Awesome

kedro-mlflow-example

Overview

This project demonstrates in a simple manner how to integrate MLflow with a Kedro codebase. The Medium post with detailed instructions can be found here

To get started:

• Create a Conda environment with Python 3.6 - conda create -n my_env python=3.6
• Install kedro - pip install kedro==0.15.4
• Clone the repository and cd into the project root
• Install dependencies - kedro install
• Run the project - mlflow run .

The following documentation is standard for Kedro projects.

This project was generated using Kedro 0.15.4 by running:

kedro new

Take a look at the documentation to get started.

Rules and guidelines

In order to get the best out of the template:

• Please don't remove any lines from the .gitignore file provided
• Make sure your results can be reproduced by following a data engineering convention, e.g. the one we suggest here
• Don't commit any data to your repository
• Don't commit any credentials or local configuration to your repository
• Keep all credentials or local configuration in conf/local/

Installing dependencies

Dependencies should be declared in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

kedro install

Running Kedro

You can run your Kedro project with:

kedro run

Testing Kedro

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests with the following command:

kedro test

To configure the coverage threshold, please have a look at the file .coveragerc.

Working with Kedro from notebooks

In order to use notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

For using Jupyter Lab, you need to install it:

pip install jupyterlab

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

You can also start Jupyter Lab:

kedro jupyter lab

And if you want to run an IPython session:

kedro ipython

Running Jupyter or IPython this way provides the following variables in scope: proj_dir, proj_name, conf, io, parameters and startup_error.

Converting notebook cells to nodes in a Kedro project

Once you are happy with a notebook, you may want to move your code over into the Kedro project structure for the next stage in your development. This is done through a mixture of cell tagging and Kedro CLI commands.

By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/.

kedro jupyter convert <filepath_to_my_notebook>

Note: The name of the Python file matches the name of the original notebook.

Alternatively, you may want to transform all your notebooks in one go. To this end, you can run the following command to convert all notebook files found in the project root directory and under any of its sub-folders.

kedro jupyter convert --all

Ignoring notebook output cells in git

In order to automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.

Note: Your output cells will be left intact locally.

Package the project

In order to package the project's Python code in .egg and / or a .wheel file, you can run:

kedro package

After running that, you can find the two packages in src/dist/.

Building API documentation

To build API docs for your code using Sphinx, run:

kedro build-docs

See your documentation by opening docs/build/html/index.html.

Building the project requirements

To generate or update the dependency requirements for your project, run:

kedro build-reqs

This will copy the contents of src/requirements.txt into a new file src/requirements.in which will be used as the source for pip-compile. You can see the output of the resolution by opening src/requirements.txt.

After this, if you'd like to update your project requirements, please update src/requirements.in and re-run kedro build-reqs.