Machine Learning Pipeline Versioning Tutorial

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 on 3 tools:

• DVC
• MLflow tracking
• mlvtools

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

Prerequisites

For this tutorial, you must be familiar with the following tools:

• virtualenv or condaenv
• make
• git
• python

Tools Overview

DVC is an open-source version control system for Machine Learning projects. It is used for versioning and sharing Machine Learning data, and reproducing Machine Learning experiments and pipeline stages.

mlvtools provides tools to generate Python scripts and DVC commands from Jupyter Notebooks.

Please have a look at the presentation.

Our main features

• Notebook parametrized conversion (mlvtools)
• Pipelining (DVC and mlvtools)
• Data x Code x Hyperparameters versioning (DVC and mlvtools)

Standard Versioning Process Establishment

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

Initial project

Starting from an existing project composed of multiple Python modules 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 the 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 configurable scripts
│   │   ├─ mlvtools_augment_train_data.py
│   │   ├─ ..
│── README.md
│── requirements.yml
│── setup.cfg
│── setup.py

Applying the process

For each Jupyter notebook a Python parameterizable and executable script is generated. This script makes it easier to version code and automate pipeline executions.

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

Each time a DVC step is run a DVC meta file ([normalized_notebook_name].dvc) is created. This metadata file represents a pipeline step, it is the DVC result of a step execution. Those files must be tracked 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 corresponds 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 script using ipynb_to_python.

   ipynb_to_python -n ./pipeline/notebooks/[notebook_name] -o ./pipeline/steps/[python_script_name]
   

5. Ensure Python 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 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 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 ./[normalized 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 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 scripts. They are generated from Jupyter notebooks, so changes should be done in Jupyter notebooks and then scripts should be re-generated.

Tutorial

Environment

To complete this tutorial clone this repository:

git clone https://github.com/peopledoc/mlvtools-tutorial

Create and a Python virtual environment, and activate it:

virtualenv --python python3 venv
source venv/bin/activate

Install requirements:

make develop

All other steps are explained in each use case.

Cases

• How DVC works
• mlvtools pipeline features (on simple cases)
• Going further with more realistic use cases:
  • Use Case 1: Build and Reproduce a Pipeline
  • Use Case 2: Create a new version of a pipeline (Run an experiment)
  • Use Case 3: Build a Pipeline from an Existing Pipeline
  • Use Case 4: Hyperparameter optimisation and fine-tuning

Talks

• PyData Paris - March 2019 Meetup: talk
• PyData Amsterdam - May 2019: tutorial slides and video