TensorFlow Debugger: A Game-Changer for Machine Learning Workflows

TensorFlow Debugger (tfdbg) is a tool for debugging TensorFlow programs. It allows you to pause the execution of your TensorFlow program, examine tensors and variables, and step through the code line by line one after another to better understand how the program is running.

The stochasticity that machine learning algorithms have makes debugging difficult, as does the fact that the algorithms are frequently conducted on distant machines using specialised HW accelerators.

The use of symbolic execution (also known as graph mode), which improves the runtime performance of the training session but limits the ability to freely read any tensors in the graph—a capability that is crucial for debugging—makes TensorFlow debugging even more challenging.

To use tfdbg, you will need to add debug hooks to your TensorFlow program. These hooks define points in the code where the debugger will pause and allow you to examine the state of the program. You can then use tfboard to visualize the debug data and interact with the debugger.

Here is an example of how to use tfdbg in a TensorFlow program:

import tensorflow as tf

# Add a debug hook to the tensor x
x = tf.Variable(tf.zeros([2, 2]))
x_debug = tf.debugging.set_debug_tensor(x, "x_debug")

# Build the rest of the model as usual
y = tf.matmul(x_debug, x_debug)

with tf.Session() as sess:
# Initialize the variables
  sess.run(tf.global_variables_initializer())

# Run the model and pause at the debug hook
  sess.run(y, debug_urls=["file:///tmp/tfdbg_1"])

You can then use the TensorFlow Debugger CLI to connect to the debug session and interact with the debugger. For more information, see the TensorFlow Debugger documentation: https://www.tensorflow.org/tensorflow/tensorflow_debugger

For storing checkpoints, TensorFlow provides tools like the Keras model checkpoint callback. The only thing left to do is choose how frequently to take these images by calculating the cost of an unforeseen pause in the training session against the overhead of storing checkpoints.

Building your application in a modular way is a crucial strategy for producing debuggable programmes. This would entail the capability to test various portions of the training pipeline, such as the dataset, the loss function, various model layers, and callbacks, independently when applied to a TensorFlow training loop.