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Janani Ravi

This course covers the important aspects of performing distributed training of PyTorch models, using the multiprocessing, data-parallel, and distributed data-parallel approaches. It also discusses which you can host PyTorch models for prediction.

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This course covers the important aspects of performing distributed training of PyTorch models, using the multiprocessing, data-parallel, and distributed data-parallel approaches. It also discusses which you can host PyTorch models for prediction.

PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models, while still using Python-native support for debugging and visualization. In this course, Deploying PyTorch Models in Production: PyTorch Playbook you will gain the ability to leverage advanced functionality for serializing and deserializing PyTorch models, training, and then deploying them for prediction. First, you will learn how the load_state_dict and the torch.save() and torch.load() methods complement and differ from each other, and the relative pros and cons of each. Next, you will discover how to leverage the state_dict which is a handy dictionary with information about parameters as well as hyperparameters. Then, you will see how the multiprocessing, data-parallel, and distributed data-parallel approaches to distributed training can be used in PyTorch. You will train a PyTorch model on a distributed cluster using high-level estimator APIs. Finally, you will explore how to deploy PyTorch models using a Flask application, a Clipper cluster, and a serverless environment. When you’re finished with this course, you will have the skills and knowledge to perform distributed training and deployment of PyTorch models and utilize advanced mechanisms for model serialization and deserialization.

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What's inside

Syllabus

Course Overview
Persisting and Loading PyTorch Models
Implementing Training Using Single and Multiple Processors
Implementing Distributed Training on Multiple Machines
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Deploying PyTorch Models to Production

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Utilizes the latest PyTorch techniques for training and deployment of models for production deployment
Taught by Janani Ravi, a recognized expert in deep learning using PyTorch
Explores a range of advanced topics, including serialization, deserialization, distributed training, and model deployment
Suitable for learners with prior experience in deep learning and PyTorch
Covers the multiprocessing, data-parallel, and distributed data-parallel approaches to distributed training in PyTorch
Provides hands-on experience through labs and interactive materials

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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 Deploying PyTorch Models in Production: PyTorch Playbook with these activities:
Read the book 'Deep Learning with PyTorch'
Gain a comprehensive understanding of PyTorch through a reputable guide.
Show steps
  • Acquire a copy of the book 'Deep Learning with PyTorch'.
  • Create a dedicated reading schedule.
  • Take notes, highlight important sections, and engage with the material actively.
Engage in Peer Discussions
Connect with peers to share knowledge, discuss challenges, and enhance your understanding of PyTorch.
Browse courses on Online Collaboration
Show steps
  • Join online forums or discussion groups specific to PyTorch.
  • Participate in discussions, ask questions, and contribute your own insights.
  • Connect with other learners and form study groups for deeper collaboration.
Join a PyTorch study group
Engage with fellow learners and exchange knowledge through discussions in online study groups.
Browse courses on PyTorch
Show steps
  • Reach out to other students in the course.
  • Propose a study group meeting, either virtually or in person.
  • Collaborate on practice exercises, discuss concepts, and provide mutual support.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete tutorial from Pluralsight
Practice distributed training of PyTorch models using tutorials from a provider like Pluralsight.
Browse courses on PyTorch
Show steps
  • Identify the preferred resource from Pluralsight
  • Set aside time to work through the tutorial
  • Review the course materials to reinforce your understanding
Read a PyTorch technical blog
Stay abreast of the latest advancements in PyTorch by exploring technical blogs.
Browse courses on PyTorch
Show steps
  • Find a reputable PyTorch blog
  • Identify 2 to 3 recent blog posts to read.
  • Summarize the key takeaways from the blogs.
Build a simple PyTorch model and deploy it using Docker
Apply your skills to build and deploy a practical PyTorch model.
Show steps
  • Design a simple PyTorch model for a specific task.
  • Train and validate your model using appropriate datasets.
  • Containerize your model using Docker.
  • Deploy the container to a cloud platform.
  • Evaluate the performance and fine-tune the deployment.
Participate in PyTorch hackathons or competitions
Challenge yourself and gain valuable experience by participating in coding competitions.
Show steps
  • Identify suitable PyTorch hackathons or competitions.
  • Form a team or participate individually.
  • Develop innovative solutions to the proposed challenges.
  • Submit your solutions for evaluation.
  • Reflect on your performance and identify areas for improvement.

Career center

Learners who complete Deploying PyTorch Models in Production: PyTorch Playbook will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Develop and maintain machine learning models for a variety of applications, including natural language processing, computer vision, and speech recognition. This course can help build a foundation in PyTorch, a leading machine learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Machine Learning Engineer, this course may be useful for building a foundation.
Research Scientist
Conduct research on the application and development of machine learning models in scientific and engineering scenarios. This course introduces the essential elements of the PyTorch framework, and includes training and deployment of PyTorch models for prediction. For those interested in understanding the process of deploying a PyTorch model, this course may be useful to build a foundation for developing as a Research Scientist.
Data Scientist
Analyze data to extract insights and develop machine learning models to solve business problems. This course includes training and deployment of PyTorch models for prediction, and can help build a foundation for developing as a Data Scientist. The course introduces the essential elements of the PyTorch framework, which may be useful for a role involving data science.
Software Engineer
Design, develop, and maintain software systems. This course may help build a foundation in PyTorch, a leading machine learning framework, which may be useful for developing as a Software Engineer. The course introduces the essential elements of the PyTorch framework, and includes training and deployment of PyTorch models for prediction.
Deep Learning Engineer
Develop and maintain deep learning models for a variety of applications, including natural language processing, computer vision, and speech recognition. This course can help build a foundation in PyTorch, a leading deep learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Deep Learning Engineer, this course may be useful for building a foundation.
Artificial Intelligence Engineer
Develop and maintain artificial intelligence systems. This course can help build a foundation in PyTorch, a leading machine learning framework. If you aspire to be an Artificial Intelligence Engineer, this course may be useful for building a foundation, as it includes training and deployment of PyTorch models for prediction.
Natural Language Processing Engineer
Develop and maintain natural language processing systems. This course includes training and deployment of PyTorch models for prediction, and can help build a foundation for developing as a Natural Language Processing Engineer. The course introduces the essential elements of the PyTorch framework, which may be useful for a role involving natural language processing.
Computer Vision Engineer
Develop and maintain computer vision systems. This course includes training and deployment of PyTorch models for prediction, and can help build a foundation for developing as a Computer Vision Engineer. The course introduces the essential elements of the PyTorch framework, which may be useful for a role involving computer vision.
Robotics Engineer
Design, develop, and maintain robotic systems. This course may help build a foundation in PyTorch, a leading machine learning framework, which may be useful for developing as a Robotics Engineer. The course introduces the essential elements of the PyTorch framework, and includes training and deployment of PyTorch models for prediction.
Data Analyst
Analyze data to identify trends and patterns. This course can help build a foundation in PyTorch, a leading machine learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Data Analyst, this course may be useful for building a foundation.
Business Analyst
Analyze business data to identify opportunities and risks. This course may help build a foundation in PyTorch, a leading machine learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Business Analyst, this course may be useful for building a foundation.
Product Manager
Develop and manage products. This course may help build a foundation in PyTorch, a leading machine learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Product Manager, this course may be useful for building a foundation.
Marketing Manager
Develop and manage marketing campaigns. This course may help build a foundation in PyTorch, a leading machine learning framework. If you aspire to be a Marketing Manager, this course may be useful for building a foundation, as it includes training and deployment of PyTorch models for prediction.
Sales Manager
Develop and manage sales teams. This course may help build a foundation in PyTorch, a leading machine learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Sales Manager, this course may be useful for building a foundation.
Customer Success Manager
Manage customer relationships and ensure customer satisfaction. This course may help build a foundation in PyTorch, a leading machine learning framework, and includes training and deployment of PyTorch models for prediction. If you aspire to be a Customer Success Manager, this course may be useful for building a foundation.

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 Deploying PyTorch Models in Production: PyTorch Playbook.
Provides a comprehensive overview of deep learning with PyTorch, covering topics such as model building, training, and deployment. It also includes a number of hands-on exercises that will help you learn how to use PyTorch effectively.
Provides a comprehensive overview of deep learning with Python. It covers all the basics of deep learning, including model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use deep learning effectively in Python.
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers all the basics of machine learning, including data preparation, model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use these libraries effectively.
Provides a comprehensive overview of deep learning. It covers all the basics of deep learning, including model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use deep learning effectively.
Provides a comprehensive overview of pattern recognition and machine learning. It covers all the basics of pattern recognition and machine learning, including model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use pattern recognition and machine learning effectively.
Provides a practical introduction to machine learning for hackers. It covers all the basics of machine learning, including data preparation, model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use machine learning effectively in your own projects.
Provides a comprehensive overview of statistical learning. It covers all the basics of statistical learning, including model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use statistical learning effectively.
Provides a practical introduction to machine learning. It covers all the basics of machine learning, including data preparation, model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use machine learning effectively in your own projects.
Provides a practical introduction to machine learning with C++. It covers all the basics of machine learning, including data preparation, model building, training, and evaluation. It also includes a number of helpful exercises that will help you learn how to use machine learning effectively in C++.

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