Overview
Code example
Copy and paste the following code into tf-profile.py.
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])
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
python tf-profile.py
Compress the logs folder
tar -zcvf ./logs.tar.gz ./logs
Download the tarball file with sftp and/or hal-ondemand.
Decompress the tarball file
tar -zxvf ./logs.tar.gz
Install the tensorboard profile plugin in your python environment.
pip install tensorboard_plugin_profile
Launch the tensorboard with profiler installed.
tensorboard --logdir ./logs
Remote Profiling on HAL system
Coming soon...