mloq 0.0.63

Package for initializing ML projects following ML Ops best practices.

ML Ops Quickstart

ML Ops Quickstart is a tool for initializing Machine Learning projects following ML Ops best practices.

Setting up new repositories is a time-consuming task that involves creating different files and configuring tools such as linters, docker containers and continuous integration pipelines. The goal of mloq is to simplify that process, so you can start writing code as fast as possible.

mloq generates customized templates for Python projects with focus on Maching Learning. An example of the generated templates can be found in mloq-template.

1. Installation

mloq is tested on Ubuntu 18.04+, and supports Python 3.6+.

Install from pypi

pip install mloq

Install from source

git clone https://github.com/FragileTech/ml-ops-quickstart.git
cd ml-ops-quickstart
pip install -e .

2. Usage

2.1 Command line interface

Options:

• --file -f: Name of the configuration file. If file it's a directory it will load the mloq.yml file present in it.

• --overwrite -o: Rewrite files that already exist in the target project.

• --interactive -i: Missing configuration data can be defined interactively from the CLI.

Usage examples

Arguments:

• OUTPUT_DIRECTORY: Path to the target project.

To set up a new repository from scratch interactively in the curren working directory:

mloq setup -i .

To load a mloq.yml configuration file from the current repository, and initialize the directory example, and overwrite all existing files with no interactivity:

mloq setup -f . -o example

5. License

ML Ops Quickstart is released under the MIT license.

6. Contributing

Contributions are very welcome! Please check the contributing guidelines before opening a pull request.

7. Roadmap

• Improve documentation and test coverage.
• Configure sphinx to build the docs automatically.
• Expose all api as a CLI interface
• Add new customization options.