Deep learning (DL) training jobs bring some unique challenges to existing cluster
managers, such as unpredictable training times, an all-or-nothing execution model, and …

Efficiently scheduling deep learning jobs on large-scale GPU clusters is crucial for job
performance, system throughput, and hardware utilization. It is getting ever more …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific
knowledge to improve latency and efficiency of training deep learning models in a GPU …

Modern distributed machine learning (ML) training workloads benefit significantly from
leveraging GPUs. However, significant contention ensues when multiple such workloads are …

Deep learning (DL) is becoming increasingly popular in many domains, including computer
vision, speech recognition, self-driving automobiles, etc. GPU can train DL models efficiently …

Deep learning training on a shared GPU cluster is becoming a common practice. However,
we observe severe sharing anomaly in production multi-tenant clusters where jobs in some …

Modern GPU clusters support Deep Learning training (DLT) jobs in a distributed manner.
Job scheduling is the key to improve the training performance, resource utilization and …

Training Deep Learning (DL) model requires multiple resource types, including CPUs,
GPUs, storage IO, and network IO. Advancements in DL have produced a wide spectrum of …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …