Quick Example on Intel CPU and GPU

Installation

If you are using a machine using more than one kind of processor or core and includes an the Intel GPU, please refer to Intel XPU Software Installation
Otherwise, please refer to Intel CPU Software Installation

Code

Use TensorFlow to compute graph: Conv -> ReLU activation -> Bias

quick_example.py

import numpy as np
import sys

import tensorflow as tf

# Conv + ReLU activation + Bias
N = 1
num_channel = 3
input_width, input_height = (5, 5)
filter_width, filter_height = (2, 2)

x = np.random.rand(N, input_width, input_height, num_channel).astype(np.float32)
weight = np.random.rand(filter_width, filter_height, num_channel, num_channel).astype(np.float32)
bias = np.random.rand(num_channel).astype(np.float32)

conv = tf.nn.conv2d(x, weight, strides=[1, 1, 1, 1], padding='SAME')
activation = tf.nn.relu(conv)
result = tf.nn.bias_add(activation, bias)

print(result)
print('Finished')

Execute the Code

python quick_example.py

Example Output

With successful execution, it will print out the following results:

...
tf.Tensor(
[[[[3.479142   2.7296917  4.6456823 ]
   [4.077278   3.9259825  5.3000765 ]
   [3.3999124  3.0527704  4.0656753 ]
   [2.85485    2.7297122  3.9373732 ]
   [2.4818356  2.1455178  2.4929404 ]]

  [[3.6422923  2.718459   4.7090344 ]
   [3.988714   3.3391027  4.875052  ]
   [3.6461415  2.9349675  4.327398  ]
   [3.298973   2.3905785  4.1704025 ]
   [1.9154005  1.6926193  1.9677248 ]]

  [[3.481086   2.9746864  3.8941312 ]
   [3.3221133  2.5479512  4.197306  ]
   [3.305706   2.9873173  4.5597944 ]
   [3.250221   3.118212   3.8672705 ]
   [1.949225   1.2636094  1.5300783 ]]

  [[3.1403804  2.1729176  3.6628485 ]
   [3.2607155  2.6342418  3.9381838 ]
   [2.6761076  2.5063303  3.4718971 ]
   [2.8880196  2.1658201  3.3787665 ]
   [2.1193419  1.42261    2.318963  ]]

  [[1.8809638  1.6514435  2.3549364 ]
   [1.8598063  1.517385   1.9702091 ]
   [1.9260886  1.3804817  2.381424  ]
   [1.6027272  1.7787259  1.9631021 ]
   [0.93901324 1.2134862  0.89942324]]]], shape=(1, 5, 5, 3), dtype=float32)
Finished

Notes

In this example, it is not necessary to import intel_extension_for_tensorflow, and no need to call any of its APIs.
If installed as the intel-extension-for-tensorflow[cpu], then the script will choose CPU as the backend and be executed on the CPU automatically; while if installed as intel-extension-for-tensorflow[xpu], then the default backend will be GPU and the script will be executed on the GPU.