Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Updated
Dec 16, 2025 - Python
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Open Machine Learning Compiler Framework
On-device AI across mobile, embedded and edge for PyTorch
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
TensorLy: Tensor Learning in Python.
Deep learning with spiking neural networks (SNNs) in PyTorch.
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
Read and write Neuroglancer datasets programmatically.
Hyper optimized contraction trees for large tensor networks and einsums
Phase-Amplitude Coupling under Python
Tensor Train Toolbox
Keras implementation of Neural Graph Fingerprints as proposed by Duvenaud et al., 2015
TensorLy-Torch: Deep Tensor Learning with TensorLy and PyTorch
Provides a systematic and extensible way to build, train, evaluate, and tune deep learning models using PyTorch.
Framework agnostic python runtime for RWKV models
Machine Learning with Symbolic Tensors
depth map computation
Visualize PyTorch tensors with a single line of code.
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