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The process of designing model architectures based on sparse tensor network topologies.
Distinct from Sparse Model Architectures: Focuses on the network topology of sparse tensors rather than general conditional computation or routing.
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Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with sparse inputs and outputs. It provides a model-parallel training framework and a GPU-accelerated sparse engine to support memory-intensive networks. The framework is specifically designed for recommendation system training and large-scale sparse learning. It enables the distribution of large weight matrices and embedding tables across multiple GPU devices to handle models that exceed the memory capacity of a single processor. The project covers a broad range of capabilities in
Enables constructing machine learning models using scalable sparse tensor networks to handle large-scale data.