Pytorch
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PyTorch is an open source machine learning library that provides two main features: tensor computing with strong GPU acceleration and built-in support for deep neural networks through an autodiff tape-based system. It includes packages for optimization algorithms, neural networks, multiprocessing, utilities, and computer vision tasks. PyTorch uses an imperative programming style and defines computation graphs at runtime, compared to TensorFlow which uses both static and dynamic graphs.
Pytorch
- 2. 1/1 Initial release October 2016 PyTorch provides two high-level features: open source machine learning library Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU) Deep neural networks built on a tape-based autodiff system Facebook
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- 4. 1/1 Component Description torch a Tensor library like NumPy, with strong GPU support torch.autograd a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch torch.optim A package implementing various optimization algorithms. torch.nn a neural networks library deeply integrated with autograd designed for maximum flexibility torch.multiprocessing Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training torch.utils DataLoader and other utility functions for convenience torchvision Deep Learning Modules
- 6. 1/1 TensorFlow vs. PyTorch PyTorch TensorFlow PyTorch is closely related to the lua-based Torch framework which is actively used in Facebook. TensorFlow is developed by Google Brain and actively used at Google. PyTorch is relatively new compared to other competitive technologies. TensorFlow is not new and is considered as a to-go tool by many researchers and industry professionals. PyTorch includes everything in imperative and dynamic manner. TensorFlow includes static and dynamic graphs as a combination. Computation graph in PyTorch is defined during runtime. TensorFlow do not include any run time option. PyTorch includes deployment featured for mobile and embedded frameworks. TensorFlow works better for embedded frameworks.
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