Metadata-Version: 2.1
Name: mltraq
Version: 0.0.101
Summary: Open source experiment tracking API with ML performance analysis.
Home-page: https://mltraq.com/
License: BSD-3
Author: Michele Dallachiesa
Author-email: michele.dallachiesa@sigforge.com
Requires-Python: >=3.10.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Provides-Extra: complete
Provides-Extra: dask
Provides-Extra: pgsql
Requires-Dist: cloudpickle (>=2.2.0)
Requires-Dist: colorama (>=0.4.6)
Requires-Dist: dask[complete] (>=2022.11.0,<2023.0.0); extra == "dask" or extra == "complete"
Requires-Dist: joblib (>=1.1.1)
Requires-Dist: pandas (>=1.5.3)
Requires-Dist: psycopg[binary] (>=3.1.17,<4.0.0); extra == "pgsql" or extra == "complete"
Requires-Dist: scikit-learn (>=1.1.3,<2.0.0); extra == "complete"
Requires-Dist: sqlalchemy (>=2.0.0)
Requires-Dist: sqlalchemy-utils (>=0.38.3)
Requires-Dist: tabulate (>=0.9.0,<0.10.0); extra == "complete"
Requires-Dist: tqdm (>=4.64.1)
Requires-Dist: ulid-py (>=1.1.0,<2.0.0)
Project-URL: Repository, https://github.com/elehcimd/mltraq
Description-Content-Type: text/markdown

<p align="center">
<img width="33%" height="33%" src="https://mltraq.com/assets/img/logo-black.svg" alt="MLtraq Logo">
</p>

<p align="center">
<img src="https://www.mltraq.com/assets/img/badges/test.svg" alt="Test">
<img src="https://www.mltraq.com/assets/img/badges/coverage.svg" alt="Coverage">
<img src="https://www.mltraq.com/assets/img/badges/python.svg" alt="Python">
<img src="https://www.mltraq.com/assets/img/badges/pypi.svg" alt="PyPi">
<img src="https://www.mltraq.com/assets/img/badges/license.svg" alt="License">
<img src="https://www.mltraq.com/assets/img/badges/code-style.svg" alt="Code style">
</p>

---

Open source **experiment tracking API** with **ML performance analysis** to build better models faster, facilitating collaboration and transparency within the team and with stakeholders.

---

* **Documentation**: [https://www.mltraq.com](https://www.mltraq.com)
* **Source code**: [https://github.com/elehcimd/mltraq](https://github.com/elehcimd/mltraq)

---

## Key features

* **Immediate**: start tracking experiments with a few lines of code.
* **Collaborative**: Backup and upstream experimental results with your team.
* **Interoperable**: Access the data anywhere with SQL, Pandas and Python API.
* **Flexible**: Track structured types including Numpy arrays and Pandas frames/series.
* **Steps library**: Use pre-built "steps" for tracking, testing, analysis and reporting.
* **Execution engine**: Define and execute parametrized experiment pipelines.

## Requirements

* **Python >=3.10**
* **SQLAlchemy**, **Pandas**, and **Joblib** (installed as dependencies)

## Installation

```
pip install mltraq
```

## License

This project is licensed under the terms of the [BSD 3-Clause License](https://mltraq.com/license).


