How to Debug TensorFlow Graph Execution

Introduction to Debugging TensorFlow Graph Execution

Debugging TensorFlow can sometimes be a daunting task, especially if you're dealing with the intricate details of graph execution. Understanding how TensorFlow executes your graph and where problems might arise is crucial for effectively dealing with unexpected results or performance issues.

Setting Up Your Environment

Before debugging, ensure you have a proper environment set up. TensorFlow's debugging tools work best with its latest versions, so checking your installation is crucial:

pip install --upgrade tensorflow

Optionally, you can set up a virtual environment to maintain a clean setup:

python -m venv tensorflow-env
source tensorflow-env/bin/activate  # On Windows use `tensorflow-env\Scripts\activate`

Using tf.compat.v1.disable_eager_execution

In TensorFlow 2.x, eager execution is on by default. To debug graph execution, you may want to switch it off:

import tensorflow as tf

# Disabling eager execution
tf.compat.v1.disable_eager_execution()

Turning off eager execution allows you to see errors pertinent to graph building stage.

Employing TensorFlow's Debugging Features

TensorFlow provides several debugging utilities. Let’s discuss some key approaches you can employ.

1. tf.print for Debugging

Unlike print() functions in Python, tf.print ensures the data is evaluated when defined inside graph contexts:

x = tf.constant([1.0, 2.0, 3.0], name='x')
# Example of using tf.print
print_op = tf.print('x:', x)
with tf.control_dependencies([print_op]):
   out = tf.add(x, x)
sess = tf.compat.v1.Session()
sess.run(out)

2. TensorFlow Profiler

The TensorFlow Profiler is a tool that provides insights into the model performances and helps to identify bottlenecks:

tensorboard --logdir=./logs

Execute the command above and open your browser to use the detailed profiling view of your TensorFlow execution logs.

Debugging with TensorBoard

TensorBoard is an essential dashboard tool for debugging and visualizing TensorFlow programs. Ensure your operations are logged correctly:

writer = tf.compat.v1.summary.FileWriter('./logs', sess.graph)
# Ensure your session runs the writer operation
writer.flush()

Opening the logs with TensorBoard:

tensorboard --logdir=./logs

Interpreting the Stack Trace

Stack traces can also offer valuable insights. When you encounter errors, look for the operation and the specific piece of code associated with the error:

try:
    sess.run(out)

except tf.errors.InvalidArgumentError as e:
    print("Error detected:", e)

Deep Dive into TensorFlow Graphs with tf.debugging

Use tf.debugging to assert shapes and detect NaNs/infs:

# Shape assertion
tf.debugging.assert_shapes([(x, [3])])
# NaNs detection
tf.debugging.check_numerics(x, message='Checking numerical issues')

Conclusion

Efficient debugging of TensorFlow’s graph execution involves understanding and utilizing various debugging mechanisms such as tf.print, TensorFlow Profiler, TensorBoard, and tf.debugging. These techniques, when combined, empower developers to track, analyze, and rectify performance or logical issues positively affecting both productivity and outcomes.