The definitive list that answers "I want to do X in Python, which tool should I use?"
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🤗 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.
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🗃 Open source self-hosted web archiving. Takes URLs/browser history/bookmarks/Pocket/Pinboard/etc., saves HTML, JS, PDFs, media, and more...
Open source software that helps you create and deploy high-frequency crypto trading bots
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
Refine high-quality datasets and visual AI models
mal - Make a Lisp
Blender addons to make the bridge between Blender and geographic data
Run Windows software and games on Linux
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Emulator for rapid prototyping of Software Defined Networks
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
This repository is a getting started guide to Singer.
Automatic and platform-independent unpacker for Windows binaries based on emulation
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
Phantom types for Python.
Hardware testing for the software world. Real or virtual, local or remote, human, automated or agentic.
Navigate to variable's definition with a click in JupyterLab (or with a few key strokes)
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Build system in D, Python, Ruby, Javascript or Lua
Build autonomous AI agents that interact with blockchains — in minutes, not months.
Mock an SSH server and define all commands it supports (Python, Twisted)
trycast parses JSON-like values whose shape is defined by TypedDicts and other standard Python type hints.