August 31, 2021
How Computational Graphs are Constructed in PyTorch
In the previous post we went over the theoretical foundations of automatic differentiation and reviewed the implementation in PyTorch. In this post, we will be showing the parts of PyTorch involved in creating the graph and executing it. In order to understand the following contents, please read @ezyang’s wonderful blog post about PyTorch internals.
August 23, 2021
Announcing PyTorch Developer Day 2021
We are excited to announce PyTorch Developer Day (#PTD2), taking place virtually from December 1 & 2, 2021. Developer Day is designed for developers and users to discuss core technical developments, ideas, and roadmaps.
August 18, 2021
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
In this blog post, we describe the first peer-reviewed research paper that explores accelerating the hybrid of PyTorch DDP (torch.nn.parallel.DistributedDataParallel) [1] and Pipeline (torch.distributed.pipeline) - PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models (Transformers such as BERT [2] and ViT [3]), published at ICML 2021.
August 03, 2021
What’s New in PyTorch Profiler 1.9?
PyTorch Profiler v1.9 has been released! The goal of this new release (previous PyTorch Profiler release) is to provide you with new state-of-the-art tools to help diagnose and fix machine learning performance issues regardless of whether you are working on one or numerous machines. The objective is to target the execution steps that are the most costly in time and/or memory, and visualize the work load distribution between GPUs and CPUs.
June 27, 2021
Everything You Need To Know About Torchvision’s SSDlite Implementation
In the previous article, we’ve discussed how the SSD algorithm works, covered its implementation details and presented its training process. If you have not read the previous blog post, I encourage you to check it out before continuing.
June 23, 2021
The torch.linalg module: Accelerated Linear Algebra with Autograd in PyTorch
Linear algebra is essential to deep learning and scientific computing, and it’s always been a core part of PyTorch. PyTorch 1.9 extends PyTorch’s support for linear algebra operations with the torch.linalg module. This module, documented here, has 26 operators, including faster and easier to use versions of older PyTorch operators, every function from NumPy’s linear algebra module extended with accelerator and autograd support, and a few operators that are completely new. This makes the torch...
June 18, 2021
An Overview of the PyTorch Mobile Demo Apps
PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency is reduced, privacy preserved, and models can run on mobile devices anytime, anywhere.
