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Overview
Code
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example
Copy and paste the following code into tf-profile.py.
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from datetime import datetime import os import tensorflow from tensorflow.keras.datasets import mnist from tensorflow import keras from tensorflow.keras import layers (train_images, train_labels), (test_images, test_labels) = mnist.load_data() model = keras.Sequential([ layers.Dense(512, activation="relu"), layers.Dense(10, activation="softmax") ]) model.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) train_images = train_images.reshape((60000, 28 * 28)) train_images = train_images.astype("float32") / 255 test_images = test_images.reshape((10000, 28 * 28)) test_images = test_images.astype("float32") / 255 # Create a TensorBoard callback logs = "logs/" + datetime.now().strftime("%Y%m%d-%H%M%S") tboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir = logs, histogram_freq = 1, profile_batch = '10,20') model.fit(train_images, train_labels, epochs=10, batch_size=128, callbacks = [tboard_callback]) |
Local
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profiling on your own computer
Run the code with command
Code Block language bash python tf-profile.py
- compress
Compress the logs folder
Code Block language bash tar -zcvf ./logs.tar.gz ./logs
Download the tarball file with sftp and/or hal-ondemand.
Decompress the tarball file
Code Block language bash tar -zxvf ./logs.tar.gz
Install the tensorboard profile plugin in your python environment.
Code Block language bash pip install tensorboard_plugin_profile