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A friendly introduction to distributed training (ML Tech Talks)
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Google Cloud Developer Advocate Nikita Namjoshi introduces how distributed training models can dramatically reduce machine learning training times, explains how to make use of multiple GPUs with Data Parallelism vs Model Parallelism, and explores Synchronous vs Asynchronous Data Parallelism.
Chapters:
0:00 - Introduction
00:17 - Agenda
00:37 - Why distributed training?
1:49 - Data Parallelism vs Model Parallelism
6:05 - Synchronous Data Parallelism
18:20 - Asynchronous Data Parallelism
23:41 Thank you for watching
#TensorFlow #MachineLearning #ML
product: TensorFlow - General;
Chapters:
0:00 - Introduction
00:17 - Agenda
00:37 - Why distributed training?
1:49 - Data Parallelism vs Model Parallelism
6:05 - Synchronous Data Parallelism
18:20 - Asynchronous Data Parallelism
23:41 Thank you for watching
#TensorFlow #MachineLearning #ML
product: TensorFlow - General;
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