# amznlabs/amazon-dsstne

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4,395 stars · 728 forks · C++ · Apache-2.0 · archived

## Links

- GitHub: https://github.com/amznlabs/amazon-dsstne
- awesome-repositories: https://awesome-repositories.com/repository/amznlabs-amazon-dsstne.md

## Description

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 including distributed GPU computation, sparse dataset processing, and the construction of scalable sparse tensor networks. These utilities allow for the execution of high-performance machine learning operations and model scaling across GPU clusters.

## Tags

### Artificial Intelligence & ML

- [Model Parallelism](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/model-training-pipelines/model-parallelism.md) — Provides a model-parallel training framework to split large weight matrices across multiple GPUs.
- [Distributed GPU Computing](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-gpu-computing.md) — Distributes training and prediction tasks across multiple GPUs to increase processing speed and memory capacity. ([source](https://github.com/amznlabs/amazon-dsstne#readme))
- [Distributed GPU Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-gpu-training.md) — Enables distributing neural network training and prediction workloads across multiple GPUs to increase memory and speed.
- [Embedding Table Sharding](https://awesome-repositories.com/f/artificial-intelligence-ml/embedding-adaptation-utilities/vocabulary-embedding-adapters/embedding-table-sharding.md) — Supports partitioning massive embedding tables across distributed graphics processors to handle memory-intensive models.
- [Recommendation Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/localization-model-training/recommendation-model-training.md) — Designed for training deep learning recommendation models with weight matrices that exceed single-GPU memory.
- [Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-frameworks.md) — Provides a distributed training framework that scales large weight matrices across multiple GPUs for memory-intensive networks.
- [Recommendation Models](https://awesome-repositories.com/f/artificial-intelligence-ml/recommendation-models.md) — Serves as a toolkit specifically for building deep learning recommendation models with sparse inputs and outputs.
- [Sparse Computing Kernels](https://awesome-repositories.com/f/artificial-intelligence-ml/sparse-computing-kernels.md) — Implements specialized computational kernels to accelerate sparse neural network operations on GPU hardware.
- [Sparse Tensor Network Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-libraries/sparse-tensor-network-libraries.md) — Provides a library for building and training machine learning models using scalable sparse tensor networks.
- [Sparse Computation Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings/sparse-embeddings/sparse-operation-accelerators/sparse-computation-engines.md) — Provides a GPU-accelerated sparse engine using custom kernels to process sparse datasets without zero-value expansion.
- [Sparse Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-dataset-processing/sparse-learning.md) — Supports large-scale sparse learning by processing vast datasets with mostly zero values efficiently.
- [Large-Scale Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training.md) — Supports building large-scale recommendation models using model-parallel scaling for weight matrices. ([source](https://github.com/amznlabs/amazon-dsstne#readme))
- [Sparse Tensor Network Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/sparse-model-architectures/sparse-tensor-network-modeling.md) — Enables constructing machine learning models using scalable sparse tensor networks to handle large-scale data. ([source](https://github.com/amznlabs/amazon-dsstne#readme))
- [Sparse-to-Dense Embedding Tables](https://awesome-repositories.com/f/artificial-intelligence-ml/speaker-embeddings/embedding-management/feature-embedding-tables/sparse-to-dense-embedding-tables.md) — Provides embedding tables that map high-dimensional sparse inputs directly to dense vectors without matrix expansion.

### Scientific & Mathematical Computing

- [Distributed Tensor Networks](https://awesome-repositories.com/f/scientific-mathematical-computing/tensor-operations/tensor-network-executors/distributed-tensor-networks.md) — Provides a distributed tensor network to organize large-scale model computations as a graph spread across a GPU cluster.
- [Sparse Data Processing](https://awesome-repositories.com/f/scientific-mathematical-computing/sparse-data-processing.md) — Executes computations on sparse datasets using custom GPU kernels to maintain performance without expanding zero entries. ([source](https://github.com/amznlabs/amazon-dsstne#readme))

### Part of an Awesome List

- [Deep Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning-frameworks.md) — Library for building deep learning recommendation systems.
- [General Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/general-machine-learning.md) — Library for training and deploying deep neural networks at scale.