Ben Schaechter’s Post

It's no secret that NVIDIA GPUs deliver fantastic performance that is needed for ML model training workloads. Take a look at this blog post where Riccardo Freschi and Elamaran Shanmugam show you how you can use AWS open source Observability services to monitor such workloads easily with minimum effort. https://lnkd.in/dv9a28bM #aws #observability #gpu #nvidia #prometheus #grafana #opentelemetry

To run many AI/ML workloads, you need GPUs or other specialized hardware. However, GPU access in the cloud is more complex than simply spinning up a new node type. Kubernetes offers scalability and dynamic resource allocation for AI/ML workloads running on GPU worker nodes. Fairwinds fully supports artificial intelligence (AI) and machine learning (ML) workloads in Kubernetes by providing GPU-enabled nodes. Fairwinds manages drivers for GPU vendor support, as well as autoscaling, which makes it trivial for our users to utilize GPU resources for use by AI/ML workloads. Learn more https://bit.ly/4c9ob7V

Running ML workload on Kubernetes cluster a one of the big trend. Good to learn how to monitor GPU workloads on EKS. #aws #eks #gpu

NVIDIA's acquisition of Run:ai marks a strategic move to address the escalating complexity of AI deployments. With AI workloads spanning cloud, edge, and on-premises infrastructure, efficient orchestration becomes even more important. Run:ai's Kubernetes-based platform streamlines workload management and optimizes resource utilization across diverse environments. By integrating with popular Kubernetes variants and supporting a spectrum of AI tools, this acquisition underscores NVIDIA's commitment to empowering enterprises with the agility and efficiency needed to navigate the AI landscape effectively - a growing part of the market landscape, according to Futurum research. NVIDIA Run:ai Techstrong Group The Futurum Group https://lnkd.in/e9m739tU

CloudWatch Container Insights now announces Accelerated Compute Observability on EKS https://ift.tt/vB34D51 Amazon CloudWatch Container Insights with Enhanced Observability for EKS now auto-discovers critical health metrics from your AWS accelerators Trainium and Inferentia, and AWS high performance network adapters (Elastic Fabric Adapters) as well as NVIDIA GPUs. You can visualize these out-of-the-box metrics in curated Container Insights dashboards to help monitor your accelerated infrastructure and optimize your AI workloads for operational excellence.  via Recent Announcements https://ift.tt/iHC94o7 April 23, 2024 at 07:41PM #aws #cloudcomputing

Besides GenAI, the Run:ai main focus has been on #MultiTenancy in #Kubernetes, something we've pursued at CLASTIX since 2020 with Project Capsule first, then with #Kamaji. The next challenge is solving fallacies of GPU renting, such as inefficient reservation queue, no multi cloud portability, unless a huge infrastructure cognitive load. That's what we've built at CLASTIX with Adriano Pezzuto: k8s.gpu allows you to consume GPU resources in a serverless way, renting them from multiple providers, for a true pay-per-use. We've been able to create k8s clusters in less than 16 seconds, we'll make possible to span GPU workloads worldwide in less than a minute.

Amazon CloudWatch Container Insights with Enhanced Observability for EKS now auto-discovers critical health metrics from your AWS accelerators Trainium and Inferentia, and AWS high performance network adapters (Elastic Fabric Adapters) as well as NVIDIA GPUs. You can visualize these out-of-the-box metrics in curated Container Insights dashboards to help monitor your accelerated infrastructure and optimize your AI workloads for operational excellence.

Amazon CloudWatch Container Insights with Enhanced Observability for EKS now auto-discovers critical health metrics from your AWS accelerators Trainium and Inferentia, and AWS high performance network adapters (Elastic Fabric Adapters) as well as NVIDIA GPUs. You can visualize these out-of-the-box metrics in curated Container Insights dashboards to help monitor your accelerated infrastructure and optimize your AI workloads for operational excellence. https://lnkd.in/e25SXZE4

🤔 Ever wondered - What if I can rent my GPU or use someone's else idle GPU? Think no more, Vast.ai is what you are looking for! 🔥 ✏️ Vast.ai connects data centers and professionals running the Vast hosting software with users who can find the best deals for GPU compute according to their specific requirements. Vast.ai GPU rentals are ~3-5X cheaper than current alternatives. This can help millions of underutilized consumer GPUs around the world enter the cloud computing market for the first time 🤩 🔗 Check it out here: https://lnkd.in/dEHc8w3n #deeplearning #gpu #gpucomputing #nvidia #ai #artificialintelligence #aiops #devops

Traditional ML workloads driven by the transformational power of generative AI often require substantial compute capacity for handling colossal datasets. But the surge in demand for GPUs has left many in a tight spot. With Amazon EC2 Capacity Blocks for ML, Amazon Web Services (AWS) has made it possible to access GPU compute capacity with ease and without long-term commitments. This revolutionary offering lets users reserve NVIDIA GPUs in Amazon EC2 UltraClusters, perfectly tailored for high-performance ML workloads. Organizations can decide the cluster size, the start date, and the duration, making access to GPU compute capacity more reliable and predictable. They can enjoy the benefits of Nvidia H100 Tensor Core GPU instances in cluster sizes ranging from 1 to 64 instances, each with 8 GPUs. Plus, they get the option to book time up to 14 days in advance in 1-day increments. And the instances will automatically terminate when the allotted time has elapsed. When #EnterpriseBuildsOnAWS they can reimagine the future with flexible GPU provisioning, making their journey smoother and more cost-effective. #aws #awscloud #amazonwebservices