Distributed machine learning with Python : accelerating model training and serving with distributed systems /
Chapter 2: Parameter Server and All-Reduce -- Technical requirements -- Parameter server architecture -- Communication bottleneck in the parameter server architecture -- Sharding the model among parameter servers -- Implementing the parameter server -- Defining model layers -- Defining the parameter...
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| Format: | Licensed eBooks |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2022.
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| Online-Zugang: | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3242106 |
| Zusammenfassung: | Chapter 2: Parameter Server and All-Reduce -- Technical requirements -- Parameter server architecture -- Communication bottleneck in the parameter server architecture -- Sharding the model among parameter servers -- Implementing the parameter server -- Defining model layers -- Defining the parameter server -- Defining the worker -- Passing data between the parameter server and worker -- Issues with the parameter server -- The parameter server architecture introduces a high coding complexity for practitioners -- All-Reduce architecture -- Reduce -- All-Reduce -- Ring All-Reduce. |
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| Beschreibung: | Pros and cons of pipeline parallelism. |
| Beschreibung: | 1 online resource (284 pages) : color illustrations |
| ISBN: | 1801817219 9781801817219 9781801815697 |