Request Demo

Deploying ML Workloads Leveraging NVIDIA Multi-lnstance CPU with OpenShift and cnvrg.io

GPUs are the powerhouses of machine learning and deep learning workloads. And now, with the release of the NVIDIA A100 Tensor Core GPU and NVIDIA DGX A100 system, GPU performance and capabilities for AI computing have skyrocketed, providing unprecedented acceleration at every scale and enabling data scientists to focus on innovation. 

In this webinar, we will discuss the benefits of a modern AI infrastructure, with DGX A100 at its core, Red Hat OpenShift providing Kubernetes managed services, paired with cnvrg.io’s industry-leading resource management and MLOps capabilities for IT and AI teams. The NVIDIA Multi-Instance GPU (MIG) feature can partition each A100 GPU into as many as seven GPU accelerators for optimal utilization, effectively expanding access to every user and application. The new MIG capability has the power to transform how data scientists work, and opens the possibilities for a significantly wider access to utilization of GPU technology.

We will present how the new MIG integration on cnvrg.io delivers accelerated ML workloads of all sizes to AI teams, using OpenShift for advanced orchestration. 

You will learn how to:

  • Leverage NVIDIA MIG technology to expand the performance and value of each NVIDIA A100 Tensor Core GPU
  • Execute diverse workloads with MIG instances on Kubernetes infrastructure
  • Share GPUs and MIG instances with cnvrg.io meta-scheduler, accomplishing high utilization and data science productivity
  • Deliver self service attachment of MIG instances to any ML job
  • Build an ML pipeline with multiple jobs with right-sized GPU and guaranteed quality of service (QoS) for every job

GPUs are the powerhouses of machine learning and deep learning workloads. And now, with the release of the NVIDIA A100 Tensor Core GPU and NVIDIA DGX A100 system, GPU performance and capabilities for AI computing have skyrocketed, providing unprecedented acceleration at every scale and enabling data scientists to focus on innovation. 

In this webinar, we will discuss the benefits of a modern AI infrastructure, with DGX A100 at its core, Red Hat OpenShift providing Kubernetes managed services, paired with cnvrg.io’s industry-leading resource management and MLOps capabilities for IT and AI teams. The NVIDIA Multi-Instance GPU (MIG) feature can partition each A100 GPU into as many as seven GPU accelerators for optimal utilization, effectively expanding access to every user and application. The new MIG capability has the power to transform how data scientists work, and opens the possibilities for a significantly wider access to utilization of GPU technology.

We will present how the new MIG integration on cnvrg.io delivers accelerated ML workloads of all sizes to AI teams, using OpenShift for advanced orchestration. 

You will learn how to:

  • Leverage NVIDIA MIG technology to expand the performance and value of each NVIDIA A100 Tensor Core GPU
  • Execute diverse workloads with MIG instances on Kubernetes infrastructure
  • Share GPUs and MIG instances with cnvrg.io meta-scheduler, accomplishing high utilization and data science productivity
  • Deliver self service attachment of MIG instances to any ML job
  • Build an ML pipeline with multiple jobs with right-sized GPU and guaranteed quality of service (QoS) for every job
cnvrg- Data Science Platform
cnvrg.io - ML Platform

AI:OS for Machine Learning

Get Started
Schedule Demo
small_c_popup.png

Join the innovative enterprises using cnvrg.io to build, train & deploy their models faster.

schedule a demo today

CloseMenu

Announcing CORE, a free ML Platform for the community to help data scientists focus more on data science and less on technical complexity

Download cnrvg CORE for Free

By submitting this form, I agree to cnvrg.io’s
privacy policy and terms of service.

With cnvrg.io we were able to increase our model throughput by up to 50% and on average by 30% when comparing to RESTful APIs. cnvrg.io also allows us to monitor our models in production, set alerts and retrain with high-level automation ML pipelines
Avi Gabay
Avi GabayDirector of Architecture at Playtika
As with many data science professionals, our team is hard to please. They want the flexibility to use any language, and the ability to write their own custom packages to improve performance, set configurations, etc. With cnvrg.io, this is the first time I’ve heard our data scientists and analysts say ‘when can we have it’.
Alexander Ryabov
Alexander RyabovHead of Data Services & Business Intelligence at Wargaming.net
Working in a hybrid cloud environment has major advantages but can be increasingly complex to manage, especially for AI workloads. cnvrg.io has the potential to enable us to operate in a hybrid cloud environment seamlessly. The new ML infrastructure dashboard could fill a major need in connecting our infrastructure to ML projects. It provides visibility into our on-prem GPU clusters and cloud resources, paving the way for increasing the utilization and ROI of our GPUs.
Bruce King
Bruce KingData Science Technologist at Seagate Technology Advanced Analytics Group
Lightricks is a research-driven company, which is why we’ve built a team with some of the top computer vision researchers in the country. cnvrg.io ensures our highly qualified researchers are focused on building the industry-leading AI technology that we are now world renown for, instead of spending time on engineering, configuration and DevOps.
Ofir Bibi
Ofir BibiHead of Research at Lightricks
Previous
Next