kedro | Python framework | Machine Learning library
kandi X-RAY | kedro Summary
kandi X-RAY | kedro Summary
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
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Top functions reviewed by kandi - BETA
- Creates a pipeline for the given parameters .
- Builds a data set from the given catalog .
- Formats an object .
- Replace source code for a specific package .
- Creates a new node with decorators
- Creates a node from the given functions .
- Finds the syntactic replacements for a given module .
- Replace package_name with original name and destination_name
- Parse a dataset definition .
- Creates a kedroro session .
kedro Key Features
kedro Examples and Code Snippets
Lines of Code : 66
License : No License
Copy# nodes.py
import pandas as pd
from .lib import inscribe_triangles
def circles_to_triangles(circles: pd.DataFrame) -> pd.DataFrame:
"""
Takes a collection of circles, inscribes a triangle in each circle,
returns the collection of insc # my-proj/pipelinies/data_engineering/pipeline
from kedro.pipeline import node
from .nodes import split_data
pipeline = [
node(
split_data,
["example_iris_data", "params:example_test_data_ratio"],
dict(
train_ Usage: find-kedro [OPTIONS]
Options:
--file-patterns TEXT glob-style file patterns for Python node module
discovery
--patterns TEXT prefixes or glob names for Python pipeline, node,
Community Discussions
Trending Discussions on kedro
QUESTION
I was looking at iris project example provided by kedro. Apart from logging the accuracy I also wanted to save the predictions and test_y as a csv.
This is the example node provided by kedro.
...ANSWER
Answered 2022-Jan-24 at 15:31Kedro actually abstracts this part for you. You don't need to access the datasets via their Python API.
Your report_accuracy method does need to be tweaked to return the DataFrame instead of None.
Your node needs to be defined as such:
QUESTION
I want to save Kedro memory dataset in azure as a file and still want to have it in memory as my pipeline will be using this later in the pipeline. Is this possible in Kedro. I tried to look at Transcoding datasets but looks like not possible. Is there any other way to acheive this?
...ANSWER
Answered 2022-Jan-18 at 15:22I would try explicitly saving the dataset to Azure as part of your node logic, i.e. with catalog.save(). Then you can feed the dataset to downstream nodes in memory using the standard node inputs and outputs.
QUESTION
I'm quite new using Kedro and after installing kedro in my conda environment, I'm getting the following error when trying to list my catalog:
Command performed: kedro catalog list
Error:
kedro.io.core.DataSetError: An exception occurred when parsing config for DataSet
df_medinfo_raw: ObjectParquetDataSetcannot be loaded fromkedro.extras.datasets.pandas. Please see the documentation on how to install relevant dependencies for kedro.extras.datasets.pandas.ParquetDataSet:
I installed kedro trough conda-forge: conda install -c conda-forge "kedro[pandas]". As far as I understand, this way to install kedro also installs the pandas dependencies.
I tried to read the kedro documentation for dependencies, but it's not really clear how to solve this kind of issue.
My kedro version is 0.17.6.
...ANSWER
Answered 2022-Jan-15 at 12:10Try installing using pip
QUESTION
I would like to create my own Kedro starter. I have tried to replicate the relevant portions of the pandas iris starter. I have a cookiecutter.json file with what I believe are appropriate mappings, and I have changed the repo and package directory names as well as any references to Kedro version such that they work with cookie cutter.
I am able to generate a new project from my starter with kedro new --starter=path/to/my/starter. However, the newly created project uses the default values for the project, package, and repo names, without prompting me for any input in the terminal.
Have I misconfigured something? How can I create a starter that will prompt users to override the defaults when creating new projects?
Here are the contents of cookiecutter.json in the top directory of my starter project:
ANSWER
Answered 2022-Jan-06 at 16:55I think you may be missing prompts.yml
https://github.com/quantumblacklabs/kedro/blob/main/kedro/templates/project/prompts.yml
Full instructions can be found here: https://kedro.readthedocs.io/en/stable/07_extend_kedro/05_create_kedro_starters.html
QUESTION
Is there any way to access the kedro pipeline environment name? Actually below is my problem.
I am loading the config paths as below
...ANSWER
Answered 2021-Dec-15 at 20:27You don't need to define config paths, config loader etc unless you are trying to override something.
If you are using kedro 0.17.x, the hooks.py will look something like this.
Kedro will pass, base, local and the env you specified during runtime in conf_paths into ConfigLoader.
QUESTION
I currently work with Kedro (from quantum black https://kedro.readthedocs.io/en/stable/01_introduction/01_introduction.html) as a framework for deployment oriented framework to code collaboratively. It is a great framework to develop machine learning in a team.
I am looking for an R equivalent.
My main issue is that I have teams of data scientists that develop in R, but each team is developing in different formats.
I wanted to make them follow a common framework to develop deployment ready R code, easy to work on in 2 or 3-people teams.
Any suggestions are welcome
...ANSWER
Answered 2021-Dec-15 at 15:10Not on a very prominent scale as kedro but i can think of the below :
- Local project of a R Expert : https://github.com/Jeniffen/projectr
- Pipeliner on Tidyverse : https://cran.r-project.org/web/packages/pipeliner/index.html
QUESTION
I am using PartitionedDataSet to load multiple csv files from azure blob storage. I defined my data set in the datacatalog as below.
...ANSWER
Answered 2021-Dec-05 at 06:25Move load_args inside dataset
QUESTION
I launched ipython session and trying to load a dataset.
I am running
df = catalog.load("test_dataset")
Facing the below error
NameError: name 'catalog' is not defined
I also tried %reload_kedro but got the below error
UsageError: Line magic function `%reload_kedro` not found.
Even not able to load context either. I am running the kedro environment from a Docker container. I am not sure where I am going wrong.
...ANSWER
Answered 2021-Nov-23 at 12:43new in 0.17.5 there is a fallback option, please run the following commands in your Jupyter/IPython session:
QUESTION
I would like to log the git_sha parameter on Mlflow as shown in the documentation. What appears to me here, is that simply running the following portion of code should be enough to get git_sha logged in the Mlflow UI. Am I right ?
...ANSWER
Answered 2021-Nov-17 at 14:26Whilst it's heavily encouraged to use git with Kedro it's not required and as such no part of Kedro (except kedro-starters if we're being pedantic) is 'aware' of git.
In your before_pipeline_hook there it is pretty easy for you to retrieve the info via the techniques documented here. It seems trivial for the whole codebase, a bit more involved if you want to say provide pipeline specific hashes.
QUESTION
According to Kedro's documentation, Azure Blob Storage is one of the available data sources. Does this extend to ADLS Gen2 ?
Haven't tried Kedro yet, but before I invest some time on it, I wanted to make sure I could connect to ADLS Gen2.
Thank you in advance !
...ANSWER
Answered 2021-Nov-12 at 10:27Yes this works with Kedro. You're actually pointing a really old version of the docs, nowadays all filesystem based datasets in Kedro use fsspec under the hood which means they work with S3, HDFS, local and many more filesystems seamlessly.
The ADLS Gen2 is supported by ffspec via the underlying adlfs library which is documented here.
From a Kedro point of view all you need to do is declare your catalog entry like so:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install kedro
You can use kedro like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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