Construct, train, and scale state-of-the-art Mixture of Experts (MoE) models. This course covers advanced architectural designs, sophisticated training methodologies including routing and load balancing optimization, and effective strategies for distributed scaling of sparse expert models.
Prerequisites Advanced Deep Learning knowledge
Level:
Advanced MoE Architectures
Analyze and implement sophisticated MoE architectural variants beyond basic designs.
Expert Routing Mechanisms
Understand and implement advanced routing algorithms and gating networks for conditional computation.
MoE Training Dynamics
Address challenges in MoE training, including load balancing, router optimization, and expert specialization.
Distributed Training Optimization
Apply advanced distributed training techniques specifically tailored for sparse MoE models.
Scaling Strategies for MoE
Implement efficient scaling strategies combining model, data, and pipeline parallelism for MoEs.
Performance Analysis and Tuning
Profile, analyze, and tune the performance of large-scale MoE models in distributed environments.