Distributed deep learning has become increasingly popular in recent years due to the increasing size of datasets and the need for faster training times. Horovod and Keras are two tools that can be used together to distribute deep learning across multiple GPUs or even multiple machines.

Horovod is an open-source distributed training framework developed by Uber. It is designed to work with deep learning frameworks such as TensorFlow, Keras, and PyTorch. Horovod uses a technique called ring-allreduce to efficiently distribute the gradients across multiple GPUs or machines. This technique allows each GPU or machine to compute the gradients on a subset of the data and then combine them with the gradients from the other GPUs or machines to update the model parameters.

Keras is a high-level deep learning framework that provides an easy-to-use interface for building and training deep neural networks. Keras supports multiple backends, including TensorFlow, which makes it easy to integrate with Horovod. Keras also provides a number of pre-trained models that can be used for a variety of tasks, such as image classification, object detection, and natural language processing.

To use Horovod with Keras, you first need to install both frameworks and their dependencies. Once you have done that, you can modify your Keras code to use Horovod for distributed training. The first step is to initialize Horovod and configure it to use the appropriate number of GPUs or machines. This can be done using the following code:

“`
import horovod.keras as hvd

# Initialize Horovod
hvd.init()

# Configure Keras to use Horovod
config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())
K.set_session(tf.Session(config=config))
“`

This code initializes Horovod and sets the visible device list to the local rank of the current GPU or machine. This ensures that each GPU or machine is assigned a unique rank that can be used to identify it during training.

The next step is to modify your Keras code to use Horovod for distributed training. This can be done by wrapping your Keras model with Horovod’s DistributedOptimizer. This optimizer takes a regular Keras optimizer as an argument and uses it to compute the gradients. The gradients are then distributed across the GPUs or machines using Horovod’s ring-allreduce technique. Here is an example of how to use the DistributedOptimizer:

“`
# Define your Keras model
model = …

# Create a Keras optimizer
optimizer = keras.optimizers.Adam(lr=0.001)

# Wrap the optimizer with Horovod’s DistributedOptimizer
optimizer = hvd.DistributedOptimizer(optimizer)

# Compile the model with the distributed optimizer
model.compile(loss=’categorical_crossentropy’,
optimizer=optimizer,
metrics=[‘accuracy’])
“`

This code defines a Keras model and creates an Adam optimizer with a learning rate of 0.001. The optimizer is then wrapped with Horovod’s DistributedOptimizer and used to compile the model. This ensures that the gradients are distributed across the GPUs or machines during training.

Once you have modified your Keras code to use Horovod, you can run it on multiple GPUs or machines using a variety of tools, such as MPI or Kubernetes. Horovod provides a number of examples and tutorials that demonstrate how to use it with different deep learning frameworks and on different hardware configurations.

In conclusion, distributed deep learning with Horovod and Keras is a powerful technique that can significantly reduce training times and improve the accuracy of deep neural networks. By using Horovod’s ring-allreduce technique and Keras’ easy-to-use interface, you can distribute deep learning across multiple GPUs or machines with minimal effort. If you are working with large datasets or complex models, distributed deep learning with Horovod and Keras is definitely worth exploring.