Parallelo e cloud
Addestrare reti profonde su più GPU, cluster e cloud, utilizzando Parallel Computing Toolbox™. Ampliare il Deep Learning con più GPU in locale o su cluster e addestrare più reti in modo interattivo o in lavori batch. Per saperne di più sulle opzioni, vedere Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud.
Funzioni
deep.gpu.deterministicAlgorithms | Set determinism of deep learning operations on the GPU to get reproducible results (Da R2024b) |
deep.gpu.fastAttentionAlgorithms | Disable fast attention algorithms used by deep learning operations on the GPU (Da R2026a) |
Argomenti
Nozioni di base su parallelismo, GPU e cloud
- 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 MATLAB on Multiple GPUs
Speed up deep neural network training using multiple GPUs locally or in the cloud. - Run Experiments in Parallel
Run multiple simultaneous trials or one trial at a time on multiple workers. - Deep Learning with Big Data
Train deep neural networks using large amounts of data. - Resolve GPU Memory Issues
Troubleshoot errors when MATLAB cannot allocate the requested GPU memory.
Addestramento nel cloud
- 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.
- Cloud AI Workflow Using MathWorks Cloud Center
Example workflows for training, importing data, and optimizing a deep neural network in the cloud using MathWorks Cloud Center.
- Cloud AI Workflow Using the Deep Learning Container
Example workflows for training, importing data, and optimizing a deep neural network in the cloud using the Deep Learning Container.
Personalizzazione dei loop di addestramento
- 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. - 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.
Esempi in primo piano
Train Network in the Cloud Using Automatic Parallel Support
Train a convolutional neural network using MATLAB® automatic support for parallel training.
Train Network Using Automatic Multi-GPU Support
Use multiple GPUs on your local machine for deep learning training using automatic parallel support.
Compare Classification Network Architectures Using Experiment
Train a deep learning network for classification using Experiment Manager.
Compare Dropout Probabilities and Filter Configurations for Image Regression Using Experiment
Train a deep learning network for regression using Experiment Manager.
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.
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].
Send Deep Learning Batch Job to Cluster
Train a deep neural network on a cluster while you continue working in MATLAB® or when you close it.
Use batch to Train Multiple Deep Learning Networks
Train multiple networks in batch jobs using a cluster so that you can continue working or close MATLAB® during training.
Use parfor to Train Multiple Deep Learning Networks
Use a parfor loop to perform a parameter sweep on a training option.
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.
