epfml / LocalSGD-CodeLinks
☆46Updated 5 years ago
Alternatives and similar repositories for LocalSGD-Code
Users that are interested in LocalSGD-Code are comparing it to the libraries listed below
Sorting:
- Implementation of (overlap) local SGD in Pytorch☆33Updated 4 years ago
- Code for the signSGD paper☆86Updated 4 years ago
- Sparsified SGD with Memory: https://arxiv.org/abs/1809.07599☆58Updated 6 years ago
- ☆74Updated 5 years ago
- Practical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727☆146Updated 7 months ago
- Partial implementation of paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING"☆31Updated 4 years ago
- Atomo: Communication-efficient Learning via Atomic Sparsification☆27Updated 6 years ago
- Code for "Adaptive Gradient Quantization for Data-Parallel SGD", published in NeurIPS 2020.☆30Updated 4 years ago
- FedNAS: Federated Deep Learning via Neural Architecture Search☆54Updated 3 years ago
- vector quantization for stochastic gradient descent.☆35Updated 5 years ago
- SGD with compressed gradients and error-feedback: https://arxiv.org/abs/1901.09847☆30Updated 10 months ago
- Sketched SGD☆28Updated 4 years ago
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆90Updated 2 years ago
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆52Updated 4 years ago
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆104Updated 5 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
- QSGD-TF☆21Updated 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
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- GRACE - GRAdient ComprEssion for distributed deep learning☆140Updated 10 months ago
- implement distributed machine learning with Pytorch + OpenMPI☆51Updated 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 10 months ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 3 years ago
- Code for paper: Variance Reduced Local SGD with Lower Communication Complexity☆12Updated 5 years ago
- PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models. ICML 2021☆56Updated 3 years ago
- gTop-k S-SGD: A Communication-Efficient Distributed Synchronous SGD Algorithm for Deep Learning☆36Updated 5 years ago
- Decentralized SGD and Consensus with Communication Compression: https://arxiv.org/abs/1907.09356☆70Updated 4 years ago
- Official Pytorch implementation of "DBS: Dynamic Batch Size for Distributed Deep Neural Network Training"☆23Updated 3 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago