PyTorch Releases Version 1.7 With New Features Like CUDA 11, New APIs for FFTs, And Nvidia A100 Generation GPUs Support

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Source: https://github.com/pytorch/pytorch

Team PyTorch has recently released the latest version of PyTorch 1.7, with many changes included in the package. 

Significant highlights of the python package are: 

  • It officially supports CUDA 11 with binaries available at www.pytorch.org.
  • It supports NumPy compatible Fast Fourier transforms (FFT) via torch.fft. (Beta)
  • It supports Nvidia A100 generation GPUs and native TF32 format. (Prototype)
  • It supports distributed training on Windows. (Prototype)
  • New updates are introduced to profiling and performance for remote procedure call (RPC), TorchScript, and Stack traces in the autograd profiler. (Stable)
  • In touch audio, it has added support for speech rec (wav2letter), text to speech (WaveRNN), and source separation (ConvTasNet). (Stable)
  • In torchvision, it now supports Tensor inputs, batch computation, GPU, and TorchScript.
  • The native image I/O for JPEG and PNG formats are also added in PyTorch 1.7. (Stable)
https://pytorch.org/blog/pytorch-1.7-released/

PyTorch is a widely used, open-source deep learning platform used for writing neural network layers in Python. Developers worldwide use it for a smooth transition from research to production. 

PyTorch-1.7

It provides two high-level features:

  • Tensor computation (like NumPy) with strong GPU acceleration
  • Deep neural networks built on a tape-based auto-grad system

It is used mainly: 

  1. As a replacement for NumPy to utilize the power of GPUs 
  2. As a Deep Learning platform to gain maximum strength and speed

PyTorch has delivered many applications:

  1. Image classification (MNIST) using Convents.
  2. Variational Auto-Encoders. 
  3. World-level language modeling using LSTM RNNs
  4. Generative Adversarial Networks (DCGAN)
  5. Superresolution using an efficient sub-pixel convolutional neural network

The latest version, PyTorch 1.7.0 is available for installation on the website: www.pytorch.org