tf.config.experimental.get_visible

View source on GitHub

Return a list of physical devices visible to the host runtime.

View aliases

Main aliases

tf.config.experimental.list_physical_devices

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.config.experimental.list_physical_devices, tf.compat.v1.config.list_physical_devices

tf.config.list_physical_devices(
    device_type=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Use a GPU Eager execution Distributed training with TensorFlow Mixed precision Random number generation	Customization basics: tensors and operations Text classification with TensorFlow Hub: Movie reviews Text Classification with Movie Reviews Graph regularization for sentiment classification using synthesized graphs Graph regularization for document classification using natural graphs

Physical devices are hardware devices present on the host machine. By default all discovered CPU and GPU devices are considered visible.

This API allows querying the physical hardware resources prior to runtime initialization. Thus, giving an opportunity to call any additional configuration APIs. This is in contrast to tf.config.list_logical_devices, which triggers runtime initialization in order to list the configured devices.

The following example lists the number of visible GPUs on the host.

physical_devices = tf.config.list_physical_devices('GPU')
print("Num GPUs:", len(physical_devices))
Num GPUs: ...

However, the number of GPUs available to the runtime may change during runtime initialization due to marking certain devices as not visible or configuring multiple logical devices.

Args
`device_type`	(optional string) Only include devices matching this device type. For example "CPU" or "GPU".

Returns
List of discovered `tf.config.PhysicalDevice` objects

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.config / experimental / experimental.get_visible_devices

View aliases

Used in the notebooks

Args

Returns