Cargando…
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2022.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 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. |
|---|---|
| Notas: | Pros and cons of pipeline parallelism. |
| Descripción Física: | 1 online resource (284 pages) : color illustrations |
| ISBN: | 1801817219 9781801817219 |