Episode 54: Scaling AI: From Colab to Clusters — A Practitioner’s Guide to Distributed Training and Inference
Vanishing Gradients
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Navigating Distributed Computing with Slurm and GPUs
This chapter explores the complexities of using Slurm for distributed computing, emphasizing its user-friendly approach compared to Kubernetes. It provides insights into advanced distributed training techniques like distributed data parallelism, the importance of checkpoints in model training, and the practical scaling of AI processes. Furthermore, it discusses the decision-making process for selecting training methods based on cost and performance, supported by essential resources for understanding model training intricacies.
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