Kedro-MlOps

This project intends and define a Dev(ML)Ops pipeline On Kedro

Overview

This repo is divided into sessions in which you are tasked to develop different aspects of a DevOps pipeline. Bear in mind that for each exercise there is a task branch in which you will have the basic setup to start your exercise.

In the main branch you will find the terminated product of all exercises so feel free to compare your results with it. Below I am going to list the exercises of this repo along with their respective guide:

1. Develop a CI pipeline
2. Develop a CD pipeline

Setup

Configure a Virtual Environment

To install de project you must have conda and setup a Python3.7 environment as follows:

conda create --name Kedro-MlOps python=3.7 -y

Then you need to activate your virtualenv

conda activate Kedro-MlOps

Note: if you are using windows you may need to use a cmd shell instead of a powershell to activate a conda environment

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

pip install -r src/requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

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

kedro test

To configure the coverage threshold, go to the .coveragerc file.

Project dependencies

To generate or update the dependency requirements for your project:

kedro build-reqs

This will pip-compile the contents of src/requirements.txt into a new file src/requirements.lock. You can see the output of the resolution by opening src/requirements.lock.

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

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, catalog, and startup_error.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r src/requirements.txt you will not need to take any extra steps before you use them.

Jupyter

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

pip install jupyter

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

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using 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. 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

How to ignore notebook output cells in git

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 retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project

Run your project in Airflow

The easiest way to run your project in Airflow is by installing the Astronomer CLI and follow the following instructions:

Package your project:

kedro package

Copy the package at the root of the project such that the Docker images created by the Astronomer CLI can pick it up:

cp src/dist/*.whl ./

Generate a catalog file with placeholders for all the in-memory datasets:

kedro catalog create --pipeline=__default__

Edit the file conf/base/catalog/__default__.yml and choose a way to persist the datasets rather than store them in-memory. E.g.:

example_train_x:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_train_x.pkl
example_train_y:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_train_y.pkl
example_test_x:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_test_x.pkl
example_test_y:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_test_y.pkl
example_model:
  type: pickle.PickleDataSet
  filepath: data/06_models/example_model.pkl
example_predictions:
  type: pickle.PickleDataSet
  filepath: data/07_model_output/example_predictions.pkl

Install the Kedro Airflow plugin and convert your pipeline into an Airflow dag:

pip install kedro-airflow
kedro airflow create -t dags/

Run your local Airflow instance through Astronomer:

astro dev start