TensorFlow's pluggable device architecture adds new device support as separate plug-in packages that are installed alongside the official TensorFlow package.
The mechanism requires no device-specific changes in the TensorFlow code. It relies on C APIs to communicate with the TensorFlow binary in a stable manner. Plug-in developers maintain separate code repositories and distribution packages for their plugins and are responsible for testing their devices.
Use device plugins
To use a particular device, like one would a native device in TensorFlow, users only have to install the device plug-in package for that device. The following code snippet shows how the plugin for a new demonstration device, Awesome Processing Unit (APU), is installed and used. For simplicity, this sample APU plug-in only has one custom kernel for ReLU:
# Install the APU example plug-in package $ pip install tensorflow-apu-0.0.1-cp36-cp36m-linux_x86_64.whl ... Successfully installed tensorflow-apu-0.0.1
With the plug-in installed, test that the device is visible and run an operation on the new APU device:
import tensorflow as tf # TensorFlow registers PluggableDevices here. tf.config.list_physical_devices() # APU device is visible to TensorFlow. [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:APU:0', device_type='APU')] a = tf.random.normal(shape=, dtype=tf.float32) # Runs on CPU. b = tf.nn.relu(a) # Runs on APU. with tf.device("/APU:0"): # Users can also use 'with tf.device' syntax. c = tf.nn.relu(a) # Runs on APU. with tf.device("/CPU:0"): c = tf.nn.relu(a) # Runs on CPU. @tf.function # Defining a tf.function def run(): d = tf.random.uniform(shape=, dtype=tf.float32) # Runs on CPU. e = tf.nn.relu(d) # Runs on APU. run() # PluggableDevices also work with tf.function and graph mode.
PluggableDevice for macOS GPUs: