tf.DeviceSpec in Tensorflow with example

Hi guys, This tutorial is to briefly understand about tf.DeviceSpec in Tensorflow. The “tf.DeviceSpec” is one of the Tensorflow’s API that describes where the state/object is located and where the computations happen.

The DeviceSpec consists of FIVE optional parameters that we can specify:

  1. Job: Job name.
  2. Replica: Replica index.
  3. Task: Task index.
  4. Device type: Device type string. It can either be CPU or GPU.
  5. Device index: Device index.

If a DeviceSpec is partially specified, it will merge with other DeviceSpec available.

DeviceSpec Example

# Importing the libraries
import tensorflow as tf
from tensorflow import DeviceSpec

# Creating a DeviceSpec object 
device1 = DeviceSpec(job ="add", device_type ="GPU", device_index = 0) 
  
# Print the result
print(device1)

First, we have imported the libraries that we will be using. At line 6, we are specifying the optional parameters in the DeviceSpec and storing it in a variable and then it is printed.

Output:

<tensorflow.python.framework.device_spec.DeviceSpecV2 object at 0x000001D13E3E0FA8>

As we can see, The address of the DeviceSpec object is printed in the output.

To get a string format of the specified DeviceSpec, we can use the “to_string()” method.

print(device1.to_string())

Output:

/job:add/device:GPU:0

The above output shows the string format of the DeviceSpec. We can see the specified job, device_type, and device_index in the output.

Creating a DeviceSpec object by using the “from_string” method

A DeviceSpec can also be created by using a string. We will use the ‘from_string‘ method to create a new DeviceSpec.

new_dev = DeviceSpec.from_string('job:all/device:GPU:0')
print('address :' ,new_dev)
print('device_spec :' ,new_dev.to_string())

In the first line, we have used a from_string method and a string is specified into it. The string consists of the job, device_type, and device_index.

Output:

address : <tensorflow.python.framework.device_spec.DeviceSpecV2 object at 0x000001D13E447D68>
device_spec : /job:all/device:GPU:0

In the above output, we have printed the address of the DeviceSpec object and its specification.

Merging of partially specified DeviceSpec.

We can merge two partially specified DeviceSpec and make a new DeviceSpec by using the method “make_merged_spec“.

# Specifying First DeviceSpec
device_spec1 = tf.DeviceSpec(job='amd')
# Specifying Second DeviceSpec
device_spec2 = tf.DeviceSpec(device_type='GPU')
# Combining both first and second DeviceSpec
combined_spec = device_spec1.make_merged_spec(device_spec2)
# Printing all Devicespec
print('device_Spec1 :',device_spec1.to_string())
print('device_Spec2 :',device_spec2.to_string())
print('combined_spec :',combined_spec.to_string())

Here, we have created two partially specified DeviceSpec and created a new one by combining them.

Output:

device_Spec1 : /job:amd
device_Spec2 : /device:GPU:*
combined_spec : /job:amd/device:GPU:*

In the above output, it is clear that the two partially specified DeviceSpec have been successfully combined.

I hope that you will find this article helpful in understanding TensorFlow DeviceSpec. Thank You.

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