January 2018
Intermediate to advanced
474 pages
10h 23m
English
Content preview from Mastering TensorFlow 1.x
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Executing graphs across compute devices - CPU and GPGPU
A graph can be divided into multiple parts and each part can be placed and executed on separate devices, such as a CPU or GPU. You can list all the devices available for graph execution with the following command:
from tensorflow.python.client import device_libprint(device_lib.list_local_devices())
We get the following output (your output would be different, depending on the compute devices in your 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: 2202031001192109390 physical_device_desc: "device: 0, name: Quadro ...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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