Course Overview
This course covers the basics of distributed AI in the cloud, focusing on deep learning and parallelism. It introduces the concept of distributed training models and topologies, including data parallelism and model parallelism. The course also explores the challenges and communication overhead associated with deep learning, highlighting the importance of considering compute, memory, and communication. Additionally, it discusses Intel's Habana Gaudi platform and its flexible topologies, as well as the comparison between CPU, GPU, and XPU on model parallelism. By the end of the course, learners will understand the fundamentals of deep learning and parallelism, the importance of network topology, and the benefits and challenges of using different types of parallelism.
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Chapter Overview
This course covers the basics of distributed AI in the cloud, focusing on deep learning and parallelism. It introduces the concept of distributed training models and topologies, including data parallelism and model parallelism. The course also explores the challenges and communication overhead associated with deep learning, highlighting the importance of considering compute, memory, and communication. Additionally, it discusses Intel's Habana Gaudi platform and its flexible topologies, as well as the comparison between CPU, GPU, and XPU on model parallelism. By the end of the course, learners will understand the fundamentals of deep learning and parallelism, the importance of network topology, and the benefits and challenges of using different types of parallelism.