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Lambda GPU Cloud

Validate Lambda Managed Kubernetes with Polyaxon

Lambda Managed Kubernetes provides GPU nodes with the NVIDIA and network operators, storage classes, and cluster validation tooling already integrated. Your team still owns workload namespaces, access policy, storage use, and the Polyaxon queues exposed to users.

Outcome

A Lambda capacity class represented in Polyaxon with a repeatable check of the exact namespace, queue, GPU, and storage path used in production.

Start the guideConnect a compute cluster to Polyaxon

Infrastructure responsibilities

Lambda operates the cluster foundation

Lambda provisions the GPU infrastructure and maintains the managed Kubernetes, GPU, network, and storage integrations.

Your team owns application policy

Namespace access, workload configuration, storage semantics, secrets, quotas, and upgrade sign-off remain customer decisions.

Polyaxon connects the compute cluster

Installing Polyaxon Agent (compute cluster) connects this Kubernetes cluster to the control plane. Its queues and presets apply placement, connections, permissions, and lifecycle controls.

Prerequisites

  • A Lambda 1-Click Cluster with Managed Kubernetes enabled and working kubectl access.
  • A reviewed namespace, RBAC policy, registry access, storage class, and data persistence plan.
  • The cluster connected to Polyaxon by installing Polyaxon Agent (compute cluster), with a queue or preset targeting the intended GPU capacity.

Validate the infrastructure

  1. 1

    Validate the Lambda GPU pool

    Schedule a small diagnostic through the same Polyaxon queue, namespace, and storage connection production jobs will use.

    version: 1.1kind: componentname: gpu-readinessrun:  kind: job  container:    image: nvidia/cuda:12.8.1-base-ubuntu22.04    command: ["bash", "-lc"]    args: ["nvidia-smi"]    resources:      limits:        nvidia.com/gpu: "1"

    Keep Lambda placement in the selected queue or preset. This published CUDA image checks GPU visibility only; pin a tag or digest compatible with your driver. Polyaxon does not provision the 1-Click Cluster.

  2. 2

    Submit the operation

    Run the component through the target Polyaxon project, queue, preset, and approval path.

    polyaxon run -f lambda-readiness.yaml -q agent-name/queue-name
  3. 3

    Check the execution target

    Verify node placement, GPU visibility, framework compatibility, storage throughput, DNS, and Polyaxon log and artifact collection.

    polyaxon ops dashboard

Production checklist

Document which storage classes are persistent and which paths use local NVMe scratch space.
Keep namespace RBAC, compute-cluster connection settings, connections, queues, and presets in version control.
Run the readiness operation through the same queue and namespace production workloads use.
Revalidate workloads during Lambda's upgrade sign-off process before accepting a maintenance window.
Run a collective communication test on the exact nodes and interfaces used by distributed training.

Troubleshooting

The operation remains Pending

Compare the requested GPU count and queue selector with available Lambda nodes, then inspect taints, tolerations, quota, and autoscaling state.

Data disappears after the operation exits

Confirm that outputs target a persistent storage class or external object storage rather than node-local NVMe.

The connected compute cluster cannot submit or collect logs

Verify the Polyaxon Agent deployment, namespace RBAC, service account, network policy, registry access, and control-plane connectivity.

Sources

Official platform, library, model, and Polyaxon references used by this guide.

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