Clusters et clouds
Si votre task de calcul est trop importante ou trop lente pour votre ordinateur local, vous pouvez décharger votre calcul sur un cluster sur site ou dans le cloud pour exécuter votre code MATLAB® avec des modifications minimales. Essayez Parallel > Discover Clusters dans la barre d’outils MATLAB pour savoir si vous disposez déjà d’un cluster disponible.
Si vous disposez déjà d'un cluster avec un planificateur, vous pouvez intégrer MATLAB à celui-ci en utilisant MATLAB Parallel Server™. Alternativement, si vous ne disposez pas d'un planificateur existant, MATLAB Parallel Server fournit le MATLAB Job Scheduler.
Fonctions
Classes
Exemples et procédures
Configuration du cluster
- Discover Clusters and Use Cluster Profiles
Find out how to work with cluster profiles and discover cloud clusters. - Scale Up from Desktop to Cluster
Develop your parallel MATLAB® code on your local machine and scale up to a cluster. - Process Big Data in the Cloud
This example shows how to access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data. - Scale Up Parallel Code to Large Clusters
Discover options to scale your parallel MATLAB code to use large HPC clusters.
- Benchmark Your Cluster with the HPC Challenge
This example shows how to evaluate the performance of a compute cluster with the HPC Challenge Benchmark.
Deep Learning
- Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud (Deep Learning Toolbox)
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 (Deep Learning Toolbox)
Speed up deep neural network training using multiple GPUs locally or in the cloud. - Train Network Using Automatic Multi-GPU Support (Deep Learning Toolbox)
This example shows how to use multiple GPUs on your local machine for deep learning training using automatic parallel support. - Use parfor to Train Multiple Deep Learning Networks (Deep Learning Toolbox)
This example shows how to use aparforloop to perform a parameter sweep on a training option. - Use parfeval to Train Multiple Deep Learning Networks (Deep Learning Toolbox)
This example shows how to useparfevalto perform a parameter sweep on the depth of the network architecture for a deep learning network and retrieve data during training. - Train Deep Learning Networks in Parallel (Deep Learning Toolbox)
This example shows how to run multiple deep learning experiments on your local machine. - Train Network in Parallel with Custom Training Loop (Deep Learning Toolbox)
This example shows how to set up a custom training loop to train a network in parallel. - Work with Deep Learning Data in AWS (Deep Learning Toolbox)
This example shows how to upload data to an Amazon S3™ bucket. - Use batch to Train Multiple Deep Learning Networks (Deep Learning Toolbox)
This example shows how to train multiple networks in batch jobs using a cluster so that you can continue working or close MATLAB® during training.
Concepts
- Spécifier vos préférences pour le traitement parallèle
Spécifier vos préférences et créer automatiquement un pool parallèle.
- Set Environment Variables on Workers
Copy system environment variables from the client to workers in a cluster.
Informations connexes
- Parallélisation et cloud (Deep Learning Toolbox)
- Installation (MATLAB Parallel Server)
- Réduisez le temps d'obtention des résultats avec MATLAB en utilisant le calcul parallèle
Sélection d՚exemples
Scale Up from Desktop to Cluster
Develop your parallel MATLAB® code on your local machine and scale up to a cluster.
Process Big Data in the Cloud
Access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data.
