BigDL: Distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray
-
Updated
Apr 26, 2024 - Jupyter Notebook
BigDL: Distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray
Intel® End-to-End AI Optimization Kit
Distributed training of DNNs • C++/MPI Proxies (GPT-2, GPT-3, CosmoFlow, DLRM)
Comparison of distributed machine learning techniques applied to openly available datasets
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines.
Distributed deep learning framework based on pytorch/numba/nccl and zeromq.
Implemented training strategies to help improve bottlenecks and to improve the training speed while maintaining the quality of our GANs.
Scalable NLP model fine-tuning and batch inference with Ray and Anyscale
SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training
🚨 Prediction of the Resource Consumption of Distributed Deep Learning Systems
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.
RocketML Deep Neural Networks
Distributed Deep Learning experiments with the BigDL framework over Databricks
SHUKUN Technology Co.,Ltd Algorithm intern (2020/12-2021/5). Multi-GPU, Multi-node training for deep learning models. Horovod, NVIDIA clara train sdk, configuration tutorial,performance testing.
This repository contains the implementation of a wide variety of Deep Learning Projects in different applications of computer vision, NLP, federated, and distributed learning. These projects include university projects and projects implemented due to interest in Deep Learning.
An implementation of a distributed ResNet model for classifying CIFAR-10 and MNIST datasets.
Eager-SGD is a decentralized asynchronous SGD. It utilizes novel partial collectives operations to accumulate the gradients across all the processes.
Distributed Tensorflow, Keras and BigDL on Apache Spark
Yelp review classification using CNN model with horovod on HPC cluster
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…
Add a description, image, and links to the distributed-deep-learning topic page so that developers can more easily learn about it.
To associate your repository with the distributed-deep-learning topic, visit your repo's landing page and select "manage topics."