Debugging and Monitoring TensorFlow Programs from Pluralsight

This course goes deep into two specific tools in the TensorFlow toolkit - tfdbg and TensorBoard. These tools can be used to examine the internal state of TensorFlow programs and to visualize execution metrics and state.

An important facet of building good ML models is the ability to debug TensorFlow code when your models do not converge. Traditional debuggers fall short in this regard which is why tfdbg and TensorBoard are important skills in your toolkit.

In this course, Debugging and Monitoring TensorFlow Programs, you will learn how you can adapt TensorFlow commands and library functions to help debug your programs in addition to learning specialized tools like tfdbg and Tensorboard.

First, you will go over TensorFlow's special features to debug your code. Partial graph executions, tf.Print() and tf.Assert() statements, traditional Python debuggers and the tf.py_func() to interpose arbitrary Python code into your computation graph all help debug the graph build phase.

Next, you will see that the specialized TensorFlow debugger tfdbg works very much like traditional Python debuggers but has the ability to step into session.run() statements and display the state of your computation graph at every step. It also has filters like the has_inf_or_nan which allows you to break at the exact point your model begins to diverge.

Finally, you will be shown Tensorboard, which is a browser-based tool that helps you visualize your computation graph and view how control flows through your code. In addition, it can be used to display execution metrics and the current state of your program.

After finishing this course, you will be closer to mastering TensorFlow through equipping you with important tools to build and debug robust machine learning models.

What's inside

Syllabus

Course Overview

Introducing TensorFlow Debugging Methods

Applying tfdbg to Common Use-cases

Visualizing TensorFlow Using TensorBoard

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores debugging techniques for TensorFlow computation graphs, which is a core skill for ML developers

Taught by instructors recognized for their expertise in TensorFlow debugging and monitoring

Covers advanced debugging tools like tfdbg and TensorBoard, which are industry standard for TensorFlow debugging

Provides a detailed overview of TensorFlow's debugging features, enabling learners to leverage built-in capabilities for efficient debugging

Suitable for intermediate to advanced TensorFlow users seeking to enhance their debugging skills

Learners are advised to have prior experience with TensorFlow and debugging concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Debugging and Monitoring TensorFlow Programs with these activities:

Review Python debugging techniques

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Debugging TensorFlow code often involves Python fundamentals. Review some of them for a better understanding.

Browse courses on Python

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Read a tutorial on Python debugging techniques.
Try out the debugging techniques on a simple Python script.

Gather resources on TensorFlow debugging

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Create a repository of resources to help you with TensorFlow debugging.

Browse courses on TensorFlow

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Find resources on TensorFlow debugging.
Organize the resources into a document or folder.

Read 'TensorFlow for Deep Learning' by Nishant Shukla

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Supplement your knowledge of TensorFlow and TensorBoard from a popular reference book.

View Machine Learning with TensorFlow on Amazon

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Read the first three chapters of the book.

Six other activities

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Subscribe to Tensorboard tutorial channel

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Get familiar with how TensorBoard works.

Browse courses on TensorFlow

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Subscribe on Youtube
Watch the first 3 videos on the channel and take notes on important concepts.

TensorBoard logging for debugging

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Practice debugging your TensorFlow code using TensorBoard.

Browse courses on TensorFlow

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Find a tutorial on TensorBoard logging for debugging.
Follow the tutorial and debug your code.

Join a study group for TensorFlow and TensorBoard

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Learn from and collaborate with other students.

Browse courses on TensorFlow

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Find a study group for TensorFlow and TensorBoard.
Join the study group and participate in the discussions.

Build a neural network using TensorFlow and debug it using TensorBoard

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Apply your understanding of TensorFlow and TensorBoard to debugging a neural network.

Browse courses on TensorFlow

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Find a tutorial on building a neural network using TensorFlow.
Follow the tutorial and build a neural network.
Debug your neural network using TensorBoard.

Write a blog post on how to debug TensorFlow code using TensorBoard

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Share your knowledge of TensorFlow and TensorBoard with others.

Browse courses on TensorFlow

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Write an outline for your blog post.
Write the first draft of your blog post.
Edit and revise your blog post.
Publish your blog post.

Mentor a junior developer on TensorFlow debugging

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Share your knowledge with others and reinforce your own understanding.

Browse courses on TensorFlow

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Find a junior developer who needs help with TensorFlow debugging.
Provide guidance and support to the junior developer.

Career center

Learners who complete Debugging and Monitoring TensorFlow Programs will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, evaluate, and maintain machine learning models and algorithms. This course helps prepare you for this role by teaching you the tools used to debug and monitor the state of machine learning models as they run. This can help ensure the accuracy and effectiveness of the deployed models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists gather, analyze, and interpret data to extract meaningful insights. This course may be useful to this role by empowering you to ensure that as you build models to analyze and extract insights from data, the models are developed and debugged properly.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and test software systems. This course can be useful by teaching you how to debug and monitor TensorFlow programs, which are a common framework used in software development.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, transform, and analyze data to identify trends and patterns and communicate data-driven insights to stakeholders. This course may be useful to this role as it can help you better understand how to debug and monitor the tools that you use to analyze and work with data.

See salaries and explore the career path for Data Analyst

Machine Learning Researcher

Machine Learning Researchers conduct research to advance the field of machine learning. This course may be useful to this role by providing you with tools to debug and monitor the programs that you use to conduct your research.

See salaries and explore the career path for Machine Learning Researcher

Quantitative Analyst

Quantitative Analysts use mathematical and statistical modeling to analyze data and make investment recommendations. This course may be useful to this role as it can help you better understand how to debug and monitor the models that you use to make investment recommendations.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research Scientists conduct scientific research to advance knowledge in a particular field. This course may be useful to this role by providing you with tools to debug and monitor the programs that you use to conduct your research.

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Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and test artificial intelligence systems. This course can be useful to this role by providing you with tools to debug and monitor the artificial intelligence systems that you build.

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Deep Learning Engineer

Deep Learning Engineers design, develop, and test deep learning models. This course can be useful to this role by providing you with tools to debug and monitor the deep learning models that you build.

See salaries and explore the career path for Deep Learning Engineer

Software Developer

Software Developers design, develop, and test software systems. This course can be useful to this role by teaching you how to debug and monitor TensorFlow programs, which are a common framework used in software development.

See salaries and explore the career path for Software Developer

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course may be useful to this role as it can help you better understand how to debug and monitor the data pipelines that you build.

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Systems Engineer

Systems Engineers design, develop, and maintain computer systems. This course may be useful to this role by providing you with tools to debug and monitor the systems that you develop.

See salaries and explore the career path for Systems Engineer

Business Intelligence Analyst

Business Intelligence Analysts collect, analyze, and interpret data to provide insights to businesses. This course may be useful to this role by providing you with tools to debug and monitor the programs that you use to analyze and work with data.

See salaries and explore the career path for Business Intelligence Analyst

Computer Scientist

Computer Scientists research and develop new computing technologies. This course may be useful to this role by providing you with tools to debug and monitor the programs that you develop.

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Technical Writer

Technical Writers create documentation for technical products and services. This course may be useful to this role by providing you with a deeper understanding of the tools and technologies that you write about.

See salaries and explore the career path for Technical Writer