A Python framework for creating reproducible, maintainable and modular data science code.
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Updated
May 30, 2023 - Python
A Python framework for creating reproducible, maintainable and modular data science code.
Aim
Machine Learning automation and tracking
Visualise your Kedro data and machine-learning pipelines and track your experiments.
Code for Kaggle and Offline Competitions
A Clojure machine learning library
SEML: Slurm Experiment Management Library
Metadata store for Production ML
Deploy MLflow with HTTP basic authentication using Docker
GitHub Action That Retrieves Model Runs From Weights & Biases
More light-weight pytorch experiment management library!
A powerful and easy to use Python framework for experiment tracking and incremental computing
A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management
2 Lines of code to track ML experiments + EDA + check into Github
Tutorial on experiment tracking and reproducibility for Machine Learning projects with DVC
Custom ML tracking experiment and debugging tools.
The Python Component System (PCS) is an API and CLI for building, running, and sharing Python code. AgentOS is a set of libraries built on top of PCS that make it easy to build, run, and share agents that use Reinforcement Learning.
Lightning Talk about sacred at PyData Berlin
CmdInterface enables detailed logging of command line and python experiments in a very lightweight manner (coding wise). It wraps your command line or python function calls in a few lines of python code and logs everything you might need to reproduce the experiment later on or to simply check what you did a couple of years ago.
Add a description, image, and links to the experiment-tracking topic page so that developers can more easily learn about it.
To associate your repository with the experiment-tracking topic, visit your repo's landing page and select "manage topics."