Skip to content

Commit

Permalink
Merge remote-tracking branch 'upstream/main'
Browse files Browse the repository at this point in the history
  • Loading branch information
@dchourasia
dchourasia committed Jan 4, 2025
2 parents f88d22a + a5a6d39 commit ff2bfb3
Show file tree
Hide file tree
Showing 4 changed files with 560 additions and 0 deletions.
306 changes: 306 additions & 0 deletions tests/kfto/kfto_mnist_training_test.go
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,306 @@
/*
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"
"testing"

kftov1 "github.com/kubeflow/training-operator/pkg/apis/kubeflow.org/v1"
. "github.com/onsi/gomega"
. "github.com/project-codeflare/codeflare-common/support"

corev1 "k8s.io/api/core/v1"
"k8s.io/apimachinery/pkg/api/resource"
metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
)

func TestPyTorchJobMnistCpu(t *testing.T) {
runKFTOPyTorchMnistJob(t, 0, 2, "", GetCudaTrainingImage(), "resources/requirements.txt")
}
func TestPyTorchJobMnistWithCuda(t *testing.T) {
runKFTOPyTorchMnistJob(t, 1, 1, "nvidia.com/gpu", GetCudaTrainingImage(), "resources/requirements.txt")
}

func TestPyTorchJobMnistWithROCm(t *testing.T) {
runKFTOPyTorchMnistJob(t, 1, 1, "amd.com/gpu", GetROCmTrainingImage(), "resources/requirements-rocm.txt")
}

func runKFTOPyTorchMnistJob(t *testing.T, numGpus int, workerReplicas int, gpuLabel string, image string, requirementsFile string) {
test := With(t)

// Create a namespace
namespace := test.NewTestNamespace()

mnist := ReadFile(test, "resources/mnist.py")
requirementsFileName := ReadFile(test, 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,
})

outputPvc := CreatePersistentVolumeClaim(test, namespace.Name, "50Gi", corev1.ReadWriteOnce)
defer test.Client().Core().CoreV1().PersistentVolumeClaims(namespace.Name).Delete(test.Ctx(), outputPvc.Name, metav1.DeleteOptions{})

// Create training PyTorch job
tuningJob := createKFTOPyTorchMnistJob(test, namespace.Name, *config, gpuLabel, numGpus, workerReplicas, outputPvc.Name, image)
defer test.Client().Kubeflow().KubeflowV1().PyTorchJobs(namespace.Name).Delete(test.Ctx(), tuningJob.Name, *metav1.NewDeleteOptions(0))

// Make sure the PyTorch job is running
test.Eventually(PyTorchJob(test, namespace.Name, tuningJob.Name), TestTimeoutDouble).
Should(WithTransform(PyTorchJobConditionRunning, Equal(corev1.ConditionTrue)))

// 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)

}

func createKFTOPyTorchMnistJob(test Test, namespace string, config corev1.ConfigMap, gpuLabel string, numGpus int, workerReplicas int, outputPvcName string, baseImage string) *kftov1.PyTorchJob {
var useGPU = false
var backend string

if numGpus > 0 {
useGPU = true
backend = "nccl"
} else {
backend = "gloo"
}

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{
ObjectMeta: metav1.ObjectMeta{
Labels: map[string]string{
"app": "kfto-mnist",
},
},
Spec: corev1.PodSpec{
Affinity: &corev1.Affinity{
PodAntiAffinity: &corev1.PodAntiAffinity{
RequiredDuringSchedulingIgnoredDuringExecution: []corev1.PodAffinityTerm{
{
LabelSelector: &metav1.LabelSelector{
MatchLabels: map[string]string{
"app": "kfto-mnist",
},
},
TopologyKey: "kubernetes.io/hostname",
},
},
},
},
Containers: []corev1.Container{
{
Name: "pytorch",
Image: baseImage,
ImagePullPolicy: corev1.PullIfNotPresent,
Command: []string{
"/bin/bash", "-c",
fmt.Sprintf(`mkdir -p /tmp/lib && export PYTHONPATH=$PYTHONPATH:/tmp/lib && \
pip install --no-cache-dir -r /mnt/files/requirements.txt --target=/tmp/lib && \
python /mnt/files/mnist.py --epochs 3 --save-model --output-path /mnt/output --backend %s`, backend),
},
VolumeMounts: []corev1.VolumeMount{
{
Name: config.Name,
MountPath: "/mnt/files",
},
{
Name: "tmp-volume",
MountPath: "/tmp",
},
{
Name: "output-volume",
MountPath: "/mnt/output",
},
},
Resources: corev1.ResourceRequirements{
Limits: corev1.ResourceList{
corev1.ResourceCPU: resource.MustParse("1"),
corev1.ResourceMemory: resource.MustParse("6Gi"),
},
},
},
},
Volumes: []corev1.Volume{
{
Name: config.Name,
VolumeSource: corev1.VolumeSource{
ConfigMap: &corev1.ConfigMapVolumeSource{
LocalObjectReference: corev1.LocalObjectReference{
Name: config.Name,
},
},
},
},
{
Name: "tmp-volume",
VolumeSource: corev1.VolumeSource{
EmptyDir: &corev1.EmptyDirVolumeSource{},
},
},
{
Name: "output-volume",
VolumeSource: corev1.VolumeSource{
PersistentVolumeClaim: &corev1.PersistentVolumeClaimVolumeSource{
ClaimName: outputPvcName,
},
},
},
},
RestartPolicy: corev1.RestartPolicyOnFailure,
},
},
},
"Worker": {
Replicas: Ptr(int32(workerReplicas)),
RestartPolicy: kftov1.RestartPolicyOnFailure,
Template: corev1.PodTemplateSpec{
ObjectMeta: metav1.ObjectMeta{
Labels: map[string]string{
"app": "kfto-mnist",
},
},
Spec: corev1.PodSpec{
Affinity: &corev1.Affinity{
PodAntiAffinity: &corev1.PodAntiAffinity{
RequiredDuringSchedulingIgnoredDuringExecution: []corev1.PodAffinityTerm{
{
LabelSelector: &metav1.LabelSelector{
MatchLabels: map[string]string{
"app": "kfto-mnist",
},
},
TopologyKey: "kubernetes.io/hostname",
},
},
},
},
Containers: []corev1.Container{
{
Name: "pytorch",
Image: baseImage,
ImagePullPolicy: corev1.PullIfNotPresent,
Command: []string{
"/bin/bash", "-c",
fmt.Sprintf(`mkdir -p /tmp/lib && export PYTHONPATH=$PYTHONPATH:/tmp/lib && \
pip install --no-cache-dir -r /mnt/files/requirements.txt --target=/tmp/lib && \
python /mnt/files/mnist.py --epochs 3 --save-model --backend %s`, backend),
},
VolumeMounts: []corev1.VolumeMount{
{
Name: config.Name,
MountPath: "/mnt/files",
},
{
Name: "tmp-volume",
MountPath: "/tmp",
},
},
Resources: corev1.ResourceRequirements{
Limits: corev1.ResourceList{
corev1.ResourceCPU: resource.MustParse("1"),
corev1.ResourceMemory: resource.MustParse("6Gi"),
},
},
},
},
Volumes: []corev1.Volume{
{
Name: config.Name,
VolumeSource: corev1.VolumeSource{
ConfigMap: &corev1.ConfigMapVolumeSource{
LocalObjectReference: corev1.LocalObjectReference{
Name: config.Name,
},
},
},
},
{
Name: "tmp-volume",
VolumeSource: corev1.VolumeSource{
EmptyDir: &corev1.EmptyDirVolumeSource{},
},
},
},
RestartPolicy: corev1.RestartPolicyOnFailure,
},
},
},
},
},
}

if useGPU {
// Update resource lists for GPU (NVIDIA/ROCm) usecase
tuningJob.Spec.PyTorchReplicaSpecs["Master"].Template.Spec.Containers[0].Resources.Limits[corev1.ResourceName(gpuLabel)] = resource.MustParse(fmt.Sprint(numGpus))
tuningJob.Spec.PyTorchReplicaSpecs["Worker"].Template.Spec.Containers[0].Resources.Limits[corev1.ResourceName(gpuLabel)] = resource.MustParse(fmt.Sprint(numGpus))

tuningJob.Spec.PyTorchReplicaSpecs["Master"].Template.Spec.Containers[0].Env = []corev1.EnvVar{
{
Name: "NCCL_DEBUG",
Value: "INFO",
},
}
tuningJob.Spec.PyTorchReplicaSpecs["Worker"].Template.Spec.Containers[0].Env = []corev1.EnvVar{
{
Name: "NCCL_DEBUG",
Value: "INFO",
},
}

// 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,
},
}
}

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
}
Loading

0 comments on commit ff2bfb3

Please sign in to comment.