With the growing complexity of deep learning models and the emergence of Large Language Models (LLMs) and generative AI, scaling training efficiently and cost-effectively has become an urgent need. Enter Ray Train, a cutting-edge library designed specifically for seamless, production-ready distributed deep learning.
In this talk, we will take a deep dive into the architecture of Ray Train, emphasizing its advanced resource scheduling and the simplicity of its APIs designed for effortless ecosystem integrations. We will cover a detailed breakdown of Ray Train's design, from its robust architecture to its exclusive features for LLM training, including Distributed Checkpointing and the seamless Ray Data Integration.
• Ray Train offers production-ready open-source solutions for large-scale distributed training.
• Ray Train seamlessly integrates into the deep learning ecosystem (such as PyTorch, Lightning, HuggingFace) with easy-to-use APIs.
• Ray Train accelerates your LLM development with built-in fault tolerance and resource management capabilities.
Yunxuan Xiao is a software engineer at Anyscale, where he works on the open-source Ray Libraries. He is passionate about scaling AI workloads and making machine learning more accessible and efficient.
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