⚠️ End-of-life notice.
Effective immediately, HP has ended development, maintenance, and support of this project. The repo will be available in read-only mode until September 1st, 2024, when it will be deleted. You are welcome to create a copy and keep the project going.

ML-Git

ML-Git is a tool which provides a Distributed Version Control system to enable efficient dataset management. Like its name emphasizes, it is inspired in git concepts and workflows, ML-Git enables the following operations:

Manage a repository of different datasets, labels and models.
Distribute these ML artifacts between members of a team or across organizations.
Apply the right data governance and security models to their artifacts.

If you are seeking to learn more about ML-Git, access ML-Git Page.

How to install

Prerequisites:

From repository:

pip install ml-git

From source code:

Download ML-Git from repository and execute commands below:

cd ml-git/
pip install .

How to uninstall

pip uninstall ml-git

How to configure

1 - As ML-Git leverages git to manage ML entities metadata, it is necessary to configure user name and email address:

git config --global user.name "Your User"
git config --global user.email "your_email@example.com"

2 - OPTIONAL CONFIGURATIONS

2.1 - Some ML-Git commands have a wizard to help you during their execution. Those commands have the --wizard option available to enable this wizard. However, you can configure the wizard to be enabled by default on all supported commands by running the following command:
```
ml-git repository config --set-wizard=enabled
```
2.2 - You can also allow commands and options to be autocompleted with a [Tab] key press. For that, take a look at the following link ML-Git Shell Completion Support.

3 - Storage:

ML-Git needs a configured storage to store data from managed artifacts. Please take a look at the ML-Git architecture and internals documentation to better understand how ML-Git works internally with data.

To configure the storage see documentation about supported storages and how to configure each one.

4 - ML-Git project:

An ML-Git project is an initialized directory that will contain a configuration file to be used by ML-Git in managing entities. To configure it you can use the basic steps to configure the project described in first project documentation.

Usage

ml-git --help
Usage: ml-git [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.

Commands:
  clone       Clone an ml-git repository ML_GIT_REPOSITORY_URL
  datasets    Management of datasets within this ml-git repository.
  labels      Management of labels sets within this ml-git repository.
  models      Management of models within this ml-git repository.
  repository  Management of this ml-git repository.

Basic commands

ml-git clone <repository-url>

ml-git clone https://github.com/user/ml_git_configuration_file_example.git

If you prefer to create a new directory to clone into:

ml-git clone https://github.com/user/ml_git_configuration_file_example.git my-project-dir

If you prefer keep git tracking files in the project:

ml-git clone https://github.com/user/ml_git_configuration_file_example.git --track

ml-git <ml-entity> create

This command will help you to start a new project, it creates your project artifact metadata:

ml-git datasets create --categories="computer-vision, images" --bucket-name=your_bucket --import=../import-path --mutability=strict dataset-ex

Demonstration video:

ml-git <ml-entity> status

Show changes in project workspace:

ml-git datasets status dataset-ex

Demonstration video:

ml-git <ml-entity> add

Add new files to index:

ml-git datasets add dataset-ex

To increment version:

ml-git datasets add dataset-ex --bumpversion

Add an specific file:

ml-git datasets add dataset-ex data/file_name.ex

Demonstration video:

ml-git <ml-entity> commit

Consolidate added files in the index to repository:

ml-git datasets commit dataset-ex

Demonstration video:

ml-git <ml-entity> push

Upload metadata to remote repository and send [chunks](docs/mlgit_internals.md) to storage:

ml-git datasets push dataset-ex

Demonstration video:

ml-git <ml-entity> checkout

Change workspace and metadata to versioned ml-entity tag:

ml-git datasets checkout computer-vision__images__dataset-ex__1

Demonstration video:

How to contribute

Your contributions are always welcome!

Fork the repository into your own GitHub
Clone the repository to your local machine
Create a new branch for your changes using the following pattern (feature | bugfix | hotfix)/branch_name. Example: feature/sftp_storage_implementation
Make changes and test
Push the changes to your repository
Create a Pull Request from your forked repository to the ML-Git repository with comprehensive description of changes

Another way to contribute with the community is creating an issue to track your ideas, doubts, enhancements, tasks, or bugs found. If an issue with the same topic already exists, discuss on the issue.