Thanks for answering @User 👑

Saving data with DataCatalog https://stackoverflow.com/questions/68923747/saving-data-with-datacatalog

Has anyone served a custom MLFlow model and that was trained and saved in kedro via

kedro-mlflow

? I have trained a custom mock model in which I define operations to save internal data sources to an mlflow run and load them into the desired custom

mlflow.pyfunc.PythonModel

. I am able to serve the model using

--no-conda

. Without this flag I get an error raised in which

ModuleNotFoundError: No module named '<kedro_package_name>'

. In my MLModel file I set

loader_module: <kedro_package_name>.extras.mlflow.loader_cosine_model

. I can also provide any other additional details if anyone can help!

Hi, I think you have declared your PipelineML object as in this demo: https://github.com/Galileo-Galilei/kedro-mlflow-tutorial/blob/4c85c357162a85093f0875fe3085fbd9ebe2e4be/src/kedro_mlflow_tutorial/hooks.py#L60-L70 You can see that the it is possible to specify the ``conda_env`` here. It accepts either a path or a dictionnary. I suggest in the tutorial to use ``{your_kedro_package}=={__version__} ``, because in an enterprise setup, I often deploy the code of my package in an internal Nexus /Pypi, so it can be downloaded when needed. It won't work for you if you haven't publish your package on PyPI fist, because conda tries to to pip install it. 4 solutions: - pass the dictionary of your requirements instead of the default one in the ``pipeline_ml_factory`` call - pass the path to your requirements.txt or conda.yml file ``pipeline_ml_factory`` call - publish your package on PyPI so conda can download it (not recommended for public projects, but likely the best solution in an enterprise setup) - create an empty conda env, activate it, install manually your package inside it (``pip install -e /path/to/kedro/package/src``) and call ``mlflow serve`` inside it.

Hello everyone. how to get the current run_id inside a node when using SequentialRunner.run (not using KedroSession nor KedroContext) ?

It’s present in the hooks https://kedro.readthedocs.io/en/latest/kedro.framework.hooks.specs.NodeSpecs.html#kedro.framework.hooks.specs.NodeSpecs.before_node_run Further reading - https://kedro.readthedocs.io/en/latest/07_extend_kedro/02_hooks.html

I know about hooks, but I don't see in the documentation how to register hooks without KedroSession ? I am using SequentialRunner.run to run my pipelines not KedroSession.

How to let kedro execute nodes in sequence https://stackoverflow.com/questions/69126849/how-to-let-kedro-execute-nodes-in-sequence

Ah I’m sorry I miss understood. If you’re not following the standard structure I think your only option is to maybe define a custom runner

Running a kedro pipeline with inputs and outputs defined in the command line https://stackoverflow.com/questions/69129846/running-a-kedro-pipeline-with-inputs-and-outputs-defined-in-the-command-line

While running kedro - ImportError: Bad git executable https://stackoverflow.com/questions/69132675/while-running-kedro-importerror-bad-git-executable

Hi everyone! I would like to be able to pass command line arguments (like input path and output path) from the command line to update the catalog. I sow the possibility of using kedro.config.TemplatedConfigLoader, but it requires to manually define the global_dict variables in the hooks. Is there a way to intercept arguments defined through the CLI and use them in the TemplatedConfigLoader ? Furthermore, thinking about the problem with dynamic catalogues and reproducibility, It would be nice to be able to save the rendered jinja2 catalog such that if one would want to reproduce it, he/she would just have to run the pipeline with the saved catalogue. What would be the Kedro way of doing this? Thanks for your help!

We've actually been talking about a lot of issues related to configuration here: https://github.com/quantumblacklabs/kedro/issues/891 Do you want to pop a comment on this GitHub issue?

I also have a configuration related question: Can I run

kedro run --env test

and still have my settings in 'test' overwritten by my settings in 'local'? I think it only works when NOT specifying an environment

otherwise my settings in conf/local are ignored

You would have to alter the templates config loader to do this - IIRC there is a post on this very topic by @Ignacio if you search for it

Hmm, I am thinking about opening a feature request for that because in my current project I am always using an environment - therefore 'local' does not work at all. Can you point me to the post by @User ? I was not able to find it in git

I think this is it https://discord.com/channels/778216384475693066/846330075535769601/875275273430515795

Interesting results! Thanks for pointing me to it!

Hello everyone. I’d like an opinion on a scalability issue I’m facing, mostly because I’m new to Kedro, and maybe yall can point me to the best way out of this. My problem is that I have a generic enough pipeline that can be applied to some factory equipments. Currently it attends only one but the client wants to scale this up. A project characteristic is that the catalog and parameters change substantially between the applications. And a restriction is that we want to be able to run the pipelines independently as needed, meaning that not only raw data should be changed between them, but the outputs too. I’m worried about duplicating code because it makes maintenance difficult and I also worried about losing the convenient environment usage to separate domains

local

,

production_vm

,

production_cloud

, etc. That said … how can I keep the pipeline reusable with substantial changes in catalog and parameters? Thanks for reading! 👍

P.S.: It's not that I haven't found ways out of it ... but my idea is to check on best practices, and avoid future reworks.

You can use nested environment in this case. In

conf/

, you can maintain the following structure:

conf/
|__ app1/
    |__ local
    |__ vm
|__ app2/

And run the pipeline with

kedro run --env="app1/local"

Thanks for the idea @User, following your example, will it respect a default parameter file for each app?

conf/
├── app1/
│   ├── parameters.yml
│   ├── local/
│   │   └── catalog.yml
│   └── vm/
│       ├── catalog.yml
└── app2

will it read the

conf/app1/parameters.yml

if passed

--env="app1/local"

?

@Waldrill it should take any of the keys present in those nested parameters files as overrides

Cool @User, was the environment made for this kind of separation (between applications I mean) or it is just a work around? I don't want to use something that might not be designed for it and therefore struggle with it in the feature releases.

No it’s a core part of Kedro

The canonical example is staging/prod/test