How to train your Neural Network using the GPU on your MacBook?

When I changed my laptop last time back in 2018, I wanted a machine to explore the intricacies of Neural Networks and AI in general. Then, I decided to go for a MacBook Pro (15-inch, 2018). It was an expensive choice, but its robust performance has never fallen short with handling Data Science and middle-complex AI projects. However, I never leveraged its GPU for training purposes until now! In the following sections, I will show you how to get the advantage of this graphic processor already included on your MacBook Pro. For this matter, I will start presenting you the library used to bridge our beloved DL-libraries with our hardware.


Install PlaidML

You can follow this tutorial for the installation of PlaidML. Here are the steps, though, for setting up everything on your macOS:

  1. Make sure your computer is listed on Apple’s compatibility list with support for OpenCL.
  2. Create an environment for using PlaidML.
python3 -m venv plaidml-venv
source plaidml-venv/bin/activate 
  1. Install PlaidML with Keras and set up PlaidML to use your preferred computing device.
    • You will be asked whether to use experimental device support. In any case, you can type in “n” and then return to choose using default config devices.
    • I choose AMD Radeon pro 560 as it is the most powerful one available.
pip install -U plaidml-keras
  1. Test the installation by running MobileNet in plaidbench.
pip install plaidml-keras plaidbench
plaidbench keras mobilenet

Building a simple CNN classifier

You can either code the following using Jypyter Notebook or in a script.

The first part is when you specify the usage of PlaidML as Keras Backend. Otherwise, it would be using TensorFlow by default.

# Importing PlaidML. Make sure you follow this order
import plaidml.keras
import os
os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"

Here, you import the libraries needed, download the dataset, and split it into a training set and a testing set.

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

# Download fashion dataset from Keras
fashion_mnist = keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()

# Reshape and normalize the data
x_train = x_train.astype('float32').reshape(60000,28,28,1) / 255
x_test = x_test.astype('float32').reshape(10000,28,28,1) / 255

Next, you build a simple CNN (Convolutional Neural Network) using Keras Sequential model.

# Build a CNN model. You should see "INFO:plaidml:Opening device xxx" after you run this chunk
model = keras.Sequential()
model.add(keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1))) 
model.add(keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(keras.layers.Dense(256, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))

Following, you compile the model using adam optimizer, the Sparse Categorical Crossentropy as loss function with the accuracy as a metric.

# Compile the model

Finally, you fit the model and test the accuracy:

import time
start = time.process_time()

# Fit the model on training set, y_train,

# Evaluate the model on test set
score = model.evaluate(x_test, y_test, verbose=0)
# Print test accuracy
print('\n', 'Test accuracy:', score[1])

# your code here    
print(time.process_time() - start)

At this point, you have trained a CNN with around 90% accuracy in about 4 minutes. You can compare the performance in following this novel library and using the traditional way (TensorFlow as backend). In my case, the performance enhancement was about 15 times faster than using a CPU.

Making use of your GPU power as a MacBook Pro user will definitively enable you to handle more complicated problems. Enjoy!

If you want to know more about PlaidML, visit its Github repository.

This post was inspired by Frank Xu in Towards Data Science.

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