Thanks for this, that PR is exactly the reason I started looking for the order.

@noklam maybe we should include a diagram in the docs as part of that PR, I've also just had

after_command_run

merged on the CLI side

That would be super helpful. We probably need to make a draw.io version (this one is just miro board), or if mermaid chart works, we can also do that like this PR. https://github.com/kedro-org/kedro/pull/1392

Hello, I am trying to run the pipeline using SDK I have found that documentation https://kedro.readthedocs.io/en/stable/nodes_and_pipelines/run_a_pipeline.html?highlight=Run#run-pipelines-with-io is not up to date since in kedro 0.18.0 the SequntialRunner.run method requires

hook_manager

Unfortunately all the documentation says nothing about

hook_manager

and how to initialize it. Moreover, the documentation code gives error since calling

run(pipeline, catalog=catalog)

yields

ErrorMessage "TypeError: run() missing 1 required positional argument: 'hook_manager'

see https://github.com/kedro-org/kedro/pull/1466/files

This will be fixed shortly in 0.18.1

Any help with running the pipeline in 0.18.0v ? I have tried with the aforementioned PR solution using NullPlugin but I got an error than None is not iterable 😦

It's late here in London the team will get back to you in the morning

You would normally just do

session.run()

instead of calling runner directly. If you absolutely need the runner for some reason, this is a hack work for 0.18.0 but there is no guarantee this will continue to work for coming version.

from kedro.framework.session.session import _create_hook_manager
print(runner.run(greeting_pipeline, data_catalog, _create_hook_manager())

Many thanks. That helped. I will definitely try to start session and the

session.run()

. It is just that I have found in documentation that "Running pipeline" should create the runner first.

You are correct, the documentatiom is trying to explain internally how does it works, and also how you can create these Runner object in a standalone mode. In practice, you would create a Kedro project, the session will take care of creation of datacatalog, runner etc.

Hello everyone! I have the same problem here, I managed to run the pipeline using >

from kedro.framework.hooks import _create_hook_manager

>

print(runner.run(greeting_pipeline, data_catalog, _create_hook_manager())

but I would like to use session. Any help on how I could get or create the session to use for the

session.run()

command? Is there a way to pass to the session a specific datacatalog? (I am using this to perform some tests and I created a fake data catalog to do this) Thank you in advance!

https://kedro.readthedocs.io/en/stable/tutorial/spaceflights_tutorial.html Hi @eleonora.picca, I would suggest following the tutorial is the best way to understand how individual components fit together. You only interact with

session

in interactive mode (Jupyter/Ipython), the most common way to interact with kedro is via the CLI,

kedro run

which execute the pipeline. Under the hood, it will create all necessary components like

session

,

context

,

catalog

for you. To start a new kedro project, you would do

kedro new

. You can do

kedro new --starter=spaceflights

which will create a template project for you with more advance kedro features. Then you can run a pipeline via

kedro run

. The tutorial above will guide you step by step how to creating these project in practice.

Thank you @noklam ! But what if I wanted to specify, for testing purposes, a fake catalog that I specifically created before the

session.run

? The

runner.run

has the DataCatalog argument, while

session.run

doesn't, but in this case would be useful to be able to pass a specific DataCatalog. Thank you again for your time

Can I ask for more details? What are you trying to test? You are correct that

session.run()

doesn't have the data catalog argument because it is managed by the session itself.

I am trying to test locally some pipelines, passing test data as inputs that I create using Python classes because it is easier in this way because data are Spark parquets. So I created a pipeline tester that takes the pipeline as argument and basically runs it giving this fake data as a catalog

Would it be easier to just have a environment called "test", where you have test data fixture and just trigger the test with

kedro run --env=test

? or optionally

kedro run --env=test --pipeline=TARGET_PIPELINE

?

Hi guys. I am having trouble using the DataCatalog module in Jupyter Notebook. The problem is that I don't know how to specify custom datasets. When I try to load using DataCatalog.from_config() using my own catalog.yml I get the following error:

DataSetError: An exception occurred when parsing config for DataSet `val_csv_glebas`:
Class `local_pipeline.extras.io.vector_datasets.ShpVectorDataset` not found or one of its dependencies has not been installed.

How can I make the module

local_pipeline.extras.io.vector_datasets.ShpVectorDataset

be known to DataCatalog class?

So this means there is an error in your definition. The underlying error should be further up in the stack trace. A good way to debug is to import the class in the notebook and try configuring it in python not YAML Lastly you shouldn't need to create the catalog yourself, if you do

kedro jupyter notebook

it will be registered for you

True. I was experimenting with the DataCatalog so I could get it in a standalone way but it is better to do like this. Thanks for the tip.

Mono repo Kedro project https://stackoverflow.com/questions/71997005/mono-repo-kedro-project

Hi this might have been answered elsewhere but I was not able to find anything. I have seen on several github issues that one way of doing a package deployment is to include the

conf

as part of the

src/

file. Is there any good example on how that actually looks like? I guess what I am trying to figure out is once it's there do people add it to

package_data

of setup.py and then somehow change the

CONF_SOURCE

variable in settings.py or are there better approaches. Use case is an airflow deployment

Hi this might have been answered

Hello, I am wondering if there is any possibility to list all available versions of kero's catalog versioned dataset ?

So in a kedro ipython session you can access the live catalog object pull them out, but I don't think there is a public API

What do you mean by "pull them out". I think I can load the item providing its version. That's great. But I would like to list the available versions of catalog's item, first.

Get available Versions of versioned dataset

Is it possible to access project parameters from inside a hook?

Sure is - it's actually just a dataset in the catalog