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Snehan Kekre
This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput.
Prerequisites:
Read more
This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput.
Prerequisites:
Read more
This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput.
Prerequisites:
In order to successfully complete this project, you should be competent in Python programming, understand deep learning and what inference is, and have experience building deep learning models in TensorFlow and its Keras API.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/f99wou/optimize
What's inside
Syllabus
Optimize TensorFlow Models For Deployment with TensorRT
Welcome to this is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a comprehensive approach to optimizing Tensorflow models for inference
Offers hands-on, guided instruction to effectively apply NVIDIA's TensorRT
Taught by Snehan Kekre, an experienced instructor
Students should have prior knowledge in Python programming, deep learning, and TensorFlow/Keras
Requires access to a development environment with specific software and libraries installed
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Save Optimize TensorFlow Models For Deployment with TensorRT to your list so you can find it easily later:
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Reviews summary
Tensorflow model optimization for deployment
Learners say this TensorRT course is well-received and highly-rated for its clear explanations of concepts, engaging assignments, and up-to-date content. According to students, the course is well-organized and informative with a good mix of theory and hands-on practice.
Clear and comprehensive instruction.
""The first to introduce such a rare and important topic.""
""Great workshop, all the concepts were very well explained.""
""Excelent and compresed way of explaining TensorRT.""
Engaging and practical assignments.
""Awesome project. Thank you so much.""
""This is something that I was looking for. I've studied a lot of theories about TensorRT but this project gives a clear view of how to do it.""
""Very nice project.""
Some course content is outdated.
""It's a very cool course, but it's outdated and underlying working environment won't let you proceed with practise just at the middle.""
""good content, but some code is out of date, especially the package installation part.""
""Very nice project. The only note is that the installation of TensorRT is outdated.""
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 Optimize TensorFlow Models For Deployment with TensorRT with these
activities:
Review TensorFlow Basics for TensorRT Optimization
Show steps
Revisit essential TensorFlow basics to ensure you have a solid foundation for optimizing models with TensorRT.
Browse courses on
TensorFlow
Show steps
-
Review TensorFlow data structures and operations
-
Refresh your knowledge on TensorFlow layers and models
-
Revisit TensorFlow training and evaluation
Review Deep Learning Concepts for TensorRT Optimization
Show steps
Review fundamental deep learning concepts to strengthen your understanding of TensorRT optimization techniques.
Browse courses on
TensorFlow
Show steps
-
Revisit basic deep learning concepts
-
Refresh your knowledge on neural networks
-
Review different types of deep learning models
Optimizing TensorFlow Models using FP32 Precision
Show steps
Practice optimizing models using the FP32 precision level to improve inference performance.
Browse courses on
TensorFlow
Show steps
-
Create a TensorFlow model
-
Convert the model to TensorRT using FP32 precision
-
Evaluate the optimized model's performance
Five other activities
Expand to see all activities and additional details
Show all eight activities
Optimizing TensorFlow Models using FP16 Precision
Show steps
Practice optimizing models using the FP16 precision level to further improve inference performance.
Browse courses on
TensorFlow
Show steps
-
Create a TensorFlow model
-
Convert the model to TensorRT using FP16 precision
-
Evaluate the optimized model's performance
Optimizing TensorFlow Models using INT8 Precision
Show steps
Practice optimizing models using the INT8 precision level to achieve the highest level of inference performance.
Browse courses on
TensorFlow
Show steps
-
Create a TensorFlow model
-
Convert the model to TensorRT using INT8 precision
-
Evaluate the optimized model's performance
Tuning TF-TRT Parameters for Optimal Performance
Show steps
Practice tuning TF-TRT parameters to balance accuracy and performance for inference.
Browse courses on
TensorFlow
Show steps
-
Identify the TF-TRT parameters to tune
-
Adjust the parameters and evaluate the model's performance
-
Repeat until the optimal parameters are found
Blog Post: Best Practices for TensorRT Optimization
Show steps
Write a blog post sharing your learnings and insights on best practices for optimizing TensorFlow models with TensorRT.
Browse courses on
TensorFlow
Show steps
-
Research best practices for TensorRT optimization
-
Write a blog post outline
-
Write the blog post content
-
Proofread and edit the blog post
-
Publish the blog post
Contribute to an Open Source TensorRT Project
Show steps
Contribute to an open source TensorRT project to gain practical experience and deepen your understanding.
Browse courses on
TensorFlow
Show steps
-
Identify an open source TensorRT project to contribute to
-
Read the project documentation and codebase
-
Identify an area to contribute to
-
Implement your contribution and test it
-
Submit a pull request to the project
Review TensorFlow Basics for TensorRT Optimization
Show steps
Revisit essential TensorFlow basics to ensure you have a solid foundation for optimizing models with TensorRT.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review TensorFlow data structures and operations
- Refresh your knowledge on TensorFlow layers and models
- Revisit TensorFlow training and evaluation
Review Deep Learning Concepts for TensorRT Optimization
Show steps
Review fundamental deep learning concepts to strengthen your understanding of TensorRT optimization techniques.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Revisit basic deep learning concepts
- Refresh your knowledge on neural networks
- Review different types of deep learning models
Optimizing TensorFlow Models using FP32 Precision
Show steps
Practice optimizing models using the FP32 precision level to improve inference performance.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Create a TensorFlow model
- Convert the model to TensorRT using FP32 precision
- Evaluate the optimized model's performance
Five other activities
Expand to see all activities and additional details
Show all eight activities
Optimizing TensorFlow Models using FP16 Precision
Show steps
Practice optimizing models using the FP16 precision level to further improve inference performance.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Create a TensorFlow model
- Convert the model to TensorRT using FP16 precision
- Evaluate the optimized model's performance
Optimizing TensorFlow Models using INT8 Precision
Show steps
Practice optimizing models using the INT8 precision level to achieve the highest level of inference performance.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Create a TensorFlow model
- Convert the model to TensorRT using INT8 precision
- Evaluate the optimized model's performance
Tuning TF-TRT Parameters for Optimal Performance
Show steps
Practice tuning TF-TRT parameters to balance accuracy and performance for inference.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify the TF-TRT parameters to tune
- Adjust the parameters and evaluate the model's performance
- Repeat until the optimal parameters are found
Blog Post: Best Practices for TensorRT Optimization
Show steps
Write a blog post sharing your learnings and insights on best practices for optimizing TensorFlow models with TensorRT.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Research best practices for TensorRT optimization
- Write a blog post outline
- Write the blog post content
- Proofread and edit the blog post
- Publish the blog post
Contribute to an Open Source TensorRT Project
Show steps
Contribute to an open source TensorRT project to gain practical experience and deepen your understanding.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify an open source TensorRT project to contribute to
- Read the project documentation and codebase
- Identify an area to contribute to
- Implement your contribution and test it
- Submit a pull request to the project
Career center
Learners who complete Optimize TensorFlow Models For Deployment with TensorRT will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in designing, developing, and deploying deep learning models. Optimizing these models for deployment is a critical step to ensure efficient and performant inference. A course on "Optimize TensorFlow Models For Deployment with TensorRT" provides Deep Learning Engineers with hands-on experience in optimizing TensorFlow models using NVIDIA's TensorRT, which can enhance their ability to deliver high-quality deep learning applications.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models, and optimizing these models for deployment is a crucial part of their job. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can provide Machine Learning Engineers with the skills and knowledge they need to optimize their models for better performance and efficiency. Moreover, the course's emphasis on NVIDIA's TensorRT can be particularly valuable for Machine Learning Engineers who are working with NVIDIA hardware.
Data Scientist
Data Scientist
Data Scientists utilize various techniques to extract insights from data, including machine learning and deep learning models. Optimizing these models for deployment is essential to ensure their effective application in real-world scenarios. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can equip Data Scientists with the necessary skills to optimize their models for better performance and efficiency, enhancing their ability to deliver valuable insights and solutions.
Solutions Architect
Solutions Architect
Solutions Architects design and develop software solutions for clients, and optimizing these solutions for performance and efficiency is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Solutions Architects who are working on designing and developing machine learning or deep learning solutions, providing them with the skills and knowledge to optimize their solutions for better performance and efficiency, particularly when using NVIDIA hardware.
DevOps Engineer
DevOps Engineer
DevOps Engineers bridge the gap between development and operations teams, and optimizing software systems for performance and efficiency is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit DevOps Engineers who are working on deploying machine learning or deep learning models, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, particularly when using NVIDIA hardware.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and manage cloud computing systems, and optimizing these systems for performance and efficiency is a critical aspect of their job. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Cloud Engineers who are working on deploying machine learning or deep learning models on cloud platforms, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA GPUs.
Technical Consultant
Technical Consultant
Technical Consultants provide technical advice and guidance to clients, and optimizing software systems for performance and efficiency is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Technical Consultants who are working with clients on deploying machine learning or deep learning models, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA hardware.
Systems Engineer
Systems Engineer
Systems Engineers design, build, and maintain complex systems, and optimizing these systems for performance and efficiency is a key aspect of their job. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Systems Engineers who are working on deploying machine learning or deep learning models, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA hardware.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and develop solutions, and optimizing the efficiency and performance of their data analysis pipelines is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Business Analysts who are working on deploying machine learning or deep learning models for business analysis, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA hardware.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems, and optimizing the efficiency and performance of their data analysis pipelines is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Data Analysts who are working on deploying machine learning or deep learning models for data analysis, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA hardware.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks, and optimizing the efficiency and performance of their data analysis pipelines is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Risk Analysts who are working on deploying machine learning or deep learning models for risk analysis, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA hardware.
Product Manager
Product Manager
Product Managers are responsible for the development and management of software products, and optimizing these products for performance and efficiency is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Product Managers who are working on machine learning or deep learning products, providing them with the skills and knowledge to optimize their products for better performance and efficiency, particularly when using NVIDIA hardware.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data, and optimizing the efficiency and performance of their models is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Quantitative Analysts who are working on deploying machine learning or deep learning models for quantitative analysis, providing them with the skills and knowledge to optimize their deployments for better performance and efficiency, especially when using NVIDIA hardware.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems, and optimizing these systems for performance and efficiency is a key aspect of their role. A course on "Optimize TensorFlow Models For Deployment with TensorRT" can benefit Software Engineers who are working on machine learning or deep learning applications, providing them with the skills and knowledge to optimize their software for better performance and efficiency, particularly when using NVIDIA hardware.
Research Scientist
Research Scientist
A course on "Optimize TensorFlow Models For Deployment with TensorRT" may be useful for a career as a Research Scientist. Research Scientists are constantly developing and improving machine learning models, and optimizing these models for deployment is an important part of that process. The provided course can provide a deeper understanding of the tools and techniques used to optimize TensorFlow models, which can help Research Scientists improve the performance and efficiency of their models.
For more career information including salaries, visit:
OpenCourser.com/course/f99wou/optimize
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Optimize TensorFlow Models For Deployment with TensorRT.
Provides a comprehensive overview of TensorFlow and how to use it for deep learning. It covers the fundamentals of TensorFlow, including its architecture, operations, and data structures.
Provides a collection of recipes for solving common problems in TensorFlow 2.0. It covers a wide range of topics, including data loading, model building, training, and evaluation.
Provides a practical guide to deep learning using Java. It covers the fundamentals of deep learning, including model building, training, and evaluation.
Save
Provides a practical guide to deep learning using R and the Keras API. It covers the fundamentals of deep learning, including model building, training, and evaluation.
Provides a comprehensive overview of deep learning for natural language processing. It covers the fundamentals of deep learning, as well as specific applications to NLP tasks such as text classification, machine translation, and question answering.
Provides a practical guide to deep learning using JavaScript. It covers the fundamentals of deep learning, including model building, training, and evaluation.
Practical guide to building deep learning models using Python. It covers the basics of deep learning, as well as advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a hands-on introduction to machine learning using Python. It covers the basics of machine learning, as well as advanced topics such as natural language processing and computer vision.
Save
Comprehensive reference on deep learning. It covers the basics of deep learning, as well as advanced topics such as generative adversarial networks and deep reinforcement learning.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers the basics of machine learning, as well as advanced topics such as Bayesian inference and kernel methods.
Provides a probabilistic perspective on machine learning. It covers the basics of machine learning, as well as advanced topics such as Bayesian inference and graphical models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f99wou/optimize
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Provides a comprehensive approach to optimizing Tensorflow models for inference
Offers hands-on, guided instruction to effectively apply NVIDIA's TensorRT
Taught by Snehan Kekre, an experienced instructor
Students should have prior knowledge in Python programming, deep learning, and TensorFlow/Keras
Requires access to a development environment with specific software and libraries installed
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