September 05, 2023
Automated trace collection and analysis
In this blog, we share how we enabled the collection and analysis of PyTorch Profiler traces for training workloads without any user side code instrumentation. We leveraged Dynolog - an open source daemon for CPU and GPU telemetry to collect PyTorch Profiler traces, and analyzed the collected traces using Holistic Trace Analysis - an open source library for analyzing PyTorch Profiler traces. This toolchain has allowed engineers at Meta to accelerate their performance optimization workflows. T...
August 31, 2023
PyTorch/XLA SPMD: Scale Up Model Training and Serving with Automatic Parallelization
Today, we are delighted to announce PyTorch/XLA SPMD: the integration of GSPMD into PyTorch with an easy to use API. PyTorch developers seeking superior performance and scale can train and serve the largest neural networks while maximizing utilization of AI accelerators, such as Google Cloud TPUs.
August 24, 2023
Large Scale Training of Hugging Face Transformers on TPUs With PyTorch/XLA FSDP
AI is transforming many industries through advanced capabilities such as understanding and generating language, answering questions, and delivering accurate recommendations. These capabilities are fueled by ever-increasing size and complexity of AI models, which require vast amounts of computing power to train.
August 01, 2023
AMD's Journey to Openness and Performance
AMD has gained progress in building a robust software stack that supports an open ecosystem of models, libraries, frameworks, and tools. With proven platforms gaining momentum, there is significance of a leadership software stack and an optimized ecosystem for achieving application performance. PyTorch is a key part of AMD’s AI journey, and AMD’s Victor Peng, AMD President and Soumith Chintala, founder of PyTorch discussed the latest progress at the DC & AI Keynote on June 12.
