A powerful GPU cluster doesn't guarantee efficient use of compute resources.
In our new case study, we share how we helped a research lab solve the challenge of sharing GPUs for ML workloads.
One of the most interesting aspects of the project was the choice between MIG and time-slicing. Both approaches allow multiple tasks to run on a single GPU, but they work differently and aren't suitable for every type of hardware.
In this case study, we cover:
• the differences between MIG and time-slicing;
• how we built an MLOps platform on Kubernetes with GitOps;
• why we chose open source and avoided vendor lock-in.
If you work with Kubernetes, ML infrastructure, or GPU clusters, this breakdown might be useful: https://evrone.com/cases/relab
In our new case study, we share how we helped a research lab solve the challenge of sharing GPUs for ML workloads.
One of the most interesting aspects of the project was the choice between MIG and time-slicing. Both approaches allow multiple tasks to run on a single GPU, but they work differently and aren't suitable for every type of hardware.
In this case study, we cover:
• the differences between MIG and time-slicing;
• how we built an MLOps platform on Kubernetes with GitOps;
• why we chose open source and avoided vendor lock-in.
If you work with Kubernetes, ML infrastructure, or GPU clusters, this breakdown might be useful: https://evrone.com/cases/relab