| epfml/powersgd |
112 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
mit |
Python |
| Practical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727 |
| synxlin/deep-gradient-compression |
106 |
|
0 |
0 |
over 4 years ago |
0 |
|
2 |
other |
Python |
| [ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training |
| sands-lab/grace |
98 |
|
0 |
0 |
almost 4 years ago |
0 |
|
2 |
bsd-2-clause |
Python |
| GRACE - GRAdient ComprEssion for distributed deep learning |
| aghiles/microexr |
17 |
|
0 |
0 |
almost 12 years ago |
0 |
|
1 |
other |
C++ |
| A lightweight subset of the OpenEXR library. |
| Ockhius/hdr_tonemapping_fattal02 |
12 |
|
0 |
0 |
about 9 years ago |
0 |
|
0 |
|
C++ |
| Fattal02 HDR Tone mapping operator. Gradient Domain High Dynamic Range Compression. |
| hwang595/ATOMO |
12 |
|
0 |
0 |
over 7 years ago |
0 |
|
2 |
|
Python |
| Atomo: Communication-efficient Learning via Atomic Sparsification |
| Tejalsjsu/DeepGradientCompression |
11 |
|
0 |
0 |
over 6 years ago |
0 |
|
1 |
|
Python |
| It is implementation of Research paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING". Deep gradient compression is a technique by which the gradients are compressed before they are being sent. This approach greatly reduces the communication bandwidth and thus improves multi node training. |
