Training a Model with Limited Memory using Mixed Precision and Gradient Checkpointing

Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

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.…

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