Metadata-Version: 2.1
Name: niceml
Version: 0.9.1
Summary: Welcome to niceML 🍦, a Python-based MLOps framework that uses TensorFlow and Dagster. This framework streamlines the development, and maintenance of machine learning models, providing an end-to-end solution for building efficient and scalable pipelines.
Keywords: tensorflow,scikit-learn,streamlit
Author: Denis Stalz-John
Author-email: denis.stalz-john@codecentric.de
Requires-Python: >=3.8, !=2.7.*, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*, !=3.7.*, !=3.11.*
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Provides-Extra: tensorflow
Provides-Extra: tensorflow-macos
Provides-Extra: visu
Requires-Dist: albumentations (>=1.3.0,<2.0.0)
Requires-Dist: altair (>=4.2.2,<5.0.0) ; extra == "visu"
Requires-Dist: cattrs (>=22.2.0,<23.0.0)
Requires-Dist: click (>=8.1.3,<9.0.0)
Requires-Dist: copier (>=7.2.0,<8.0.0)
Requires-Dist: cryptography (>=41.0.0,<42.0.0)
Requires-Dist: cython (>=0.29.34,<0.30.0)
Requires-Dist: dagit (>=1.3.3,<2.0.0)
Requires-Dist: dagster (>=1.4,<1.5)
Requires-Dist: dagster-mlflow (>=0.20.14,<0.21.0)
Requires-Dist: fastparquet (>=2023.2.0,<2024.0.0)
Requires-Dist: hydra-core (>=1.3.2,<2.0.0)
Requires-Dist: invoke (>=1.4.1,<2)
Requires-Dist: isodate (>=0.6.1,<0.7.0)
Requires-Dist: mkdocs-gen-files (>=0.5.0,<0.6.0)
Requires-Dist: networkx (>=3.1,<4.0)
Requires-Dist: opencv-python (>=4.7.0.72,<5.0.0.0)
Requires-Dist: pandas (==1.5.0)
Requires-Dist: pandera (>=0.14.5,<0.15.0)
Requires-Dist: pillow (>=10.1.0,<11.0.0)
Requires-Dist: protobuf (>=3.0.0,<4.0.0)
Requires-Dist: pympler (>=1.0.1,<2.0.0)
Requires-Dist: python-dotenv (>=1.0.0,<2.0.0)
Requires-Dist: pyyaml (>=6.0,<7.0)
Requires-Dist: requests (>=2.31.0,<3.0.0)
Requires-Dist: schema (>=0.7.5,<0.8.0)
Requires-Dist: scikit-learn (>=1.2.2,<2.0.0)
Requires-Dist: scipy (>=1.8)
Requires-Dist: streamlit (>=1.28.0,<2.0.0) ; extra == "visu"
Requires-Dist: tensorflow (>=2.12,<2.13) ; extra == "tensorflow"
Requires-Dist: tensorflow-macos (>=2.12,<2.13) ; extra == "tensorflow-macos"
Requires-Dist: tensorflow-metal (>=1.0,<1.1) ; extra == "tensorflow-macos"
Requires-Dist: toml (>=0.10.2,<0.11.0)
Requires-Dist: tornado (>=6.3.2,<7.0.0)
Project-URL: homepage, https://niceml.io
Project-URL: repository, https://github.com/codecentric-oss/niceml
Description-Content-Type: text/markdown

# This is the readme for niceML
[![PyPI](https://img.shields.io/pypi/v/niceml)](
https://pypi.org/project/niceml/
)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/niceml)
[![🧪 Pytest](
https://github.com/codecentric-oss/niceml/actions/workflows/pytest.yaml/badge.svg)](
https://github.com/codecentric-oss/niceml/actions/workflows/pytest.yaml)
![GitHub commit activity](
https://img.shields.io/github/commit-activity/m/codecentric-oss/niceml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](
https://opensource.org/licenses/MIT)

**niceML** is a tool to help you set up your machine learning projects faster. 
It provides pipelines for a variety of ML tasks, like

- **Object Detection**,
- **Semantic Segmentation**,
- **Regression**,
- **Classification**
- and others.

All you have to do is configure your pipeline, and you're ready to go!

You can also add your own components to the build-in dashboard, 
where you can compair the results and performance of your ML models.

Further documentation is available at [niceML.io](https://niceml.io).

A lot more documentation will follow soon!

