Armed with nothing more than an Apache Spark toting SUSE Linux laptop, you have all the trappings required to prototype the application of Machine Learning against your data-science needs. From programmability in Scala, Java or Python, to built-in support for Machine Learning via MLlib, Spark is an exceedingly effective enabler that allows you to rapidly produce results. Of course, as soon as your prototyping proves successful, you'll want to scale out to embrace the volume, variety and velocity that characterizes today's demands in Big Data Analytics ... in production. Because Spark is as comfortable on an isolated laptop as it is in a distributed-computing environment, addressing these ‘Big Data’ requirements in production boils down to effectively and efficiently embracing SUSE Linux containers, servers, clusters and clouds. As case studies will illustrate, this transition from prototype to production can be made successfully.

1. www.univa.com
www.univa.com
Ian Lumb
Solutions Architect
SUSE, Booth #1681
SC17, Denver, CO
November 16, 2017
Scaling Machine Learning
for SUSE Linux
Containers, Servers,
Clusters and Clouds

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Deep Learning from Twitter?

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Text Classification via Apache Spark
Karau et al., Learning Spark, O’Reilly, 2015

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PyTorch
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▪ Python package that provides
▪ Tensor computation – strong GPU acceleration, efficient memory usage
o Integrated with NVIDIA CuDNN and NCCL libraries
▪ Deep Neural Networks built on a tape-based autograd system
▪ Can leverage numpy, scipy and Cython as needed
▪ Available tutorials include Natural Language Processing (NLP)
http://pytorch.org/about/

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Word Vectors
https://adriancolyer.files.wordpress.com/2016/04/word2vec-distributed-representation.png?w=600

6. Next Steps: Scaling …
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OUTIN
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GPUs Accelerate ALL Major Frameworks
www.univa.com
Source: http://on-demand.gputechconf.com/gtc/2016/presentation/s6847-stephen-jones-accelerate-deep-learning.pdf

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TensorFlow
www.univa.com
Source: https://blog.paperspace.com/which-ml-framework-should-i-use/

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Sensing GPUs in Univa Grid Engine
▪ A custom load sensor is included as a pre-compiled binary as well as
via C source code
▪ Requires the NVIDIA Management Library (NVML) support provided
with the CUDA driver
▪ CUDA-specific metrics, for the driver and all GPU devices, are
captured via new complexes that are automatically configured upon
installation of the sensor
▪ The sensor provides CUDA-specific metrics in near real time using
the standard Grid Engine protocol for custom load sensors
▪ Now CUDA-specific metrics can factor into scheduling decisions, be
monitored and reported via Univa UniSight and more …

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UGE RSMAPs Address the GPU-Mgmt Gap
▪ A consumable complex attribute specific to Univa Grid Engine - in
essence, a mathematical data structure (e.g., multiset or bag)
▪ Allows conflict-free assignment of arbitrary strings (e.g., GPU_0,
GPU_1 … GPU_N) to individual units (e.g., within dense arrays of
GPUs) that can be configured with host or global scope
▪ Includes within-a-host capability to bind CPUs and GPUs via
topology masks
▪ Extends the resource abstraction to include both a quantitative
amount plus an identifier that can be exposed via environment
variables, interfaces (CLIs, APIs) and more …
▪ Facilitates management of mapped, consumable resources to HPC
jobs via the Univa Grid Engine scheduler
▪ The Resource Map (RSMAP) allows GPUs to be fully integrated as
workload-managed resources for HPC

12. RSMAP’d GPUs in Univa Grid Engine

13. Monitoring & Reporting Upon GPU Jobs

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THANK YOU
Ian Lumb
ilumb@univa.com
Solutions Architect