Accelerate AI Workload Performance using Intel® Extension for TensorFlow* | Intel Software

In this tutorial, we will explore how to accelerate AI workloads using the Intel® Extension for TensorFlow*.

Intel has collaborated with Google to develop the Intel® Extension for TensorFlow*, which is a set of tools, libraries, and optimizations that enable developers to accelerate deep learning workloads on Intel® processors. This extension provides enhancements for various compute and storage capabilities of the processor, thus improving the performance and efficiency of AI applications.

To get started with accelerating AI workloads using the Intel® Extension for TensorFlow*, follow the steps below:

Step 1: Install TensorFlow and Intel® Extension for TensorFlow*

The first step is to install TensorFlow and the Intel® Extension for TensorFlow* on your system. You can download and install TensorFlow using pip, as follows:

pip install tensorflow

Next, download and install the Intel® Extension for TensorFlow* by running the following command:

pip install intel-tensorflow

Step 2: Enable Intel® Extension for TensorFlow* optimizations

To enable the optimizations provided by the Intel® Extension for TensorFlow*, you need to set the environment variable TF_ENABLE_ONEDNN_OPTS to 1. This can be done using the following command:

export TF_ENABLE_ONEDNN_OPTS=1

Alternatively, you can also set this environment variable in your Python script before importing TensorFlow, as shown below:

import os
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "1"
import tensorflow as tf

Step 3: Run your TensorFlow code

Once you have installed TensorFlow and the Intel® Extension for TensorFlow and enabled the optimizations, you can now run your TensorFlow code as usual. The Intel® Extension for TensorFlow will automatically apply the optimizations to accelerate your AI workloads on Intel® processors.

For example, you can run a simple neural network training script using TensorFlow with the Intel® Extension for TensorFlow* as follows:

import tensorflow as tf

# Define a simple neural network model
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

# Compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Load the MNIST dataset
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Train the model
model.fit(x_train, y_train, epochs=5)

# Evaluate the model
model.evaluate(x_test,  y_test, verbose=2)

By following these steps, you can accelerate your AI workloads using the Intel® Extension for TensorFlow* and take advantage of the optimizations provided for Intel® processors. This will help you improve the performance and efficiency of your deep learning applications.

I hope this tutorial was helpful in getting you started with AI workload acceleration using the Intel® Extension for TensorFlow*. Happy coding!