March 14, 2022
Introducing PyTorch Fully Sharded Data Parallel (FSDP) API
Recent studies have shown that large model training will be beneficial for improving model quality. During the last 3 years, model size grew 10,000 times from BERT with 110M parameters to Megatron-2 with one trillion. However, training large AI models is not easy—aside from the need for large amounts of computing resources, software engineering complexity is also challenging. PyTorch has been working on building tools and infrastructure to make it easier.
March 10, 2022
PyTorch 1.11, TorchData, and functorch are now available
We are excited to announce the release of PyTorch 1.11 (release notes). This release is composed of over 3,300 commits since 1.10, made by 434 contributors. Along with 1.11, we are releasing beta versions of TorchData and functorch.
March 10, 2022
Introducing TorchRec, and other domain library updates in PyTorch 1.11
We are introducing the beta release of TorchRec and a number of improvements to the current PyTorch domain libraries, alongside the PyTorch 1.11 release. These updates demonstrate our focus on developing common and extensible APIs across all domains to make it easier for our community to build ecosystem projects on PyTorch. Highlights include:
March 02, 2022
Understanding LazyTensor System Performance with PyTorch/XLA on Cloud TPU
Introduction
February 24, 2022
Case Study: Amazon Ads Uses PyTorch and AWS Inferentia to Scale Models for Ads Processing
Amazon Ads uses PyTorch, TorchServe, and AWS Inferentia to reduce inference costs by 71% and drive scale out.
February 23, 2022
Introducing TorchRec, a library for modern production recommendation systems
We are excited to announce TorchRec, a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production.
February 08, 2022
Practical Quantization in PyTorch
Quantization is a cheap and easy way to make your DNN run faster and with lower memory requirements. PyTorch offers a few different approaches to quantize your model. In this blog post, we’ll lay a (quick) foundation of quantization in deep learning, and then take a look at how each technique looks like in practice. Finally we’ll end with recommendations from the literature for using quantization in your workflows.
