Projects in Awesome Lists tagged with distributed-deep-learning

A curated list of projects in awesome lists tagged with distributed-deep-learning .

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https://github.com/intel/bigdl

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

analytics-zoo apache-spark bigdl deep-neural-network distributed-deep-learning keras-tensorflow python pytorch scala

Last synced: 14 May 2025

https://github.com/dkeras-project/dkeras

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

data-parallelism deep-learning deep-neural-networks distributed distributed-deep-learning distributed-keras-engine distributed-systems keras keras-classification-models keras-models keras-neural-networks keras-tensorflow machine-learning neural-network parallel-computing plaidml python ray tensorflow tensorflow-models

Last synced: 02 May 2025

https://github.com/zoranzhao/deepthings

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

deep-neural-networks distributed-deep-learning edge-computing internet-of-things iot-edge-clusters

Last synced: 22 Sep 2025

https://github.com/guanhuawang/sensai

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

cifar-10 cifar-100 cifar10 cifar100 cnn-classification deep-learning deep-neural-networks distributed-deep-learning distributed-machine-learning distributed-systems imagenet imagenet1k machine-learning mlsys mobilenet-v2 resnet shufflenet-v2 sysml vgg

Last synced: 15 Apr 2025

https://github.com/ParCIS/Chimera

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

distributed-deep-learning pipeline-parallelism transformers

Last synced: 13 Apr 2025

https://github.com/shigangli/eager-sgd

Eager-SGD is a decentralized asynchronous SGD. It utilizes novel partial collectives operations to accumulate the gradients across all the processes.

distributed-deep-learning gradient-averaging partial-allreduce

Last synced: 13 Aug 2025

https://github.com/shigangli/wagma-sgd

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 parallel scalability.

distributed-deep-learning model-averaging partial-allreduce

Last synced: 13 Aug 2025