how to choose dml device when use tensorflow-directml-plugin

[bingoct]

Enviroment

software	version
win 10 pro	10.0.19044
cpu	amd 4650G
amd gpu driver 22.9.2	22.9.2
python	3.8.10
tensorflow-cpu	2.10.0
tensorflow-directml-plugin	0.1.1.dev221004

Desciption

>>> import tensorflow as tf
>>> devices = tf.config.experimental.list_physical_devices()
>>> for device in devices:
...     print(device)
...
PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')

Running the keras mnist demo, it seem still works on cpu.

Expectation

i try tf1-directml. it works both on gpu in win10-based and wsl2-based.

software	version
wsl2(ubuntu)	20.04LTS
wsl2(kernel)	5.10.60.1
python	3.7.15
tensorflow-directml	1.15.8

>>> devices = tf.config.experimental.list_physical_devices()
>>> for device in devices:
...     print(device)    # gpu.name正是TF的gpu。
...
PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')
PhysicalDevice(name='/physical_device:DML:0', device_type='DML')

Run same code above, it works on gpu.

btw, it's really exicted to use directml with integrated-gpu. In this small scale network, there is a significant improvement.

CPU avg Epoch  15s 18ms/step
GPU avg Epoch  9s 186us/sample

[argman]

you can set environment variable DML_VISIBLE_DEVICES as in cuda