October 26, 2021
Accelerating PyTorch with CUDA Graphs
Today, we are pleased to announce a new advanced CUDA feature, CUDA Graphs, has been brought to PyTorch. Modern DL frameworks have complicated software stacks that incur significant overheads associated with the submission of each operation to the GPU. When DL workloads are strong-scaled to many GPUs for performance, the time taken by each GPU operation diminishes to just a few microseconds and, in these cases, the high work submission latencies of frameworks often lead to low utilization of ...
October 21, 2021
PyTorch 1.10 Release, including CUDA Graphs APIs, Frontend and Compiler Improvements
We are excited to announce the release of PyTorch 1.10. This release is composed of over 3,400 commits since 1.9, made by 426 contributors. We want to sincerely thank our community for continuously improving PyTorch.
October 21, 2021
New Library Releases in PyTorch 1.10, including TorchX, TorchAudio, TorchVision
Today, we are announcing a number of new features and improvements to PyTorch libraries, alongside the PyTorch 1.10 release. Some highlights include:
September 08, 2021
Announcing PyTorch Annual Hackathon 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
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.
