8 citations · 13 across the 3 of their papers we have counts for
3 papers
Automap: Towards Ergonomic Automated Parallelism for ML Models
Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis +8
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline…
Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization
Valentin Dalibard, Michael Schaarschmidt, Eiko Yoneki
We present an optimizer which uses Bayesian optimization to tune the system parameters of distributed stochastic gradient descent (SGD). Given a specific context, our goal is to qu…
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection
Michael Schaarschmidt, Felix Gessert, Valentin Dalibard +1
Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforce…