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README.md

POC versioning Machine Learning pipeline

The aim of this repository is to show a way to handle pipelining and versioning of a Machine Learning project.

Processes exposed during this tutorial are based of 3 existing tools:

Use cases are based on a text classification task on 20newsgroup dataset. A dummy tutorial is also available to show tools mechanisms.

Requirements:

Before starting, you must be familiar with the following commands:

  • virtualenv or condaenv
  • make
  • git
  • python3

Tools Overview

DVC: an open-source tool for data science and machine learning projects. Use to version, share and reproduce.

MLflow tracking: API and UI to log and visualize metrics obtained during experiments.

MLV-tools: provides a set of tools to enhance Jupyter Notebooks conversion and DVC versioning and pipelining.

Please have a look to the presentation

Our main features

Standard Versioning Process Establishment

Goal: find a way to version code, data and pipelines.

Existing project

Starting from an existing project composed of Python 3 module(s) and a set of Jupyter notebooks, we want to create an automated pipeline in order to version, share and reproduce experiments.

│── classifier
│   ├── aggregate_classif.py
│   ├── __init__.py
│   ├── extract.py
│   └── ...
│── notebooks
│   ├── Augment train data.ipynb
│   ├── Check data and split and train.ipynb
│   ├── Extract data.ipynb
│   ├── Learn text classifier.ipynb
│   ├── Learn aggregated model.ipynb
│   ├── Preprocess image data.ipynb
│   └── Train CNN classifier on image data.ipynb
│── README.md
│── requirements.yml
│── setup.cfg
│── setup.py

The data flow is processed by applying steps and intermediary results are versioned using metadata files. These steps are defined in Jupyter notebooks, which are then converted to Python scripts.

Keep in mind that:

  • The reference for the code of the step remains in Jupyter notebook
  • Pipelines are structured according to their inputs and outputs
  • Hyperparameters are pipeline inputs

Project after refactoring

│── classifier
│   ├── aggregate_classif.py
│   ├── __init__.py
│   ├── extract.py
│   └── ...
│── notebooks
│   ├── Augment train data.ipynb
│   ├── Check data and split and train.ipynb
│   ├── Extract data.ipynb
│   ├── Learn text classifier.ipynb
│   ├── Learn aggregated model.ipynb
│   ├── Preprocess image data.ipynb
│   └── Train CNN classifier on image data.ipynb
│── pipeline
│   ├── dvc                                        ** DVC pipeline steps 
│   │   ├─ mlvtools_augment_train_data_dvc              
│   │   ├─ ..
│   ├── scripts                                    ** Notebooks converted into Python 3 configurable scripts
│   │   ├─ mlvtools_augment_train_data.py
│   │   ├─ ..
│── README.md
│── requirements.yml
│── setup.cfg
│── setup.py

Notebooks converted into configurable Python 3 scripts: obtained by Jupyter notebook conversion.

DVC pipeline steps: DVC command applied on generated Python 3 scripts

Applying the process

For each Jupyter notebook a Python 3 parameterizable and executable script is generated. It is the way to version code and be able to automatize its run.

Pipelines are composed of DVC steps. Those steps can be generated directly from the Jupyter notebook based on parameters describe in the Docstring. (notebook -> python script -> DVC command)

Each time a DVC step is run a DVC meta file ([normalize_notebook_name].dvc) is created. This metadata file represent a pipeline step, it is the DVC result of a step execution. Those files must be tacked using Git. They are used to reproduce a pipeline..

Application:

For each step in the tutorial the process remain the same.

  1. Write a Jupyter notebook which correspond to a pipeline step. (See Jupyter notebook syntax section in MLVtools documentation)

  2. Test your Jupyter notebook.

  3. Add it under git.

  4. Convert the Jupyter notebook into a configurable and executable Python 3 script using ipynb_to_python.

      ipynb_to_python -n ./pipeline/notebooks/[notebook_name] -o ./pipeline/steps/[python_script_name]
    
  5. Ensure Python 3 executable and configurable script is well created into ./pipeline/steps/[python_script_name].

      ./pipeline/steps/[python_script_name] -h
    
  6. Create a DVC commands to run the Python 3 script using DVC.

      gen_dvc -i ./pipeline/steps/[python_script_name] \
                  --out-dvc-cmd ./scripts/cmd/[dvc_cmd_name] 
    
  7. Ensure DVC command is well created.

  8. Add generated command and Python 3 script under git.

  9. Add step inputs under DVC.

  10. Run DVC command ./scripts/cmd/[dvc_cmd_name].

  11. Check DVC meta file is created ./[normalize notebook _name].dvc

  12. Add DVC meta file under git/

Key Features

Need Feature
Ignore notebook cell # No effect
DVC input and ouptuts :dvc-in, :dvc-out
Add extra parameters :dvc-extra
Write DVC whole command :dvc-cmd
Convert Jupiter Notebook to Python 3 script ipynb_to_python
Generate DVC command gen_dvc
Create a pipeline step from a Jupiter Notebook ipynb_to_python, gen_dvc
Add a pipeline step with different IO Copy DVC step then edit inputs, outputs and meta file name
Reproduce a pipeline dvc repro [metafile]
Reproduce a pipeline with no cache dvc repro -f [metafile]
Reproduce a pipeline after an algo change dvc repro -f [metafile] or run impacted step individually then complete the pipeline.

It is allowed to modify or duplicate a DVC command to change an hyperparameter or run a same step twice with different parameters.

It is a bad idea to modify generated Python 3 scripts. They are generated from Jupyter notebooks, so changes should be done in them and then scripts should be re-generated.

Tutorial

Environment

To complete this tutorial clone this repository:

git clone https://github.com/peopledoc/mlv-tools-tutorial

Activate your Python 3 virtual environment.

Install requirements:

make develop

All other steps are explain in each use cases.

Cases

Talks

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