Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-
optimizing inter-dependent factors both at the per-job level and at the cluster-wide level …

DNN models across many domains continue to grow in size, resulting in high resource
requirements for effective training, and unpalatable (and often unaffordable) costs for …

When using distributed machine learning (ML) systems to train models on a cluster of worker
machines, users must configure a large number of parameters: hyper-parameters (eg the …

Efficient resource scheduling is essential for maximal utilization of expensive deep learning
(DL) clusters. Existing cluster schedulers either are agnostic to machine learning (ML) …

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model
is trained with updates computed on mobile devices while keeping the data of users local …

Deep learning (DL) is growing in popularity for many data analytics applications, including
among enterprises. Large business-critical datasets in such settings typically reside in …

Deep learning models are trained on servers with many GPUs, and training must scale with
the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel …

Elasticity—scaling out or in depending upon resource demand or availability—allows a
system to improve its efficiency or performance. This leads to potentially significant cost …