Reduce Cost by 75% with Fractional GPU for Deep Learning Inference
In this post, we’ll address how fractionalizing GPU for deep learning inference workloads with lower computational needs can save 50-75% of the cost of deep
Scan Computers Announces Partnership with Run:AI
Scan Computers, a leading UK IT provider, is proud to announce its new partnership with Run:AI, a company virtualising AI infrastructure.
Run:AI helps London’s AI Centre speed up machine learning, particularly in the fight against Covid-19
After installing Run:AI, the AI Centre can run more experiments, utilize resources more efficiently, and speed up completion time for training data science models July
Run:AI creates first fractional GPU sharing for Kubernetes deep learning workloads
By creating multiple logical GPUs on a single resource, Run:AI has built another key part of AI virtualization technology Tel Aviv — Wednesday, May 6,
Run:AI COVID-19 Response: How can we help you?
Like most of you, we have been following the Coronavirus pandemic and tracking its rapid spread. As we watch the world, but particularly countries like
Run:AI announces General Availability of its K8s-based deep learning virtualization platform
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,
Breaking Static GPU Allocations with Guaranteed Quotas
– 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
How Can IT Support Emerging Data Science Initiatives?
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
We Open-Sourced a Gradient Accumulation Tool to Enable Using Large Batch Sizes Even When GPU Memory is Limited
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
Challenges in GPU Machine Scheduling for AI and ML Workloads
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
Can New Approaches to GPU Machine Management Speed Delivery of Deep Learning Projects?
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
What We’re Reading: Three AI Posts that Got our Attention – vol. 1
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