If you are new to cloud clusters, see Getting Started with Cloud Center.
In Cloud Center, click Create a Cluster.
On the Create Cluster page, specify your cluster options.
Specify a cluster name and click Create Cluster to try a default cluster. Cloud Center prompts you if you need to create a new SSH key. You might want to configure other cluster settings, such as cluster size, machine types and storage options. For example, for deep learning, choose a Machine type with GPUs such as the P2 or G3 instances.
|Give this cluster a name||Specify a name.|
|MATLAB Version||Select the same version as your local desktop client MATLAB®.|
|Automatically terminate cluster||Select a timeout for the cluster so that it shuts down automatically.|
|Cluster Log Level||Change the cluster log level. If you need to diagnose cluster issues with support engineers, increase the log level for more detail. Log levels above Medium can negatively impact performance.|
|Location & Network|
Select the Region where your cluster will run. Consider your location and connectivity. Select a Network that meets the requirements for Connecting a Desktop Computer (Client Machine) to MATLAB Parallel Server Running on the Amazon EC2 Cloud. You can only use the Amazon Virtual Private Cloud (VPC) network type with Cloud Center. For more information, see Configure AWS VPC for Cloud Center.
|Use a dedicated headnode||Enabled (default) - Add a headnode instance that only runs management services (for example, the job manager), and does not host any MATLAB workers. Cloud Center uses the instance type shown in Headnode Machine Type (read-only). This mode improves performance. For details, see Use a Dedicated Headnode Instance for Management Services.|
Disabled - The headnode shares the job manager and workers. This mode minimizes machine cost, but can reduce performance. For details see Use a Shared Instance for Management Services.
|Worker Machine Type|
Choose an instance that suits your application. Types vary by hardware specification, including number of cores, memory, and GPU support. For details, see Choose Supported EC2 Instance Machine Types.
For deep learning, choose a machine type with GPUs such as the P2 or G3 instances. P2s have GPUs with high performance for general computation. G3s have GPUs with high single-precision performance for deep learning, image processing, and computer vision.
|Workers per Machine||The maximum number of workers per machine depends on your selected Worker Machine Type, and it corresponds to the number of physical CPU cores.|
|Allow cluster to auto-resize||Enabled - The number of machines in your cluster will shrink or grow depending on the amount of work submitted to the cluster. Set Workers in Cluster to the maximum number of workers you want in the cluster. You must use a dedicated headnode to enable the auto-resize feature. For more information, see Resize Clusters Automatically.|
Disabled (default) - The number of machines in your cluster remains fixed at the number of workers set by Workers in Cluster.
|Workers in Cluster|
Choose the number of workers, using the Upper Limit menu. If you select a number greater than the Workers per Machine, you see the Machines in Cluster information update to show more than one machine. Cloud Center supports a maximum of 1024 workers per cluster.
The Initial Count field shows the number of workers your cluster will start with. If Allow cluster to auto-resize is disabled, the Initial Count field matches your Upper Limit selection.
If Allow cluster to auto-resize is enabled, the Upper Limit menu sets the maximum number of workers for your cluster, in increments of Workers per Machine. The Initial Count field is zero. You cluster starts with zero workers and can resize up to the maximum number of workers. For more information, see Resize Clusters Automatically.
|Cluster Shared Storage, Local Machine Storage||If you have data stored in an Amazon S3 bucket, then you can use datastores in MATLAB to directly access the data without needing any storage on the cluster. For details, see Transfer Data To Amazon S3 BucketsTransfer Data To Amazon S3 Buckets. You can also select the cluster and local storage options when creating your cluster. For details, see Cluster File System and Storage.|
If you do not have a key, Cloud Center prompts you to create one. AWS
requires an SSH key to start EC2 instances. Click create a new
key, in the dialog box, enter a name, and click
Download Key. Your browser might require you to
identify a location. You get a root access key file with the extension
If you want to log in as root to your cloud cluster machines, you need the SSH key. Cluster machines have no password, so you use a key to log in using SSH. Cloud Center also provides a nonroot user access key file, which is unique to each cluster. For details on the user access key file, see Download SSH Key Identity File.
If you have existing keys, select from the keys for the specified region of your AWS account, or create a new key. Otherwise, Cloud Center uses the previously selected key or the first key listed alphabetically in the AWS account.
|Operating System Image (AMI)||If you have created a custom AMI, you can select it. See Create a Custom Amazon Machine Image (AMI).|
Click Create Cluster to create and start your cluster machines. The cluster starts a number of machines (instances) determined by your choices of number of workers and workers per machine. Cloud Center displays the cluster status Starting, and indicates the interim status of all the cluster machines.
It can take up to several minutes for a cluster to completely start up. The status indicates the stages of the process. To get status on any individual cluster machine, click Headnode or Worker expanders.
When the cluster is started and ready for use, Cloud Center displays the cluster status as Online.
For next steps using your new cluster, discover the cluster from MATLAB. See Discover Clusters.
This figure shows an example of Create Cluster settings.
This figure shows a typical cluster status after starting a standard 18-worker cluster with a 2-hour time limit.
If the cluster fails to start completely, its status will indicate that. For information on the failure, click the appropriate Headnode or Worker expander to read the respective log. Often you can shut down your failed cluster and attempt to start it again.
To access a cluster created in your account, use Discover Clusters from MATLAB. See Discover Clusters.
Alternatively, it can be useful to download and share the cluster profile. When your cloud cluster is starting or online, click MATLAB Cluster Profile to save a cluster profile from Cloud Center onto your local machine, allowing you to access that cluster from MATLAB and the Cluster Profile Manager. Save the profile in a folder accessible from your client MATLAB. Any user who imports this cluster profile can access the cluster. See Import Cluster Profiles.