Soptq / Dynamic_Load_Balance_DistributedDNNLinks
Official Pytorch implementation of "DBS: Dynamic Batch Size for Distributed Deep Neural Network Training"
☆23Updated 3 years ago
Alternatives and similar repositories for Dynamic_Load_Balance_DistributedDNN
Users that are interested in Dynamic_Load_Balance_DistributedDNN are comparing it to the libraries listed below
Sorting:
- ☆46Updated 5 years ago
- Partial implementation of paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING"☆31Updated 4 years ago
- FedNAS: Federated Deep Learning via Neural Architecture Search☆55Updated 3 years ago
- Implementation of (overlap) local SGD in Pytorch☆33Updated 4 years ago
- PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models. ICML 2021☆56Updated 3 years ago
- Practical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727☆147Updated 7 months ago
- Sparsified SGD with Memory: https://arxiv.org/abs/1809.07599☆58Updated 6 years ago
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- Codes for paper "Few Shot Network Compression via Cross Distillation", AAAI 2020.☆32Updated 5 years ago
- vector quantization for stochastic gradient descent.☆35Updated 5 years ago
- Source code of ICLR2020 submisstion: Zeno++: Robust Fully Asynchronous SGD☆13Updated 5 years ago
- ☆22Updated 4 years ago
- Implementation of Parameter Server using PyTorch communication lib☆42Updated 6 years ago
- Understanding Top-k Sparsification in Distributed Deep Learning☆24Updated 5 years ago
- [ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training☆222Updated 11 months ago
- Atomo: Communication-efficient Learning via Atomic Sparsification☆27Updated 6 years ago
- [NeurIPS‘2021] "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", Geng Yuan, Xiaolong Ma, Yanzhi Wang et al…☆18Updated 3 years ago
- ☆75Updated 6 years ago
- MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. Published in CVPR 2020☆37Updated 4 years ago
- ☆22Updated 5 years ago
- GRACE - GRAdient ComprEssion for distributed deep learning☆140Updated 11 months ago
- Pytorch implementation of our paper accepted by IEEE TNNLS, 2022 -- Distilling a Powerful Student Model via Online Knowledge Distillation☆28Updated 3 years ago
- Code for "Adaptive Gradient Quantization for Data-Parallel SGD", published in NeurIPS 2020.☆30Updated 4 years ago
- Create tiny ML systems for on-device learning.☆20Updated 3 years ago
- Code for the signSGD paper☆88Updated 4 years ago
- [NeurIPS 2021] “Stronger NAS with Weaker Predictors“, Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang W…☆27Updated 2 years ago
- [ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, De…☆45Updated last year
- A Sparse-tensor Communication Framework for Distributed Deep Learning☆13Updated 3 years ago
- Pytorch deep learning object detection using CINIC-10 dataset.☆22Updated 5 years ago
- gTop-k S-SGD: A Communication-Efficient Distributed Synchronous SGD Algorithm for Deep Learning☆36Updated 5 years ago