Inside TensorFlow: tfdistributeStrategy faq

instructor Instructor: TensorFlow instructor-icon
duration Duration: 1.00 duration-icon

tf.distribute.Strategy is a class with multiple implementations for data parallelism, parameter servers and workers, central storage, mirrored variables, all-reduce algorithms, and one device strategies. It provides a way to parallelize computations across multiple devices, allowing for faster training and inference.

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Go to class

Course Feature

costCost:

Free

providerProvider:

Youtube

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

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Updated in [February 21st, 2023]

1. You can learn about the different implementations of TensorFlow's tfdistributeStrategy, such as data parallelism, parameter servers and workers, central storage, mirrored variables, all-reduce algorithm, ring all-reduce, hierarchical all-reduce, and one device strategy.


2. You can learn how to use the tfdistributeStrategy with both Keras and Estimator, and understand the differences between mirrored and per-replica values.


3. You can learn how to use the tfdistributeStrategy to support computations, and understand the concept of modes and the default strategy.


4. You can learn how to use the tfdistributeStrategy to update state, and understand the concept of replica vs. variable locality. You can also learn how to use the merge_call(fn, args) function to average loss using the global batch size.


5. You can learn how to use the tfdistributeStrategy to calculate a mean metric, and understand the example provided.

[Applications]
The application of this course, Inside TensorFlow: tfdistributeStrategy, can be seen in the training of models using Keras and Estimator. It can also be used to understand the concept of mirrored vs. per-replica values, modes, replica vs. variable locality, and the one standard pattern for updating state. Additionally, the merge_call(fn, args) function can be used as a secret weapon for optimizer implementation.

[Career Paths]
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[Education Paths]
1. Bachelor's Degree in Computer Science: A Bachelor's Degree in Computer Science is a great way to gain a comprehensive understanding of the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. This degree will also provide students with the opportunity to develop their skills in areas such as artificial intelligence, machine learning, and data science. As technology continues to evolve, the demand for computer science professionals is expected to grow, making this degree a great choice for those looking to pursue a career in the field.

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