Tabrizian / learning-to-quantize
Code for "Adaptive Gradient Quantization for Data-Parallel SGD", published in NeurIPS 2020.
☆30Updated 4 years ago
Alternatives and similar repositories for learning-to-quantize:
Users that are interested in learning-to-quantize are comparing it to the libraries listed below
- Partial implementation of paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING"☆31Updated 4 years ago
- vector quantization for stochastic gradient descent.☆34Updated 4 years ago
- Sparsified SGD with Memory: https://arxiv.org/abs/1809.07599☆58Updated 6 years ago
- ☆46Updated 5 years ago
- SGD with compressed gradients and error-feedback: https://arxiv.org/abs/1901.09847☆30Updated 8 months ago
- ☆29Updated 5 years ago
- Implementation of (overlap) local SGD in Pytorch☆33Updated 4 years ago
- Code for the signSGD paper☆83Updated 4 years ago
- FedNAS: Federated Deep Learning via Neural Architecture Search☆54Updated 3 years ago
- Understanding Top-k Sparsification in Distributed Deep Learning☆24Updated 5 years ago
- Federated Dynamic Sparse Training☆30Updated 2 years ago
- [ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training☆217Updated 9 months 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☆68Updated 4 years ago
- Create tiny ML systems for on-device learning.☆20Updated 3 years ago
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- ☆74Updated 5 years ago
- LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning (2021 IEEE/ACM Symposium on Edge Co…☆42Updated 2 years ago
- Federated Learning with Partial Model Personalization☆42Updated 2 years ago
- It is implementation of Research paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING". Deep g…☆18Updated 5 years ago
- Code and checkpoints of compressed networks for the paper titled "HYDRA: Pruning Adversarially Robust Neural Networks" (NeurIPS 2020) (ht…☆92Updated 2 years ago
- Sketched SGD☆28Updated 4 years ago
- [ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time☆42Updated 2 years ago
- Implementation of the FedPM framework by the authors of the ICLR 2023 paper "Sparse Random Networks for Communication-Efficient Federated…☆28Updated 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
- Measuring and predicting on-device metrics (latency, power, etc.) of machine learning models☆66Updated 2 years ago
- PyTorch implementation of ICML 2017 paper, SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Paral…☆18Updated 7 years ago
- GRACE - GRAdient ComprEssion for distributed deep learning☆137Updated 8 months ago
- Practical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727☆147Updated 5 months ago
- ☆25Updated 3 years ago