fix: [alert] KubeDeploymentRolloutStuck (ai) (#750)

Reviewed-on: k.renner/homelab#748

kubernetes/apps/llama-server/deployment.yaml

@@ -25,6 +25,13 @@ metadata:
     app.kubernetes.io/name: llama-server
 spec:
   replicas: 1
+  # The startupProbe below sanctions a 40 min cold download, but the default
+  # progressDeadlineSeconds is 600. Left implicit the Deployment declares
+  # ProgressDeadlineExceeded at 10 minutes while the probe is still legitimately
+  # waiting, and KubeDeploymentRolloutStuck fires 15 min after that on a rollout
+  # that is fine. Kept above the startup budget, with room for the image pull
+  # and scheduling that happen before the first probe.
+  progressDeadlineSeconds: 3000
   # A rolling update would load a second copy of the weights onto the same
   # iGPU and into the same node's RAM before releasing the first.
   strategy:

kubernetes/apps/llama-server/embed.yaml

@@ -19,6 +19,11 @@ metadata:
     app.kubernetes.io/name: llama-embed
 spec:
   replicas: 1
+  # Same reasoning as llama-server: the startupProbe budget (600s) must stay
+  # below progressDeadlineSeconds, whose default is also 600 — exactly equal, so
+  # any cold download that uses most of its budget trips
+  # KubeDeploymentRolloutStuck on a rollout that is still progressing normally.
+  progressDeadlineSeconds: 900
   strategy:
     type: Recreate
   selector:

kubernetes/apps/llama-server/pvc.yaml

@@ -1,10 +1,20 @@
 ---
-# Holds the GGUF weights, downloaded once by the Deployment's initContainer.
-# Prune-disabled so retiring this Kustomization does not throw away several
-# GiB of model files that would have to be pulled from Hugging Face again.
+# Holds the GGUF weights, downloaded once by llama-server itself via --hf-repo.
+# Prune-disabled so retiring this Kustomization does not throw away tens of GiB
+# of model files that would have to be pulled from Hugging Face again.
 #
-# ReadWriteMany so a second replica or a one-off benchmark Job can mount the
-# same weights; the models are read-only at inference time.
+# ReadWriteMany so a second replica, a one-off benchmark Job, or a candidate
+# model being evaluated can mount the same weights; the models are read-only at
+# inference time.
+#
+# Sized well beyond the two deployed models. The cache is shared by every
+# candidate under evaluation (scripts/embed-eval-real.sh), and on a slow
+# connection the download is the long pole — a shared cache lets the large
+# candidates pull in the background while the small ones are already being
+# measured, and means a model is never fetched twice across runs.
+#
+# CephFS supports online expansion (allowVolumeExpansion on the storage class),
+# so growing this does not disturb the running pods.
 apiVersion: v1
 kind: PersistentVolumeClaim
 metadata:
@@ -18,4 +28,4 @@ spec:
   storageClassName: ceph-filesystem
   resources:
     requests:
-      storage: 40Gi
+      storage: 120Gi