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PyTorch has a unique way of building neural networks: using and replaying a tape recorder. Such as slicing, indexing, mathematical operations, linear algebra, reductions.Īnd they are fast! Dynamic Neural Networks: Tape-Based Autograd We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the If you use NumPy, then you have used Tensors (a.k.a.

A deep learning research platform that provides maximum flexibility and speed.Įlaborating Further: A GPU-Ready Tensor Library.

A replacement for NumPy to use the power of GPUs.

Useful for data loading and Hogwild trainingĭataLoader and other utility functions for convenience Python multiprocessing, but with magical memory sharing of torch Tensors across processes.

Dynamic Neural Networks: Tape-Based AutogradĪt a granular level, PyTorch is a library that consists of the following components: ComponentĪ Tensor library like NumPy, with strong GPU supportĪ tape-based automatic differentiation library that supports all differentiable Tensor operations in torchĪ compilation stack (TorchScript) to create serializable and optimizable models from PyTorch codeĪ neural networks library deeply integrated with autograd designed for maximum flexibility.Our trunk health (Continuous Integration signals) can be found at. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed. Deep neural networks built on a tape-based autograd system.

Tensor computation (like NumPy) with strong GPU acceleration.PyTorch is a Python package that provides two high-level features:

Clementine 1.4.0 RC1 (887) download the new version for android