Debugging Deep Learning Code: A Comprehensive Guide

Have you ever been stuck on an error for way too long? I remember a time when I spent over two weeks on a seemingly trivial typo that didn’t crash my program but returned inexplicable results. It was maddening, and I literally couldn’t sleep because of it. I’m sure you’ve faced similar frustrations in your coding journey. In this fourth episode of the “Deep Learning in Production” series, we will focus on how to debug deep learning code effectively and how to use logging to catch bugs and errors before deploying our models. We will use TensorFlow to showcase examples, following the image segmentation project we’ve built in the previous articles. However, the principles we discuss apply equally to PyTorch and other AI frameworks.

As mentioned in the introduction of this series, machine learning is just ordinary software and should be treated as such. One of the most essential parts of the software development lifecycle is debugging. Proper debugging can help eliminate future pains when our algorithms are up and running and being used by real users, making our systems robust and reliable. It is also integral in the early stages of coding to speed up the development of our algorithms.

How to Debug Deep Learning?

Debugging deep learning models can be more challenging than debugging traditional software for several reasons:

1. Poor Model Performance Doesn’t Always Indicate Bugs: Sometimes, a model may perform poorly due to issues unrelated to the code itself, such as insufficient data or inappropriate model architecture.

2. Long Iteration Cycles: The process of building, training, and testing a model can be time-consuming, making it harder to pinpoint issues quickly.

3. Data Errors: Training and testing data can contain errors or anomalies that affect model performance.

4. Hyperparameter Sensitivity: The final accuracy of a model can be heavily influenced by hyperparameters, complicating the debugging process.

5. Non-Deterministic Behavior: Many machine learning algorithms are probabilistic, leading to variability in results.

6. Static Computation Graphs: Frameworks like TensorFlow and CNTK use static computation graphs, which can complicate debugging.

Given these challenges, the best approach to debugging is to simplify the machine learning model development process as much as possible. Start with a very simple algorithm, using only a handful of features, and gradually expand by adding features and tuning hyperparameters while keeping the model simple. Once you find a satisfactory set of features, you can incrementally increase the model’s complexity, keeping track of the metrics until the results meet your application’s requirements.

However, even with a simplified approach, bugs and anomalies will occur. When they do, it’s time to leverage Python’s debugging capabilities.

Python Debugger (Pdb)

The Python debugger (Pdb) is part of the standard Python library and allows you to monitor the state of your program while it runs. The most important command in any debugger is the breakpoint, which you can set anywhere in your code. When the debugger hits a breakpoint, it pauses execution and gives you access to the values of all variables at that point, along with the traceback of Python calls.

You can interact with the Python Debugger in two ways: via the command line or through an Integrated Development Environment (IDE). While using the terminal is possible, it can be tedious. IDEs like PyCharm make the debugging process much more user-friendly.

In PyCharm, you can set a breakpoint (indicated by a red dot) and run your program in debug mode. When the execution hits the breakpoint, you can inspect variable values and step through your code line by line to identify issues. This approach allows you to avoid cluttering your code with print statements, making debugging cleaner and more efficient.

Debugging Data: Schema Validation

Now that we have a way to find bugs in our code, let’s address another common source of errors in machine learning: data. Data is rarely in perfect form; it may contain corrupted data points, missing values, or inconsistent formats. To catch these issues before training or prediction, schema validation is a powerful technique.

A schema acts as a contract for the format of your data. It can be defined as a JSON file containing all the required features for a model, along with their types and formats. For example, if your input data consists of images, your schema might look like this:

SCHEMA = {
  "type": "object",
  "properties": {
    "image": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "array",
          "items": {
            "type": "array",
            "items": {
              "type": "number"
            }
          }
        }
      }
    }
  },
  "required": ["image"]
}

This schema defines an object with a property called "image," which is a four-dimensional array. Schema validation is particularly useful when your model is deployed in a production environment and accepts user data. By validating incoming data against the schema, you can catch abnormalities before they cause issues.

Logging: An Essential Tool

Logging is a crucial aspect of troubleshooting application and infrastructure performance. When your code runs in a production environment, such as on Google Cloud, you can’t always debug directly. Instead, logs provide a clear picture of what’s happening in your application, helping you discover exceptions and errors.

While print statements might seem sufficient for logging, they fall short in several ways:

• Severity Levels: Logs can have different severity levels (DEBUG, INFO, WARNING, ERROR, CRITICAL), allowing you to filter messages based on importance.
• Output Channels: You can direct logs to various output channels, such as files, HTTP endpoints, or email, rather than being limited to the console.
• Timestamps: Logs automatically include timestamps, providing context for when events occurred.
• Configurable Formats: The format of log messages can be easily customized.

To implement logging in Python, you can use the built-in logging module. Here’s a simple example:

import logging

logging.warning('Warning: Our pants are on fire...')

For a more structured approach, you can create a logger configuration file and a utility function to manage logging across your application. This allows you to maintain a consistent logging strategy and easily adjust settings as needed.

Useful TensorFlow Debugging and Logging Functions

If you’re using TensorFlow, there are several built-in functions that can help with debugging and logging:

1. tf.print: A built-in print function for tensors that can be used in both eager and graph modes.
2. tf.Variable.assign: Allows you to assign values to a variable during runtime, enabling you to test different scenarios.
3. tf.summary: Provides an API to write summary data into files, which can be visualized using TensorBoard.
4. tf.debugging: A set of assert functions tailored for tensors, allowing you to validate data, weights, and models.
5. tf.debugging.enable_check_numerics: This function will cause your code to error out if any operation’s output tensor contains infinity or NaN.

These functions can significantly ease the debugging process in TensorFlow, making it easier to identify and resolve issues.

Conclusion

In this article, we explored how to debug deep learning code using Python’s debugger and PyCharm, discussed data debugging through schema validation, and highlighted the importance of logging. We also provided a list of TensorFlow functions that can alleviate pain points in debugging and logging deep learning code.

As we move forward in this series, we will delve into data processing techniques such as vectorization, batching, prefetching, and parallel execution. If you’re interested in similar content for PyTorch, feel free to reach out to us on our social media channels.

Stay tuned for our next installment, where we’ll continue to build robust and reliable deep learning models. Happy coding!