Event Details

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Machine Learning benchmarking with OpenStack and Kubernetes

Deep Learning and Cloud Platforms are transforming the field of Machine Learning from theory to practice. However, implementation differences across frameworks and inference engines make the comparison of benchmark results difficult. SPEC and TPCC benchmarks are not accurate due to the complex interactions between implementation choices such as batch size, hyperparameters, or numerical precision. To address this complexity requires systematic benchmarking that is both representative of real-world use cases and valid across different software/hardware platforms.

This talk will present the best Machine Learning benchmarking tools to use with OpenStack and Kubernetes. We will show how MLPerf and Thoth help data scientists to improve their system performance and fully benefit from their CPUs, GPUs, or FPGAs. We will share insights and lessons learned over the journey of key Machine Learning training and inference use cases selection.

What can I expect to learn?

We will share insights and lessons learned over the journey of the selection of key machine learning training and inference workloads representative of important production use cases for CPUs, GPUs and FPGAs

Monday, November 4, 11:40am-12:20pm (3:40am - 4:20am UTC)
Difficulty Level: Intermediate
Red Hat, Senior Principal Product Manager
Erwan Gallen est Senior Principal Product Manager pour Red Hat OpenShift et Red Hat OpenStack Services on OpenShift. Erwan gère au sein de la Business Unit Hybrid Platforms de Red Hat la stratégie pour l'accélération matérielle de l'intelligence artificielle (IA). Erwan a occupé divers postes, notamment en tant que Release Manager d'un cloud public et CTO d'un groupe de médias en ligne.... FULL PROFILE