November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Executing graphs across compute devices – CPU and GPGPU
A graph can be partitioned into several parts, and each part can be placed and executed on different devices, such as a CPU or GPU. All of the devices that are available for graph execution can be listed with the following command:
from tensorflow.python.client import device_libprint(device_lib.list_local_devices())
The output is listed as follows (the output for your machine will be different because this will depend on the available compute devices in your specific system):
[name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 12900903776306102093 , name: "/device:GPU:0" device_type: "GPU" memory_limit: 611319808 locality { bus_id: 1 } incarnation: ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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