Platform For AI:TensorBoard

Storage Configuration

• Object Storage Service (OSS) dataset endpoint: oss://your-bucket-name.oss-region-internal.aliyuncs.com/dlc-training-data/

• Dataset mount path within container: /mnt/data/

TensorBoard Logging Setup

Specify the storage location for summary logs using TensorFlow's SummaryWriter:

SummaryWriter('/mnt/data/output/runs/experiment_name')

Complete sample implementation:

import tensorflow as tf

# Initialize SummaryWriter with designated logging directory
log_dir = "/mnt/data/output/runs"
writer = tf.summary.create_file_writer(log_dir)

# Training loop with comprehensive metric logging
for epoch in range(num_epochs):
    # Core training logic implementation
    train_loss = calculate_loss()
    train_accuracy = calculate_accuracy()
    
    # Scalar metric logging to TensorBoard
    with writer.as_default():
        tf.summary.scalar("train_loss", train_loss, step=epoch)
        tf.summary.scalar("train_accuracy", train_accuracy, step=epoch)
    
    # Periodic advanced metric logging
    if epoch % 10 == 0:
        tf.summary.histogram("model_weights", model.weights, step=epoch)
        tf.summary.image("prediction_samples", sample_predictions, step=epoch)

# Critical: Close SummaryWriter to ensure log persistence
writer.close()
print(f'Training completed successfully. Logs saved to: {log_dir}')

Create a TensorBoard instance

1. Log on to the PAI console. Select the region where your instance resides in the top navigation bar and select a workspace. Then, click Enter Deep Learning Containers (DLC).

2. On the page that appears, click TensorBoard in the Actions column of the job that you want to manage. In the TensorBoard panel, click Create TensorBoard.

3. On the Create TensorBoard page, configure the parameters and click OK. The following tables describe the parameters.

   • Basic Information

     Parameter	Description
     Name	The name of the TensorBoard instance.
     Datasets	Configuration Type: You can select Mount Dataset, Mount OSS, and By Task. We recommend that you select Mount Dataset. Summary Path: The path in which the TensorBoard summary logs are stored. You can obtain the complete path from the SummaryWriter class in the training code. Sample configurations for the example: Mount Dataset: Select a dataset and enter the relative path of the summary directory in the dataset. Mount OSS: Select an OSS storage path and enter the relative path of the summary directory in OSS. By Task: Select a desired DLC job and enter the complete path of the log files in the container.

   • Resource Configuration

     The following table describes the supported resource types.

     Resource type	Description
     Free Quota	The system provides you with a certain amount of free resources. Each instance can use up to 2 vCPUs and 4 GiB of memory. If the amount of the free quota cannot meet your business requirements, you can disable instances that run on free quotas to release free resources and use the released free resources to create the TensorBoard instance.
     Lingjun AI Computing Service	Public Resources: uses the pay-as-you-go billing method. Only general computing uses public resources. You can select an instance type based on your business requirements. Resource Quota: uses the subscription billing method. You must purchase computing resources and create quotas before you specify this parameter. You must configure the following parameters together with this parameter: Note This feature is available only to users in the whitelist. If you want to use this feature, contact your account manager to configure the whitelist. Priority: the priority of a TensorBoard instance. Valid values: 1 to 9. The value 1 indicates the lowest priority. Job Resource: the resources that you use to run a TensorBoard instance. The resources include the number of vCPUs and the Memory (GiB). The unit of the memory size is GiB.

   • VPC

     If you use Public Resources to create a TensorBoard instance, the virtual private cloud (VPC)-related parameters are available.

     • If you do not configure a VPC, Internet connection is used. In this case, the system may stutter during TensorBoard instance startup or when you view the reports due to the limited bandwidth of the Internet connection.

     • To ensure sufficient network bandwidth and stable performance, we recommend that you configure a VPC.

       Select a VPC, a vSwitch, and a security group in the current region. After you complete the configuration, the cluster in which the TensorBoard instance runs can access the services in the selected VPC and use the security group that you specified to control access.

       Important

       If the TensorBoard instance uses a dataset that requires a VPC, such as a Cloud Parallel File Storage (CPFS) dataset or a NAS dataset that has a mount target in the VPC, you must configure a VPC.

4. Go to the TensorBoard page to view the analysis report.

   1. In the left-side navigation pane of the workspace page, choose AI Computing Asset Management > Job.

   2. On the TensorBoard tab, if the Status of the TensorBoard instance is Running, click View TensorBoard in the Actions column.

      The TensorBoard page appears.

Manage a TensorBoard instance

Perform the following steps to manage a TensorBoard instance:

1. Log on to the PAI console. Select the region where your instance resides in the top navigation bar and select a workspace. Then, click Enter Jobs.

2. On the TensorBoard tab of the page that appears, perform the following operations to manage a TensorBoard instance.

   • Start a TensorBoard instance

     Click Start in the Actions column to restart a stopped TensorBoard instance.

   • View the details of a TensorBoard instance

     Click the name of the TensorBoard instance. On the TensorBoard instance details page, view Basic Information and Configuration Information.

   • View associated DLC jobs

     View the number of the DLC jobs that you associate with the TensorBoard instance. On the Tensorboard tab, move the pointer over the icon in the Associated Task column to view the ID of the associated DLC job. Click the ID to go to the details page of the DLC job.

   • View associated datasets

     View the number of the datasets that you associate with the TensorBoard instance. On the Tensorboard tab, move the pointer over the icon in the Associated Dataset column to view the ID of the associated dataset. Click the ID to go to the details page of the dataset.

   • View the running duration

     View the running duration of the TensorBoard instance. The running duration starts when the instance is started. After you stop the TensorBoard instance, the running duration is reset. On the Tensorboard tab, view the running duration of the TensorBoard instance in the Running Duration column.

   • Stop a TensorBoard instance

     • Click Stop in the Actions column of the TensorBoard instance.

     • Click Auto-stop Settings in the Actions column of the TensorBoard instance to specify the time at which you want the instance to automatically stop.

References

You can create a TensorBoard instance for a DLC job on the AI Computing Asset Management > Job page. For more information, see Create and manage TensorBoard instances.