In this talk, Wanchao Liang, Software Engineer at Meta Pytorch Team, explores the technology advancements of PyTorch Distributed, and dives into the details of how multi-dimensional parallelism is made possible to train Large Language Models by composing different PyTorch native distributed training APIs.
In this talk, Wanchao Liang, Software Engineer at Meta Pytorch Team, explores the technology advancements of PyTorch Distributed, and dives into the details of how multi-dimensional parallelism is made possible to train Large Language Models by composing different PyTorch native distributed training APIs.
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Videos
TorchTitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo that showcases PyTorch's latest distributed training features in a clean, minimal codebase.
In this talk, Tianyu will share TorchTitan’s design and optimizations for the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its performance, composability, and scalability.
As large-scale machine learning becomes increasingly GPU-centric, modern high-performance hardware like NVMe storage and RDMA networks (InfiniBand or specialized NICs) are becoming more widespread. To fully leverage these resources, it’s crucial to build a balanced architecture that avoids GPU underutilization. In this talk, we will explore various strategies to address this challenge by effectively utilizing these advanced hardware components. Specifically, we will present experimental results from building a Kubernetes-native distributed caching layer, utilizing NVMe storage and high-speed RDMA networks to optimize data access for PyTorch training.