📝 Guest post: How to Measure Your GPU Cluster Utilization, and Why That Matters*
Was this email forwarded to you? Sign up here In this article, Run:AI’s team introduces Why measure GPU cluster utilization?Ask any data scientist what they think their GPU cluster’s utilization is, and they’ll probably say it’s above 60%–at least. When AI teams and models begin to scale, it can certainly feel like compute resources are always in use, which is extremely frustrating for data scientists who are eager to train, validate and test their models. As one customer recently put it, “We need a scheduler…otherwise, we might have blood on the floor as users will fight for the GPUs.” Yikes. But the truth is, most GPU clusters are at less than 20% utilization. Why the disconnect between guesstimate and reality? A mismatch between allocation (an amount or portion of a resource assigned to a particular user) and utilization (the amount practically and effectively being used). It’s almost impossible to get an accurate measurement of GPU cluster utilization without a tool, even in the most advanced teams running AI in production. For example, when we met autonomous vehicle leaders Wayve, they had 100% of their resources allocated, but less than 45% utilized at any given time. Because GPUs were statically assigned to researchers, when the researchers weren’t using their assigned GPUs others could not access them, creating the illusion that GPUs for model training were at capacity. Meanwhile, if IT has no visibility into utilization, they might misdiagnose the problem, assuming it’s time to purchase more hardware. Over time, you can end up with an ever-growing cluster of expensive GPUs, and very little ROI to show for it. We open-sourced What is |
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📨 Edge#191: MPI – the Fundamental Enabler of Distributed Training
Tuesday, May 17, 2022
In this issue: we discuss the fundamental enabler of distributed training: message passing interface (MPI); +Google's paper about General and Scalable Parallelization for ML Computation Graphs; +
📌Event: Join the Largest Conference on MLOps: 3rd Annual MLOps World 2022! 🎉
Monday, May 16, 2022
We are happy to support the 3rd Annual MLOps World 2022! The MLOps World Committee would like to invite you this June 9-10th for a truly must-attend event, and an unforgettable experience in Toronto,
Google’s Big ML Week
Sunday, May 15, 2022
Weekly news digest curated by the industry insiders
📌 Last chance! Join us at apply() – the ML Data Engineering Conference
Friday, May 13, 2022
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📝 Guest post: It's Time to Use Semi-Supervised Learning for Your CV models*
Thursday, May 12, 2022
In this article, Masterful AI's team suggests that instead of throwing more training data at a deep learning model, one should consider semi-supervised learning (SSL) to unlock the information in
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