Use Arena to submit distributed TensorFlow training jobs in a Kubernetes cluster - Container Service for Kubernetes

This topic describes how to use the Arena client to submit distributed TensorFlow training jobs that run in the parameter server (PS)-worker architecture. You can use TensorBoard to visualize training results.

Prerequisites

A Container Service for Kubernetes (ACK) cluster that contains GPU-accelerated nodes is created.
Nodes in the cluster can access the Internet.
Configure the Arena client.
A persistent volume claim (PVC) is created for the ACK cluster and the datasets used in this topic are downloaded to the corresponding persistent volume (PV). For more information, see Configure a shared NAS volume.

Background information

In this topic, the source training code is downloaded from a Git repository. The datasets are stored in a shared File Storage NAS (NAS) volume that is mounted by using a PV and a PVC. In this example, a PVC that is named training-data is created. The PVC uses a shared PV. The datasets are stored in the tf_data directory of the shared PV.

Procedure

Run the following command to query the available GPU resources in the cluster:

arena top node

Expected output:

NAME                       IPADDRESS     ROLE    STATUS  GPU(Total)  GPU(Allocated)
cn-huhehaote.192.16x.x.xx  192.1xx.x.xx  master  ready   0           0
cn-huhehaote.192.16x.x.xx  192.1xx.x.xx  master  ready   0           0
cn-huhehaote.192.16x.x.xx  192.1xx.x.xx  master  ready   0           0
cn-huhehaote.192.16x.x.xx  192.1xx.x.xx  <none>  ready   2           0
cn-huhehaote.192.16x.x.xx  192.1xx.x.xx  <none>  ready   2           0
cn-huhehaote.192.16x.x.xx  192.1xx.x.xx  <none>  ready   2           0
-----------------------------------------------------------------------------------------
Allocated/Total GPUs In Cluster:
0/6 (0%)

The output shows that three GPU-accelerated nodes can be used to run training jobs.

Run the arena submit tfjob/tf [--flag] command to submit a distributed TensorFlow training job.

The following sample code provides an example on how to submit a distributed TensorFlow training job that runs in the PS-worker architecture. The training job runs on one PS node and two worker nodes.

    arena submit tf --name=tf-dist         \
              --working-dir=/root \
              --gpus=1              \
              --workers=2              \
              --worker-image=kube-ai-registry.cn-shanghai.cr.aliyuncs.com/kube-ai/tensorflow:1.5.0-devel-gpu  \
              --sync-mode=git \
              --sync-source=https://code.aliyun.com/xiaozhou/tensorflow-sample-code.git \
              --ps=1              \
              --ps-image=kube-ai-registry.cn-shanghai.cr.aliyuncs.com/kube-ai/tensorflow:1.5.0-devel   \
              --data=training-data:/mnt \
              --tensorboard \
              --logdir=/mnt/tf_data/logs \
              "python code/tensorflow-sample-code/tfjob/docker/mnist/main.py --log_dir /mnt/tf_data/logs  --data_dir /mnt/tf_data/"

Expected output:

configmap/tf-dist-tfjob created
configmap/tf-dist-tfjob labeled
service/tf-dist-tensorboard created
deployment.apps/tf-dist-tensorboard created
tfjob.kubeflow.org/tf-dist created
INFO[0000] The Job tf-dist has been submitted successfully
INFO[0000] You can run `arena get tf-dist --type tfjob` to check the job status

The following table describes the parameters in the preceding sample code block.

Parameter	Required	Description	Default
--name	Yes	Specifies the name of the job that you want to submit. The name must be globally unique.	N/A
--working-dir	No	Specifies the directory where the command is executed.	/root
--gpus	No	Specifies the number of GPUs that are used by the worker nodes where the training job runs.	0
--workers	No	Specifies the number of worker nodes.	1
--image	This parameter is required if you do not specify --worker-image for worker nodes or --ps-image for PS nodes.	Specifies the address of the image that is used to deploy the runtime. If you do not specify --worker-image or --ps-image, both worker nodes and PS nodes use the same image address.	N/A
--worker-image	This parameter is required if you do not specify --image.	Specifies the address of the image for worker nodes. If --image is also specified, this parameter overwrites the --image parameter.	N/A
--sync-mode	No	Specifies the synchronization mode. Valid values: git and rsync. The git mode is used in this example.	N/A
--sync-source	No	The address of the repository from which the source code is synchronized. This parameter is used in combination with the --sync-mode parameter. The git mode is used in this example. Therefore, you must specify a Git repository address, such as the URL of a project on GitHub or Alibaba Cloud. The source code is downloaded to the code/ directory under --working-dir. The directory is /root/code/tensorflow-sample-code in this example.	N/A
--ps	This parameter is required for distributed TensorFlow training jobs.	Specifies the number of PS nodes.	0
--ps-image	This parameter is required if you do not specify --image.	Specifies the image address for PS nodes. If --image is also specified, this parameter overwrites the --image parameter.	N/A
--data	No	Mounts a shared PV to the runtime where the training job runs. The value of this parameter consists of two parts that are separated by a colon (`:`). Specify the name of the PVC on the left side of the colon. To obtain the name of the PVC, run the `arena data list` command. This command queries the PVCs that are available for the specified cluster. Specify the path to which the PV claimed by the PVC is mounted on the right side of the colon. This way, your training job can retrieve the data stored in the corresponding PV claimed by the PVC. Note Run the `arena data list` command to query the PVCs that are available for the specified cluster. `NAME ACCESSMODE DESCRIPTION OWNER AGE training-data ReadWriteMany 35m` If no PVC is available, you can create one. For more information, see Configure a shared NAS volume.	N/A
--tensorboard	No	Specifies that TensorBoard is used to visualize training results. You can set the --logdir parameter to specify the path from which TensorBoard reads event files. If you do not specify this parameter, TensorBoard is not used.	N/A
--logdir	No	Specifies the path from which TensorBoard reads event files. You must specify both this parameter and the --tensorboard parameter.	/training_logs

Important

If you use a non-public Git repository, run the following command to submit a training job:

 arena submit tf \
        ...
        --sync-mode=git \
        --sync-source=https://code.aliyun.com/xiaozhou/tensorflow-sample-code.git \
        --env=GIT_SYNC_USERNAME=yourname \
        --env=GIT_SYNC_PASSWORD=yourpwd \
        "python code/tensorflow-sample-code/tfjob/docker/mnist/main.py

In the preceding code block, the Arena client synchronizes the source code by using the git-sync project. You can customize the environment variables that are defined in the git-sync project.

Run the following command to query the status of all submitted jobs:

arena list

Expected output:

NAME     STATUS     TRAINER  AGE  NODE
tf-dist  RUNNING    TFJOB    58s  192.1xx.x.xx
tf-git   SUCCEEDED  TFJOB    2h   N/A

Run the following command to query the GPU resources that are used by the jobs:

arena top job

Expected output:

NAME     GPU(Requests)  GPU(Allocated)  STATUS     TRAINER  AGE  NODE
tf-dist  2              2               RUNNING    tfjob    1m   192.1xx.x.x
tf-git   1              0               SUCCEEDED  tfjob    2h   N/A


Total Allocated GPUs of Training Job:
2

Total Requested GPUs of Training Job:
3

Run the following command to query the GPU resources in the cluster:

arena top node

Expected output:

NAME                       IPADDRESS     ROLE    STATUS  GPU(Total)  GPU(Allocated)
cn-huhehaote.192.1xx.x.xx  192.1xx.x.xx  master  ready   0           0
cn-huhehaote.192.1xx.x.xx  192.1xx.x.xx  master  ready   0           0
cn-huhehaote.192.1xx.x.xx  192.1xx.x.xx  master  ready   0           0
cn-huhehaote.192.1xx.x.xx  192.1xx.x.xx  <none>  ready   2           1
cn-huhehaote.192.1xx.x.xx  192.1xx.x.xx  <none>  ready   2           1
cn-huhehaote.192.1xx.x.xx  192.1xx.x.xx  <none>  ready   2           0
-----------------------------------------------------------------------------------------
Allocated/Total GPUs In Cluster:
2/6 (33%)

Run the following command to query detailed information about the task:

arena get tf-dist

Expected output:

STATUS: RUNNING
NAMESPACE: default
PRIORITY: N/A
TRAINING DURATION: 1m

NAME     STATUS   TRAINER  AGE  INSTANCE          NODE
tf-dist  RUNNING  TFJOB    1m   tf-dist-ps-0      192.1xx.x.xx
tf-dist  RUNNING  TFJOB    1m   tf-dist-worker-0  192.1xx.x.xx
tf-dist  RUNNING  TFJOB    1m   tf-dist-worker-1  192.1xx.x.xx

Your tensorboard will be available on:
http://192.1xx.x.xx:31870

Note

TensorBoard is used in this example. Therefore, you can find the URL of TensorBoard in the last two rows of the job information. If TensorBoard is not used, the last two rows are not returned.

Use a browser to view the training results in TensorBoard.
1. Run the following command to map TensorBoard to the local port 9090:
  Important
  Port forwarding set up by using kubectl port-forward is not reliable, secure, or extensible in production environments. It is only for development and debugging. Do not use this command to set up port forwarding in production environments. For more information about networking solutions used for production in ACK clusters, see Ingress overview.
```
kubectl port-forward svc/tf-dist-tensorboard 9090:6006
```
2. Visit localhost:9090 in your browser to view data on TensorBoard as shown in the following figure.

Run the following command to print the log of the job:

arena logs tf-dist

Expected output:

WARNING:tensorflow:From code/tensorflow-sample-code/tfjob/docker/mnist/main.py:120: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
...
Accuracy at step 960: 0.9691
Accuracy at step 970: 0.9677
Accuracy at step 980: 0.9687
Accuracy at step 990: 0.968
Adding run metadata for 999
Total Train-accuracy=0.968

After you run the preceding command, the log of worker-0 is printed by default. To print the log of a specified node, you can obtain the name of the specified node from the job information and run the arena logs $job_name -i $instance_name command to print the log.

Example:

arena get tf-dist

Expected output:

STATUS: SUCCEEDED
NAMESPACE: default
PRIORITY: N/A
TRAINING DURATION: 1m

NAME     STATUS     TRAINER  AGE  INSTANCE          NODE
tf-dist  SUCCEEDED  TFJOB    5m   tf-dist-ps-0      192.16x.x.xx
tf-dist  SUCCEEDED  TFJOB    5m   tf-dist-worker-0  192.16x.x.xx
tf-dist  SUCCEEDED  TFJOB    5m   tf-dist-worker-1  192.16x.x.xx

Your tensorboard will be available on:
http://192.16x.x.xx:31870

Run the following command to print the log of the job:

arena logs tf-dist -i tf-dist-worker-1

Expected output:

WARNING:tensorflow:From code/tensorflow-sample-code/tfjob/docker/mnist/main.py:120: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
...
Accuracy at step 970: 0.9676
Accuracy at step 980: 0.968
Accuracy at step 990: 0.967
Adding run metadata for 999
Total Train-accuracy=0.967

You can run the arena logs $job_name -f command to print the job log in real time and run the arena logs $job_name -t N command to print N lines from the bottom of the log. You can also run the arena logs --help command to query parameters for printing logs.

The following sample code provides an example on how to print N lines from the bottom of the log:

arena logs tf-dist -t 5

Expected output:

Accuracy at step 9970: 0.9834
Accuracy at step 9980: 0.9828
Accuracy at step 9990: 0.9816
Adding run metadata for 9999
Total Train-accuracy=0.9816