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Add MNIST test to run multi-node distributed training using KFTO
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| /* | ||
| Copyright 2023. | ||
| 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 | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| 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. | ||
| */ | ||
|
|
||
| package kfto | ||
|
|
||
| import ( | ||
| "bytes" | ||
| "fmt" | ||
| "os" | ||
| "testing" | ||
|
|
||
| . "github.com/onsi/gomega" | ||
| . "github.com/project-codeflare/codeflare-common/support" | ||
|
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| corev1 "k8s.io/api/core/v1" | ||
| "k8s.io/apimachinery/pkg/api/resource" | ||
| metav1 "k8s.io/apimachinery/pkg/apis/meta/v1" | ||
|
|
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| kftov1 "github.com/kubeflow/training-operator/pkg/apis/kubeflow.org/v1" | ||
| ) | ||
|
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| func TestPyTorchJobMnistCpu(t *testing.T) { | ||
| runKFTOPyTorchMnistJob(t, 0, "", GetCudaTrainingImage(), "resources/requirements.txt") | ||
| } | ||
|
|
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| func TestPyTorchJobMnistWithCuda(t *testing.T) { | ||
| runKFTOPyTorchMnistJob(t, 1, "nvidia.com/gpu", GetCudaTrainingImage(), "resources/requirements.txt") | ||
| } | ||
|
|
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| func TestPyTorchJobMnistWithROCm(t *testing.T) { | ||
| runKFTOPyTorchMnistJob(t, 1, "amd.com/gpu", GetROCmTrainingImage(), "resources/requirements-rocm.txt") | ||
| } | ||
|
|
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| func runKFTOPyTorchMnistJob(t *testing.T, numGpus int, gpuLabel string, image string, requirementsFile string) { | ||
| test := With(t) | ||
|
|
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| // Create a namespace | ||
| namespace := test.NewTestNamespace() | ||
|
|
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| workingDirectory, err := os.Getwd() | ||
| test.Expect(err).ToNot(HaveOccurred()) | ||
|
|
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| mnist, err := os.ReadFile(workingDirectory + "/resources/mnist.py") | ||
| test.Expect(err).ToNot(HaveOccurred()) | ||
|
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| requirementsFileName, err := os.ReadFile(workingDirectory + "/" + requirementsFile) | ||
| if numGpus > 0 { | ||
| mnist = bytes.Replace(mnist, []byte("accelerator=\"has to be specified\""), []byte("accelerator=\"gpu\""), 1) | ||
| } else { | ||
| mnist = bytes.Replace(mnist, []byte("accelerator=\"has to be specified\""), []byte("accelerator=\"cpu\""), 1) | ||
| } | ||
| config := CreateConfigMap(test, namespace.Name, map[string][]byte{ | ||
| // MNIST Ray Notebook | ||
| "mnist.py": mnist, | ||
| "requirements.txt": requirementsFileName, | ||
| }) | ||
|
|
||
| // Create PVC for trained model | ||
| outputPvc := CreatePersistentVolumeClaim(test, namespace.Name, "50Gi", corev1.ReadWriteMany) | ||
| defer test.Client().Core().CoreV1().PersistentVolumeClaims(namespace.Name).Delete(test.Ctx(), outputPvc.Name, metav1.DeleteOptions{}) | ||
|
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| // Create training PyTorch job | ||
| tuningJob := createKFTOPyTorchMnistJob(test, namespace.Name, *config, gpuLabel, numGpus, outputPvc.Name, image) | ||
| defer test.Client().Kubeflow().KubeflowV1().PyTorchJobs(namespace.Name).Delete(test.Ctx(), tuningJob.Name, *metav1.NewDeleteOptions(0)) | ||
|
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| // Make sure the PyTorch job is running | ||
| test.Eventually(PyTorchJob(test, namespace.Name, tuningJob.Name), TestTimeoutDouble). | ||
| Should(WithTransform(PyTorchJobConditionRunning, Equal(corev1.ConditionTrue))) | ||
|
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| // Make sure the PyTorch job succeeded | ||
| test.Eventually(PyTorchJob(test, namespace.Name, tuningJob.Name), TestTimeoutDouble).Should(WithTransform(PyTorchJobConditionSucceeded, Equal(corev1.ConditionTrue))) | ||
| test.T().Logf("PytorchJob %s/%s ran successfully", tuningJob.Namespace, tuningJob.Name) | ||
|
|
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| } | ||
|
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| func createKFTOPyTorchMnistJob(test Test, namespace string, config corev1.ConfigMap, gpuLabel string, numGpus int, outputPvcName string, baseImage string) *kftov1.PyTorchJob { | ||
|
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| var useGPU = false | ||
| var backend = "" | ||
|
|
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| if gpuLabel == "nvidia.com/gpu" && numGpus > 0 { | ||
| useGPU = true | ||
| backend = "nccl" | ||
| } | ||
| if gpuLabel == "amd.com/gpu" && numGpus > 0 { | ||
| useGPU = true | ||
| backend = "mpi" | ||
| } | ||
| if backend == "" { | ||
| backend = "gloo" | ||
| } | ||
|
|
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| tuningJob := &kftov1.PyTorchJob{ | ||
| TypeMeta: metav1.TypeMeta{ | ||
| APIVersion: corev1.SchemeGroupVersion.String(), | ||
| Kind: "PyTorchJob", | ||
| }, | ||
| ObjectMeta: metav1.ObjectMeta{ | ||
| GenerateName: "kfto-mnist-", | ||
| }, | ||
| Spec: kftov1.PyTorchJobSpec{ | ||
| PyTorchReplicaSpecs: map[kftov1.ReplicaType]*kftov1.ReplicaSpec{ | ||
| "Master": { | ||
| Replicas: Ptr(int32(1)), | ||
| RestartPolicy: kftov1.RestartPolicyOnFailure, | ||
| Template: corev1.PodTemplateSpec{ | ||
| Spec: corev1.PodSpec{ | ||
| Containers: []corev1.Container{ | ||
| { | ||
| Name: "pytorch", | ||
| Image: baseImage, | ||
| ImagePullPolicy: corev1.PullIfNotPresent, | ||
| Command: []string{ | ||
| "/bin/bash", "-c", | ||
| fmt.Sprintf(`python -m venv /tmp/venv && \ | ||
| source /tmp/venv/bin/activate && \ | ||
| pip install --no-cache-dir -r /mnt/files/requirements.txt && \ | ||
| python /mnt/files/mnist.py --epochs 1 --save-model --backend %s`, backend), | ||
| }, | ||
| Env: []corev1.EnvVar{ | ||
| { | ||
| Name: "MASTER_ADDR", | ||
| Value: "mnist-pytorch-distributed-master-0.mnist-pytorch-distributed-master", | ||
| }, | ||
| { | ||
| Name: "MASTER_PORT", | ||
| Value: "29500", | ||
| }, | ||
| { | ||
| Name: "WORLD_SIZE", | ||
| Value: "3", | ||
| }, | ||
| }, | ||
| VolumeMounts: []corev1.VolumeMount{ | ||
| { | ||
| Name: config.Name, | ||
| MountPath: "/mnt/files", | ||
| ReadOnly: true, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| Volumes: []corev1.Volume{ | ||
| { | ||
| Name: config.Name, | ||
| VolumeSource: corev1.VolumeSource{ | ||
| ConfigMap: &corev1.ConfigMapVolumeSource{ | ||
| LocalObjectReference: corev1.LocalObjectReference{ | ||
| Name: config.Name, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| RestartPolicy: corev1.RestartPolicyOnFailure, | ||
| }, | ||
| }, | ||
| }, | ||
| "Worker": { | ||
| Replicas: Ptr(int32(1)), | ||
| RestartPolicy: kftov1.RestartPolicyOnFailure, | ||
| Template: corev1.PodTemplateSpec{ | ||
| Spec: corev1.PodSpec{ | ||
| Containers: []corev1.Container{ | ||
| { | ||
| Name: "pytorch", | ||
| Image: baseImage, | ||
| ImagePullPolicy: corev1.PullIfNotPresent, | ||
| Command: []string{ | ||
| "/bin/bash", "-c", | ||
| fmt.Sprintf(`python -m venv /tmp/venv && \ | ||
| source /tmp/venv/bin/activate && \ | ||
| pip install --no-cache-dir -r /mnt/files/requirements.txt && \ | ||
| python /mnt/files/mnist.py --epochs 1 --save-model --backend %s`, backend), | ||
| }, | ||
| Env: []corev1.EnvVar{ | ||
| { | ||
| Name: "MASTER_ADDR", | ||
| Value: "mnist-pytorch-distributed-master-0.mnist-pytorch-distributed-master", | ||
| }, | ||
| { | ||
| Name: "MASTER_PORT", | ||
| Value: "29500", | ||
| }, | ||
| { | ||
| Name: "WORLD_SIZE", | ||
| Value: "3", | ||
| }, | ||
| }, | ||
| VolumeMounts: []corev1.VolumeMount{ | ||
| { | ||
| Name: config.Name, | ||
| MountPath: "/mnt/files", | ||
| ReadOnly: true, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| Volumes: []corev1.Volume{ | ||
| { | ||
| Name: config.Name, | ||
| VolumeSource: corev1.VolumeSource{ | ||
| ConfigMap: &corev1.ConfigMapVolumeSource{ | ||
| LocalObjectReference: corev1.LocalObjectReference{ | ||
| Name: config.Name, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| RestartPolicy: corev1.RestartPolicyOnFailure, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| } | ||
|
|
||
| if useGPU { | ||
| // Update resource lists | ||
| tuningJob.Spec.PyTorchReplicaSpecs["Master"].Template.Spec.Containers[0].Resources = corev1.ResourceRequirements{ | ||
| Limits: corev1.ResourceList{ | ||
| corev1.ResourceCPU: resource.MustParse("2"), | ||
| corev1.ResourceMemory: resource.MustParse("8Gi"), | ||
| corev1.ResourceName(gpuLabel): resource.MustParse(fmt.Sprint(numGpus)), | ||
| }, | ||
| } | ||
| tuningJob.Spec.PyTorchReplicaSpecs["Worker"].Template.Spec.Containers[0].Resources = corev1.ResourceRequirements{ | ||
| Limits: corev1.ResourceList{ | ||
| corev1.ResourceCPU: resource.MustParse("2"), | ||
| corev1.ResourceMemory: resource.MustParse("8Gi"), | ||
| corev1.ResourceName(gpuLabel): resource.MustParse(fmt.Sprint(numGpus)), | ||
| }, | ||
| } | ||
|
|
||
| // Update tolerations | ||
| tuningJob.Spec.PyTorchReplicaSpecs["Master"].Template.Spec.Tolerations = []corev1.Toleration{ | ||
| { | ||
| Key: gpuLabel, | ||
| Operator: corev1.TolerationOpExists, | ||
| }, | ||
| } | ||
| tuningJob.Spec.PyTorchReplicaSpecs["Worker"].Template.Spec.Tolerations = []corev1.Toleration{ | ||
| { | ||
| Key: gpuLabel, | ||
| Operator: corev1.TolerationOpExists, | ||
| }, | ||
| } | ||
| } | ||
|
|
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| tuningJob, err := test.Client().Kubeflow().KubeflowV1().PyTorchJobs(namespace).Create(test.Ctx(), tuningJob, metav1.CreateOptions{}) | ||
| test.Expect(err).NotTo(HaveOccurred()) | ||
| test.T().Logf("Created PytorchJob %s/%s successfully", tuningJob.Namespace, tuningJob.Name) | ||
|
|
||
| return tuningJob | ||
| } |
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