fix(runtimes): propagate trainer environment variables to worker processes #3454
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| /* | ||
| Copyright 2025 The Kubeflow Authors. | ||
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| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||
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| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| */ | ||
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| package jobset | ||
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| import ( | ||
| "testing" | ||
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| corev1 "k8s.io/api/core/v1" | ||
| metav1 "k8s.io/apimachinery/pkg/apis/meta/v1" | ||
| batchv1ac "k8s.io/client-go/applyconfigurations/batch/v1" | ||
| corev1ac "k8s.io/client-go/applyconfigurations/core/v1" | ||
| "k8s.io/utils/ptr" | ||
| jobsetv1alpha2ac "sigs.k8s.io/jobset/client-go/applyconfiguration/jobset/v1alpha2" | ||
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| trainer "github.com/kubeflow/trainer/v2/pkg/apis/trainer/v1alpha1" | ||
| "github.com/kubeflow/trainer/v2/pkg/constants" | ||
| "github.com/kubeflow/trainer/v2/pkg/runtime" | ||
| utiltesting "github.com/kubeflow/trainer/v2/pkg/util/testing" | ||
| ) | ||
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| func TestBuilderTrainerEnvPropagation(t *testing.T) { | ||
| testCases := map[string]struct { | ||
| trainJobEnv []corev1.EnvVar | ||
| initialPodEnv []corev1ac.EnvVarApplyConfiguration | ||
| expectedEnv []corev1.EnvVar | ||
| }{ | ||
| "variables propagated": { | ||
| trainJobEnv: []corev1.EnvVar{{Name: "CUSTOM_VAR", Value: "custom_value"}}, | ||
| expectedEnv: []corev1.EnvVar{{Name: "CUSTOM_VAR", Value: "custom_value"}}, | ||
| }, | ||
| "no variables propagated (empty case)": { | ||
| trainJobEnv: []corev1.EnvVar{}, | ||
| expectedEnv: []corev1.EnvVar{}, | ||
| }, | ||
| "merge with existing variables": { | ||
| trainJobEnv: []corev1.EnvVar{{Name: "CUSTOM_VAR", Value: "custom_value"}}, | ||
| initialPodEnv: []corev1ac.EnvVarApplyConfiguration{ | ||
| *corev1ac.EnvVar().WithName("EXISTING_VAR").WithValue("existing_value"), | ||
| }, | ||
| expectedEnv: []corev1.EnvVar{ | ||
| {Name: "EXISTING_VAR", Value: "existing_value"}, | ||
| {Name: "CUSTOM_VAR", Value: "custom_value"}, | ||
| }, | ||
| }, | ||
| } | ||
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| for name, tc := range testCases { | ||
| t.Run(name, func(t *testing.T) { | ||
| // Setup TrainJob | ||
| trainJob := utiltesting.MakeTrainJobWrapper(metav1.NamespaceDefault, "test-job"). | ||
| Trainer(utiltesting.MakeTrainJobTrainerWrapper(). | ||
| Env(tc.trainJobEnv...). | ||
| Obj()). | ||
| Obj() | ||
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| // Setup runtime info for MPI (launcher is NOT a node) | ||
| info := &runtime.Info{ | ||
| RuntimePolicy: runtime.RuntimePolicy{ | ||
| MLPolicySource: utiltesting.MakeMLPolicySourceWrapper(). | ||
| MPIPolicy(nil, trainer.MPIImplementationOpenMPI, nil, ptr.To(false)). | ||
| Obj(), | ||
| }, | ||
| } | ||
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| // Create JobSet spec with initial environment | ||
| container := corev1ac.Container().WithName(constants.Node) | ||
| for i := range tc.initialPodEnv { | ||
| container.WithEnv(&tc.initialPodEnv[i]) | ||
| } | ||
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| jobSetSpec := jobsetv1alpha2ac.JobSetSpec().WithReplicatedJobs( | ||
| jobsetv1alpha2ac.ReplicatedJob(). | ||
| WithName(constants.Node). | ||
| WithTemplate(batchv1ac.JobTemplateSpec(). | ||
| WithSpec(batchv1ac.JobSpec(). | ||
| WithTemplate(corev1ac.PodTemplateSpec(). | ||
| WithSpec(corev1ac.PodSpec().WithContainers(container)), | ||
| ), | ||
| ), | ||
| ), | ||
| ) | ||
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| builder := NewBuilder(jobsetv1alpha2ac.JobSet("test-job", metav1.NamespaceDefault).WithSpec(jobSetSpec)) | ||
| builder.Trainer(info, trainJob) | ||
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| // Verify results | ||
| var actualEnv []corev1.EnvVar | ||
| for _, rJob := range builder.Spec.ReplicatedJobs { | ||
| if *rJob.Name == constants.Node { | ||
| for _, c := range rJob.Template.Spec.Template.Spec.Containers { | ||
| if *c.Name == constants.Node { | ||
| for _, env := range c.Env { | ||
| actualEnv = append(actualEnv, corev1.EnvVar{Name: *env.Name, Value: *env.Value}) | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| if len(actualEnv) != len(tc.expectedEnv) { | ||
| t.Fatalf("Expected %d environment variables, got %d", len(tc.expectedEnv), len(actualEnv)) | ||
|
There was a problem hiding this comment. The existing tests in |
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| } | ||
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| for i, expected := range tc.expectedEnv { | ||
| if actualEnv[i].Name != expected.Name || actualEnv[i].Value != expected.Value { | ||
| t.Errorf("At index %d: expected %s=%s, got %s=%s", i, expected.Name, expected.Value, actualEnv[i].Name, actualEnv[i].Value) | ||
| } | ||
| } | ||
| }) | ||
| } | ||
| } | ||
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This change is broader than just MPI. The old condition was gated on
isRunLauncherAsNode, so it only kicked in for MPI configs. Now any replicated job namednode(PyTorch, DeepSpeed, etc.) will get env vars andresourcesPerNodeinjected unconditionally. Is that the intent here? If so, might be worth noting that in the PR description since it changes behavior for all runtimes, not just MPI.