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Gradient Compression Techniques · Awesome GitHub Repositories

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Awesome GitHub RepositoriesGradient Compression Techniques

Methods for quantizing and compressing gradient data to reduce network overhead during distributed training.

Distinguishing note: Specifically targets gradient transmission efficiency in distributed training, distinct from general data compression.

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  • deepspeedai/DeepSpeed

    deepspeedai/DeepSpeed

    41,638View on GitHub↗

    DeepSpeed 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

    Gradient data is compressed and quantized before network transmission to minimize bandwidth bottlenecks during large-scale distributed training sessions.

    Pythonbillion-parameterscompressiondata-parallelism
    41,638View on GitHub↗