wenwei202 / terngradView external linksLinks
Ternary Gradients to Reduce Communication in Distributed Deep Learning (TensorFlow)
☆182Nov 19, 2018Updated 7 years ago
Alternatives and similar repositories for terngrad
Users that are interested in terngrad are comparing it to the libraries listed below
Sorting:
- QSGD-TF☆21May 15, 2019Updated 6 years ago
- Sparsified SGD with Memory: https://arxiv.org/abs/1809.07599☆58Oct 25, 2018Updated 7 years ago
- Code for the signSGD paper☆93Jan 12, 2021Updated 5 years ago
- Caffe for Sparse and Low-rank Deep Neural Networks☆383Mar 8, 2020Updated 5 years ago
- Layer-wise Sparsification of Distributed Deep Learning☆10Jul 6, 2020Updated 5 years ago
- ☆77Jun 7, 2019Updated 6 years ago
- vector quantization for stochastic gradient descent.☆35May 12, 2020Updated 5 years ago
- Stochastic Gradient Push for Distributed Deep Learning☆170Apr 5, 2023Updated 2 years ago
- Atomo: Communication-efficient Learning via Atomic Sparsification☆28Dec 9, 2018Updated 7 years ago
- ☆12Nov 15, 2018Updated 7 years ago
- Understanding Top-k Sparsification in Distributed Deep Learning☆24Nov 15, 2019Updated 6 years ago
- PMLS-Caffe: Distributed Deep Learning Framework for Parallel ML System☆193May 10, 2018Updated 7 years ago
- Sketched SGD☆28Jul 4, 2020Updated 5 years ago
- DRACO: Byzantine-resilient Distributed Training via Redundant Gradients☆23Dec 9, 2018Updated 7 years ago
- ☆33Dec 3, 2019Updated 6 years ago
- Caffe: a fast open framework for deep learning.☆13Jul 19, 2016Updated 9 years ago
- MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning☆12Apr 26, 2021Updated 4 years ago
- ☆28Oct 21, 2020Updated 5 years ago
- Code example for the ICLR 2018 oral paper☆152May 31, 2018Updated 7 years ago
- a high performance system for customized-precision distributed deep learning☆12Dec 10, 2020Updated 5 years ago
- Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets☆28Apr 22, 2016Updated 9 years ago
- Parallel SGD, done locally and remote☆14May 19, 2016Updated 9 years ago
- A compressed adaptive optimizer for training large-scale deep learning models using PyTorch☆25Nov 26, 2019Updated 6 years ago
- Ristretto: Quantization and compression of large AI models. Author: Philipp Gysel.☆288Jan 24, 2026Updated 2 weeks ago
- Implementation of Ternary Weight Networks In Caffe☆63Nov 29, 2016Updated 9 years ago
- A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments.☆132Feb 21, 2022Updated 3 years ago
- code for the paper "A Statistical Framework for Low-bitwidth Training of Deep Neural Networks"☆29Oct 31, 2020Updated 5 years ago
- Collective communications library with various primitives for multi-machine training.☆1,396Updated this week
- LIBBLE by Parameter Server☆17Sep 17, 2018Updated 7 years ago
- Code for "Adaptive Gradient Quantization for Data-Parallel SGD", published in NeurIPS 2020.☆30Jan 14, 2021Updated 5 years ago
- LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks☆246Aug 30, 2022Updated 3 years ago
- Sparse Recurrent Neural Networks -- Pruning Connections and Hidden Sizes (TensorFlow)☆74Jul 25, 2020Updated 5 years ago
- Implementation of (overlap) local SGD in Pytorch☆34Jul 12, 2020Updated 5 years ago
- Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019☆54May 8, 2020Updated 5 years ago
- ☆45Apr 14, 2017Updated 8 years ago
- Analyze network performance in distributed training☆20Oct 20, 2020Updated 5 years ago
- It is implementation of Research paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING". Deep g…☆18Aug 14, 2019Updated 6 years ago
- ☆21Nov 18, 2022Updated 3 years ago
- Implement for ``Learning Deep Features via Congenerous Cosine Loss for Person Recognition''☆175Dec 11, 2017Updated 8 years ago