Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Updated
May 27, 2024 - Python
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Open deep learning compiler stack for cpu, gpu and specialized accelerators
Hyper optimized contraction trees for large tensor networks and einsums
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Tensor Train Toolbox
Tensor Network State Packages
Batch multidimensional indexing for pytorch
Math on (Hyper-Dual) Tensors with Trailing Axes
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
Estimation of elastic waves velocities in anisotropic solids
Abstract your array operations.
Tensor-like types – with variadic shapes – that support both static and runtime type checking, and convenient parsing
Read and write Neuroglancer datasets programmatically.
Simple yet powerful tensor manipulation library with a clear and verbose notation for reliable and readable code
Python Module for PyTorch Tensor Visualisation in CUDA Eliminating CPU Transfer
Economic analysis tool using tensor PCA modeling to interpolate GNP values, integrating tensor product, PCA, and linear regression for better interpolation.
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