osdc-runners-nodepools

name	osdc-runners-nodepools
description	OSDC runners, NodePools, BuildKit, GitHub Actions constraints, EKS node taints, image mirroring, and the runner/nodepool change checklist. Applies to ~/meta/ci-infra/osdc. Load when modifying runners, nodepools, BuildKit, or node configurations.

OSDC Runners, NodePools & BuildKit

Quality Gates

Before declaring runner/nodepool work complete: run just lint and just test from osdc/. All 13 linters and unit tests must pass — these gates are mandatory per the project CLAUDE.md.

Runner & NodePool Change Checklist (MANDATORY)

When changing runner definitions (modules/arc-runners/defs/, modules/arc-runners-h100/defs/, modules/arc-runners-b200/defs/) or NodePool definitions (modules/nodepools/defs/, modules/nodepools-h100/defs/, modules/nodepools-b200/defs/), you MUST update the following scripts/python/ files to stay in sync:

File	What to update
scripts/python/instance_specs.py	INSTANCE_SPECS (vcpu, memory_gib, memory_mi, gpu, arch — used by analyze_node_utilization.py, generate_buildkit.py, collect_instance_memory.py, simulate_cluster.py), ENI_MAX_PODS (AWS-stock max-pods used by simulators and BuildKit sizing)
scripts/python/pytorch_workload_data.py	OLD_TO_NEW_LABEL (update old->new runner name mappings when names change)
scripts/python/simulate_cluster.py	Uses analyze_node_utilization functions — verify simulation still works
scripts/python/simulate_cluster_cli.py	CLI entry point for simulation — re-run to validate packing
integration-tests/workflows/integration-test.yaml.tpl	Update runs-on labels if runner names changed. Jobs are wrapped in # BEGIN_<TAG> / # END_<TAG> markers; if you add a new gate, register it in TAG_REQUIREMENTS in integration-tests/scripts/python/phases.py so the orchestrator strips jobs on clusters missing the required modules.
docs/runner_naming_convention.md	Update runner name examples and mapping tables

Verification: After any runner/nodepool change, run just analyze-utilization to confirm packing efficiency and just test to verify all scripts agree on the new values.

DaemonSet impact: When changing runner/job pod resources or adding new instance types, account for per-node DaemonSet overhead. The BuildKit pod-sizing algorithm subtracts a fixed 300m CPU / 440Mi memory budget (DAEMONSET_OVERHEAD_CPU_M / DAEMONSET_OVERHEAD_MEM_MI in modules/buildkit/scripts/python/generate_buildkit.py) before dividing per-node capacity. Cluster-wide base DaemonSets that fit inside this budget (manifests under base/kubernetes/): registry-mirror-config, node-performance-tuning, algif-mitigation, dirtyfrag-mitigation, image-cache-janitor, nvidia-device-plugin, plus DCGM on GPU nodes. nodelocaldns (25m CPU / 100Mi memory) is under base/kubernetes/nodelocaldns/ and deployed via its own deploy.sh. Per-module DaemonSets include cache-enforcer (module) and runner-hooks-warmer (declared in modules/arc/kubernetes/hooks-warmer.yaml, runs in the arc-runners namespace, but pinned to node-fleet=c7i-runner via nodeSelector). Helm/EKS-addon overhead (kube-prometheus-stack node-exporter, Alloy logging, kube-proxy, vpc-cni, ebs-csi-node) is enumerated in scripts/python/daemonset_overhead.py (HELM_DAEMONSETS + EKS_ADDON_DAEMONSETS). The live computed total is available via uv run scripts/python/daemonset_overhead.py. New cluster-wide DaemonSets MUST tolerate the standard taints listed in the EKS Node Taints section below.

GitHub Actions Workflow Constraints

All self-hosted runner pods run in containerMode: kubernetes-novolume. Workflow jobs typically need a container: image — containerless jobs may be rejected by the runner-container-hooks. Runner image is digest-pinned per OSDC commit by modules/arc-runners/scripts/python/resolve_runner_version.py — never use :latest and there is no Renovate bot. Runner-container-hooks fork: the runner-hooks-warmer DaemonSet (modules/arc/kubernetes/hooks-warmer.yaml, deployed by the arc module into the arc-runners namespace) pins v0.8.15 (https://github.com/jeanschmidt/runner-container-hooks/releases/tag/v0.8.15) — this is the version actually placed on each node. A stale comment in runner.yaml.tpl still references v0.8.13; the warmer DaemonSet wins. The wait-for-hooks init container (Alpine, polls /mnt/host-hooks/dist/index.js for up to 300s) snapshots the patched hooks into an emptyDir consumed by every runner pod.

Runner image resolution (per-commit digest pinning)

modules/arc-runners/deploy.sh resolves the runner image in this order:

1. Operator override: if arc.runner_image_tag is set in clusters.yaml (under the cluster's arc: block), the deploy uses ghcr.io/actions/actions-runner:<tag> (tag-only, no digest). The resolver is bypassed entirely — no GitHub API call, no crane call, ConfigMap untouched. Use this as the rollback escape hatch.
2. Auto-resolve: otherwise resolve_runner_version.py is invoked. It uses the SHA of the most recent commit touching anything under osdc/ as a lookup key into the arc-runner-version-lock ConfigMap (namespace osdc-system, key history.json, capped at 20 entries newest-first). Cache hit returns the locked tag@digest. Cache miss calls GitHub /releases/latest, resolves the digest via crane digest, prepends the entry, and writes back with optimistic-concurrency (resourceVersion); 409 retries up to 5 times.
3. Final fallback: generate_runners.py line ~275 contains cluster_config.get("runner_image", "ghcr.io/actions/actions-runner:2.333.1") — but in practice deploy.sh always exports RUNNER_IMAGE, so the literal 2.333.1 only ever appears via unit tests, never via deploy.

The three arc-runners* modules (arc-runners, arc-runners-h100, arc-runners-b200) deploy concurrently and share the same lock ConfigMap. The H100 and B200 modules exec into the base arc-runners/deploy.sh with ARC_RUNNERS_DEFS_DIR / ARC_RUNNERS_OUTPUT_DIR / ARC_RUNNERS_MODULE_NAME overrides, so they inherit the resolver result. See docs/runner-image-autoresolve.md for the full ConfigMap shape, failure modes, and rollback paths.

There is no longer a .github/workflows/osdc-auto-update-deploy-prod.yml — deploy-time resolution replaced the Renovate flow. The smoke test modules/arc-runners/tests/smoke/test_runner_version_lock.py validates the ConfigMap shape and cross-checks the locked entry against the deployed AutoscalingRunnerSet pod templates.

EKS Node Taints

Base nodes: CriticalAddonsOnly=true:NoSchedule. All base workloads (Harbor, DaemonSets, Karpenter, control plane) must tolerate this.

Workload (Karpenter) nodes — every NodePool emits these taints:

• node-fleet=<fleet-name>:NoSchedule — fleet isolation (e.g. c7i-runner, g5, m8g)
• instance-type=<instance>:NoSchedule — per-instance-type taint
• nvidia.com/gpu=true:NoSchedule — GPU pools only

IPv6-only EKS: the cluster runs on IPv6-only pod networking (ip_family = "ipv6", commit a6b4c8c / PR #576). Pod IPs are allocated from a /80 IPv6 prefix per node via VPC CNI prefix delegation; the service CIDR is auto-assigned by EKS in fd00:ec2::/108 (ULA). There is no per-AZ NodePool fan-out, no bucket label, no ENIConfig CR, and no pod_cidr_buckets map in clusters.yaml — the IPv4 + Custom Networking bucket scheme described in the abandoned INCREASE_IPV4.md plan was superseded by the IPv6 migration and is NOT in the current generator.

Startup taints (cleared by per-DaemonSet init containers via the shared taint_remover.py at end-of-init; registry in modules/nodepools/scripts/python/generate_nodepools.py:STARTUP_TAINTS):

• node-init.osdc.io/registry-mirror=true:NoSchedule — emitted on every cluster; cleared by registry-mirror-config
• node-init.osdc.io/perf-tuning=true:NoSchedule — emitted on every cluster; cleared by node-performance-tuning
• node-init.osdc.io/algif-mitigation=true:NoSchedule — emitted on every cluster (TEMPORARY, removed in lockstep with the DaemonSet once AL2023 kernel 6.12.85+ is rolled out); cleared by algif-mitigation
• node-init.osdc.io/dirtyfrag-mitigation=true:NoSchedule — emitted on every cluster (TEMPORARY, removed in lockstep with the DaemonSet once AL2023 kernel 6.1.170+ or 6.12.83+ is rolled out); cleared by dirtyfrag-mitigation
• node-init.osdc.io/cache-enforcer=true:NoSchedule — emitted only on clusters with cache-enforcer in NODEPOOLS_ENABLED_MODULES AND on nodepool defs that pass the per-def applies_when predicate. The predicate (generate_nodepools.py:62-65) skips release-runner pools (extra_labels.osdc.io/runner-class == "release") because the cache-enforcer DaemonSet has matching DoesNotExist nodeAffinity and never schedules there — emitting the taint would deadlock those nodes. Cleared by cache-enforcer.

STARTUP_TAINTS registry is validated at generator startup (generate_nodepools.py:_validate_startup_taints_registry) — each entry's module field must match a sibling directory under modules/, catching typos before they ship.

Workload pods do NOT tolerate node-init.osdc.io/* — they wait for every applicable taint to be removed. Runner pod tolerations are node-fleet, instance-type, plus nvidia.com/gpu on GPU job pods.

Image Mirroring

For image mirroring details (bootstrap images, ECR, Harbor proxy cache), see the osdc-harbor skill. Cluster-wide DaemonSets that pull from already-mirrored upstreams (e.g. nodelocaldns from registry.k8s.io) do NOT need a per-image pre-mirror entry — the existing registry-mirror-config proxy cache handles them lazily on first pull (~30-60s ImagePullBackOff possible on a fresh Karpenter node before containerd hosts.toml is written).

BuildKit Build Service

BuildKit (moby/buildkit:v0.29.0) runs as two Deployments in the buildkit namespace — one per architecture. Runner job pods invoke buildctl — no Kubernetes API access required.

• Architecture: Dual-arch fleet with per-arch Deployments and Services
  • buildkitd-arm64 — Graviton (script default m8gd.24xlarge; real clusters override — both staging clusters and the prod fleets use m7gd.16xlarge), Service: buildkitd-arm64.buildkit:1234
  • buildkitd-amd64 — Intel (default m6id.24xlarge), Service: buildkitd-amd64.buildkit:1234
  • buildkitd — combined Service (round-robin across both arches, for arch-agnostic builds)
• Sizing: Dynamically computed by modules/buildkit/scripts/python/generate_buildkit.py from instance specs. Guaranteed QoS (requests == limits), static CPU pinning, max-parallelism=1 (one build at a time per pod), 2 pods per node by default (overridable per-arch via buildkit.amd64_pods_per_node / buildkit.arm64_pods_per_node)
• NUMA: BuildKit EC2NodeClasses use topologyManagerPolicy: restricted + topologyManagerScope: container + prefer-closest-numa-nodes: true — stricter than the nodepool generator's best-effort default. Relevant when debugging BuildKit scheduling failures on multi-NUMA hosts.
• Startup taint: BuildKit nodepools also carry their own git-cache-not-ready=true:NoSchedule startup taint (in addition to the cluster-wide node-init.osdc.io/* ones). The git-cache rsync init clears it before pods can schedule.
• Instance types: Configurable via clusters.yaml (buildkit.arm64_instance_type, buildkit.amd64_instance_type)
• Scaling: Configurable via clusters.yaml (buildkit.replicas_per_arch, default 12 in defaults:; the bash fallback in deploy.sh is 4, used only if neither defaults: nor the cluster sets a value)
• Autoscaling (KEDA): Enabled via buildkit.autoscaling.enabled: true (currently true on arc-cbr-production and both lf-prod-aws-ue1/ue2 and both staging clusters; false elsewhere). Requires the keda module to be in the cluster's module list. Per-arch _min/_max/_fallback knobs live under buildkit.autoscaling.* in clusters.yaml. ScaledObjects are generated by generate_buildkit.generate_autoscaling_yaml and applied from generated/autoscaling.yaml. With autoscaling on, Deployments omit the static replicas: line and add terminationGracePeriodSeconds: 8100 for in-flight builds.
• Storage: NVMe instance storage (RAID0) for build cache + git object cache
• Registry mirrors: buildkitd.toml routes FROM image pulls through Harbor for docker.io, ghcr.io, nvcr.io, registry.k8s.io, quay.io (public.ecr.aws not mirrored — no rate limits)
• Network access: NetworkPolicy restricts ingress to pods from arc-runners namespace only
• Load balancing: HAProxy buildkitd-lb (least-connections) distributes buildctl connections across buildkitd pods per architecture. Backends are discovered via headless Service DNS (buildkitd-arm64-pods, buildkitd-amd64-pods, buildkitd-pods — clusterIP: None) with resolve-prefer ipv6 on each server-template line because under IPv6-only EKS the pod IPs only emit AAAA records.

Targeting an architecture

# Build an ARM64 image
buildctl --addr tcp://buildkitd-arm64.buildkit:1234 build --output type=image,name=$IMAGE,push=true ...

# Build an x86_64 image
buildctl --addr tcp://buildkitd-amd64.buildkit:1234 build --output type=image,name=$IMAGE,push=true ...

# Multi-arch: build both, then combine with crane
crane index append -t $IMAGE -m $IMAGE-arm64 -m $IMAGE-amd64

Using the git cache in Dockerfiles

The git-cache rsync runs on BuildKit nodes (same as runner nodes). The buildkitd pod mounts the cache at /opt/git-cache. Pass it as a named build context, then bind-mount in the Dockerfile and set GIT_ALTERNATE_OBJECT_DIRECTORIES:

buildctl ... --opt context:gitcache=local:gitcache --local gitcache=/opt/git-cache ...

RUN --mount=type=bind,from=gitcache,source=pytorch/pytorch.git/objects,target=/tmp/git-objects \
    GIT_ALTERNATE_OBJECT_DIRECTORIES=/tmp/git-objects \
    git clone https://github.com/pytorch/pytorch /workspace

Module-Specific Operational Knowledge

BuildKit Pod Sizing Algorithm

Pod resource requests are computed by modules/buildkit/scripts/python/generate_buildkit.py from a static instance spec table:

1. Look up total vCPU + memory for the instance type
2. Subtract kubelet reserved resources
3. Subtract DaemonSet overhead (300m CPU, 440Mi memory)
4. Apply 10% margin
5. Divide by pods_per_node (default: 2)

This ensures exactly N pods fit per node with Guaranteed QoS (requests == limits -> static CPU pinning). NVMe instance storage uses instanceStorePolicy: RAID0 on EC2NodeClass (nodeadm handles formatting/mounting) — instance types must have d suffix.

When adding a new instance type: update BOTH INSTANCE_SPECS (vcpu/memory/gpu/arch) and ENI_MAX_PODS (AWS-stock max-pods) in scripts/python/instance_specs.py.

EC2NodeClass userData

Generated EC2NodeClass userData is multipart MIME. The application/node.eks.aws part always carries kubelet config (cpuManagerPolicy, topologyManagerPolicy/Scope, log size/files). Defaults: cpuManagerPolicy: static, topologyManagerPolicy: best-effort, topologyManagerScope: container. BuildKit overrides this to topologyManagerPolicy: restricted + prefer-closest-numa-nodes: true for tight NUMA placement — it is the only generator that doesn't use best-effort. Most pools stop at the kubelet block — registry mirrors, CPU governor, GPU persistence, and the algif_aead / DirtyFrag CVE blacklists are handled by base DaemonSets (registry-mirror-config, node-performance-tuning, algif-mitigation, dirtyfrag-mitigation).

EC2NodeClass metadataOptions — IPv6 IMDS

Generated EC2NodeClass templates set spec.metadataOptions.httpProtocolIPv6: enabled. Under IPv6-only EKS the instance metadata service (IMDS) is reachable at [fd00:ec2::254] instead of the IPv4 link-local 169.254.169.254. Workloads, kubelet, and AWS SDKs that talk to IMDS must do so over IPv6; enabling httpProtocolIPv6 is what makes the IMDS endpoint listen on the IPv6 link-local address. Applied to: every nodepool generator output (modules/nodepools/scripts/python/generate_nodepools.py) and the pypi-cache nodepool template (modules/pypi-cache/kubernetes/ec2nodeclass.yaml.tpl).

GPU pools (H100, B200) optionally append a text/x-shellscript MIME part via the per-def user_data_script field — used today for one-shot containerd registry mirror config that must be in place before any image pull. Prefer DaemonSets where possible; reserve user_data_script for boot-critical setup only.

TEMPORARY mitigations — two kernel-mod blacklist DaemonSets with identical shape and the same lifecycle:

• base/kubernetes/algif-mitigation.yaml — modprobe blacklist for CVE-2026-31431 ("Copy Fail" algif_aead LPE). Remove once nodes are on a kernel 6.12.85+ AL2023 AMI. AMI pinnings tagged with TODO(CVE-2026-31431) markers are the trigger points: clusters.yaml, modules/nodepools/scripts/python/generate_nodepools.py, modules/buildkit/scripts/python/generate_buildkit.py, modules/pypi-cache/kubernetes/ec2nodeclass.yaml.tpl. Watch https://explore.alas.aws.amazon.com/CVE-2026-31431.html.
• base/kubernetes/dirtyfrag-mitigation.yaml — modprobe blacklist for CVE-2026-43284 + CVE-2026-43500 ("DirtyFrag" page-cache write LPEs in xfrm-ESP / RxRPC). Also drops the page cache to evict any DirtyFrag-poisoned pages. Same TEMPORARY lifecycle as algif-mitigation.

NodePool Def File Schema

modules/nodepools/defs/*.yaml supports three top-level forms (detected by the generator):

nodepool: — single instance type (one NodePool from one file). Currently unused; most "single-instance" pools are written as a fleet: with one entry instead.

nodepool:
  name: example
  instance_type: c7i.16xlarge
  arch: amd64                  # auto-detected if omitted
  node_disk_size: 100
  gpu: true
  has_nvme: true
  topology_manager_policy: single-numa-node   # default best-effort
  topology_manager_scope: pod                 # default container
  baremetal: true                             # uses NODEPOOLS_BAREMETAL_CONSOLIDATE_AFTER (1h)
  exclude_regions: [us-west-1]
  capacity_type: on-demand                    # or "reserved" for Capacity Blocks
  capacity_reservation_ids: [cr-xxxx]         # only with capacity_type: reserved
  user_data_script: scripts/h100-node-setup.sh # optional shell MIME part
  node_compactor: true                        # per-def override of cluster default

fleet: — multi-instance fleet (one NodePool per instance, all sharing a node-fleet label/taint). Used by every current def — including single-instance GPU pools (p4d.yaml, nodepools-h100/defs/p5.yaml, nodepools-b200/defs/p6.yaml), which are fleets with one instance entry.

fleet:
  name: g5
  arch: amd64
  gpu: true
  exclude_regions: [us-west-1]
  instances:
    - type: g5.48xlarge
      weight: 100              # Karpenter prefers higher weights
      node_disk_size: 600
      has_nvme: true
      baremetal: true          # per-instance optional

fleets: — multi-fleet file (several fleets in one YAML). Detected by the generator but unused in the current defs.

-large companion fleets (dual-fleet pattern): full-node 8-GPU runners (l-bx86iavx512-88-1000-a100-8, l-bx86iamx-176-1800-h100-8, l-bx86iamx-176-1800-b200-8) override node_fleet to point at dedicated -large companion pools: p4d-large, p5-large, p6-b200-large (plus CPU equivalents c7i-large, c7a-large, r7a-large, m7g-metal, m8g-large, g4dn-metal, g5-large). The companion fleets deliberately do NOT set single-numa-node because an 8-GPU pod spans both NUMA nodes and would hit TopologyAffinityError under single-numa-node. The packed multi-tenant pool keeps the strict NUMA policy; the -large companion stays at best-effort.

NUMA defaults: topologyManagerPolicy: best-effort for everything by default. Only the packed GPU pools p4d, p5, p6 pin single-numa-node with pod scope. Their -large companions stay at best-effort so the whole-node 8-GPU runner can span both NUMA nodes.

Not in the schema: there is no bucket, max_pods, pod_cidr_buckets, or per-AZ fan-out. EC2NodeClasses are emitted without spec.kubelet.maxPods (kubelet uses the AWS-stock default per ENI_MAX_PODS). An IPv4 + Custom Networking bucket scheme was considered (INCREASE_IPV4.md) but the codebase moved to IPv6-only EKS in PR #576; do not add these fields — they will be silently ignored by the generator.

Generated YAMLs contain CLUSTER_NAME_PLACEHOLDER — deploy.sh does sed replacement at apply time with the actual cluster name.

Runner Def File Schema

modules/arc-runners/defs/*.yaml (and arc-runners-h100/defs, arc-runners-b200/defs):

runner:
  name: l-x86iamx-22-225-h100   # ~42 char limit (see docs/runner_naming_convention.md)
  instance_type: p5.48xlarge    # determines node-fleet name (split on ".")
  node_fleet: g5-48xlarge       # optional explicit fleet name; defaults to instance_type.split(".")[0]
  vcpu: 22                      # CAPACITY_AWARE_WORKFLOW_CPU on listener
  memory: 225Gi                 # CAPACITY_AWARE_WORKFLOW_MEMORY
  disk_size: 200                # Gi (per workflow pod)
  gpu: 1                        # 0 for CPU runners
  proactive_capacity: 1         # optional warm pool size (default 0). Reduced fleet-wide in PR #772; most CPU defs now sit at 0-5.
  max_burst_capacity: 250       # optional cap on listener burst (default 0 = unlimited)
  hud_failure_base_capacity: 30 # optional additive floor for HUD-failure fallback (default 0, clamped [0,1000], warn >100). Surge formula: ProactiveCapacity*HUDFailureMultiplier + HUDFailureBaseCapacity. Not capped by clusters.yaml; def value applies in all clusters.
  max_runners: 8                # optional concurrency cap; omit for unlimited (Karpenter scales). May also be a dict: { default: 8, arc-cbr-production-uw1: 48 } — `default` key is mandatory; per-cluster keys override.
  runner_group: default         # GitHub runner group; forced to default for repo-scoped URLs. Cluster-level `arc-runners.runner_group` in clusters.yaml overrides per-def values.
  runner_class: ""              # optional isolation label (e.g. "release")

Use node_fleet when a runner needs its own dedicated fleet name distinct from the instance family (e.g. to pin the 8-GPU runner to a -large companion pool, or to isolate release-class runners). The reserved name c7i-runner is rejected — it's the legacy default fleet for plain c7i.* runners and cannot be reused as an override (scripts/python/fleet_naming.py:RESERVED_NODE_FLEET_NAMES). derive_fleet_name(instance_type, override) enforces the DNS-1123 label format on overrides.

Validation: max_runners must be a positive int OR a dict containing a default key with positive-int values per cluster id (generate_runners.py:resolve_max_runners); max_burst_capacity must be non-negative; max_burst_capacity < proactive_capacity (or < hud_failure_base_capacity) is an error, not a warning, and aborts the generate step. node_fleet, if present, must be a non-empty string with no leading/trailing whitespace.

Cluster-wide overrides (generate_runners.py):

• proactive_capacity_max: <int> at the cluster level clamps every def's proactive_capacity down to that value. Currently only the two staging clusters (meta-staging-aws-uw1, meta-staging-aws-ue1) set it (to 0); all prod clusters dropped the override in PR #771 and rely on per-def values reduced in PR #772.
• pause_runners: true at the cluster level forces max_runners=0 and hud_failure_base_capacity=0 on every scale set — cluster-wide drain switch.
• Region-exclusion auto-zeroing: if a runner's instance_type is in the cluster's excluded_instance_types (derived from the backing nodepool/fleet's exclude_regions, e.g. us-west-1 for A100/g5), max_runners, proactive_capacity, and hud_failure_base_capacity are forced to 0 so GitHub does not route jobs that would pend forever.
• arc-runners.runner_group: <name> at the cluster level pins every scale set's GitHub runner group, unless the cluster's github_config_url is repo-scoped — repo-scoped URLs force default regardless. Per-cluster groups: arc-cbr-production-uw1 -> arc-cbr-prod-uw1, meta-prod-aws-ue1 -> meta-prod-aws-ue1, lf-prod-aws-ue1/ue2 -> lf-prod-aws-ue1/ue2, staging -> meta-staging-aws-uw1/ue1. arc-cbr-production (us-east-2) does NOT set runner_group and runners land in default.

Cache-Enforcer (DaemonSet)

Uses xt_string kernel module to match domain names in TLS ClientHello SNI (port 443) and HTTP Host header (port 80). Rules in CACHE_ENFORCER iptables chain, jumped from OUTPUT and FORWARD. REJECT with tcp-reset (fast "Connection refused").

Blocked registries (forced through Harbor at harbor:30002): docker.io, registry-1.docker.io, auth.docker.io, production.cloudflare.docker.com, ghcr.io, nvcr.io, quay.io, registry.k8s.io

Blocked PyPI (REJECTed at iptables OUTPUT/FORWARD — the runner pod's pip env vars redirect to pypi-cache instead): pypi.org, files.pythonhosted.org. download.pytorch.org is NOT in PYPI_DOMAINS — workflows can still reach it directly. Runner pod PIP_INDEX_URL / UV_DEFAULT_INDEX env vars point at http://pypi-cache-cpu.pypi-cache.svc.cluster.local:8080/simple/ (Service DNS, not localhost).

NOT blocked: public.ecr.aws (no rate limits)

To add/remove blocked domains: edit REGISTRY_DOMAINS or PYPI_DOMAINS in modules/cache-enforcer/kubernetes/configmap.yaml, then just deploy-module <cluster> cache-enforcer.

Limitation: TLS Encrypted Client Hello (ECH) encrypts SNI, bypassing xt_string matching. No blocked domains currently use ECH. Migration path: Cilium CNI with toFQDNs DNS-based policies.

Dependency: cache-enforcer depends on Harbor (base) and pypi-cache module. Without pypi-cache deployed, pip installs fail entirely (traffic blocked but no cache to serve it).

Node Compactor — Karpenter Integration

Lives at base/node-compactor/ (NOT under modules/). Cluster-level default node_compactor.enabled: true in clusters.yaml. Per-def override via node_compactor: true|false in a NodePool def.

• NodePools labeled osdc.io/node-compactor: "true" are auto-discovered
• Compactor taints nodes NoSchedule (no eviction of running pods)
• Relies on Karpenter's WhenEmpty consolidation policy with consolidateAfter: 2m to delete empty tainted nodes
• On SIGTERM, compactor removes all its taints before exiting — redeploying causes temporary burst of untainted nodes
• Burst absorption: when pending pods match tainted nodes (checks tolerations, nodeSelector, affinity, resource fit), compactor temporarily removes taints. Fleet cooldown (default 900s) blocks new taints after burst untaint
• Anti-flap: per-cluster node_compactor.min_node_age_seconds (e.g. arc-cbr-production sets 900) — newly-launched nodes are exempt from tainting for this many seconds, avoiding a churn loop when Karpenter has just provisioned them for incoming workload

Runner Pod Two-Tier Resource Model

• Runner pod (750m CPU, 1Gi memory) — lightweight ARC orchestrator; mounts hook ConfigMap. Bumped from 512Mi to 1Gi to give native Node.js stdio buffers (held open by slow CRI exec during pod-density bursts) headroom — observed OOMs traced back to native buffers, not V8 heap. .NET runner caps via DOTNET_GCHeapHardLimit=C800000 (200 MiB), Node hooks via NODE_OPTIONS=--max-old-space-size=128. Resources are fixed in templates/runner.yaml.tpl, NOT in def files. Listener CAPACITY_AWARE_RUNNER_CPU/MEMORY env vars MUST stay in sync with these.
• Job pod (resources from def file vcpu/memory/disk_size/gpu) — runs actual workflow containers, gets git cache volume.
• Min runners 0; runner scaling is unlimited unless max_runners is set in the def. Prefer overspend over outage — only cap fixed-capacity reserved pools (e.g. H100/B200 Capacity Blocks).

Init container wait-for-hooks: every runner pod runs an Alpine init container that polls /mnt/host-hooks/dist/index.js (placed by the runner-hooks-warmer DaemonSet in arc-runners namespace) for up to 300s, then snapshots dist/ + .version into an emptyDir consumed by the main runner. This is a hard scheduling-gate dependency: nodes without the warmer DaemonSet ready cannot run jobs. Remove when upstream merges the patched hooks.

Four-tier PriorityClass ladder (in modules/arc/kubernetes/priority-classes.yaml):

• -10 placeholder-runner — proactive runner-capacity placeholders; preemptionPolicy: Never
• 0 arc-runner — actual runner pods; preempt placeholder-runner only
• 10 placeholder-workflow — proactive workflow-capacity placeholders; preemptionPolicy: Never
• 20 arc-workflow — actual workflow (job) pods; preempt placeholder-workflow

The pairing ensures workflow pods can claim the capacity reserved by the placeholders they replace, without runner pods clobbering workflow placeholders. Changing any value breaks the proactive-capacity preemption ladder.

Listener metrics cardinality: listenerMetrics block in the runner template enumerates an allowlist of labels per metric (counters, gauges, histograms) — strips job_name, event_name, job_workflow_ref, job_workflow_target which create unbounded series. Keeps repository, organization, enterprise, job_workflow_name, name, namespace, result. Required for Grafana Cloud billing predictability; do not add labels without a billing review.

ARC Module Split

• modules/arc/ = controller + namespaces (arc-systems, arc-runners) + runner ServiceAccount + LimitRange + hooks ConfigMap + runner-hooks-warmer DaemonSet (moved here from arc-runners in PR #746 — still runs in the arc-runners namespace but lifecycle is now owned by the arc module) + four-tier PriorityClass ladder + capacity-monitor RBAC. No terraform — pure k8s/helm. Uses fork chart oci://ghcr.io/jeanschmidt/actions-runner-controller-charts/gha-runner-scale-set-controller, version pinned in clusters.yaml arc.chart_version (currently 0.14.1-jeanschmidt.16). Controller and runner chart versions MUST match (minor mismatch deletes ARS). The cluster-wide containerd registry mirror lives at base/kubernetes/registry-mirror-config.yaml — NOT inside the arc module.
• modules/arc-runners/ = runner scale sets (the actual runners). Separate deploy cycle. Reads arc-runners.{github_config_url,github_secret_name,runner_name_prefix,runner_group} from clusters.yaml.
• modules/keda/ = KEDA operator chart (kedacore/keda, version pinned via keda.chart_version, currently 2.16.1). Required for BuildKit autoscaling. Deployed on every cluster that enables buildkit.autoscaling.enabled: true — currently meta-staging-aws-uw1, meta-staging-aws-ue1, arc-cbr-production, lf-prod-aws-ue1, lf-prod-aws-ue2 (added in PR #775). Does not directly affect runners/nodepools, but the KEDA ScaledObjects in modules/buildkit/generated/autoscaling.yaml require this module to be deployed first.

GPU Nodepools

Three NVIDIA GPU node families are supported, each with a unified single-fleet runner family providing 1/2/4/8-GPU splits via nvidia.com/gpu resource requests (NUMA single-numa-node policy).

GPU	Instance	Module	Capacity model	Notes
A10G	g5.* (multi-instance fleet: 48xl/12xl/24xl/8xl/16xl, weight-ordered)	nodepools	on-demand spot/OD	excludes us-west-1
L4 / T4 / older	g6, g4dn fleets	nodepools	on-demand	shared nodepools defs
A100 40GB SXM4	p4d.24xlarge	nodepools (p4d.yaml fleet: + companion p4d-large.yaml)	on-demand (no Capacity Block)	excludes us-west-1; packed runners l-x86iavx512-{11-125-a100, 22-250-a100-2, 44-500-a100-4} on the p4d fleet (single-numa-node), l-bx86iavx512-88-1000-a100-8 pinned via node_fleet: p4d-large (best-effort)
H100 80GB SXM5	p5.48xlarge	nodepools-h100 (p5.yaml + p5-large.yaml) + arc-runners-h100	AWS Capacity Blocks (reserved; reservation IDs are per-cluster under clusters.<id>.nodepools-h100.capacity_reservation_ids — e.g. us-east-2 single cr-0c3f05dffb85ed832, us-west-1 dual cr-04d3d1d84e127a562 + cr-09a53051589034fb8 for 48 H100s total)	All H100 defs use the per-cluster max_runners dict form. Default values (sized for one 8-GPU node): 1-GPU=8, 2-GPU=4, 4-GPU=2, 8-GPU=1. arc-cbr-production-uw1 overrides: 1-GPU=48, 2-GPU=24, 4-GPU=12, 8-GPU=6 (sized for the 6 reserved nodes). 8-GPU def pins node_fleet: p5-large. Packed p5.yaml uses user_data_script: scripts/h100-node-setup.sh
B200	p6-b200.48xlarge	nodepools-b200 (p6.yaml + p6-b200-large.yaml) + arc-runners-b200	AWS Capacity Blocks (reserved, single ID cr-0b15c0b3163f09d26 in us-east-2)	All B200 defs use plain-int max_runners (no per-cluster dict): 1-GPU=8, 2-GPU=4, 4-GPU=2, 8-GPU=1. 8-GPU def pins node_fleet: p6-b200-large. Packed p6.yaml uses user_data_script: scripts/b200-node-setup.sh

A100 nodes scale fully with workload (no warm floor — first job after consolidation pays a 5-10 min cold start). H100/B200 nodes are reserved capacity, so concurrency is capped to the reservation.

Modular Submodule Pattern (GPU pools)

nodepools-h100, nodepools-b200, arc-runners-h100, arc-runners-b200 are thin shims that delegate to the base modules (nodepools, arc-runners) with overridden defs/output paths. New GPU SKUs can be added as new sibling modules without touching the base.

modules/nodepools-h100/deploy.sh:

UPSTREAM_ROOT="${OSDC_UPSTREAM:-$(cd "$MODULE_DIR/../.." && pwd)}"
export NODEPOOLS_DEFS_DIR="$MODULE_DIR/defs"
export NODEPOOLS_OUTPUT_DIR="$MODULE_DIR/generated"
export NODEPOOLS_MODULE_NAME="nodepools-h100"
exec "$UPSTREAM_ROOT/modules/nodepools/deploy.sh" "$@"

modules/arc-runners-h100/deploy.sh does the same with ARC_RUNNERS_DEFS_DIR, ARC_RUNNERS_OUTPUT_DIR, ARC_RUNNERS_MODULE_NAME. The base nodepools/deploy.sh and arc-runners/deploy.sh honor these env vars (with fallback defaults to $MODULE_DIR/defs, etc.).

When adding a new GPU SKU, copy one of the *-h100 modules, swap the def files and reservation ID, register it in clusters.yaml modules:. Do NOT modify the base nodepools or arc-runners defs/templates.

Monitoring Component Placement

• node-exporter tolerates ALL taints (runs on every node)
• DCGM exporter: only on GPU nodes (nodeAffinity on nvidia.com/gpu.present)
• All other monitoring components: base infrastructure nodes (tolerate CriticalAddonsOnly)

Karpenter Module = Controller Only

The modules/karpenter/ module handles the controller only. NodePools are managed by modules/nodepools/ (and the GPU shims). Karpenter terraform creates: IAM Role (IRSA), SQS Queue (spot/rebalance/health events), four EventBridge Rules (spot_interruption, rebalance, instance_state_change, scheduled_change — the last from AWS Health), and discovery tags (karpenter.sh/discovery) on cluster SG and private subnets.

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Verified against commit c1c884b (IPv6-only EKS, runner-image per-commit digest pinning via resolve_runner_version.py, runner-container-hooks v0.8.15 placed by warmer DaemonSet owned by arc module, ARC fork chart 0.14.1-jeanschmidt.16, keda 2.16.1 enabling BuildKit autoscaling on staging + arc-cbr-production + lf-prod-aws-ue1/ue2, proactive_capacity reductions PR #772 and removal of proactive_capacity_max from prod PR #771, integration-test job filtering by cluster module availability PR #774 with cluster-specific runner groups PR #773).