https://docs.flyte.org/projects/cookbook/en/latest/auto/larger_apps/larger_apps_deploy.html#build-deploy-your-application-to-the-cluster Does the docker image should have the workflow code inside it?

Hey, what’s the usage of

{flyte/sandbox}.config

and what are best practices for usage as a production config? Referencing this sandbox.config

Hey everyone again 🙌 I see a stuck in triggering to the next task. My main workflow is depicted in attached pic. First

preproc_and_split

step was executed successfully:

pyflyte-execute
--inputs
<s3://my-s3-bucket/metadata/propeller/flyte-anti-fraud-ml-development-a27rchl5z9ndpw297nk8/n0/data/inputs.pb>
--output-prefix
<s3://my-s3-bucket/metadata/propeller/flyte-anti-fraud-ml-development-a27rchl5z9ndpw297nk8/n0/data/0>
--raw-output-data-prefix
<s3://my-s3-bucket/vo/a27rchl5z9ndpw297nk8-n0-0>
--checkpoint-path
<s3://my-s3-bucket/vo/a27rchl5z9ndpw297nk8-n0-0/_flytecheckpoints>
--prev-checkpoint
""
--resolver
flytekit.core.python_auto_container.default_task_resolver
--
task-module
app.workflow
task-name
preproc_and_split

On the output one should expect a small train dataset with 50k records. In Nods allocation i see a sufficient mem available. But then the first task has been succeeded i see an eternal hang in this step and flyte don’t produce next executions according to the workflow.

task_resource_defaults

conf is the next:

task_resource_defaults.yaml: |
    task_resources:
      defaults:
        cpu: 1
        memory: 3000Mi
        storage: 200Mi
      limits:
        cpu: 5
        gpu: 1
        memory: 8Gi
        storage: 500Mi

I have one task that generates a dataclasses instances on the exit and another task should takes these classes as input params:

@workflow
def main_flow() -> Forecast:
    """
    Main Flyte WorkFlow consisting of three tasks:
        -  @preproc_and_split
        -  @train_xgboost_clf
        -  @get_predictions
    """
    <http://logger.info|logger.info>(log="#START -- START Raw Preprocessing and Splitting", timestamp=None)
    train_cls, target_cls = preproc_and_split()

    <http://logger.info|logger.info>(log="#START -- START Initialize Boosting Params", timestamp=None)
    saved_mpath = train_xgboost_clf(
                            feat_cls=train_cls,
                            target_cls=target_cls,
                            xgb_params=xgb_params,
                            cust_metric=BoostingCustMetric
                         )

Where

def preproc_and_split() -> Tuple[Fraud_Raw_PostProc_Data_Class, Fraud_Raw_Target_Data_Class]:

Any advices why I faced this behaviour?

Hey, I am trying to understand how to work with launch plans. Let’s say I have this workflow and I package and deploy it. When I look in the UI the launch plan is there, but when I inspect the workflow I can’t select the launch plan. Should I package and deploy the launchplan individually or what is the preferred way? Code in comments

has anyone put the flyte services behind their own api gateway before? having issues rerouting the js assets for the UI. I am able to update the url for

/console

to

/my/url/console

but the UI still looks for the js assests in

/console/assets/*

. This is just the beginning though, will eventually need to prepend a path to

/api/v1/projects

as well so it doesn't interfere with our endpoints

Hi, I have a technical question. Background of my question. I learned that in order to chain two tasks as dependent tasks, one task’s output must be the input of the other. I have a task that depends on a task (say to remove data in a folder) that doesn’t produce any output. How do I do it?

is there a way to query flyte admin for all workflows in a project, domain, and version? i see there's a filters construct, but can't tell which filters are available

Hi! Can i set default value of workflow parameter dynamically? like date type parameter set date.now() I want always set date parameter default value to today When i run workflow on Flyte console UI.

Hi, I was working on registering a task to execute it whenever needed and i was able to register and run the task from the flyte -console but when i tried to create a execution for the same task using command line i was able to retrieve the task using the command:

flytectl get task --project flytesnacks --domain development {taskname} --latest --execFile exec_spec.yaml

and this modified the

exec_spec.yaml

and it looks like this:

iamRoleARN: ""
inputs: {}
kubeServiceAcct: ""
targetDomain: ""
targetProject: ""
task: {taskname}
version: "1.0"

and i tried to create an execution with the following command :

flytectl create execution --execFile exec_spec.yaml -p flytesnacks -d development --targetProject flytesnacks

and it throws an error like:

Error: rpc error: code = Internal desc = failed to create workflow in propeller json: error calling MarshalJSON for type *v1alpha1.Inputs: Marshal called with nil

Hi, I've setup a flyte sandbox cluster in an EC2 instance. And created a custom docker image with the required dependencies installed in it. But when I try to run any script, I am getting

ModuleNotFoundError

, though that module was installed when creating the docker image. And this is getting resolved when I

pip install

that module in the instance. So, please clarify whether on running a workflow, would flyte propeller look for the dependencies in the specified docker container or it tries to use the dependencies from the server it is running in ?

When registering workflow with the pyflyte register command. The workflow name becomes script_name.workflow_name on console. Is there any way to ignore the script file name? I want the workflow name to be just workflow_name. For example, for this https://docs.flyte.org/en/latest/getting_started/index.html#create-a-workflow the workflow name will be example.wf instead of wf.

Let’s say we have a workflow that should be associated with N different inputs, entailing N different launch plans. How would you structure/manage this in code? We have adopted an idea of managing the launch plans in a separate folder, next to the flyte workflows and thus deploying the launch plans and workflow in separate - see example below. Does this make sense for the N:1 relationship between launch plans and workflows? This entails a lot of duplication of boilerplate code in

lp.py

but it is maybe the cleanest solution to this problem?

(.venv) ☁  flyte [main] tree .
.
├── <http://in_container.mk|in_container.mk>
├── launchplans
│   └── lp.py
└── workflows
    ├── wf.py

hi everybody, i'm having some trouble running

ContainerTask

. Both a small test that I wrote myself and the cookbook example here fail when run locally. The cookbook example runs successfully when its run remotely. Any help here is appreciated!

Hi, I have a custom plugin named MyCustomTask written in a python file(say mycustomtask.py). How can I include this in the Flyte environment so that any one uses this plugin should be able to import and use it like other default plugins. Example: This is how we import Snowflake task ->

from flytekitplugins.snowflake import SnowflakeConfig, SnowflakeTask

. I need to do the same thing with my custom plugin too. ->

from flytekitplugins.mycustomtask import MyCustomTask

or similarly.

Hi, I have a question: Is there a way from within a task code execution to retrieve information on under what domain & project is it running. on? E.g. I have Development/Staging/Production domains, and when I run the workflow, I want task to load configs for the right environment.

How to use multiple images with a config file in `pyflyte register`command? I am running,

pyflyte --config config.yaml register test1.py --version 1.0.1

I have created the code as mentioned in the doc, https://docs.flyte.org/projects/cookbook/en/latest/auto/core/containerization/multi_images.html# The config.yaml is,

admin:
  # For GRPC endpoints you might want to use dns:///flyte.myexample.com
  endpoint: dns:///##############-16##<http://26454.us-west-1.elb.amazonaws.com:80|26454.us-west-1.elb.amazonaws.com:80>
  authType: Pkce
  insecure: true
logger:
  show-source: true
  level: 0
images:
  trainer: <http://ghcr.io/flyteorg/flytecookbook:core-latest|ghcr.io/flyteorg/flytecookbook:core-latest>
  predictor: moulee31/sample:1.0

The error is,

Traceback (most recent call last):
  File "/home/sanjaychouhan/.local/bin/pyflyte", line 8, in <module>
    sys.exit(main())
  File "/usr/lib/python3/dist-packages/click/core.py", line 764, in __call__
    return self.main(*args, **kwargs)
  File "/usr/lib/python3/dist-packages/click/core.py", line 717, in main
    rv = self.invoke(ctx)
  File "/usr/lib/python3/dist-packages/click/core.py", line 1137, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/usr/lib/python3/dist-packages/click/core.py", line 956, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/usr/lib/python3/dist-packages/click/core.py", line 555, in invoke
    return callback(*args, **kwargs)
  File "/usr/lib/python3/dist-packages/click/decorators.py", line 17, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/clis/sdk_in_container/register.py", line 174, in register
    registerable_entities = load_packages_and_modules(
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/tools/repo.py", line 225, in load_packages_and_modules
    registrable_entities = serialize(pkgs_and_modules, ss, str(project_root), options)
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/tools/repo.py", line 54, in serialize
    registrable_entities = get_registrable_entities(ctx, options=options)
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/tools/serialize_helpers.py", line 75, in get_registrable_entities
    get_serializable(new_api_serializable_entities, ctx.serialization_settings, entity, options=options)
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/tools/translator.py", line 578, in get_serializable
    cp_entity = get_serializable_task(entity_mapping, settings, entity)
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/tools/translator.py", line 173, in get_serializable_task
    container = entity.get_container(settings)
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/core/python_auto_container.py", line 164, in get_container
    image=get_registerable_container_image(self.container_image, settings.image_config),
  File "/home/sanjaychouhan/.local/lib/python3.8/site-packages/flytekit/core/python_auto_container.py", line 235, in get_registerable_container_image
    raise AssertionError(f"Image Config with name {name} not found in the configuration")
AssertionError: Image Config with name trainer not found in the configuration

It was working with pyflyte run command,

pyflyte --config config.yaml run --remote test1.py test_workflow

With Flyte in EKS cluster, can we mention what type of EC2 instance to use for a task? Instead of just cpu and ram.

a few general questions: 1. with many of the machine-learning tutorials, i notice that the whole workflow is typically wrapped into one task. it seems natural to me that this approach doesn't take advantage of a lot of flyte's features (caching, being able to share tasks across workflows, etc). is there a reason that most of the ML examples are written this way? 2. i often notice that after i tear down a sandbox cluster, running

docker system prune -a --volumes

frees up a sometimes surprising amount of space (i ran the mnist tutorial a few times and pruning the volumes revealed that it had used ~14GB on my machine). is there a way to reduce/mitigate this or is it just a necessary result of saving workflow executions?

Hi we’re doing some performance testing and when we start a large number of tasks at once it seems that FlytePropeller loses track of some of the running pods. For example a pod will be successful, but FlytePropeller logs the following. Full log in thread

Failed to find the Resource with name: dpp-default/g20210730154015-yjww-n0-0-dn4-0-dn108-0. Error: pods \"g20210730154015-yjww-n0-0-dn4-0-dn108-0\" not found

Flyte restarts the task and it succeeds on the 2nd or 3rd try, but this is obviously wasted work. I’m curious if this is FlytePropeller needing more CPU/Memory to accommodate or if we are overwhelming the k8s metadata server. Any thoughts would be appreciated

how might i enforce that every task execution always first runs some arbitrary setup script?

Hey, playing around with

pyflyte register

and how do adopt it in our set up. When I package code using

pyflyte package

I usually provide the dot-delineated python packages to operate on i.e.,

pyflyte --pkgs flyte.workflows package --image $REGISTRY/$REPO:$TAG -o $(PACKAGE_OUTPUT_DIR)/package.tgz

When I run

pyflyte register

I would like to do the same in order to fast-register to the same workflow i.e.,

pyflyte register flyte.workflows \
		             --version=$VERSION \
					 --image $REGISTRY/$REPO:$TAG \
					 --project $PROJECT \
	                 --domain $DOMAIN

However running that command yields

Usage: pyflyte register [OPTIONS] [PACKAGE_OR_MODULE]...
Try 'pyflyte register --help' for help.

Error: Invalid value for '[PACKAGE_OR_MODULE]...': Path 'flyte.workflows' does not exist.
gmake: *** [Makefile:42: flyte-fast-register] Error 2

Do I need to provide the path to the workflow? If I pass

flyte/workflows/wf.py

to

pyflyte register

my workflow is registered under

<http://wf.wf|wf.wf>

instead of

<http://flyte.workflows.wf.wf|flyte.workflows.wf.wf>

as with

pyflyte package

(.venv) tree .
.
├── Dockerfile
├── Makefile
├── flyte
│   ├── __init__.py
│   ├── <http://in_container.mk|in_container.mk>
│   ├── launchplans
│   │   └── lp.py
│   └── workflows
│       ├── __init__.py
│       └── wf.py

Hi, team, I’m aware of the fact that Flytekit automatically converts Python type hint into Flyte type hint.

# Creating the Multiple Linear Regression model
def build_model(model, X_train: pd.DataFrame, y_train: pd.DataFrame):
    # Use the following values: model = LinearRegression(), X_train = X_train, y_train =    y_train
    reg = model
    reg.fit(X_train, y_train)
    return reg

I’m using

int

,

float

,

pd.DataFrame

,

np.ndarray

etc. as my type hints. My question is what type hint do I use for the argument

model

and since it is returning the model, what type hint should I use for

reg

?

Regarding the hacktoberfest do you get the rewards/prizes for the other numbers too

Like if i did 4 prs and they get merged

so i get a north face hoodie, mug, tshirt or just the hoodie

Hey I want to contribute blog posts to flyte, Can someone help me get started?

hello guys I.g am stuck in installation process please help

message has been deleted

this is the msg i am getting here