03 / CORE
GPU Kubernetes
Build your first GPU Kubernetes platform.
FORMAT6 live sessions with hands-on labs
LEVELIntermediate
DURATION6 sessions
LAB ACCESSIncluded
Who it's for
- · Kubernetes engineers extending their platform to GPU workloads
- · Platform engineers standing up a GPU tenant for the first time
Prerequisites
- · Working knowledge of Kubernetes (deployments, services, nodes)
- · Basic Linux administration
What you will build
- · A GPU-enabled Kubernetes cluster from node join to scheduled workload
- · Device plugin and driver installation you configure yourself
- · A multi-tenant GPU namespace with quotas and isolation
What can break
- · GPU nodes that never register
- · Pods stuck Pending against invisible resources
- · Device plugin crash loops
CURRICULUM
01
Cluster and node architecture
- · Control plane review
- · Preparing GPU nodes
02
Drivers and runtime
- · NVIDIA driver installation
- · Container runtime GPU support
03
Device plugins and scheduling
- · NVIDIA device plugin
- · GPU-aware scheduling constraints
04
Workload deployment
- · Deploying training and inference workloads
- · Resource requests and limits for GPUs
05
Monitoring and multi-tenancy
- · GPU metrics pipelines
- · Namespace isolation and quotas
06
Troubleshooting
- · Diagnosing the platform you just built
Lab environment
Hands-on labs on real Kubernetes clusters with GPU nodes throughout all six sessions.
Outcomes
- · You can stand up a GPU Kubernetes platform without a vendor playbook
- · You can diagnose the most common GPU scheduling failures
Instructor
Built by engineers who have operated infrastructure in the real world.
Mr. Bingi, Founder
Pricing
[Pricing to be announced]
FAQ
Do I need my own GPU hardware?
No. Lab access to GPU-backed clusters is provided for the duration of the course.
Which Kubernetes distribution is used?
Core concepts apply across distributions; lab environments will be confirmed at signup.