Containerizing Deep Learning Workloads on Xeon Phi Cluster for AI Web Applications

Containerizing Deep Learning Workloads on Xeon Phi Cluster for AI Web Applications

Srivignessh Pss

Srivignessh Pss

Tempe, Arizona

AI Web App jobs require a scheduler to process Deep learning workloads. Docker containers execute these workloads in Xeon Phi Clusters.

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Description

Today's Web applications are data intensive and demand environments like tensorflow to execute the workloads. Hence, Containers are best suited to provide the framework and compute resources like CPU and memory for each workload. It decouples the app environment from the running machine/host and encapsulates all dependencies in a single portable unit. Nomad is a state of the art tool for scheduling Docker Containers. Test Model Workload is generated from Model Zoo for each framework.

Links

running-docker-containers-xeon-phi

Using Docker in high performance computing applications

Optimizing Machine Learning Workloads

Caffe Model Zoo

Intel Xeon Phi Processor for HPC

Neon model Zoo

Intel® Xeon Phi™ Processors Benefit Machine Learning/Deep Learning Apps and Frameworks

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Containerizing Deep Learning Workloads on Xeon Phi Cluster for AI Web Applications

Today's Web applications are data intensive and demand environments like tensorflow to execute the workloads. Hence, Containers are best suited to provide the framework and compute resources like CPU and memory for each workload. It decouples the app environment from the running machine/host and encapsulates all dependencies in a single portable unit. Nomad is a state of the art tool for scheduling Docker Containers. Test Model Workload is generated from Model Zoo for each framework.

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Bob Duffy

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