With hundreds of millions of customers shopping daily on Amazon, it’s critical they help them discover the right product from their massive catalog of products.
Amazon’s Deep Scalable Sparse Tensor Network Engine (DSSTNE or “Destiny”) is a deep learning framework built from the ground up to help researchers develop search and recommendation systems. With multi-GPU support, DSSTNE can automatically distribute computational workloads across all available GPUs, speeding up training of larger models without a lot of effort. As a result, DSSTNE can be used to build recommendations systems that model ten million unique products instead of being limited to hundreds of thousands possible with other solutions.
The DSSTNE roadmap includes support for the types of convolutional layers used in image processing, and additional recurrent layers needed for many natural language understanding and speech recognition tasks.
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Amazon Releases Open-Source Deep Learning Software
May 11, 2016
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AI-Generated Summary
- Amazon released DSSTNE, a deep learning framework designed to build search and recommendation systems at massive scale.
- The framework automatically distributes workloads across multiple GPUs, enabling models that cover ten million unique products instead of the hundreds of thousands typical of other solutions.
- Amazon reports that 35 percent of its product sales result from recommendations powered by this technology.
- Future development plans include adding convolutional layers for image processing and recurrent layers for natural language understanding and speech recognition.
Next Step
- Find the DSSTNE FAQ on GitHub for additional details and getting-started information.
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