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并行扩展深度学习，在GPU和云中扩展.

话题

并行扩展深度学习，在GPU和云中扩展
Options for deep learning with MATLAB^®in parallel and using multiple GPUs, locally or in the cloud.
在多个GPU上使用MATLAB进行深度学习
使用本地或云中多个GPU加快深层神经网络训练。
Deep Learning with Big Data
Train deep neural networks using large amounts of data.
云中的深度学习
Access MATLAB in the cloud for deep learning.
使用实验经理并行培训网络
一次对多个工人进行多次同时试验或一次试验。
Run Custom Training Loops on a GPU and in Parallel
通过在GPU上运行，使用多个GPU或群集并行速度加速自定义训练循环。
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:使用MATLAB深学习容器在Amazon Web服务上训练网络
- 第2步：使用MATLAB深度学习容器的Amazon S3存储桶
- 步骤3：使用MATLAB深度学习容器在云中使用实验经理

特色示例

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.

打开实时脚本

使用自动多GPU支持的火车网络万博1manbetx

使用本地机器上的多个GPU使用自动并行支持进行深度学习训练。万博1manbetx培训深度学习网络通常需要数小时或数天。通过并行计算，您可以使用多个GPU加快训练。要了解有关并行培训选项的更多信息，请参阅并行和云中的深度学习。

打开实时脚本

创建一个深度学习实验进行分类

Train a deep learning network for classification using Experiment Manager.

Open Example

创建一个深度学习实验以进行回归

使用实验经理培训深度学习网络进行回归。

Open Example

并行培训深度学习网络

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.

打开实时脚本

Use parfor to Train Multiple Deep Learning Networks

使用PARFOR循环在训练选项上执行参数扫描。

打开实时脚本

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.

打开实时脚本

Upload Deep Learning Data to the Cloud

Upload data to an Amazon S3 bucket.

打开实时脚本

与自定义培训循环并行的火车网络

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.

打开实时脚本

Train Network Using Federated Learning

使用联合学习训练网络。联合学习是一种使您能够以分布式，分散的方式训练网络[1]。

打开实时脚本

将深度学习批处理工作发送到集群

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

打开实时脚本

使用MATLAB深学习容器在Amazon Web服务上训练网络

使用MATLAB®在AmazonEC2®实例上使用MATLAB®在云中训练深度学习模型。

打开实时脚本

使用MATLAB深度学习容器的Amazon S3存储桶

使用存储在Amazon S3™存储桶中的培训数据训练您的深度学习模型，并将受过训练的模型保存到云中。

打开实时脚本

使用MATLAB深度学习容器在云中使用实验经理

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.

打开实时脚本