After the structure of the training and test files was established, I coded a PyTorch Dataset class to read data into memory and serve the data up in batches using a PyTorch DataLoader object. A ...

This project provides an implementation of the BERT model, as described in the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", using PyTorch. In addition to ...

TorchOpt provides distributed training features based on the PyTorch RPC module for better training speed and multi-node multi-GPU support. Different from the MPI-like parallelization paradigm, which ...

Introduced in PyTorch 1.4 as an experimental feature, the RPC framework provides mechanisms for running PyTorch functions on remote machines and thus allows training models across multiple ...

In this paper, we present novel visualization strategies for inspecting, displaying, browsing, comparing, and visualizing deep neural networks (DNN) and their internal state during training. Despite ...

PyTorch 1.10 is production ready, with a rich ecosystem of tools and libraries for deep learning, computer vision, natural language processing, and more. Here's how to get started with PyTorch.