete all pods and service in kubernetes •

docker system prune -a

for a fresh start 🙂 • created prefect service/deployments for k8s (using latest images) • docker build my app image • created service/deployments for my app for k8s Any ideas? THANKS!

I want to share the results of my long day of bug hunting. It all comes down to this screenshot: on the left, the visualization of my graph that does not work (note there are two merge nodes). On the right, the one that is fixed. I was composing three different graphs, and using

get_terminal_tasks()

to determine how to connect these graphs. Something has changed on the way

switch/merge

is managed and this created several terminal_tasks on one of my flows. The solution for me was to set the reference task of each flow and use

get_reference_task

instead of

get_terminal_tasks

Anyways, needed to share the small win after a frustrating bug-hunting session (because the bug would present itself randomly due to

set

not having an order)

Hi guys! Beginner to prefect here - very much excited to get up and running on it! I am currently trying to translate our current, very basic/limited pipeline over to prefect - after which we will iterate on it and take advantage of prefect's offerings. Here is our current pipeline - with several extraneous stuff removed:

class BasePipeline:
    """
    A series of services that mutate a document object.
    The flow consists of a single 'source',
    followed by x number of 'processors', followed by a 'sink.'
    The document object is passed along these services in a linear fashion.
    A pipeline configuration object is used to define the type of services
    and the order of the processors.
    """

    def __init__(self, blueprints: "PipelineConfiguration"):
        """
        Initializes and configures services from PipelineConfiguration
        blueprints.
        :param blueprints: a pipeline configuration object
        """
        self.project = blueprints.project
        self.source: Source = start_source(blueprints.source)
        self.sinks: List[Sink] = [start_sink(sink_blueprint) for sink_blueprint in blueprints.sinks]
        self.processors: List[Processor] = [start_processor(processor_blueprint) for processor_blueprint in blueprints.processors]

    def __call__(self, metrics: bool = False, failfast: bool = False) -> None:
        """
        Passes a document through the pipeline.
        """

        for doc in self.source.pull():
            for processor in self.processors:
                processor(doc)
            for sink in self.sinks:
                sink.push(doc)

And here is my translation attempt:

@task
def init_source(project: str, source_config: Blueprint):
    return start_source(project, source_config)


@task
def init_sink(project: str, sink_config: Blueprint):
    return start_sink(project, sink_config)


@task
def init_processor(project: str, processor_config: Blueprint):
    return start_processor(project, processor_config)


@task
def run_source(source: Source):
    return source.pull()


@task
def run_sink(sink: Sink, data: Document):
    return sink.push(data)


@task
def run_processor(processor: Processor, data: Document):
    return processor(data)


def execute(pipeline_config: PipelineConfiguration):
    project = pipeline_config.project
    with Flow("test-flow") as flow:
        # Service Initialization
        source = init_sink(project, pipeline_config.source)
        processors = [init_processor(project, processor_config) for processor_config in pipeline_config.processors]
        sinks = [init_sink(project, sink_config) for sink_config in pipeline_config.sinks]

        # Perform ETL
        datastream = run_source(source)
        for doc in datastream:
            for processor in processors:
                doc = run_processor(processor, doc)
            for sink in sinks:
                run_sink(sink, doc)

    flow.run()

The issue i'm running into is it is saying datastream is not iterable. While this may be a syntax issue on my end, the output of source.pull() is a generator. Are generators supported in prefect? If not - does anyone have an optimal workaround?

Hey guys, maybe this has been discussed before, but; wouldn't it be good to have a forum for this? That way it would be easier for someone to find answers. There is a lot of valuable information in this chat!

Hello all! Is there a way to delete existing Cloud Hook?

Hi everyone, I have a question about running

async

tasks.

from prefect import task, Flow

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

@task
async def transform(some_list):
    transformed = [x + 1 for x in some_list]
    return transformed

@task
async def load(transformed):
    print('load somewhere')
    
with Flow('async testing') as flow:
    extract_nums = extract()
    transformed = transform(extract_nums)
    load(transformed)

flow.run()

This creates a flow and all, but how do I actually run this flow asynchronously? Posted in #random

I have a flow that is passing pandas DataFrames between tasks. When it runs in Prefect Cloud, I get this logging error that I don't see locally:

April 22nd 2020 at 12:02:34pm EDT | CloudHandler
CRITICAL 
Failed to write log with error: Object of type ndarray is not JSON serializable

Hi all, I'm doing multiple flow.run()'s in different threads - but when I do this output caching breaks. I'm trying to debug, and I think the issue is that the prefect context isn't shared between runs in multiple threads. Docs here: https://docs.prefect.io/api/latest/utilities/context.html#context-2 state that context is thread safe, so I tried creating a context using

shared_context = prefect.utilities.context.Context()

and then passing that to run with

my_flow.run(context=shared_context)

but this doesn't seem to solve my problem. Would appreciate any pointer in the right direction!

I'm having a little trouble. I am trying to refactor a large Flow into a few smaller pieces. If I initialize this

MetaModel

class in the flow it will be successful. If i try to load it in the flow will crash at

fit_model

.

# meta = init_meta_model(problem, tinydb, use_default_models)
    # ^^^ this does not work
    meta = MetaModel(problem, tinydb, use_default_models=True)
    model_path = get_models(meta)

    fit_models = fit_model.map(
        model_path=model_path,
        train_data=unmapped(train_data),
        target=unmapped(train_target),
        problem=unmapped(problem),
    )
###
@task
def init_meta_model(problem, db, use_default_models=True):
    meta = MetaModel(problem, db, use_default_models=True)
    return meta
@task
def fit_model(model_path, train_data, target, problem):
    model = joblib.load(model_path)
    model.fit(X=train_data, y=target)
    joblib.dump(model, model_path)

MetaModel.models = ["PATH_TO_MODEL"]

Sorry, got one more question 🙂 - looking at the ETL examples where you are doing something like extracting a list of values, mapping them to a transform (map reduce type operation) and then finally using the reduced transform result load that into e.g. a database. Is there a way to have this fan-out instead, in other words not have the reduce function for the map and end with one task. Not sure if that makes sense, so would something like 1 extract task produces 10 results, and that kicks off 10 parallel transforms, each producing one output that is then passed to 10 parallel load tasks be possible?

Something I’m not quite sure how to do in

prefect

is use control flow to have an optional task inline in a sequence of tasks. Something like:

with Flow('flow') as flow:
    do_optional_step = Parameter('do_optional_step')
    
    x = Parameter('x')
    y = x + 1
    y = ifelse(do_optional_step, 2*y, y)

This doesn’t work as intended, but is what I’d like to be able to do. If I were doing this as a pure python function, it would be

def run_flow_steps(x, do_optional_step):
    y = x + 1
    if do_optional_step:
        y = 2*y
    return y

Hi everyone! I am new here, wandered my way over here after reading up on dask etl jobs that leverage prefect. Anyone running their ETL jobs like that?

I have a question abow flow control. If I have a flow that looks like this and run the flow with

DaskExecutor

, it will run

first_condition

and

second_condition

asynchronously correct? And if I use

LocalExecutor

, it will run

first_condition

and then

second condition

synchronously?

Question about flow versions, Is a flow execution always tied to the flow version when it was registered? If I run a flow and it fails due to some buggy code, fix the code and rerun this failed flow, will it run the version with the bug again or the latest registered version? I have tried this process but after hitting the retry button, it stucked at a pending state. I have the agent running this time.

Hello, one question regarding new-type `Result`s: what is planned to happen to

store_safe_value

? At first sight, it seems to be redundant to `Result`'s formatted location.

Quick question regarding the new open-sourced ui server 🙌 What ports and services should be facing the external world (if I was deploying this in some out of premises server or cluster)? The

ui:8080

is obvious, but I could not make it work without adding

graphql:4201

too, and I am not sure if that’s safe…

Hi folks. Setting up prefect server on a local network box, let's call it

server1

. When I access the page at

<http://server1:8080>

it starts making network calls to the graphql backend at

<http://localhost:4200>

. Surely this is configurable but I can't seem to find out where. Here's what I've tried so far: • editing

service.ui.graphql_url

in my ~/.prefect/config.toml on server1 • creating a new env and setting

environments.server1.services_host

to

server1

• exporting

PREFECT_SERVER__GRAPHQL_URL="<http://server1:4200>"

I must be missing something obvious here. Any ideas?

Hi team - is there any concept of a task timeout in prefect currently ?

And just as I side note I have been playing with the graphql API and to say it is exceptional is putting it very lightly.

I do have one request actually related to caching - is there plan for first-class support for exceptions ?

The

docker-compose.yml

(in prefect/cli) that is ran upon

prefect server start

brings up a postgres instance. If I already had a postgres instance running, is it possible through the CLI to omit bringing up the postgres instance that is in the

docker-compose.yml

? Similar to

docker-compose up

.

I've having a little trouble using the SQLiteQuery task. https://docs.prefect.io/api/latest/tasks/sqlite.html#prefect-tasks-database-sqlite-sqlitequerytask I'm trying to pass the db name as a parameter, but it is not working. The query task does not accept parameters, only string. I've attached a concise version of my code and error message below.

def test_meta_model_regression():
    meta_model_run = meta_model_flow.run(
        db = "test.db",
    )
    assert meta_model_run.message == "All reference tasks succeeded."

with Flow("meta_model_flow") as meta_model_flow:
    db = Parameter("db")
    models = SQLiteQuery(db,"SELECT identifier FROM models")

>> TypeError: expected str, bytes or os.PathLike object, not Parameter

Is there some way to pass the parameter "test.db" to the query?