tensorboard-plugin-profile

import tensorflow as tf from datetime import datetime import os # Ensure log directory exists log_dir = os.path.join("logs", "profile", datetime.now().strftime("%Y%m%d-%H%M%S")) os.makedirs(log_dir, exist_ok=True) # Dummy model and data for profiling model = tf.keras.models.Sequential([ tf.keras.layers.Dense(10, activation='relu', input_shape=(10,)), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy') data = tf.random.normal(shape=(100, 10)) labels = tf.random.uniform(shape=(100, 1), maxval=2, dtype=tf.int64) # Option 1: Programmatic profiling with tf.profiler.experimental print(f"Starting programmatic profile, data will be in {log_dir}") with tf.profiler.experimental.Profile(log_dir): model.fit(data, labels, epochs=2, batch_size=32) # Option 2: Using TensorBoard Keras Callback for profiling specific batches # tb_callback = tf.keras.callbacks.TensorBoard( # log_dir=log_dir, # profile_batch='1,3' # Profile batches 1 to 3 # ) # model.fit(data, labels, epochs=2, batch_size=32, callbacks=[tb_callback]) print("Profiling data generated. To view, run TensorBoard in your terminal:") print(f"tensorboard --logdir={os.path.abspath('logs')}") print("Then open your browser to http://localhost:6006/#profile")