Use TensorBoard in Amazon SageMaker Studio Classic

Important

As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see Amazon SageMaker Studio.

The following doc outlines how to install and run TensorBoard in Amazon SageMaker Studio Classic.

Note

This guide shows how to open the TensorBoard application through a SageMaker Studio Classic notebook server of an individual SageMaker AI domain user profile. For a more comprehensive TensorBoard experience integrated with SageMaker Training and the access control functionalities of SageMaker AI domain, see TensorBoard in Amazon SageMaker AI.

Prerequisites

This tutorial requires a SageMaker AI domain. For more information, see Amazon SageMaker AI domain overview

Set Up TensorBoardCallback

1. Launch Studio Classic, and open the Launcher. For more information, see Use the Amazon SageMaker Studio Classic Launcher

2. In the Amazon SageMaker Studio Classic Launcher, under Notebooks and compute resources, choose the Change environment button.

3. On the Change environment dialog, use the dropdown menus to select the TensorFlow 2.6 Python 3.8 CPU Optimized Studio Classic Image.

4. Back to the Launcher, click the Create notebook tile. Your notebook launches and opens in a new Studio Classic tab.

5. Run this code from within your notebook cells.

6. Import the required packages.

   import os
   import datetime
   import tensorflow as tf

7. Create a Keras model.

   mnist = tf.keras.datasets.mnist
   
   (x_train, y_train),(x_test, y_test) = mnist.load_data()
   x_train, x_test = x_train / 255.0, x_test / 255.0
   
   def create_model():
     return tf.keras.models.Sequential([
       tf.keras.layers.Flatten(input_shape=(28, 28)),
       tf.keras.layers.Dense(512, activation='relu'),
       tf.keras.layers.Dropout(0.2),
       tf.keras.layers.Dense(10, activation='softmax')
     ])

8. Create a directory for your TensorBoard logs

   LOG_DIR = os.path.join(os.getcwd(), "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))

9. Run training with TensorBoard.

   model = create_model()
   model.compile(optimizer='adam',
                 loss='sparse_categorical_crossentropy',
                 metrics=['accuracy'])
                 
                 
   tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=LOG_DIR, histogram_freq=1)
   
   model.fit(x=x_train,
             y=y_train,
             epochs=5,
             validation_data=(x_test, y_test),
             callbacks=[tensorboard_callback])

10. Generate the EFS path for the TensorBoard logs. You use this path to set up your logs from the terminal.

    EFS_PATH_LOG_DIR = "/".join(LOG_DIR.strip("/").split('/')[1:-1])
    print (EFS_PATH_LOG_DIR)

    Retrieve the EFS_PATH_LOG_DIR. You will need it in the TensorBoard installation section.

Install TensorBoard

1. Click on the  Amazon SageMaker Studio Classic button on the top left corner of Studio Classic to open the Amazon SageMaker Studio Classic Launcher. This launcher must be opened from your root directory. For more information, see Use the Amazon SageMaker Studio Classic Launcher

2. In the Launcher, under Utilities and files, click System terminal.

3. From the terminal, run the following commands. Copy EFS_PATH_LOG_DIR from the Jupyter notebook. You must run this from the /home/sagemaker-user root directory.

   pip install tensorboard
   tensorboard --logdir <EFS_PATH_LOG_DIR>

Launch TensorBoard

1. To launch TensorBoard, copy your Studio Classic URL and replace lab? with proxy/6006/ as follows. You must include the trailing / character.

   https://<YOUR_URL>.studio.region.sagemaker.aws/jupyter/default/proxy/6006/

2. Navigate to the URL to examine your results.