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Build a Machine Learning Testbed Based on Kubernetes and Nvidia GPU

Nvidia GPUs have become the defacto standard for running machine learning jobs. From entry-level graphics cards to the Pascal 100 GPUs in the cloud, data scientists are relying on Nvidia for training and inferencing machine learning models.

On the infrastructure front, Kubernetes has become the standard for running modern applications. It has evolved from running stateless workloads to transactional databases.

Nvidia has been slowly but steadily adding support for containers and Kubernetes. Today, it is possible to access GPUs from containers and Kubernetes pods. Almost all the Containers-as-a-Service (CaaS) providers expose Nvidia K80 and P100 GPUs through Kubernetes.

Even though we can access GPUs in the public cloud, nothing beats building our own GPU-based development machine running Kubernetes. Depending on your budget, you can choose from an entry-level GTX 1050 Ti series to high-end TITAN X GPUs to power your testbed.

I recently built a custom machine based on the humble GeForce GTX 1050 TiGPU. Being a fan of Kubernetes, I wanted to run a single-node cluster to run my machine learning experiments. By no means, this matches the horsepower delivered by K80s and P100s available in the public cloud. But, this is sufficient to explore GPU-based deep learning frameworks such as TensorFlow and Caffe.

In this tutorial, I am going to walk you through the steps involved in building a GPU-backed, single-node Kubernetes cluster.

Kubernetes on Nvidia GPUs is available in preview. Please note that this configuration is not recommended for production environments.

Read the entire article at The New Stack

Janakiram MSV is an analyst, advisor, and architect. Follow him on Twitter,  Facebook and LinkedIn.

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Janakiram MSV heads the Cloud Infrastructure Services at Aditi Technologies. He was the founder and CTO of Get Cloud Ready Consulting, a niche Cloud Migration and Cloud Operations firm that recently got acquired by Aditi Technologies. In his current role, he leads a highly talented engineering team that focuses on migrating and managing applications deployed on Amazon Web Services and Microsoft Windows Azure Infrastructure Services.
Janakiram is an industry analyst with deep understanding of Cloud services. Through his speaking, writing and analysis, he helps businesses take advantage of the emerging technologies. He leverages his experience of engaging with the industry in developing informative and practical research, analysis and authoritative content to inform, influence and guide decision makers. He analyzes market trends, new products / features, announcements, industry happenings and the impact of executive transitions.
Janakiram is one of the first few Microsoft Certified Professionals on Windows Azure in India. Demystifying The Cloud, an eBook authored by Janakiram is downloaded more than 100,000 times within the first few months. He is the Chief Editor of a popular portal on Cloud called www.CloudStory.in that covers the latest trends in Cloud Computing. Janakiram is an analyst with the GigaOM Pro analyst network where he analyzes the Cloud Services landscape. He is a guest faculty at the International Institute of Information Technology, Hyderabad (IIIT-H) where he teaches Big Data and Cloud Computing to students enrolled for the Masters course. As a passionate speaker, he has chaired the Cloud Computing track at premier events in India.
He has been the keynote speaker at many premier conferences, and his seminars are attended by thousands of architects, developers and IT professionals. His sessions are rated among the best in every conference he participates.
Janakiram has worked at the world-class product companies including Microsoft Corporation, Amazon Web Services and Alcatel-Lucent. Joining as the first employee of Amazon Web Services in India, he was the AWS Technology Evangelist. Prior to that, Janakiram spent 10 years at Microsoft Corporation where he was involved in selling, marketing and evangelizing the Microsoft Application Platform and Tools.