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
cdinto 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.
In order to get the best out of the template:
- Please don't remove any lines from the
.gitignorefile 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/
Dependencies should be declared in src/requirements.txt for pip installation and src/environment.yml for conda installation.
To install them, run:
kedro install
You can run your Kedro project with:
kedro run
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.
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.
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
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.
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/.
To build API docs for your code using Sphinx, run:
kedro build-docs
See your documentation by opening docs/build/html/index.html.
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.