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clusters and clouds -凯发k8网页登录

discover cluster resources, and work with cluster profiles

if your computing task is too big or too slow for your local computer, you can offload your calculation to a cluster onsite or in the cloud to run your matlab® code with minimal changes. try parallel > discover clusters in the matlab toolstrip to find out if you already have a cluster available.

if you already have a cluster with a scheduler, you can integrate matlab with it using matlab parallel server™. alternatively, if you do not have an existing scheduler, then matlab parallel server provides matlab job scheduler.

functions

create cluster object
parpoolcreate parallel pool on cluster
get current parallel pool
shut down cloud cluster
start cloud cluster
wait for cloud cluster to change state
examine or set default cluster profile
export one or more profiles to file
import cluster profiles from file
save modified cluster properties to its current profile
save cluster properties to specified profile
configure settings for parallel computing toolbox client session or matlab parallel server workers

classes

parallel pool of workers
access cluster properties and behaviors
run command on client and all workers in parallel pool

examples and how to

cluster setup

deep learning

  • (deep learning toolbox)
    explore options for deep learning with matlab in parallel and using multiple gpus, locally or in the cloud.
  • (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.
  • (deep learning toolbox)
    this example shows how to use a parfor loop to perform a parameter sweep on a training option.
  • (deep learning toolbox)
    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.
  • (deep learning toolbox)
    this example shows how to run multiple deep learning experiments on your local machine.
  • (deep learning toolbox)
    this example shows how to set up a custom training loop to train a network in parallel.
  • (deep learning toolbox)
    this example shows how to upload data to an amazon s3 bucket.
  • (deep learning toolbox)
    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.

concepts

  • specify your parallel preferences

    specify your preferences, and automatically create a parallel pool.


  • how to use plugin scripts to set up generic schedulers.


  • copy system environment variables from the client to workers in a cluster.

related information

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