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Overview
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This guide will show our users how to use the TensorFlow Profiler to profile the execution of your TensorFlow code.
Code 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 Profiling
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The tensorflow.keras.callbacks.TensorBoard command will create a tensorboard callback and profile_batch will pick batch number 10 to batch number 20.
Local profiling on your own computer
Run the code with command
Code Block language bash python tf-profile.pyCompress the logs folder
Code Block language bash tar -zcvf ./logs.tar.gz ./logsDownload the tarball file with sftp and/or hal-ondemand.
Decompress the tarball file
Code Block language bash tar -zxvf ./logs.tar.gzInstall the tensorboard profile plugin in your python environment.
Code Block language bash pip install tensorboard_plugin_profileLaunch the tensorboard with profiler installed.
Code Block language bash tensorboard --logdir ./logs- Open the tensorboard dashboard in your web browser. (Google Chrome is strongly recommended)
Remote Profiling on HAL system
Coming soon...