Use TensorBoard in Amazon SageMaker Studio
The following doc outlines how to install and run TensorBoard in Amazon SageMaker Studio.
Note
This guide shows how to open the TensorBoard application through a SageMaker Studio notebook server of an individual SageMaker Domain user profile. For a more comprehensive TensorBoard experience integrated with SageMaker Training and the access control functionalities of SageMaker Domain, see Use TensorBoard to debug and analyze training jobs in Amazon SageMaker.
Prerequisites
This tutorial requires an Amazon SageMaker Studio Domain. For more information, see Onboard to Amazon SageMaker Domain
Set Up TensorBoardCallback
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Launch Studio, and open the Launcher. For more information, see Use the Amazon SageMaker Studio Launcher
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In the Amazon SageMaker Studio Launcher, under
Notebooks and compute resources
, choose the Change environment button. -
On the Change environment dialog, use the dropdown menus to select the
TensorFlow 2.3 Python 3.7(optimized for CPU)
Studio Image. -
Back to the Launcher, click the Create notebook tile. Your notebook launches and opens in a new Studio tab.
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Run this code from within your notebook cells.
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Import the required packages.
import os import datetime import tensorflow as tf
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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') ])
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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"))
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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])
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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
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Click on the
Amazon SageMaker Studio
button on the top left corner of Studio to open the Amazon SageMaker Studio Launcher. This launcher must be opened from your root directory. For more information, see Use the Amazon SageMaker Studio Launcher -
In the Launcher, under
Utilities and files
, clickSystem terminal
. -
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
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To launch TensorBoard, copy your Studio URL and replace
lab?
withproxy/6006/
as follows. You must include the trailing/
character.https://
<YOUR_URL>
.studio.region.sagemaker.aws/jupyter/default/proxy/6006/ -
Navigate to the URL to examine your results.