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Parallel and 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, on GPUs, and in the Cloud.

Topics

Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud
Explore options for deep learning with MATLAB^® in parallel and using multiple GPUs, locally or in the cloud.
Deep Learning with Big Data
Train deep neural networks using large amounts of data.
Use Experiment Manager to Train Networks in Parallel
Run multiple simultaneous trials or one trial at a time on multiple workers. (Since R2020b)
Deep Learning with MATLAB on Multiple GPUs
Speed up deep neural network training using multiple GPUs locally or in the cloud.
Run Custom Training Loops on a GPU and in Parallel
Speed up custom training loops by running on a GPU, in parallel using multiple GPUs, or on a cluster.
Resolve GPU Memory Issues
Troubleshoot errors when MATLAB cannot allocated the requested GPU memory
Deep Learning in the Cloud
Access MATLAB in the cloud for deep learning.
Work with Deep Learning Data in the Cloud

Example workflows for uploading data to the cloud.
- Work with Deep Learning Data in AWS
- Work with Deep Learning Data in Azure
Cloud AI Workflow Using the Deep Learning Container

An example workflow for training, importing data, and optimizing a deep neural network in the cloud using the Deep Learning Container.
- STEP 1: Train Network on Amazon Web Services Using MATLAB Deep Learning Container
- STEP 2: Use Amazon S3 Buckets with MATLAB Deep Learning Container
- STEP 3: Use Experiment Manager in the Cloud with MATLAB Deep Learning Container

Featured Examples

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 on a cluster in the cloud using MATLAB automatic parallel support.

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Train Network Using Automatic Multi-GPU Support

Use multiple GPUs on your local machine for deep learning training using automatic parallel support.

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Create a Deep Learning Experiment for Classification

Train a deep learning network for classification using Experiment Manager.

Since R2020a

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Create a Deep Learning Experiment for Regression

Train a deep learning network for regression using Experiment Manager.

Since R2020a

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Train Deep Learning Networks in Parallel

Run multiple deep learning experiments on your local machine. Using this example as a template, you can modify the network layers and training options to suit your specific application needs. You can use this approach with a single or multiple GPUs. If you have a single GPU, the networks train one after the other in the background. The approach in this example enables you to continue using MATLAB® while deep learning experiments are in progress.

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Use parfor to Train Multiple Deep Learning Networks

Use a parfor loop to perform a parameter sweep on a training option.

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Use parfeval to Train Multiple Deep Learning Networks

Use parfeval to perform a parameter sweep on the depth of the network architecture for a deep learning network and retrieve data during training.

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Work with Deep Learning Data in AWS

Upload data to an Amazon S3™ bucket.

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Work with Deep Learning Data in Azure

Set up, write to, and read from Azure® Blob Storage.

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Train Network in Parallel with Custom Training Loop

Set up a custom training loop to train a network in parallel. In this example, parallel workers train on portions of the overall mini-batch. If you have a GPU, then training happens on the GPU. During training, a DataQueue object sends training progress information back to the MATLAB client.

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Train Network Using Federated Learning

Train a network using federated learning. Federated learning is a technique that enables you to train a network in a distributed, decentralized way [1].

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Send Deep Learning Batch Job to Cluster

Send deep learning training batch jobs to a cluster so that you can continue working or close MATLAB® during training.

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Train Network on Amazon Web Services Using MATLAB Deep Learning Container

Train a deep learning network in the cloud using MATLAB® on an Amazon EC2® instance.

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Use Amazon S3 Buckets with MATLAB Deep Learning Container

Train your deep learning model with training data stored in an Amazon S3™ Bucket and save the trained model to the cloud.

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Use Experiment Manager in the Cloud with MATLAB Deep Learning Container

Fine-tune your deep learning network by using Experiment Manager in the cloud. Make use of multiple NVIDIA® high-performance GPUs on an AWS® EC2 instance to run multiple experiments in parallel. Tune the hyperparameters of your network and try different network architectures. You can sweep through a range of hyperparameters automatically and save the results for each variation. Compare the results of your experiments to find the best network.

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