Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
Code for Kaggle and Offline Competitions
PyTorch template for Deep Learning projects with support for scalable multi-GPU and multi-node training.
SEML: Slurm Experiment Management Library
Metadata store for Production ML
More light-weight pytorch experiment management library!
An Agent Skill for the DL experiment lifecycle: RUN (a GPU you own or rent) → VERIFY the number is real → DELIVER reproducible, single-source figures and tables.
GitHub Action That Retrieves Model Runs From Weights & Biases
Stop trusting your pipeline. Verify it; visually on a canvas or in Python. Self-hosted platform + library. Now, you can be able use it in Databricks and MLFlow with bundle option.
A minimal Python logger that tracks everything you try when building AI - metrics, prompts, models, etc, so you can see what changed and why.
A lightweight Python library for saving simulation results in a standardized, reproducible format.
Ralph Loop Optimizer: an AI-driven framework that turns any evaluatable codebase into a self-improving optimization loop for strategies, models, prompts, and workflows
Modern experiment tracking that follows you across servers, messengers, and LLMs.
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.
A lightweight Python CLI for batch experiments: auto-generate tasks from configs, run in parallel, and track results with real-time monitoring.
Open-source platform for reproducible AI/ML workflows, from local experimentation to distributed workers and long-lived services.
Small-scale MLOps library
Tutorial on experiment tracking and reproducibility for Machine Learning projects with DVC
An MLOps workflow for training, inference, experiment tracking, model registry, and deployment.
To associate your repository with the experiment-tracking topic, visit your repo's landing page and select "manage topics."