MLComp ====================================== .. image:: http://66.248.205.49:8111/app/rest/builds/buildType:id:MLComp_Deploy/statusIcon.svg :target: http://66.248.205.49:8111/project.html?projectId=MLComp&tab=projectOverview&guest=1 :alt: Build Status .. image:: https://img.shields.io/github/license/catalyst-team/mlcomp.svg :alt: License .. image:: https://img.shields.io/pypi/v/mlcomp.svg :target: https://pypi.org/project/mlcomp/ :alt: Pypi version .. image:: https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fmlcomp%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v :target: https://catalyst-team.github.io/mlcomp/ :alt: Docs .. image:: https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/MLcomp.png :target: https://github.com/catalyst-team/mlcomp MLComp is a distributed DAG (Directed acyclic graph) framework for machine learning with UI. The goal of MLComp is to provide tools for training, inference, creating complex pipelines (especially for computer vision) in a rapid, well manageable, way. MLComp is compatible with: Python 3.6+, Unix operation system. **Features** - Amazing UI - `Catalyst `_ support - Distributed training - Supervisor that controls computational resources - Synchronization of both code and data - Resource monitoring - Full-functionally pause and continue on UI - Auto control of the requirements - Code dumping(with syntax highlight on UI) - Kaggle integration - Hierarchical logging - Grid search - Experiments comparison - Customizing layouts .. toctree:: :caption: Overview: :maxdepth: 1 self installation usage layout grid_search filesync