Decentralized Asynchronous Training on Heterogeneous Devices
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Updated
Jul 16, 2024 - Python
Decentralized Asynchronous Training on Heterogeneous Devices
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.
Implemented training strategies to help improve bottlenecks and to improve the training speed while maintaining the quality of our GANs.
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.
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.
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…
A blockchain based neural architecture search project.
Horovod Tutorial for Pytorch using NVIDIA-Docker.
Distributed Keras Engine, Make Keras faster with only one line of code.
Simultaneous Multi-Party Learning Framework
Collection of resources for automatic deployment of distributed deep learning jobs on a Kubernetes cluster
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