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.
- Install Arena.
- 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).
Background information
In this topic, the source training code is downloaded from a Git repository. The datasets are stored in a shared Apsara 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=tensorflow/tensorflow:1.5.0-devel-gpu \ --sync-mode=git \ --sync-source=https://code.aliyun.com/xiaozhou/tensorflow-sample-code.git \ --ps=1 \ --ps-image=tensorflow/tensorflow:1.5.0-devel \ --data=training-data:/mnist_data \ --tensorboard \ --logdir=/mnist_data/tf_data/logs \ "python code/tensorflow-sample-code/tfjob/docker/mnist/main.py --log_dir /mnist_data/tf_data/logs --data_dir /mnist_data/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 thearena 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 thearena 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.
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 a job:
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. In the preceding section, you can obtain the URL of TensorBoard from the job information. The ACK cluster is deployed in another region. Therefore, you must use sshuttle to view the visualized training results in your browser.
The following sample code provides an example on how to use sshuttle as a proxy:
# you can install sshuttle==0.74 in your mac with python2.7 pip install sshuttle==0.74 # 0/0 -> 0.0.0.0/0 sshuttle -r root@39.104.xx.xxx 0/0
Note 39.104.xx. xxx is the public IP address of the ACK cluster. In addition, you must check whether port 22 is open in your security group. By default, port 22 is open.Copy the URL (http://192.1xx.x.xx:31870) that is obtained in Step 6 to the address bar of your browser and press Enter. The TensorBoard page appears. - 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 thearena logs $job_name -t N
command to print N lines from the bottom of the log. You can also run thearena 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