PyPI98Exceptionalhealth index
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PyPI95Exceptionalhealth index

graphistry/pygraphistryPyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
Python★ 2,565↓ 8,037/moOct 3, 2026
PyPI94Exceptionalhealth index
Python · Jupyter Notebook★ 5,236↓ 308.2K/moSep 20, 2026
PyPI94Exceptionalhealth index
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PyPI91Excellenthealth index

pytorch/rlA modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Python★ 3,548↓ 84.8K/moSep 5, 2026
NuGet87Excellenthealth index

SciSharp/NumSharpHigh Performance Computation for N-D Tensors in .NET, similar API to NumPy.
C#★ 1,478Sep 6, 2026
PyPI83Excellenthealth index

johnmarktaylor91/torchlensCapture every activation and gradient of any PyTorch model — forward and backward — with automatic graph visualization, rich metadata, and live interventions. Works on any architecture, including dynamic and recurrent ones.
Python★ 658↓ 1,741/moSep 13, 2026
crates.io · PyPI83Excellenthealth index
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Python★ 23↓ 2,051/moOct 13, 2026
NuGet80Excellenthealth index

dotnet/TorchSharpA .NET library that provides access to the library that powers PyTorch.
C#★ 1,851Sep 11, 2026
Python · Cuda★ 1,053↓ 300.3K/moSep 17, 2026
Python · Jupyter Notebook★ 2,952Sep 17, 2026

NevermindNilas/NeluxNeLux: High-performance video processing for Python, powered by FFmpeg and PyTorch. Ultra-fast video decoding directly to tensors.
C++ · Python★ 29↓ 3,141/moSep 11, 2026
PyPI63Moderatehealth index

c0rmac/metal-linalgQR, symmetric eigendecomposition and SVD for batches of matrices on Apple Silicon GPUs, for MLX and PyTorch. Metal kernels per regime, routed to the GPU or the CPU by policies measured per Mac. C++, Python (pip: metal-linalg, metal-linalg-torch), Swift and C.
C★ 11↓ 3,008/moOct 4, 2026
PyPI59Moderatehealth index

d4l3k/rdma4pyHigh-performance RDMA for Python and PyTorch (ibverbs, efa, NVMe-oF)
Python · Cython★ 12↓ 11.6K/moSep 15, 2026
PyPI54Moderatehealth index

chris-santiago/imbalanced-lossesimbalanced-losses is a PyTorch library of training losses for class-imbalanced classification — including Focal Loss, Smooth-AP, and Recall-at-Quantile — with built-in DDP all-gather support for globally-correct rank estimation and normalization across multi-GPU training.
Python · HTML★ 0Sep 16, 2026

KentoNishi/torch-pitch-shiftPitch-shift audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.
Python★ 139↓ 1.2M/moSep 15, 2026
Python★ 165KSep 16, 2026
Python★ 2,391Sep 13, 2026