Has anyone used ibis.TableDataset with duckdb schemas? If I set a schema on a data catalog entry I get the error

Invalid Input Error: Could not set option "schema" as a global option

.

bronze_x:
  type: ibis.TableDataset
  filepath: x.csv
  file_format: csv
  table_name: x
  backend: duckdb
  database: data.duckdb
  schema: bronze

I can reproduce this error with vanilla ibis:

con = ibis.duckdb.connect(database="data.duckdb", schema = "bronze")

Found a related question on ibis' github, it sounds like duckdb can't set the schema globally so it has to be done in the table functions. Wondering if this would require a change to ibis.TableDataset, and if so, would this pattern work the same with other backends?

Am I correct in understanding that Kedro-Pandera will only work with pandas schemas currently? I saw that it uses

pandera.io.deserialize_schema

under the hood in it's schema resolver, and that seems to be only implemented in pandera for pandas, is that right?

Hello everyone This is regarding the kubeflow plugin. I wanted to just gain some information about how kedro nodes are executed by kubeflow. Does kubeflow run each node in a separate container ?? or separate pods ?? or all of nodes are executed in the same container

Hi, everyone. Can someone confirm if its possible to use kedro-azureml with kedro>=0.19? From what I see here it's not, but wanted to confirm it if mabe the website is outdated

Hey Folks I am looking for a way to mount AWS EFS volume to my kedro pipeline which will be executed by kubeflow . I am using the kubeflow plugin. The config has below 2 options for Volumes , I am not sure which one is for what purpose 1.

volume:

    # Storage class - use null (or no value) to use the default storage
    # class deployed on the Kubernetes cluster
    storageclass: # default

    # The size of the volume that is created. Applicable for some storage
    # classes
    size: 1Gi

    # Access mode of the volume used to exchange data. ReadWriteMany is
    # preferred, but it is not supported on some environements (like GKE)
    # Default value: ReadWriteOnce
    #access_modes: [ReadWriteMany]

    # Flag indicating if the data-volume-init step (copying raw data to the
    # fresh volume) should be skipped
    skip_init: False

    # Allows to specify user executing pipelines within containers
    # Default: root user (to avoid issues with volumes in GKE)
    owner: 0

    # Flak indicating if volume for inter-node data exchange should be
    # kept after the pipeline is deleted
    keep: False

2.

# Optional section to allow mounting additional volumes (such as EmptyDir)
  # to specific nodes
  extra_volumes:
    tensorflow_step:
    - mount_path: /dev/shm
      volume:
        name: shared_memory
        empty_dir:
          cls: V1EmptyDirVolumeSource
          params:
            medium: Memory

Hey Everyone I wanted to know more about kedro CLI that we have . So there are arguments like

--env , --nodes , -- pipelines

which we pass using the

kedro run

command . So for any given plugin related to deployments like airflow , kubeflow . How can we supply these arguments ?

Hey all. I recently stumbled across "React Flow" (link) while searching for an open source graph & node drag-and-drop solution. Specifically, I am looking to create a UI for my team, which would allow for easy no-code pipeline creation, by presenting a list of available nodes (on the side, as "prewritten" functions) which can then be dragged-dropped-and-connected with the rest of the pipeline. I am unsure if something like this would... 'play nice' with kedro-viz (now and/or in the future), but is there anyone who has thought about this before? If so, what did you do about it (ideally in combination with kedro/kedro-viz)? [I have posted 2 relevant links below]

Hey there! Quick question about kedro-azureml. We are using AzureML, and we'd like to use AzureMLAssetDataset with dataset factories. After a lot of headach and debugging, it seems impossible to use both, as the way credentials are passed to the AzureMLAssetDataset is done through a hook (after_catalog_created), but the issue is that if you use a dataset_patterns (as in, declare your dataset as "{name}.csv" or something similar), the hook is called, but the patterned dataset is not instanciated yet. After all that, a before_node_run is called, and then there is a AzureMLAssetDataset._load() called, but the AzureMLAssetDataset.azure_config setter hasn't been called yet (as it is called only in the after_catalog_created hook). At first glance, it seems like a kedro-azureml issue, as AzureMLAssetDataset._load() can be called without the setter being called when used as a dataset factory. But also, it might be a kedro issue, as I think there should be an obvious way to setup credentials in that specific scenario, and I don't quite see it from the docs on hook either

Hey guys I would like to know if theres anyone that have tested the Kedro Vertex AI Plugin, on its latest version. I'm having some issues with async node runs, for some reason it is taking a lot longer than when run locally. It might be because I'm allocanting a GPU to parto of the process, but it shouldn't, in my perspective, so if anyone have any ideas or suggestions, I'll appreciate that...

Hey there, another question on the ibis.TableDataset. Just moving a bunch of our local code (duckdb) to databricks and hit a snag. We're using unity catalog (UC). I loaded raw tables into UC manually for simplicity and confirmed I can load them using an ibis connection (see screenshot 1). When I try to load this table in using the TableDataset I get an error saying "`raw_tracks` cannot be found" (see screenshot 2). I think this is because the load() method doesn't pull in database from the config...

raw_tracks:
  type: ibis.TableDataset
  table_name: raw_tracks
  connection:
    backend: pyspark
    database: comms_media_dev.dart_extensions

def load(self) -> ir.Table:
            return self.connection.table(self._table_name)

I think updating load() seems fairly simple, something like the code below works, but was the initial intent that we could pass a catalog / database through the config here? If yes on the latter I think perhaps I'm not using the spark config properly or databricks is doing something strange... posted a question about that here for context.

def load(self) -> ir.Table:
            return self.connection.table(name = self._table_name, database = self._database)

Hey folks, has anyone use the kedro-azureml plugin on a Apple M1 mac? Seem to be unable to install it locally due to a dependency on packages that are unsupported on M1 chips (azureml-sdk etc,).

Stay tuned with upcoming Kedro VSCode releases (it will probably show up in 0.3.0, we will release 0.2.3 for some bug fixes including Windows issue), we are working on improving the static catalog validation, It will validate against user virtual environment, so it's able to detect missing dependencies/third parties dependencies.

Hi Team, I'm getting an issue while using kedro-azureml using this doc - link, Able to run all steps without any issues but while the pipeline runs in Azure ML it gives the following error:

Failed to execute command group with error Container `0341a555koec4794bb36cf074f0386h-execution-wrapper` failed with status code `1` and it was not possible to extract the structured error Container `0341a555koec4794bb36cf074f0386h-execution-wrapper` exited with code 1 due to error None and we couldn't read the error due to GetErrorFromContainerFailed { last_stderr: Some("exec /mnt/azureml/cr/j/0341a555koec4794bb36cf074f0386h/cap/lifecycler/wd/execution-wrapper: no such file or directory\n") }.

Pipeline screenshot from Azure ML:

Hi Team! Anyone ever played with hyperparameter tuning frameworks within kedro? I have found several scattered pieces of info related to this topic, but no complete solutions. Ultimately, I think what I would like to set up is a way to have multiple nodes running at the same time and all contributing to the same tuning experiment. I would prefer using optuna and this is the way I would go about it based on what I have found online: 1. Create a node that creates an optuna study 2. Create N nodes that each run hyperparameter tuning in parallel. Each of them loads the optuna study and if using kedro-mlflow each hyperparameter trial can be logged into its own nested run. 3. Create a final nodes that process the results of all tuning nodes Does this sound reasonable to you? Has anyone produced such a kedro workflow already? I would love to see what it looks like. I am also wondering: • I am thinking of creating an OptunaStudyDataset for the optuna study . Has anyone attempted this already? • For creating N tuning nodes, I am thinking of using the approach presented on the GetInData blog post on dynamic pipelines. Would this be the recommended approach? Thanks!

Hi guys, I am having trouble to run my kedro from a docker build. I'm using MLflow and the

kedro_mlflow.io.artifacts.MlflowArtifactDataset

I followed the instructions for building the container from kedro-docker repo but when running, those artifacts want to access my local windows path instead of the containers path. Do you guys know what additional settings I have to make? All my settings in are pretty much vanilla. The

mlflow_tracking_uri

is set to null

"{dataset}.team_lexicon":
  type: kedro_mlflow.io.artifacts.MlflowArtifactDataset  
  dataset:
    type: pandas.ParquetDataset  
    filepath: data/03_primary/{dataset}/team_lexicon.pq 
    metadata:
      kedro-viz:
        layer: primary  
        preview_args:
            nrows: 5

Traceback (most recent call last):
  
kedro.io.core.DatasetError: Failed while saving data to dataset MlflowParquetDataset(filepath=/home/kedro_docker/data/03_primary/D1-24-25/team_lexicon.pq, load_args={}, protocol=file, save_args={}).
[Errno 13] Permission denied: '/C:'

Hello, guys, I noticed that there is no support for

log_table

method in kedro-mlflow. So I wonder what will be the right way to log additional data from a node, something that is not yet supported by the plugin? Right now I just do something like this at the end of the node function

mlflow.log_table(data_for_table, output_filename)

But I am concerned as I am not sure if it will always work and will always log the data to the correct run because I was not able to get retrieve the active run id from inside the node with

mlflow.active_run()

(it returns

None

all the time). I need this because I want to use the

Evaluation

tab in the UI to manually compare some outputs of different runs.

Hello guys! Noticed there is a typing-annotation bug in

kedro-mlflow 0.14.3

specific to

python 3.9

. It seems that a fix is already merged in the repo. When would the fix be released? Thank!

I think this belongs in plugins! If I remember correctly, there was once a pycharm friendly version of this: https://github.com/kedro-org/vscode-kedro Does that exist anywhere still?

hi folks, in case it's useful for anybody, yesterday I quickly hacked a kedro-openlineage integration, and demonstrated it using Marquez. I guess it should work with any OL consumer but you tell me 🙂 https://github.com/astrojuanlu/kedro-openlineage

happy to announce that @em-pe released

kedro-azureml

0.9.0 and

kedro-vertexai

0.12.0 with support for the most recent Kedro and Python versions. you can thank GetInData for it 👏🏼

I think Kedro

0.19.12

and the changes we did to the databricks starter (https://github.com/kedro-org/kedro-starters/pull/267) might have broken the resource creation for the

kedro-databricks

plugin @Jens Peder Meldgaard. When I do

kedro databricks bundle

the resources folder gets created, but it's empty. (cc: @Sajid Alam)

Hi @Jens Peder Meldgaard, I'm learning more about how

kedro-databricks

works and I was wondering whether it makes sense to use any of the other runners (

ThreadRunner

or

ParallelRunner

)? As far as I understand for every node we use these run parameters

--nodes name, --conf-source self.remote_conf_dir, --env self.env

. Would it make sense to allow for adding runner type too? Or if you want parallel running you should use the databricks cluster setup for that? I'm not very familiar with all the run options in Databricks, so trying to figure out where to use Kedro features and where Databricks. (cc: @Rashida Kanchwala)

Does

kedro-mlflow

support custom model flavors in datasets? I'm reading in docs that yes, but wanted to double check that this is relevant. @Yolan Honoré-Rougé

(and for the record kedro mlflow has a built in custom model to log an entire kedro pipeline which may be useful)

Hi, This issue was created for

kedro-databricks

and I am uncertain of how to resolve it - anyone who can help me figure out what to do here? 🙏 https://github.com/JenspederM/kedro-databricks/issues/135 A bit of explanation: The issue occurs when using

namespaces

for pipelines, as it prepends the

namespace

to any input and output resulting in, e.g.,

ValueError: Pipeline input(s) {'active_modelling_pipeline.X_train', 'active_modelling_pipeline.y_train'} not found in the DataCatalog

when using a

namespace

called

active_modelling_pipeline

. When nodes are executed in Databricks, each node is executed in a workflow task with a command similar to

kedro run --nodes <node-name> --conf-source <some-path> --env <some-env>

. Do I need to add the

--namespace <some-namespace>

option to the invocation to get it to correctly resolve the catalog paths?

Hi team, are there plans to make

kedro-pandera

support 1.0? @Yolan Honoré-Rougé @Nok Lam Chan

@Max Pardoe has left the channel

@SIMON TAMAYO has left the channel

Hi 👋 I would like to try and publish the kedro-viz of a kedro project on GitHub pages using the dedicated action. The issue is that my project is using uv and it seems to me the action uses system Python. I have tried a wide range of tricks unsuccessfully and no matter what I do the action sees my project and its dependencies uninstalled. I was wondering if anyone here has been running into this problem? Here is my currently failing workflow:

name: Publish and share Kedro Viz

permissions:
  pages: write
  id-token: write

on:
  pull_request:
  push:
    branches:
      - main
  workflow_dispatch:

jobs:
  deploy-viz:
    name: Deploy Kedro Viz
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - name: Install the latest version of uv
        uses: astral-sh/setup-uv@v4
        with:
          enable-cache: true
          cache-dependency-glob: "pyproject.toml"
          github-token: ${{ secrets.GITHUB_TOKEN }}
          python-version: "3.13"

      - name: Sync uv
        run: uv sync --group viz

      - name: Deploy Kedro-Viz to GH Pages
        uses: kedro-org/publish-kedro-viz@v2

Thanks a lot for your help :)

Hi everyone 👋 I am really excited to finally share a plugin I’ve been working on for over a year: kedro-dagster which allows user to orchestrate Kedro pipelines using dagster, a modern asset-oriented Python orchestrator. It also comes with a companion example repo kedro-dagster-example. It has been an amazing learning journey for me and I am grateful to the Kedro community for creating such a positive environment where all questions are answered with kindness and curiosity. A special thanks to @Deepyaman Datta for the early guidance :) Main features - Lets you map your existing Kedro pipelines into the Dagster orchestrator: auto-generate a

definitions.py

and use a

dagster.yml

in your config to specify jobs, schedules, executors etc. - Preserves your Kedro hooks intact. In particular, it works seamlessly with kedro-mlflow. - Experimentally supports Dagster partitions by fanning-out Kedro nodes acting on partitioned datasets. - The example repo is a full-blown small project showing how it can be wired up. It makes use of dynamic pipelines and also showcases distributed hyperparameter tuning using a new `optuna.StudyDataset` experimental dataset. Get started - Docs: https://kedro-dagster.readthedocs.io/ - Plugin repo: https://github.com/gtauzin/kedro-dagster - Example repo: https://github.com/gtauzin/kedro-dagster-example - Kedro-Viz of the example repo: https://gtauzin.github.io/kedro-dagster-example/ I would love your help & feedback It would mean a lot if you could: * Try it out in one of your Kedro projects * Spot issues, missing bits or docs gaps * Share how you would use it, or ideas for features/improvements * Reach out if you would like to contribute! I am looking forward to hearing what you think and how you might use it! :)