🚨 Prediction of the Resource Consumption of Distributed Deep Learning Systems
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Updated
Feb 6, 2023 - Python
🚨 Prediction of the Resource Consumption of Distributed Deep Learning Systems
Distributed Keras Engine, Make Keras faster with only one line of code.
SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training
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.
Distributed deep learning framework based on pytorch/numba/nccl and zeromq.
Collection of resources for automatic deployment of distributed deep learning jobs on a Kubernetes 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…
An implementation of a distributed ResNet model for classifying CIFAR-10 and MNIST datasets.
Horovod Tutorial for Pytorch using NVIDIA-Docker.
A blockchain based neural architecture search project.
Eager-SGD is a decentralized asynchronous SGD. It utilizes novel partial collectives operations to accumulate the gradients across all the processes.
Simultaneous Multi-Party Learning Framework
Implemented training strategies to help improve bottlenecks and to improve the training speed while maintaining the quality of our GANs.
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