110 citations · 168 across the 10 of their papers we have counts for
3 papers · 1 filter
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning
Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman
With the rapid adoption of machine learning (ML), a number of domains now use the approach of fine tuning models which were pre-trained on a large corpus of data. However, our expe…
Pufferfish: Communication-efficient Models At No Extra Cost
Hongyi Wang, Saurabh Agarwal, Dimitris Papailiopoulos
To mitigate communication overheads in distributed model training, several studies propose the use of compressed stochastic gradients, usually achieved by sparsification or quantiz…
On the Utility of Gradient Compression in Distributed Training Systems
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman +1
A rich body of prior work has highlighted the existence of communication bottlenecks in synchronous data-parallel training. To alleviate these bottlenecks, a long line of recent wo…