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An opinionated list of Python frameworks, libraries, tools, and resources
Curated list of project-based tutorials
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
FastAPI framework, high performance, easy to learn, fast to code, ready for production
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Models and examples built with TensorFlow
Apache Superset is a Data Visualization and Data Exploration Platform
Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to clo…
💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline
Scrapy, a fast high-level web crawling & scraping framework for Python.
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
The world's simplest facial recognition api for Python and the command line
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Streamlit — A faster way to build and share data apps.
Developer-first error tracking and performance monitoring
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
🎨 Diagram as Code for prototyping cloud system architectures
A toolkit for developing and comparing reinforcement learning algorithms.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more