Deep Learning in Parallel and in the Cloud

Scale up deep learning with multiple GPUs locally or in the cloud and train multiple networks interactively or in batch jobs

Train deep networks on multiple GPUs, clusters, and clouds, using Parallel Computing Toolbox™. Scale up deep learning with multiple GPUs locally or on clusters, and train multiple networks interactively or in batch jobs. To learn about options, see Scale Up Deep Learning in Parallel and in the Cloud.

Topics

Deep Learning with Big Data on GPUs and in Parallel

Train deep networks on CPUs, GPUs, clusters, and clouds, and tune options to suit your hardware.

Scale Up Deep Learning in Parallel and in the Cloud

Options for deep learning with MATLAB using multiple GPUs, locally or in the cloud.

Deep Learning with MATLAB on Multiple GPUs

Specify multiple GPUs to use locally or in the cloud for training.

Train Network Using Automatic Multi-GPU Support

This example shows how to use multiple GPUs on your local machine for deep learning training using automatic parallel support.

Train Deep Learning Networks in Parallel

This example shows how to run multiple deep learning experiments on your local machine.

Use parfor to Train Multiple Deep Learning Networks

This example shows how to use a parfor loop to perform a parameter sweep on a training option.

Use parfeval to Train Multiple Deep Learning Networks

This example shows how to use parfeval to perform a parameter sweep on the depth of the network architecture for a deep learning network and retrieve data during training.

Upload Deep Learning Data to the Cloud

This example shows how to upload data to an Amazon S3 bucket.

Send Deep Learning Batch Job to Cluster

This example shows how to send deep learning training batch jobs to a cluster so that you can continue working or close MATLAB during training.

Train Network in Parallel with Custom Training Loop

This example shows how to set up a custom training loop to train a network in parallel.

Featured Examples

Use Experiment Manager to Train Networks in Parallel

Train deep networks in parallel using Experiment Manager. Running an experiment in parallel allows you to try different training configurations at the same time. You can also use MATLAB® while the training is in progress. Parallel execution requires Parallel Computing Toolbox™.

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Train Network in the Cloud Using Automatic Parallel Support

Train a convolutional neural network using MATLAB automatic support for parallel training. Deep learning training often takes hours or days. With parallel computing, you can speed up training using multiple graphical processing units (GPUs) locally or in a cluster in the cloud. If you have access to a machine with multiple GPUs, then you can complete this example on a local copy of the data. If you want to use more resources, then you can scale up deep learning training to the cloud. To learn more about your options for parallel training, see Scale Up Deep Learning in Parallel and in the Cloud. This example guides you through the steps to train a deep learning network in a cluster in the cloud using MATLAB automatic parallel support.

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