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llmdbenchmark.teardown

Teardown phase of the benchmark lifecycle. Removes resources deployed by a previous standup, including Helm releases, namespaced resources, and cluster-scoped roles.

Step Ordering

Steps are registered in steps/__init__.py via get_teardown_steps() and execute in order:

Step Name Scope Description
00 TeardownPreflightStep global Load plan config and print summary banner showing what will be torn down
01 UninstallHelmStep global Uninstall Helm releases, OpenShift routes, and model download jobs
02 CleanHarnessStep global Remove harness resources (ConfigMaps, pods, secrets) from the harness namespace
03 DeleteResourcesStep global Delete namespaced resources in normal mode or deep mode
04 CleanClusterRolesStep global Remove cluster-scoped ClusterRoles and ClusterRoleBindings created during standup

Step Details

Step 00 -- Preflight

Loads the rendered plan config (config.yaml) from the first rendered stack. Populates context.namespace, context.deployed_methods, and other fields needed by subsequent steps. Prints a phase banner showing the cluster, namespace, model, and methods to be torn down. Fails if no rendered plan config is found.

Step 01 -- Uninstall Helm Releases

Skipped when modelservice is not in context.deployed_methods.

For each target namespace:

  • Uninstalls Helm releases matching the release prefix and model labels.
  • Deletes OpenShift routes (if on OpenShift) matching the release prefix.
  • Deletes model download jobs.

Step 02 -- Clean Harness

Removes harness resources from the harness namespace:

  • Profile ConfigMaps (matching workload- prefix or harness-related names).
  • Load generator pods (labelled app=llmdbench-harness-launcher,function=load_generator).
  • Context secrets (the secret name is read from control.contextSecretName in the plan config).

Step 03 -- Delete Resources

Two modes:

Normal mode (default): Deletes resources matching known patterns across both model and harness namespaces. Patterns differ based on deploy method:

  • Standalone patterns: standalone, download-model, testinference, lmbenchmark
  • Modelservice patterns: llm-d-benchmark-preprocesses, p2p, inference-gateway, inferencepool, httproute, llm-route, base-model, endpoint-picker, etc.

System resources (e.g. kube-root-ca.crt, odh-trusted-ca-bundle) are excluded.

Resource types checked: deployment, httproute, service, gateway, gatewayparameters, inferencepool, inferencemodel, configmap, ingress, pod, job.

Deep mode (--deep): Deletes ALL resources in both namespaces across a broad set of resource types: deployment, service, secret, gateway, inferencemodel, inferencepool, httproute, configmap, job, role, rolebinding, serviceaccount, hpa, va, servicemonitor, podmonitor, pod, pvc. On OpenShift, also deletes route resources.

Step 04 -- Clean Cluster Roles

Skipped when context.non_admin is True or when modelservice is not in context.deployed_methods.

Deletes ClusterRoles and ClusterRoleBindings matching the release prefix. Also deletes well-known modelservice ClusterRoles by suffix: modelservice-endpoint-picker, modelservice-epp-metrics-scrape, modelservice-manager, modelservice-metrics-auth, modelservice-admin, modelservice-editor, modelservice-viewer.

Dry-Run Behavior

In dry-run mode, all kubectl and helm commands are logged without execution. Each step reports what it would delete. CommandResult objects are returned with dry_run=True and exit_code=0.

Files

teardown/
├── __init__.py              -- Package marker
└── steps/
    ├── __init__.py           -- Step registry (get_teardown_steps)
    ├── step_00_preflight.py
    ├── step_01_uninstall_helm.py
    ├── step_02_clean_harness.py
    ├── step_03_delete_resources.py
    └── step_04_clean_cluster_roles.py