Stars
Release Docker builds of TimescaleDB
An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps
FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.
Collective communications library with various primitives for multi-machine training.
Zulip server and web application. Open-source team chat that helps teams stay productive and focused.
The open-source tool for building high-quality datasets and computer vision models
Opensource IDE For Exploring and Testing Api's (lightweight alternative to postman/insomnia)
Git with a cup of tea! Painless self-hosted all-in-one software development service, including Git hosting, code review, team collaboration, package registry and CI/CD
Tensors and Dynamic neural networks in Python with strong GPU acceleration
♾️ CML - Continuous Machine Learning | CI/CD for ML
Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
Temporal Planning for Reconfigurable Multirobot Systems
MARS is a cross-platform simulation and visualisation tool created for robotics research.
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Hydra is a framework for elegantly configuring complex applications
GitHub Action to build and push Docker images with Buildx
Model for Reconfigurable Multi-Robot Organizations
An extremely fast Python linter and code formatter, written in Rust.
A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
A simple yet powerful tool to turn traditional container/OS images into unprivileged sandboxes.
Reference implementations of MLPerf™ training benchmarks