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

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. 

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