Execute a Synchronous Distributed AI Workload
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To execute a synchronous multinode AI and ML job in your Kubernetes cluster, perform the following tasks on the deployment jump host. This process enables you to take advantage of data that is stored on a NetApp volume and to use more GPUs than a single worker node can provide. See the following figure for a depiction of a synchronous distributed AI job.
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Synchronous distributed jobs can help increase performance and training accuracy compared with asynchronous distributed jobs. A discussion of the pros and cons of synchronous jobs versus asynchronous jobs is outside the scope of this document. |
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The following example commands show the creation of one worker that participates in the synchronous distributed execution of the same TensorFlow benchmark job that was executed on a single node in the example in the section Execute a Single-Node AI Workload. In this specific example, only a single worker is deployed because the job is executed across two worker nodes.
This example worker deployment requests eight GPUs and thus can run on a single GPU worker node that features eight or more GPUs. If your GPU worker nodes feature more than eight GPUs, to maximize performance, you might want to increase this number to be equal to the number of GPUs that your worker nodes feature. For more information about Kubernetes deployments, see the official Kubernetes documentation.
A Kubernetes deployment is created in this example because this specific containerized worker would never complete on its own. Therefore, it doesn’t make sense to deploy it by using the Kubernetes job construct. If your worker is designed or written to complete on its own, then it might make sense to use the job construct to deploy your worker.
The pod that is specified in this example deployment specification is given a
hostNetworkvalue oftrue. This value means that the pod uses the host worker node’s networking stack instead of the virtual networking stack that Kubernetes usually creates for each pod. This annotation is used in this case because the specific workload relies on Open MPI, NCCL, and Horovod to execute the workload in a synchronous distributed manner. Therefore, it requires access to the host networking stack. A discussion about Open MPI, NCCL, and Horovod is outside the scope of this document. Whether or not thishostNetwork: trueannotation is necessary depends on the requirements of the specific workload that you are executing. For more information about thehostNetworkfield, see the official Kubernetes documentation.$ cat << EOF > ./netapp-tensorflow-multi-imagenet-worker.yaml apiVersion: apps/v1 kind: Deployment metadata: name: netapp-tensorflow-multi-imagenet-worker spec: replicas: 1 selector: matchLabels: app: netapp-tensorflow-multi-imagenet-worker template: metadata: labels: app: netapp-tensorflow-multi-imagenet-worker spec: hostNetwork: true volumes: - name: dshm emptyDir: medium: Memory - name: testdata-iface1 persistentVolumeClaim: claimName: pb-fg-all-iface1 - name: testdata-iface2 persistentVolumeClaim: claimName: pb-fg-all-iface2 - name: results persistentVolumeClaim: claimName: tensorflow-results containers: - name: netapp-tensorflow-py2 image: netapp/tensorflow-py2:19.03.0 command: ["bash", "/netapp/scripts/start-slave-multi.sh", "22122"] resources: limits: nvidia.com/gpu: 8 volumeMounts: - mountPath: /dev/shm name: dshm - mountPath: /mnt/mount_0 name: testdata-iface1 - mountPath: /mnt/mount_1 name: testdata-iface2 - mountPath: /tmp name: results securityContext: privileged: true EOF $ kubectl create -f ./netapp-tensorflow-multi-imagenet-worker.yaml deployment.apps/netapp-tensorflow-multi-imagenet-worker created $ kubectl get deployments NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE netapp-tensorflow-multi-imagenet-worker 1 1 1 1 4s -
Confirm that the worker deployment that you created in step 1 launched successfully. The following example commands confirm that a single worker pod was created for the deployment, as indicated in the deployment definition, and that this pod is currently running on one of the GPU worker nodes.
$ kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE netapp-tensorflow-multi-imagenet-worker-654fc7f486-v6725 1/1 Running 0 60s 10.61.218.154 10.61.218.154 <none> $ kubectl logs netapp-tensorflow-multi-imagenet-worker-654fc7f486-v6725 22122
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Create a Kubernetes job for a master that kicks off, participates in, and tracks the execution of the synchronous multinode job. The following example commands create one master that kicks off, participates in, and tracks the synchronous distributed execution of the same TensorFlow benchmark job that was executed on a single node in the example in the section Execute a Single-Node AI Workload.
This example master job requests eight GPUs and thus can run on a single GPU worker node that features eight or more GPUs. If your GPU worker nodes feature more than eight GPUs, to maximize performance, you might want to increase this number to be equal to the number of GPUs that your worker nodes feature.
The master pod that is specified in this example job definition is given a
hostNetworkvalue oftrue, just as the worker pod was given ahostNetworkvalue oftruein step 1. See step 1 for details about why this value is necessary.$ cat << EOF > ./netapp-tensorflow-multi-imagenet-master.yaml apiVersion: batch/v1 kind: Job metadata: name: netapp-tensorflow-multi-imagenet-master spec: backoffLimit: 5 template: spec: hostNetwork: true volumes: - name: dshm emptyDir: medium: Memory - name: testdata-iface1 persistentVolumeClaim: claimName: pb-fg-all-iface1 - name: testdata-iface2 persistentVolumeClaim: claimName: pb-fg-all-iface2 - name: results persistentVolumeClaim: claimName: tensorflow-results containers: - name: netapp-tensorflow-py2 image: netapp/tensorflow-py2:19.03.0 command: ["python", "/netapp/scripts/run.py", "--dataset_dir=/mnt/mount_0/dataset/imagenet", "--port=22122", "--num_devices=16", "--dgx_version=dgx1", "--nodes=10.61.218.152,10.61.218.154"] resources: limits: nvidia.com/gpu: 8 volumeMounts: - mountPath: /dev/shm name: dshm - mountPath: /mnt/mount_0 name: testdata-iface1 - mountPath: /mnt/mount_1 name: testdata-iface2 - mountPath: /tmp name: results securityContext: privileged: true restartPolicy: Never EOF $ kubectl create -f ./netapp-tensorflow-multi-imagenet-master.yaml job.batch/netapp-tensorflow-multi-imagenet-master created $ kubectl get jobs NAME COMPLETIONS DURATION AGE netapp-tensorflow-multi-imagenet-master 0/1 25s 25s -
Confirm that the master job that you created in step 3 is running correctly. The following example command confirms that a single master pod was created for the job, as indicated in the job definition, and that this pod is currently running on one of the GPU worker nodes. You should also see that the worker pod that you originally saw in step 1 is still running and that the master and worker pods are running on different nodes.
$ kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE netapp-tensorflow-multi-imagenet-master-ppwwj 1/1 Running 0 45s 10.61.218.152 10.61.218.152 <none> netapp-tensorflow-multi-imagenet-worker-654fc7f486-v6725 1/1 Running 0 26m 10.61.218.154 10.61.218.154 <none>
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Confirm that the master job that you created in step 3 completes successfully. The following example commands confirm that the job completed successfully.
$ kubectl get jobs NAME COMPLETIONS DURATION AGE netapp-tensorflow-multi-imagenet-master 1/1 5m50s 9m18s $ kubectl get pods NAME READY STATUS RESTARTS AGE netapp-tensorflow-multi-imagenet-master-ppwwj 0/1 Completed 0 9m38s netapp-tensorflow-multi-imagenet-worker-654fc7f486-v6725 1/1 Running 0 35m $ kubectl logs netapp-tensorflow-multi-imagenet-master-ppwwj [10.61.218.152:00008] WARNING: local probe returned unhandled shell:unknown assuming bash rm: cannot remove '/lib': Is a directory [10.61.218.154:00033] PMIX ERROR: NO-PERMISSIONS in file gds_dstore.c at line 702 [10.61.218.154:00033] PMIX ERROR: NO-PERMISSIONS in file gds_dstore.c at line 711 [10.61.218.152:00008] PMIX ERROR: NO-PERMISSIONS in file gds_dstore.c at line 702 [10.61.218.152:00008] PMIX ERROR: NO-PERMISSIONS in file gds_dstore.c at line 711 Total images/sec = 12881.33875 ================ Clean Cache !!! ================== mpirun -allow-run-as-root -np 2 -H 10.61.218.152:1,10.61.218.154:1 -mca pml ob1 -mca btl ^openib -mca btl_tcp_if_include enp1s0f0 -mca plm_rsh_agent ssh -mca plm_rsh_args "-p 22122" bash -c 'sync; echo 1 > /proc/sys/vm/drop_caches' ========================================= mpirun -allow-run-as-root -np 16 -H 10.61.218.152:8,10.61.218.154:8 -bind-to none -map-by slot -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH -mca pml ob1 -mca btl ^openib -mca btl_tcp_if_include enp1s0f0 -x NCCL_IB_HCA=mlx5 -x NCCL_NET_GDR_READ=1 -x NCCL_IB_SL=3 -x NCCL_IB_GID_INDEX=3 -x NCCL_SOCKET_IFNAME=enp5s0.3091,enp12s0.3092,enp132s0.3093,enp139s0.3094 -x NCCL_IB_CUDA_SUPPORT=1 -mca orte_base_help_aggregate 0 -mca plm_rsh_agent ssh -mca plm_rsh_args "-p 22122" python /netapp/tensorflow/benchmarks_190205/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --model=resnet50 --batch_size=256 --device=gpu --force_gpu_compatible=True --num_intra_threads=1 --num_inter_threads=48 --variable_update=horovod --batch_group_size=20 --num_batches=500 --nodistortions --num_gpus=1 --data_format=NCHW --use_fp16=True --use_tf_layers=False --data_name=imagenet --use_datasets=True --data_dir=/mnt/mount_0/dataset/imagenet --datasets_parallel_interleave_cycle_length=10 --datasets_sloppy_parallel_interleave=False --num_mounts=2 --mount_prefix=/mnt/mount_%d --datasets_prefetch_buffer_size=2000 -- datasets_use_prefetch=True --datasets_num_private_threads=4 --horovod_device=gpu > /tmp/20190814_161609_tensorflow_horovod_rdma_resnet50_gpu_16_256_b500_imagenet_nodistort_fp16_r10_m2_nockpt.txt 2>&1
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Delete the worker deployment when you no longer need it. The following example commands show the deletion of the worker deployment object that was created in step 1.
When you delete the worker deployment object, Kubernetes automatically deletes any associated worker pods.
$ kubectl get deployments NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE netapp-tensorflow-multi-imagenet-worker 1 1 1 1 43m $ kubectl get pods NAME READY STATUS RESTARTS AGE netapp-tensorflow-multi-imagenet-master-ppwwj 0/1 Completed 0 17m netapp-tensorflow-multi-imagenet-worker-654fc7f486-v6725 1/1 Running 0 43m $ kubectl delete deployment netapp-tensorflow-multi-imagenet-worker deployment.extensions "netapp-tensorflow-multi-imagenet-worker" deleted $ kubectl get deployments No resources found. $ kubectl get pods NAME READY STATUS RESTARTS AGE netapp-tensorflow-multi-imagenet-master-ppwwj 0/1 Completed 0 18m
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Optional: Clean up the master job artifacts. The following example commands show the deletion of the master job object that was created in step 3.
When you delete the master job object, Kubernetes automatically deletes any associated master pods.
$ kubectl get jobs NAME COMPLETIONS DURATION AGE netapp-tensorflow-multi-imagenet-master 1/1 5m50s 19m $ kubectl get pods NAME READY STATUS RESTARTS AGE netapp-tensorflow-multi-imagenet-master-ppwwj 0/1 Completed 0 19m $ kubectl delete job netapp-tensorflow-multi-imagenet-master job.batch "netapp-tensorflow-multi-imagenet-master" deleted $ kubectl get jobs No resources found. $ kubectl get pods No resources found.