September 08, 2021
We’re excited to announce the PyTorch Annual Hackathon 2021! This year, we’re looking to support the community in creating innovative PyTorch tools, libraries, and applications. 2021 is the third year we’re hosting this Hackathon, and we welcome you to join the PyTorch community and put your machine learning skills into action. Submissions start on September 8 and end on November 3. Good luck to everyone!
August 31, 2021
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
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
In this blog post, we describe the first peer-reviewed research paper that explores accelerating the hybrid of PyTorch DDP (
torch.nn.parallel.DistributedDataParallel)  and Pipeline (
torch.distributed.pipeline) - PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models (Transformers ...
August 03, 2021
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 visuali...
June 27, 2021
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
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 func...