Testing E2E fixes for kueue integration

.github/workflows/e2e_tests.yaml

@@ -5,13 +5,13 @@ on:
   pull_request:
     branches:
       - main
-      - 'release-*'
+      - "release-*"
       - ray-jobs-feature
     paths-ignore:
-      - 'docs/**'
-      - '**.adoc'
-      - '**.md'
-      - 'LICENSE'
+      - "docs/**"
+      - "**.adoc"
+      - "**.md"
+      - "LICENSE"
 
 concurrency:
   group: ${{ github.head_ref }}-${{ github.workflow }}
@@ -33,9 +33,9 @@ jobs:
       - name: Checkout common repo code
         uses: actions/checkout@v4
         with:
-          repository: 'project-codeflare/codeflare-common'
-          ref: 'main'
-          path: 'common'
+          repository: "project-codeflare/codeflare-common"
+          ref: "main"
+          path: "common"
 
       - name: Checkout CodeFlare operator repository
         uses: actions/checkout@v4
@@ -46,7 +46,7 @@ jobs:
       - name: Set Go
         uses: actions/setup-go@v5
         with:
-          go-version-file: './codeflare-operator/go.mod'
+          go-version-file: "./codeflare-operator/go.mod"
           cache-dependency-path: "./codeflare-operator/go.sum"
 
       - name: Set up gotestfmt
@@ -57,8 +57,8 @@ jobs:
       - name: Set up specific Python version
         uses: actions/setup-python@v5
         with:
-          python-version: '3.11'
-          cache: 'pip' # caching pip dependencies
+          python-version: "3.11"
+          cache: "pip" # caching pip dependencies
 
       - name: Setup NVidia GPU environment for KinD
         uses: ./common/github-actions/nvidia-gpu-setup
@@ -76,7 +76,7 @@ jobs:
         run: |
           cd codeflare-operator
           echo Setting up CodeFlare stack
-          make setup-e2e
+          make setup-e2e KUEUE_VERSION=v0.13.4 KUERAY_VERSION=v1.4.0
           echo Deploying CodeFlare operator
           make deploy -e IMG="${CODEFLARE_OPERATOR_IMG}" -e ENV="e2e"
           kubectl wait --timeout=120s --for=condition=Available=true deployment -n openshift-operators codeflare-operator-manager
@@ -122,10 +122,16 @@ jobs:
           pip install poetry
           poetry install --with test,docs
           echo "Running e2e tests..."
-          poetry run pytest -v -s ./tests/e2e -m 'kind and nvidia_gpu' > ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output.log 2>&1
+          poetry run pytest -v -s ./tests/e2e/ -m 'kind and nvidia_gpu' > ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output.log 2>&1
         env:
           GRPC_DNS_RESOLVER: "native"
 
+      - name: Run RayJob E2E tests
+        run: |
+          export CODEFLARE_TEST_OUTPUT_DIR=${{ env.TEMP_DIR }}
+          echo "CODEFLARE_TEST_OUTPUT_DIR=${CODEFLARE_TEST_OUTPUT_DIR}" >> $GITHUB_ENV
+          poetry run pytest -v -s ./tests/e2e/rayjob > ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output_rayjob.log 2>&1
+
       - name: Switch to kind-cluster context to print logs
         if: always() && steps.deploy.outcome == 'success'
         run: kubectl config use-context kind-cluster
@@ -135,6 +141,7 @@ jobs:
         run: |
           echo "Printing Pytest output logs"
           cat ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output.log
+          cat ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output_rayjob.log
 
       - name: Print CodeFlare operator logs
         if: always() && steps.deploy.outcome == 'success'
@@ -146,7 +153,7 @@ jobs:
         if: always() && steps.deploy.outcome == 'success'
         run: |
           echo "Printing KubeRay operator logs"
-          kubectl logs -n ray-system --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
+          kubectl logs -n default --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
 
       - name: Export all KinD pod logs
         uses: ./common/github-actions/kind-export-logs

src/codeflare_sdk/common/utils/k8s_utils.py

@@ -7,14 +7,10 @@
 from ..kubernetes_cluster import config_check, _kube_api_error_handling
 
 
-def get_current_namespace():
+def get_current_namespace():  # pragma: no cover
     """
     Retrieves the current Kubernetes namespace.
 
-    This function attempts to detect the current namespace by:
-    1. First checking if running inside a pod (reading from service account namespace file)
-    2. Falling back to reading from the current kubeconfig context
-
     Returns:
         str:
             The current namespace or None if not found.

src/codeflare_sdk/ray/cluster/build_ray_cluster.py

@@ -136,7 +136,6 @@ def build_ray_cluster(cluster: "codeflare_sdk.ray.cluster.Cluster"):
                 "enableIngress": False,
                 "rayStartParams": {
                     "dashboard-host": "0.0.0.0",
-                    "dashboard-port": "8265",
                     "block": "true",
                     "num-gpus": str(head_gpu_count),
                     "resources": head_resources,

src/codeflare_sdk/ray/cluster/cluster.py

@@ -208,6 +208,10 @@ def apply(self, force=False):
         self._throw_for_no_raycluster()
         namespace = self.config.namespace
         name = self.config.name
+
+        # Regenerate resource_yaml to reflect any configuration changes
+        self.resource_yaml = self.create_resource()
+
         try:
             self.config_check()
             api_instance = client.CustomObjectsApi(get_api_client())
@@ -387,16 +391,25 @@ def is_dashboard_ready(self) -> bool:
             bool:
                 True if the dashboard is ready, False otherwise.
         """
+
+        dashboard_uri = self.cluster_dashboard_uri()
+        if dashboard_uri is None:
+            return False
+
         try:
             response = requests.get(
-                self.cluster_dashboard_uri(),
+                dashboard_uri,
                 headers=self._client_headers,
                 timeout=5,
                 verify=self._client_verify_tls,
             )
         except requests.exceptions.SSLError:  # pragma no cover
             # SSL exception occurs when oauth ingress has been created but cluster is not up
             return False
+        except Exception:  # pragma no cover
+            # Any other exception (connection errors, timeouts, etc.)
+            return False
+
         if response.status_code == 200:
             return True
         else:
@@ -504,6 +517,8 @@ def cluster_dashboard_uri(self) -> str:
                 ):
                     protocol = "https" if route["spec"].get("tls") else "http"
                     return f"{protocol}://{route['spec']['host']}"
+            # No route found for this cluster
+            return "Dashboard not available yet, have you run cluster.up()?"
         else:
             try:
                 api_instance = client.NetworkingV1Api(get_api_client())
@@ -522,7 +537,8 @@ def cluster_dashboard_uri(self) -> str:
                         protocol = "http"
                     elif "route.openshift.io/termination" in annotations:
                         protocol = "https"
-                return f"{protocol}://{ingress.spec.rules[0].host}"
+                    return f"{protocol}://{ingress.spec.rules[0].host}"
+
         return "Dashboard not available yet, have you run cluster.up()?"
 
     def list_jobs(self) -> List:
@@ -783,6 +799,7 @@ def remove_autogenerated_fields(resource):
                 del resource[key]
             else:
                 remove_autogenerated_fields(resource[key])
+
     elif isinstance(resource, list):
         for item in resource:
             remove_autogenerated_fields(item)

src/codeflare_sdk/ray/cluster/test_config.py

@@ -1,4 +1,4 @@
-# Copyright 2022-2025 IBM, Red Hat
+# Copyright 2024 IBM, Red Hat
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

src/codeflare_sdk/ray/rayjobs/config.py

@@ -131,10 +131,8 @@ class ManagedClusterConfig:
         accelerator_configs:
             A dictionary of custom resource mappings to map extended resource requests to RayCluster resource names.
             Defaults to DEFAULT_ACCELERATORS but can be overridden with custom mappings.
-        local_queue:
-            The name of the queue to use for the cluster.
         annotations:
-            A dictionary of annotations to apply to the cluster.
+            A dictionary of annotations to apply to the Job.
         volumes:
             A list of V1Volume objects to add to the Cluster
         volume_mounts:
@@ -161,7 +159,6 @@ class ManagedClusterConfig:
     accelerator_configs: Dict[str, str] = field(
         default_factory=lambda: DEFAULT_ACCELERATORS.copy()
     )
-    local_queue: Optional[str] = None
     annotations: Dict[str, str] = field(default_factory=dict)
     volumes: list[V1Volume] = field(default_factory=list)
     volume_mounts: list[V1VolumeMount] = field(default_factory=list)
@@ -248,7 +245,6 @@ def build_ray_cluster_spec(self, cluster_name: str) -> Dict[str, Any]:
         """
         ray_cluster_spec = {
             "rayVersion": RAY_VERSION,
-            "enableInTreeAutoscaling": False,
             "headGroupSpec": self._build_head_group_spec(),
             "workerGroupSpecs": [self._build_worker_group_spec(cluster_name)],
         }
@@ -288,7 +284,6 @@ def _build_head_ray_params(self) -> Dict[str, str]:
         """Build Ray start parameters for head node."""
         params = {
             "dashboard-host": "0.0.0.0",
-            "dashboard-port": "8265",
             "block": "true",
         }
 
@@ -346,12 +341,9 @@ def _build_head_container(self) -> V1Container:
                 self.head_accelerators,
             ),
             volume_mounts=self._generate_volume_mounts(),
+            env=self._build_env_vars() if hasattr(self, "envs") and self.envs else None,
         )
 
-        # Add environment variables if specified
-        if hasattr(self, "envs") and self.envs:
-            container.env = self._build_env_vars()
-
         return container
 
     def _build_worker_container(self) -> V1Container:
@@ -373,12 +365,9 @@ def _build_worker_container(self) -> V1Container:
                 self.worker_accelerators,
             ),
             volume_mounts=self._generate_volume_mounts(),
+            env=self._build_env_vars() if hasattr(self, "envs") and self.envs else None,
         )
 
-        # Add environment variables if specified
-        if hasattr(self, "envs") and self.envs:
-            container.env = self._build_env_vars()
-
         return container
 
     def _build_resource_requirements(

src/codeflare_sdk/ray/rayjobs/rayjob.py

@@ -22,6 +22,7 @@
 import re
 import ast
 from typing import Dict, Any, Optional, Tuple
+from codeflare_sdk.common.kueue.kueue import get_default_kueue_name
 from codeflare_sdk.common.utils.constants import MOUNT_PATH
 from kubernetes import client
 from ...common.kubernetes_cluster.auth import get_api_client
@@ -59,9 +60,9 @@ def __init__(
         cluster_config: Optional[ManagedClusterConfig] = None,
         namespace: Optional[str] = None,
         runtime_env: Optional[Dict[str, Any]] = None,
-        shutdown_after_job_finishes: Optional[bool] = None,
         ttl_seconds_after_finished: int = 0,
         active_deadline_seconds: Optional[int] = None,
+        local_queue: Optional[str] = None,
     ):
         """
         Initialize a RayJob instance.
@@ -73,12 +74,11 @@ def __init__(
             cluster_config: Configuration for creating a new cluster (optional if cluster_name provided)
             namespace: The Kubernetes namespace (auto-detected if not specified)
             runtime_env: Ray runtime environment configuration (optional)
-            shutdown_after_job_finishes: Whether to shut down cluster after job finishes (optional)
             ttl_seconds_after_finished: Seconds to wait before cleanup after job finishes (default: 0)
             active_deadline_seconds: Maximum time the job can run before being terminated (optional)
+            local_queue: The Kueue LocalQueue to submit the job to (optional)
 
         Note:
-            shutdown_after_job_finishes is automatically detected but can be overridden:
             - True if cluster_config is provided (new cluster will be cleaned up)
             - False if cluster_name is provided (existing cluster will not be shut down)
             - User can explicitly set this value to override auto-detection
@@ -108,17 +108,7 @@ def __init__(
         self.runtime_env = runtime_env
         self.ttl_seconds_after_finished = ttl_seconds_after_finished
         self.active_deadline_seconds = active_deadline_seconds
-
-        # Auto-set shutdown_after_job_finishes based on cluster_config presence
-        # If cluster_config is provided, we want to clean up the cluster after job finishes
-        # If using existing cluster, we don't want to shut it down
-        # User can override this behavior by explicitly setting shutdown_after_job_finishes
-        if shutdown_after_job_finishes is not None:
-            self.shutdown_after_job_finishes = shutdown_after_job_finishes
-        elif cluster_config is not None:
-            self.shutdown_after_job_finishes = True
-        else:
-            self.shutdown_after_job_finishes = False
+        self.local_queue = local_queue
 
         if namespace is None:
             detected_namespace = get_current_namespace()
@@ -177,10 +167,6 @@ def submit(self) -> str:
                 if scripts:
                     self._handle_script_volumes_for_existing_cluster(scripts, result)
 
-            if self.shutdown_after_job_finishes:
-                logger.info(
-                    f"Cluster will be automatically cleaned up {self.ttl_seconds_after_finished}s after job completion"
-                )
             return self.name
         else:
             raise RuntimeError(f"Failed to submit RayJob {self.name}")
@@ -230,11 +216,32 @@ def _build_rayjob_cr(self) -> Dict[str, Any]:
             },
             "spec": {
                 "entrypoint": self.entrypoint,
-                "shutdownAfterJobFinishes": self.shutdown_after_job_finishes,
                 "ttlSecondsAfterFinished": self.ttl_seconds_after_finished,
+                "shutdownAfterJobFinishes": self._cluster_config is not None,
             },
         }
 
+        labels = {}
+        # If cluster_config is provided, use the local_queue from the cluster_config
+        if self._cluster_config is not None:
+            if self.local_queue:
+                labels["kueue.x-k8s.io/queue-name"] = self.local_queue
+            else:
+                default_queue = get_default_kueue_name(self.namespace)
+                if default_queue:
+                    labels["kueue.x-k8s.io/queue-name"] = default_queue
+                else:
+                    # No default queue found, use "default" as fallback
+                    labels["kueue.x-k8s.io/queue-name"] = "default"
+                    logger.warning(
+                        f"No default Kueue LocalQueue found in namespace '{self.namespace}'. "
+                        f"Using 'default' as the queue name. If a LocalQueue named 'default' "
+                        f"does not exist, the RayJob submission will fail. "
+                        f"To fix this, please explicitly specify the 'local_queue' parameter."
+                    )
+
+        rayjob_cr["metadata"]["labels"] = labels
+
         # Add active deadline if specified
         if self.active_deadline_seconds:
             rayjob_cr["spec"]["activeDeadlineSeconds"] = self.active_deadline_seconds