Help Center/ Cloud Container Engine/ Best Practices/ Batch Computing/ Deploying and Using Kubeflow in a CCE Cluster/ Training a TensorFlow Model
Updated on 2024-12-28 GMT+08:00
View PDF
Share

Training a TensorFlow Model

After Kubeflow is deployed, it is easy to use the ps-worker mode to train TensorFlow models. This section describes an official TensorFlow training example provided by Kubeflow. For details, see TensorFlow Training (TFJob).

Running the Mnist Example

  1. Deploy the TFJob resource to start training.

    Create the tf-mnist.yaml file. The following is an example: 
    apiVersion: "kubeflow.org/v1"
kind: TFJob
metadata:
  name: tfjob-simple
  namespace: kubeflow
spec:
  tfReplicaSpecs:
    Worker:
      replicas: 2
      restartPolicy: OnFailure
      template:
        spec:
          containers:
            - name: tensorflow
              image: kubeflow/tf-mnist-with-summaries:latest
              command:
                - "python"
                - "/var/tf_mnist/mnist_with_summaries.py"

  2. Create the TFJob. 

    kubectl apply -f tf-mnist.yaml

  3. View the logs after the worker running is complete. 

    kubectl -n kubeflow logs tfjob-simple-worker-0

    Information similar to the following is displayed:

    ...
Accuracy at step 900: 0.964
Accuracy at step 910: 0.9653
Accuracy at step 920: 0.9665
Accuracy at step 930: 0.9681
Accuracy at step 940: 0.9664
Accuracy at step 950: 0.9667
Accuracy at step 960: 0.9694
Accuracy at step 970: 0.9683
Accuracy at step 980: 0.9687
Accuracy at step 990: 0.966
Adding run metadata for 999

  4. Delete the TFJob. 

    kubectl delete -f tf-mnist.yaml

Using a GPU

The training can be performed in the GPU scenario. In this scenario, the cluster must contain GPU nodes and proper drivers must be installed.

  1. Specify the GPU resources in the TFJob.

    Create the tf-gpu.yaml file. The following is an example:

    This example runs in the TensorFlow distributed architecture. The ResNet50 model in the convolutional neural network (CNN) is used to train randomly generated images. A total of 32 (batch_size) images are trained each time, and the images are trained 100 times in total. Additionally, the performance (image/sec) of each training is recorded.

    apiVersion: "kubeflow.org/v1"
kind: "TFJob"
metadata:
  name: "tf-smoke-gpu"
spec:
  tfReplicaSpecs:
    PS:
      replicas: 1
      template:
        metadata:
          creationTimestamp: null
        spec:
          containers:
            - args:
                - python
                - tf_cnn_benchmarks.py
                - --batch_size=32
                - --model=resnet50
                - --variable_update=parameter_server
                - --flush_stdout=true
                - --num_gpus=1
                - --local_parameter_device=cpu
                - --device=cpu
                - --data_format=NHWC
              image: docker.io/kubeflow/tf-benchmarks-cpu:v20171202-bdab599-dirty-284af3
              name: tensorflow
              ports:
                - containerPort: 2222
                  name: tfjob-port
              resources:
                limits:
                  cpu: "1"
              workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure
    Worker:
      replicas: 1
      template:
        metadata:
          creationTimestamp: null
        spec:
          containers:
            - args:
                - python
                - tf_cnn_benchmarks.py
                - --batch_size=32
                - --model=resnet50
                - --variable_update=parameter_server
                - --flush_stdout=true
                - --num_gpus=1
                - --local_parameter_device=cpu
                - --device=gpu
                - --data_format=NHWC
              image: docker.io/kubeflow/tf-benchmarks-gpu:v20171202-bdab599-dirty-284af3
              name: tensorflow
              ports:
                - containerPort: 2222
                  name: tfjob-port
              resources:
                limits:
                  nvidia.com/gpu: 1       # Number of GPUs
              workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure

  2. Create the TFJob. 

    kubectl apply -f tf-gpu.yaml

  3. After the worker runs the job (about 5 minutes if a GPU is used), run the following command to view the result. 

    kubectl logs tf-smoke-gpu-worker-0

    Information similar to the following is displayed:

    ...
INFO|2023-09-02T12:04:25|/opt/launcher.py|27| Running warm up
INFO|2023-09-02T12:08:55|/opt/launcher.py|27| Done warm up
INFO|2023-09-02T12:08:55|/opt/launcher.py|27| Step      Img/sec loss
INFO|2023-09-02T12:08:56|/opt/launcher.py|27| 1 images/sec: 68.8 +/- 0.0 (jitter = 0.0) 8.777
INFO|2023-09-02T12:09:00|/opt/launcher.py|27| 10        images/sec: 70.4 +/- 0.4 (jitter = 1.8) 8.557
INFO|2023-09-02T12:09:04|/opt/launcher.py|27| 20        images/sec: 70.5 +/- 0.3 (jitter = 1.5) 8.090
INFO|2023-09-02T12:09:09|/opt/launcher.py|27| 30        images/sec: 70.3 +/- 0.3 (jitter = 1.6) 8.041
INFO|2023-09-02T12:09:13|/opt/launcher.py|27| 40        images/sec: 70.1 +/- 0.2 (jitter = 1.7) 9.464
INFO|2023-09-02T12:09:18|/opt/launcher.py|27| 50        images/sec: 70.1 +/- 0.2 (jitter = 1.6) 7.797
INFO|2023-09-02T12:09:23|/opt/launcher.py|27| 60        images/sec: 70.1 +/- 0.2 (jitter = 1.6) 8.595
INFO|2023-09-02T12:09:27|/opt/launcher.py|27| 70        images/sec: 70.0 +/- 0.2 (jitter = 1.7) 7.853
INFO|2023-09-02T12:09:32|/opt/launcher.py|27| 80        images/sec: 69.9 +/- 0.2 (jitter = 1.7) 7.849
INFO|2023-09-02T12:09:36|/opt/launcher.py|27| 90        images/sec: 69.8 +/- 0.2 (jitter = 1.7) 7.911
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| 100       images/sec: 69.7 +/- 0.1 (jitter = 1.7) 7.853
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| ----------------------------------------------------------------
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| total images/sec: 69.68
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| ----------------------------------------------------------------
INFO|2023-09-02T12:09:42|/opt/launcher.py|80| Finished: python tf_cnn_benchmarks.py --batch_size=32 --model=resnet50 --variable_update=parameter_server --flush_stdout=true --num_gpus=1 --local_parameter_device=cpu --device=gpu --data_format=NHWC --job_name=worker --ps_hosts=tf-smoke-gpu-ps-0.default.svc:2222 --worker_hosts=tf-smoke-gpu-worker-0.default.svc:2222 --task_index=0
INFO|2023-09-02T12:09:42|/opt/launcher.py|84| Command ran successfully sleep for ever.

    The training performance of a single GPU is 69.68 images per second.

Parent Topic: Deploying and Using Kubeflow in a CCE Cluster