TensorFlow is a widely used open-source library for machine learning and artificial intelligence. One of its powerful features is the ability to specify computation device contexts, which means directing on which hardware you want the computation to occur. This is especially valuable in complex machine learning models that can benefit from the heterogeneous computing power of CPUs and GPUs.
Understanding how to specify devices in TensorFlow can improve performance and efficiency. In this article, we will explore how to use the TensorFlow device context manager to explicitly assign operations to run on specific devices.
Using TensorFlow device Context
The tf.device context manager allows you to specify the device on which operations will run. Identifying the best device for a specific operation, such as CPU or GPU, enables optimal resource utilization. Let's look at how to implement this in TensorFlow.
Basic Syntax
To use the device context manager, you start a new block of code with tf.device as if opening a 'with' context manager:
import tensorflow as tf
# Example of assigning operations to a CPU
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0], name='a')
b = tf.constant([4.0, 5.0, 6.0], name='b')
c = a + b
print(c.numpy()) # Output: [5.0, 7.0, 9.0]
In this snippet, the addition of two tensors, a and b, happens specifically on the first CPU available on the machine.
Specifying GPU Devices
If you have a GPU on your system, you can utilize it by specifying the GPU in the device context. TensorFlow GPU devices are usually named as /device:GPU:0 and so on.
# Example of assigning operations to a GPU
with tf.device('/GPU:0'):
mat_1 = tf.constant([[1.0, 2.0], [3.0, 4.0]])
mat_2 = tf.constant([[5.0, 6.0], [7.0, 8.0]])
mat_product = tf.matmul(mat_1, mat_2)
print(mat_product.numpy()) # Output: [[19.0, 22.0], [43.0, 50.0]]
This example specifies that the matrix multiplication operation should be computed on the first GPU available.
Dynamic Device Allocation
In scenarios where you prefer dynamic device selection based on availability, you can rely on device functions to set the device dynamically:
def select_device():
if tf.config.list_physical_devices('GPU'):
return '/GPU:0'
else:
return '/CPU:0'
with tf.device(select_device()):
d = tf.random.normal([1000, 1000])
e = tf.linalg.inv(d)
This function checks if any GPUs are available, and if so, it selects the GPU; otherwise, it defaults to using the CPU.
Device Names and Custom Devices
TensorFlow also supports more granular device specifications, facilitating operations to run on custom hardware if available. For example:
# Assuming a custom hardware device is labeled as MY_DEVICE
with tf.device('/device:MY_DEVICE:0'):
custom_op = tf.constant([10.0, 20.0, 30.0])
Custom devices might be used in specific environments where additional processing units are part of the infrastructure.
Viewing All Available Devices
In TensorFlow, you can view all the available devices to decide your configurations better. Use the following code:
from tensorflow.python.client import device_lib
def get_available_devices():
devices = device_lib.list_local_devices()
for device in devices:
print(device.name)
get_available_devices()
This code provides a complete list of all devices available on your machine, both CPU, and GPU, allowing you to plan device utilization accordingly.
Conclusion
Specifying device contexts for operations in TensorFlow using the device manager allows you to optimize your applications effectively. Whether using CPUs, GPUs, or even custom devices, a strategic choice of the right device for different operations can significantly impact performance. Use these practices to fine-tune your TensorFlow models, ensuring they run faster and more efficiently on the available hardware resources.
