Amazon Dsstne

Amazon Dsstne - train deep learning on sparse… | Awesome Repos

Features

Model Parallelism - Provides a model-parallel training framework to split large weight matrices across multiple GPUs.
Distributed GPU Computing - Distributes training and prediction tasks across multiple GPUs to increase processing speed and memory capacity.
Distributed GPU Training - Enables distributing neural network training and prediction workloads across multiple GPUs to increase memory and speed.
Embedding Table Sharding - Supports partitioning massive embedding tables across distributed graphics processors to handle memory-intensive models.
Recommendation Model Training - Designed for training deep learning recommendation models with weight matrices that exceed single-GPU memory.
Model Training Frameworks - Provides a distributed training framework that scales large weight matrices across multiple GPUs for memory-intensive networks.
Recommendation Models - Serves as a toolkit specifically for building deep learning recommendation models with sparse inputs and outputs.
Sparse Computing Kernels - Implements specialized computational kernels to accelerate sparse neural network operations on GPU hardware.
Sparse Tensor Network Libraries - Provides a library for building and training machine learning models using scalable sparse tensor networks.
Sparse Computation Engines - Provides a GPU-accelerated sparse engine using custom kernels to process sparse datasets without zero-value expansion.
Distributed Tensor Networks - Provides a distributed tensor network to organize large-scale model computations as a graph spread across a GPU cluster.
Sparse Learning - Supports large-scale sparse learning by processing vast datasets with mostly zero values efficiently.
Large-Scale Model Training - Supports building large-scale recommendation models using model-parallel scaling for weight matrices.
Sparse Tensor Network Modeling - Enables constructing machine learning models using scalable sparse tensor networks to handle large-scale data.
Sparse-to-Dense Embedding Tables - Provides embedding tables that map high-dimensional sparse inputs directly to dense vectors without matrix expansion.
Sparse Data Processing - Executes computations on sparse datasets using custom GPU kernels to maintain performance without expanding zero entries.
Deep Learning Frameworks - Library for building deep learning recommendation systems.
General Machine Learning - Library for training and deploying deep neural networks at scale.

Open-source alternatives to Amazon Dsstne

Similar open-source projects, ranked by how many features they share with Amazon Dsstne.

infrasys-ai/aisystem
Infrasys-AI/AISystem
17,017View on GitHub
AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo
Jupyter Notebookaiaiinfraaisys
View on GitHub17,017
fastai/course-v3
fastai/course-v3
4,914View on GitHub
This repository is a comprehensive educational program and deep learning framework designed to teach practical deep learning using PyTorch through notebooks and code examples. It serves as a high-level library for building, training, and deploying neural networks, acting as a model training orchestrator that coordinates PyTorch models, optimizers, and loss functions. The project provides specialized toolkits for computer vision, natural language processing, and tabular data preprocessing. It distinguishes itself through advanced training controls such as discriminative learning rates, a two-w
Jupyter Notebookdata-sciencedeep-learningfastai
View on GitHub4,914
apache/incubator-mxnet
apache/incubator-mxnet
20,812View on GitHub
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap
C++
View on GitHub20,812
azure/mmlspark
Azure/mmlspark
5,228View on GitHub
Mmlspark is a distributed framework for executing machine learning models, data transformations, and AI service integrations across Apache Spark clusters. It functions as a distributed machine learning library and pipeline orchestrator, allowing users to integrate pre-trained cognitive services and custom models into large-scale batch and streaming workflows. The project is distinguished by its ability to incorporate external AI services and web APIs directly into big data pipelines for text and vision analysis. It provides a scalable model training framework that coordinates gradient boostin
Scala
View on GitHub5,228

See all 30 alternatives to Amazon Dsstne

amznlabsamazon-dsstneArchived

Features

Open-source alternatives to Amazon Dsstne

Infrasys-AI/AISystem

fastai/course-v3

apache/incubator-mxnet

Azure/mmlspark

Star history

Open-source alternatives to Amazon Dsstne

Infrasys-AI/AISystem

fastai/course-v3

apache/incubator-mxnet

Azure/mmlspark