How to profile the memoryProfileSnapshots more than 1000?

[TKH666]

I want to profile the memory usage for all op during training. Here is the code for profiling, But I found the result of profiling only records 1000 snapshots of memory allocation/deallocation. How to profile the memoryProfileSnapshots more than 1000? `loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        y_pred = model(x, training=True)
        loss = loss_fn(y, y_pred)
    gradients = tape.gradient(loss, model.trainable_weights)
    return gradients

# dummy training data
x = tf.random.normal((batch_size, input_shape[0], input_shape[1], input_shape[2]))
y = tf.ones((batch_size,))

print("Warmup...")
for k in tqdm(range(1)):
    train_step(x, y)

t0 = time.time()

print("Profiling the model...")
tf.profiler.experimental.start(logdir)
for k in range(num_iterations):
    with tf.profiler.experimental.Trace('train', step_num=k):
        train_step(x, y)
tf.profiler.experimental.stop()`