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Awesome GitHub RepositoriesDistributed Training Sharding
Strategies for partitioning model parameters and optimizer states across multiple compute nodes.
Distinguishing note: Focuses on sharding model states for memory scaling, distinct from general distributed computing frameworks.
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Awesome Distributed Training Sharding GitHub Repositories
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- deepspeedai/DeepSpeed
deepspeedai/DeepSpeed
41,638View on GitHubDeepSpeed is a high-performance library designed to scale deep learning model training and inference across massive clusters of GPUs and compute nodes. It provides a comprehensive suite of tools for distributed training, enabling the execution of models that exceed the memory capacity of single devices through advanced parameter partitioning, pipeline-based model parallelism, and memory-efficient state offloading. The framework distinguishes itself through specialized communication-efficient optimizers and hardware-aware acceleration techniques. By utilizing gradient compression, quantization
Model states and optimizer parameters are sharded across multiple compute nodes to enable training of models exceeding single-device memory.
Pythonbillion-parameterscompressiondata-parallelism
