BigDL: Distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray

Learn applied deep learning from zero to deployment using TensorFlow 1.8+

A Portable C Library for Distributed CNN Inference on IoT Edge Clusters

Intel® End-to-End AI Optimization Kit

🚨 Prediction of the Resource Consumption of Distributed Deep Learning Systems

Distributed Keras Engine, Make Keras faster with only one line of code.

Distributed training of DNNs • C++/MPI Proxies (GPT-2, GPT-3, CosmoFlow, DLRM)

SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training

RocketML Deep Neural Networks

Ok-Topk is a scheme for distributed training with sparse gradients. Ok-Topk integrates a novel sparse allreduce algorithm (less than 6k communication volume which is asymptotically optimal) with the decentralized parallel Stochastic Gradient Descent (SGD) optimizer, and its convergence is proved theoretically and empirically.

sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines.

Java based Convolutional Neural Network package running on Apache Spark framework

TensorFlow (1.8+) Datasets, Feature Columns, Estimators and Distributed Training using Google Cloud Machine Learning Engine

Distributed deep learning framework based on pytorch/numba/nccl and zeromq.

PyTorch Examples for Beginners

Collection of resources for automatic deployment of distributed deep learning jobs on a Kubernetes cluster

An implementation of a distributed ResNet model for classifying CIFAR-10 and MNIST datasets.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can be initiated without requiring that all the processes enter it. It partially reduces the data within non-overlapping groups of process, improving the…

Horovod Tutorial for Pytorch using NVIDIA-Docker.