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run: write – the Run:AI blog

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virtualization platform

Run:AI announces General Availability of its K8s-based deep learning virtualization platform

March 17, 2020

The Run:AI deep learning virtualization platform, now supporting Kubernetes, brings control and visibility to IT teams supporting data science initiatives  Tel Aviv — 17 March,

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GPU utilization

Breaking Static GPU Allocations with Guaranteed Quotas

March 15, 2020

– Dr. Ronen Dar, CTO Run:AI In a previous article, we discussed the problem of machine scheduling and complications that arise from inefficient GPU utilization

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data science lifecycle

How Can IT Support Emerging Data Science Initiatives?

February 28, 2020

This week, Omri Geller, Run:AI’s CEO and cofounder spoke on a webinar about supporting the data science lifecycle. His talk centered on the three areas

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gradient accumulation - deep learning batch size

We Open-Sourced a Gradient Accumulation Tool to Enable Using Large Batch Sizes Even When GPU Memory is Limited

January 23, 2020

Today, Run:AI published our own gradient accumulation mechanism for Keras – it’s a generic implementation, that can wrap any Keras optimizer (both a built-in one

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GPU Machine Scheduling

Challenges in GPU Machine Scheduling for AI and ML Workloads

December 19, 2019

Dr. Ronen Dar, CTO and co-founder, Run:AI – Like a huge safari animal swatting all day at a pesky fly, sophisticated projects are often hindered

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Run:AI at AI Summit NYC - GPU Machine Management

Can New Approaches to GPU Machine Management Speed Delivery of Deep Learning Projects?

December 19, 2019

At the AI Summit in NYC, Omri Geller, Run:AI CEO spoke on the subject of GPU management. This was his session topic: Can New Approaches

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AI, Machine Learning and Deep Learning

What We’re Reading: Three AI Posts that Got our Attention – vol. 1

December 16, 2019

What we’re reading from around the web on AI, Machine Learning and Deep Learning. Facebook out of Compute? This post by Tiernan Ray at ZDNet

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