Zusammenfassung
Large-scale distributed training requires significant communication bandwidth
for gradient exchange that limits the scalability of multi-node training, and
requires expensive high-bandwidth network infrastructure. The situation gets
even worse with distributed training on mobile devices (federated learning),
which suffers from higher latency, lower throughput, and intermittent poor
connections. In this paper, we find 99.9% of the gradient exchange in
distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to
greatly reduce the communication bandwidth. To preserve accuracy during
compression, DGC employs four methods: momentum correction, local gradient
clipping, momentum factor masking, and warm-up training. We have applied Deep
Gradient Compression to image classification, speech recognition, and language
modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and
Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a
gradient compression ratio from 270x to 600x without losing accuracy, cutting
the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from
488MB to 0.74MB. Deep gradient compression enables large-scale distributed
training on inexpensive commodity 1Gbps Ethernet and facilitates distributed
training on mobile.
Nutzer