In the pursuit of superior accuracy, deep learning models in areas such as natural language processing and computer vision have significantly grown in size in the past few years, frequently counted in tens to hundreds of billions of parameters. Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning engineers are constantly looking for the best way to optimize their training compute, yet are struggling with the communication overhead that can increase along with the overall cluster size.
This is why we recently launched sharded data parallelism on Amazon SageMaker, a new memory-saving distributed training technique in the SageMaker model parallel (SMP) library. Sharded data parallelism is purpose-built for extreme-scale models and uses Amazon in-house MiCS technology under the hood, a science effort to minimize the communication scale by bringing down expensive communication overhead rooted in parameter gathering and gradient synchronization. With a 30B parameter GPT-2 model with sequence

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