DSSTNE - Amazon's library for building Deep Learning models
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
What is DSSTNE - Amazon's library for building Deep Learning models?
Amazon DSSTNE is an open-source deep learning library for training and deploying recommendation models with sparse inputs and outputs. It solves the problem of scaling large neural networks for production recommendation systems by using model-parallelism across multiple GPUs and optimizing for sparse datasets common in e-commerce and personalized recommendations.
Machine learning engineers and data scientists building large-scale recommendation systems, particularly those dealing with sparse data like e-commerce product recommendations, who need production-ready, GPU-accelerated model training and deployment.
Developers choose DSSTNE for its specialized optimization for sparse data, multi-GPU scaling capabilities enabling larger models than single-GPU alternatives, and production-focused design prioritizing speed and scalability over experimental flexibility.
Overview
Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models
Use Cases
Best For
- Training deep learning recommendation models with sparse input features
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
