Keras + Tensorflow and Multiprocessing in Python
When it comes to building deep learning models in Python, Keras and Tensorflow are two of the most popular libraries used by data scientists and machine learning engineers. Keras is a high-level neural networks API that is built on top of Tensorflow. Together, they provide a powerful platform for building and training deep learning models.
One of the common challenges in training deep learning models is the time it takes to train. Deep learning models often require a large amount of data and many iterations to converge to a good solution. One way to speed up the training process is by using multiprocessing in Python.
Multiprocessing allows you to run multiple processes concurrently, taking advantage of multi-core CPUs and speeding up the training process. In the context of deep learning, this means being able to train multiple models at the same time or parallelizing the training process across multiple cores.
To use multiprocessing with Keras and Tensorflow in Python, you can use the multiprocessing module to create separate processes for training different models or for parallelizing the training process. Here’s an example of how you can use multiprocessing to train multiple models concurrently:
import multiprocessing
from keras.models import Sequential
from keras.layers import Dense
def train_model(model):
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X_train, y_train, epochs=10, batch_size=32)
models = []
for i in range(5):
model = Sequential()
model.add(Dense(32, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(1, activation='sigmoid'))
models.append(model)
pool = multiprocessing.Pool()
pool.map(train_model, models)
pool.close()
pool.join()
In this example, we create 5 different models using Keras and then use the multiprocessing.Pool class to create a pool of processes to train each model concurrently. The train_model function is defined to compile and fit each model on the training data.
By using multiprocessing with Keras and Tensorflow in Python, you can significantly speed up the training process of deep learning models and take advantage of the power of multi-core CPUs. This can be especially useful when working with large datasets or complex models that require a long training time.
