Add AMD GPU test for ray clusters

Author: ChughShilpa

.github/workflows/e2e_tests.yaml

@@ -89,7 +89,8 @@ jobs:
           export CODEFLARE_TEST_OUTPUT_DIR=${{ env.TEMP_DIR }}
 
           set -euo pipefail
-          go test -timeout 120m -v -skip "^Test.*Cpu$" ./test/e2e -json 2>&1 | tee ${CODEFLARE_TEST_OUTPUT_DIR}/gotest.log | gotestfmt
+          go test -timeout 120m -v -skip "^Test.*Cpu$|^Test.*ROCmGpu$" ./test/e2e -json 2>&1 | tee ${CODEFLARE_TEST_OUTPUT_DIR}/gotest.log | gotestfmt
+
 
       - name: Print CodeFlare operator logs
         if: always() && steps.deploy.outcome == 'success'

go.mod

@@ -11,7 +11,7 @@ require (
 	github.com/openshift/api v0.0.0-20240904015708-69df64132c91
 	github.com/openshift/client-go v0.0.0-20240904130219-3795e907a202
 	github.com/project-codeflare/appwrapper v1.0.4
-	github.com/project-codeflare/codeflare-common v0.0.0-20250306164418-eb812487be82
+	github.com/project-codeflare/codeflare-common v0.0.0-20250317102908-1c124db97844
 	github.com/ray-project/kuberay/ray-operator v1.2.2
 	go.uber.org/zap v1.27.0
 	golang.org/x/exp v0.0.0-20240719175910-8a7402abbf56

go.sum

@@ -225,8 +225,8 @@ github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 h1:Jamvg5psRI
 github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
 github.com/project-codeflare/appwrapper v1.0.4 h1:364zQLX0tsi4LvBBYNKZL7PPbNWPbVU7vK6+/kVV/FQ=
 github.com/project-codeflare/appwrapper v1.0.4/go.mod h1:A1b6bMFNMX5Btv3ckgeuAHVVZzp1G30pSBe6BE/xJWE=
-github.com/project-codeflare/codeflare-common v0.0.0-20250306164418-eb812487be82 h1:cL1K2+r1lJVwBkhXiVFr2A9DphnylJmilYDIqg/W62M=
-github.com/project-codeflare/codeflare-common v0.0.0-20250306164418-eb812487be82/go.mod h1:DPSv5khRiRDFUD43SF8da+MrVQTWmxNhuKJmwSLOyO0=
+github.com/project-codeflare/codeflare-common v0.0.0-20250317102908-1c124db97844 h1:hEjZ2pV4Fp81wytijJZ7uHWovKIqirVBA/t1F5hIrbA=
+github.com/project-codeflare/codeflare-common v0.0.0-20250317102908-1c124db97844/go.mod h1:DPSv5khRiRDFUD43SF8da+MrVQTWmxNhuKJmwSLOyO0=
 github.com/prometheus/client_golang v1.20.5 h1:cxppBPuYhUnsO6yo/aoRol4L7q7UFfdm+bR9r+8l63Y=
 github.com/prometheus/client_golang v1.20.5/go.mod h1:PIEt8X02hGcP8JWbeHyeZ53Y/jReSnHgO035n//V5WE=
 github.com/prometheus/client_model v0.0.0-20190812154241-14fe0d1b01d4/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=

test/e2e/deployment_appwrapper_test.go

@@ -45,7 +45,7 @@ func TestDeploymentAppWrapper(t *testing.T) {
 	defer func() {
 		_ = test.Client().Kueue().KueueV1beta1().ResourceFlavors().Delete(test.Ctx(), resourceFlavor.Name, metav1.DeleteOptions{})
 	}()
-	clusterQueue := createClusterQueue(test, resourceFlavor, 0)
+	clusterQueue := createClusterQueue(test, resourceFlavor, CPU)
 	defer func() {
 		_ = test.Client().Kueue().KueueV1beta1().ClusterQueues().Delete(test.Ctx(), clusterQueue.Name, metav1.DeleteOptions{})
 	}()

test/e2e/job_appwrapper_test.go

@@ -43,7 +43,7 @@ func TestBatchJobAppWrapper(t *testing.T) {
 	defer func() {
 		_ = test.Client().Kueue().KueueV1beta1().ResourceFlavors().Delete(test.Ctx(), resourceFlavor.Name, metav1.DeleteOptions{})
 	}()
-	clusterQueue := createClusterQueue(test, resourceFlavor, 0)
+	clusterQueue := createClusterQueue(test, resourceFlavor, CPU)
 	defer func() {
 		_ = test.Client().Kueue().KueueV1beta1().ClusterQueues().Delete(test.Ctx(), clusterQueue.Name, metav1.DeleteOptions{})
 	}()

test/e2e/mnist_pytorch_appwrapper_test.go

@@ -32,15 +32,15 @@ import (
 )
 
 func TestMnistPyTorchAppWrapperCpu(t *testing.T) {
-	runMnistPyTorchAppWrapper(t, "cpu", 0)
+	runMnistPyTorchAppWrapper(t, CPU)
 }
 
 func TestMnistPyTorchAppWrapperGpu(t *testing.T) {
-	runMnistPyTorchAppWrapper(t, "gpu", 1)
+	runMnistPyTorchAppWrapper(t, NVIDIA)
 }
 
 // Trains the MNIST dataset as a batch Job in an AppWrapper, and asserts successful completion of the training job.
-func runMnistPyTorchAppWrapper(t *testing.T, accelerator string, numberOfGpus int) {
+func runMnistPyTorchAppWrapper(t *testing.T, accelerator Accelerator) {
 	test := With(t)
 
 	// Create a namespace
@@ -51,7 +51,7 @@ func runMnistPyTorchAppWrapper(t *testing.T, accelerator string, numberOfGpus in
 	defer func() {
 		_ = test.Client().Kueue().KueueV1beta1().ResourceFlavors().Delete(test.Ctx(), resourceFlavor.Name, metav1.DeleteOptions{})
 	}()
-	clusterQueue := createClusterQueue(test, resourceFlavor, numberOfGpus)
+	clusterQueue := createClusterQueue(test, resourceFlavor, accelerator)
 	defer func() {
 		_ = test.Client().Kueue().KueueV1beta1().ClusterQueues().Delete(test.Ctx(), clusterQueue.Name, metav1.DeleteOptions{})
 	}()
@@ -109,7 +109,7 @@ func runMnistPyTorchAppWrapper(t *testing.T, accelerator string, numberOfGpus in
 								{Name: "MNIST_DATASET_URL", Value: GetMnistDatasetURL()},
 								{Name: "PIP_INDEX_URL", Value: GetPipIndexURL()},
 								{Name: "PIP_TRUSTED_HOST", Value: GetPipTrustedHost()},
-								{Name: "ACCELERATOR", Value: accelerator},
+								{Name: "ACCELERATOR", Value: accelerator.Type},
 							},
 							Command: []string{"/bin/sh", "-c", "pip install -r /test/requirements.txt && torchrun /test/mnist.py"},
 							VolumeMounts: []corev1.VolumeMount{