Cloud Center Release Notes

April 2022

Run MATLAB^® in AWS^® Using Cloud Center

Now you can use Cloud Center to start MATLAB in Amazon^® Web Services (AWS). You can start a single machine with MATLAB installed that you can access from a web browser or a remote desktop application. To get started, see Get Started with Cloud Center and Start MATLAB on Amazon Web Services (AWS) Using Cloud Center.

You can continue to use Cloud Center to start and manage MATLAB Parallel Server™ clusters that you can access from any MATLAB. To check supported releases, see Supported Releases for Clusters.

MATLAB shown running on a monitor and laptop screen.

September 2021

Support for MATLAB Release R2021b

You can run MATLAB R2021b on Cloud Center instances.

May 2021

Support for new instance classes
- Support for six new General Purpose instance classes: m5a, m5ad, m5d, m5dn, m5n and m5zn.
- Support for five new Compute Optimized instance classes: c5, c5a, c5ad, c5d, and c5n.
- Support for two new Accelerated Computing (GPUs) instance classes: g4dn and p3dn.
- Support for eight new Memory Optimized classes: r5, r5a, r5ad, r5b, r5d, r5dn, r5n, and x1e.
Note
c5d.xlarge is the new default headnode instance type and m5.8xlarge is the new default worker instance for clusters in a VPC.
For more details on newly added compute optimized instances and regional availability, see Choose Supported EC2 Instance Machine Types.

March 2021

Support for MATLAB Release R2021a
You can run MATLAB R2021a on Cloud Center instances.
MATLAB Worker Amazon Machine Images (AMI) now support Ubuntu 20.04 LTS
All worker AMIs now include installations of Ubuntu 20.04.
MATLAB worker Amazon Machine Images (AMI) now support CUDA 11.0 and gcc/g++ 9.3
All worker AMIs now include installations of CUDA Toolkit 11.0 and gcc/g++ 9.3. You can generate CUDA kernel objects from CU code or compile CUDA compatible source code, libraries, and executables using GPU Coder™ on GPU-enabled EC2 instances with supported GPU devices. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). You can use MathWorks^® products (such as MATLAB Coder™ or HDL Coder™) that require a gcc/g++ 9.3 or earlier compiler on MATLAB worker AMIs. For information on product requirements, see System Requirements and Supported Compilers.

September 2020

Support for MATLAB Release R2020b including one additional pretrained convolutional neural networks (CNN) for deep learning
You can run MATLAB R2020b on Cloud Center instances, including support for one additional pretrained convolutional neural network (CNN) model: EfficientNet-b0. You can use this pretrained model for classification and transfer learning. You can access the model using the function efficientnetb0 (Deep Learning Toolbox). For details, see Deep Learning in Parallel and in the Cloud (Deep Learning Toolbox) and Pretrained Deep Neural Networks (Deep Learning Toolbox).
MATLAB worker Amazon Machine Images (AMI) now support CUDA 10.2 and gcc/g++ 6.5
All worker AMIs now include installations of CUDA Toolkit 10.2 and gcc/g++ 6.5. You can generate CUDA kernel objects from CU code or compile CUDA compatible source code, libraries, and executables using GPU Coder on GPU-enabled EC2 instances with supported GPU devices. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). You can use MathWorks products (such as MATLAB Coder or HDL Coder) that require a gcc/g++ 6.5 or earlier compiler on MATLAB worker AMIs. For information on product requirements, see System Requirements and Supported Compilers.

March 2020

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
A Shareable Cluster no longer requires the “Shared With” white list (applies to all releases, except R2019b which still requires the “Shared With” white list).	Anyone with the cluster profile can access the shared cluster (except in R2019b, which still requires the "Shared With" white list).	To restrict access with a "Shared With" white list, use R2019b only.	An existing "Shared With" white list has no effect on cluster access in any version except R2019b and should not be used in any other release with the expectation that this list would restrict user access to a cluster.
You can no longer export personal cluster profiles.	For Personal clusters, the export cluster profile option is no longer available. Personal clusters were introduced in R2019b. You can still export cluster profiles for any R2019b Shareable cluster and for any R2018a - R2019a cluster.	Cluster profiles for running personal clusters can be added to MATLAB via the Discover Clusters... option under the Parallel menu in MATLAB. Cluster profiles are automatically added to MATLAB by creating a new personal clusters via the Create Cloud Cluster option on the Cluster Profile Manager in MATLAB. The Cluster Profile Manager is accessed via the Create and Manage Clusters... option under the Parallel menu in MATLAB	None

Functionality

Result

Use Instead

Compatibility Considerations

A Shareable Cluster no longer requires the “Shared With” white list (applies to all releases, except R2019b which still requires the “Shared With” white list).

Anyone with the cluster profile can access the shared cluster (except in R2019b, which still requires the "Shared With" white list).

To restrict access with a "Shared With" white list, use R2019b only.

An existing "Shared With" white list has no effect on cluster access in any version except R2019b and should not be used in any other release with the expectation that this list would restrict user access to a cluster.

You can no longer export personal cluster profiles.

For Personal clusters, the export cluster profile option is no longer available. Personal clusters were introduced in R2019b.

You can still export cluster profiles for any R2019b Shareable cluster and for any R2018a - R2019a cluster.

Cluster profiles for running personal clusters can be added to MATLAB via the Discover Clusters... option under the Parallel menu in MATLAB.

Cluster profiles are automatically added to MATLAB by creating a new personal clusters via the Create Cloud Cluster option on the Cluster Profile Manager in MATLAB. The Cluster Profile Manager is accessed via the Create and Manage Clusters... option under the Parallel menu in MATLAB

None

October 2019

Cluster Shared State
The cluster attribute Shared State enables Cloud Center to authorize users to submit jobs to or interact with a cluster from MATLAB. The Shared State may be Personal Cluster (accessible only by you) or Shareable Cluster (accessible to people you have explicitly given access to via a white list). The default Shared State attribute of a cluster is Personal Cluster. This authorization control is unrelated to accessing the cluster via SSH.
For Shareable Clusters, the person creating the cluster must expressly add authorized users to the Shared With field.
Note
Cloud Center clusters created for MATLAB releases prior to R2019b did not have clusters with the Shared State attribute. Those clusters are all implicitly shareable clusters, as any user who imports a cluster profile is authorized to submit jobs to or interact with the cluster from MATLAB. The inbound firewall rules for these clusters are managed only by Global Cluster Access rules.
For more information, see Sharing Options for Clusters.
Auto-Manage Cluster Access
The cluster attribute Auto-Manage Cluster Access allows Cloud Center to manage a cluster’s inbound firewall rules on a cluster-by-cluster basis. Auto-Manage Cluster Access is enabled by default for Personal Clusters.
Note
Clusters created prior to R2019b are Shareable Clusters, whose inbound firewall rules are managed only by Global Cluster Access rules.
For more information, see Manage Cluster Access Automatically.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2017b has been removed. For more information on the migration policy, see Requirements for Using Cloud Center.	Errors	MATLAB versions R2019b, R2019a, R2018b, or R2018a	As newer versions of MATLAB become supported, the support for older versions will be removed in future releases.

June 2019

Support for MATLAB Release R2019a Update 3 including three additional pretrained convolutional neural networks (CNN) for deep learning
- You can run MATLAB R2019a Update 3 on Cloud Center instances, including support for three additional pre-trained convolutional neural network (CNN) models: NASNet-Large, NASNet Mobile, and ShuffleNet. You can use these pretrained models for classification and transfer learning. You can access the models using the functions nasnetlarge (Deep Learning Toolbox), nasnetmobile (Deep Learning Toolbox), and shufflenet (Deep Learning Toolbox). For details, see Deep Learning in Parallel and in the Cloud (Deep Learning Toolbox) and Pretrained Deep Neural Networks (Deep Learning Toolbox).
MathWorks sign-on includes Cloud Center sign-on
- Signing in to MathWorks simultaneously signs you in to Cloud Center using your MathWorks account.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2017a has been removed. For more information on the migration policy, see Requirements for Using Cloud Center.	Errors	MATLAB versions R2019a, R2018b, R2018a, or R2017b	As newer versions of MATLAB become supported, the support for older versions will be removed in future releases.

March 2019

Support for MATLAB Release R2019a including pretrained convolutional neural networks (CNN) for deep learning
- You can run MATLAB Release R2019a on Cloud Center instances, including support for pretrained convolutional neural network (CNN) models. These networks are now available on all instances for MATLAB Release R2019a: AlexNet, DenseNet-201, GoogLeNet (trained using ImageNet and Places365 data sets), Inception-ResNet-v2, Inception-v3, MobileNet-v2, ResNet-18, ResNet-50, Resnet-101, SqueezeNet, VGG-16, VGG-19, and Xception. You can access the models using the functions alexnet (Deep Learning Toolbox), densenet201 (Deep Learning Toolbox), googlenet (Deep Learning Toolbox), inceptionresnetv2 (Deep Learning Toolbox), inceptionv3 (Deep Learning Toolbox), mobilenetv2 (Deep Learning Toolbox), resnet18 (Deep Learning Toolbox), resnet50 (Deep Learning Toolbox), resnet101 (Deep Learning Toolbox), squeezenet (Deep Learning Toolbox), vgg16 (Deep Learning Toolbox), vgg19 (Deep Learning Toolbox), and xception (Deep Learning Toolbox). You can use these pretrained models for classification and transfer learning. For details, see Deep Learning in Parallel and in the Cloud (Deep Learning Toolbox), and Pretrained Deep Neural Networks (Deep Learning Toolbox).
Automatic Cluster Resizing: Resize Cloud Center clusters on Amazon based on usage
- You can create cloud clusters using MATLAB Release R2019a that resize automatically based on usage. These clusters grow or shrink to allocate the optimal number of workers for your submitted tasks. For more information, see Resize Clusters Automatically.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2017a will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	MATLAB versions R2019a, R2018b, R2018a, or R2017b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

January 2019

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2016b has been removed. For more information on the migration policy, see Requirements for Using Cloud Center.	Errors	MATLAB versions R2018b, R2018a, R2017b, or R2017a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

December 2018

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for the Amazon EC2^® Classic network type has been removed.	Errors	Set up a new cluster using the Amazon EC2 Virtual Private Cloud (VPC) network type.	Amazon Web Services withdrew support for the EC2 Classic network for new accounts in 2013. To continue using MATLAB with cloud resources, see Configure AWS VPC for Cloud Center.
Cloud Center support for running MATLAB version R2016b will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	MATLAB versions R2018b, R2018a, R2017b, or R2017a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

October 2018

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for the Amazon EC2 Classic network type will be removed in a future release.	Warns	Set up a new cluster using the Amazon EC2 Virtual Private Cloud (VPC) network type.	Amazon Web Services withdrew support for the EC2 Classic network for new accounts in 2013. To continue using MATLAB with cloud resources, see Configure AWS VPC for Cloud Center.
Cloud Center support for running MATLAB version R2016b will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	MATLAB versions R2018b, R2018a, R2017b, or R2017a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

September 2018

Support for MATLAB Release R2018b including pretrained convolutional neural networks (CNN) for deep learning
- You can run MATLAB Release R2018b on Cloud Center instances, including support for pretrained convolutional neural network (CNN) models. These networks are now available on all instances for MATLAB Release R2018b: AlexNet, DenseNet-201, GoogLeNet, Inception-ResNet-v2, Inception-v3, ResNet-18, ResNet-50, Resnet-101, SqueezeNet, VGG-16, and VGG-19. You can access the models using the functions alexnet (Deep Learning Toolbox), densenet201 (Deep Learning Toolbox), googlenet (Deep Learning Toolbox), inceptionresnetv2 (Deep Learning Toolbox), inceptionv3 (Deep Learning Toolbox), resnet18 (Deep Learning Toolbox), resnet50 (Deep Learning Toolbox), resnet101 (Deep Learning Toolbox), squeezenet (Deep Learning Toolbox), vgg16 (Deep Learning Toolbox), and vgg19 (Deep Learning Toolbox). You can use these pretrained models for classification and transfer learning. For details, see Deep Learning in Parallel and in the Cloud (Deep Learning Toolbox), and Pretrained Deep Neural Networks (Deep Learning Toolbox).
MATLAB worker Amazon Machine Images (AMI) now support CUDA 9.0

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2016b will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	MATLAB versions R2018b, R2018a, R2017b, or R2017a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

August 2018

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2016a has been removed. For more information on the migration policy, see Requirements for Using Cloud Center.	Errors	MATLAB versions R2018a, R2017b, R2017a, or R2016b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

April 2018

Create clusters in dedicated headnode mode
- Starting this release, you can create clusters in dedicated headnode mode. When enabled, the headnode instance exclusively runs management services, such as the job manager, and does not host any MATLAB workers. This cluster architecture improves performance. For details, see Use a Dedicated Headnode Instance for Management Services.
Increased maximum number of workers to 1024
- You can now create up to 1024 worker machines in VPC networks for MATLAB R2018a onwards.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2016a will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	MATLAB versions R2018a, R2017b, R2017a, or R2016b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

March 2018

Support for MATLAB Release R2018a including pretrained convolutional neural networks (CNN) for deep learning
- You can run MATLAB Release R2018a on Cloud Center instances, including support for pretrained convolutional neural network (CNN) models. These networks are now available on all instances for MATLAB Release R2018a: AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and ResNet-101. You can access the models using the functions alexnet (Deep Learning Toolbox), vgg16 (Deep Learning Toolbox), vgg19 (Deep Learning Toolbox), googlenet (Deep Learning Toolbox), resnet50 (Deep Learning Toolbox), and resnet101 (Deep Learning Toolbox). You can use these pretrained models for classification and transfer learning. For details, see Deep Learning in Parallel and in the Cloud (Deep Learning Toolbox), and Pretrained Deep Neural Networks (Deep Learning Toolbox).

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2016a will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	MATLAB versions R2018a, R2017b, R2017a, or R2016b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

February 2018

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2016a will be removed in a future release. For more information on the migration policy, see Requirements for Using Cloud Center.	Warns	Migrate to MATLAB versions R2017b, R2017a, or R2016b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

November 2017

New pretrained convolutional neural networks (CNN) for deep learning
- You can run MATLAB Release R2017b on Cloud Center instances, including support for pretrained convolutional neural network (CNN) models. These networks are now available on all instances for MATLAB R2017b: GoogLeNet, ResNet-50, AlexNet, VGG-16, and VGG-19. You can use these pretrained models for classification and transfer learning. For details, see the white paper Deep Learning with MATLAB and Multiple GPUs, and Pretrained Deep Neural Networks (Deep Learning Toolbox).
Support for new P3 instance types
- Cloud Center supports the new P3 instance family. P3s have up to 8 NVIDIA Tesla V100 GPUs and are well suited for high performance general computation including deep learning, modeling and data analysis.
- For more details on newly added compute instances and regional availability, see Choose Supported EC2 Instance Machine Types.
Updated IAM Role creation workflow
- Cloud Center on-screen instructions are updated to help you through the latest workflow in the Amazon console to create IAM Roles.
- For more details, see Authorise Cloud Account.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2015b has been removed.	Errors	MATLAB versions R2017b, R2017a, R2016b, or R2016a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

September 2017

Support for MATLAB Release R2017b including pretrained convolutional neural networks (CNN) for deep learning
- You can run MATLAB Release R2017b on Cloud Center instances, including support for pretrained convolutional neural network (CNN) models. These networks are now available on all instances for MATLAB Release R2017a and MATLAB Release R2017b: AlexNet, VGG-16, and VGG-19. You can access the models using the functions alexnet (Deep Learning Toolbox), vgg16 (Deep Learning Toolbox), and vgg19 (Deep Learning Toolbox). You can use these pretrained models for classification and transfer learning. For details, see the white paper Deep Learning with MATLAB and Multiple GPUs, and Pretrained Deep Neural Networks (Deep Learning Toolbox).
Support for new G3 instance types
- Cloud Center supports the new G3 instance family. G3s have multiple GPUs with high single-precision performance well suited for deep learning, image processing and computer vision.
- For more details on newly added compute instances and regional availability, see Choose Supported EC2 Instance Machine Types.
Streamlined workflow for using data in the cloud
- To work with data in the cloud, you can upload to Amazon S3, then use datastores to access the data in S3 from the workers in your cluster. For details, see Transfer Data To Amazon S3 Buckets and Access Data Using MATLAB Datastore.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2015a has been removed.	Errors	MATLAB versions R2017b, R2017a, R2016b, or R2016a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.
Cloud Center support for running MATLAB versions R2015b will be removed in a future release.	Warns	MATLAB versions R2017b, R2017a, R2016b, or R2016a.

June 2017

Deep Learning: Pretrained convolutional neural network (CNN) models AlexNet, VGG-16, and VGG-19 are now available on all instances
- Three pretrained convolutional neural network (CNN) models are now available on all instances for MATLAB Release R2017a only: AlexNet, VGG-16, and VGG-19. You can access the models using the functions alexnet (Deep Learning Toolbox), vgg16 (Deep Learning Toolbox), and vgg19 (Deep Learning Toolbox). These models are SeriesNetwork (Deep Learning Toolbox) objects. You can use these pretrained models for classification and transfer learning.
  For more details, see Pretrained Deep Neural Networks (Deep Learning Toolbox).
New “Getting Started with Cloud Center” documentation
- For details, see Getting Started with Cloud Center.
Support for new Compute Optimized instance types
- c4.xlarge, with 2 physical cores and 7.5 GB Memory
- c4.2xlarge, with 4 physical cores and 15 GB Memory
- c4.4xlarge, with 8 physical cores and 30 GB Memory
- c4.8xlarge, with 16 physical cores and 60 GB Memory
Note
c4.8xlarge is the suggested default instance for clusters in a VPC.
For more details on newly added compute optimized instances and regional availability, see Choose Supported EC2 Instance Machine Types.
Support for new Memory Optimized instance types
- r4.xlarge, with 2 physical cores and 30.5 GB Memory
- r4.2xlarge, with 4 physical cores and 61 GB Memory
- r4.4xlarge, with 8 physical cores and 122 GB Memory
- r4.8xlarge, with 16 physical cores and 244 GB Memory
- r4.16xlarge, with 32 physical cores and 488 GB Memory
For more details on newly added compute optimized instances and regional availability, see Choose Supported EC2 Instance Machine Types.
Support for new Storage Optimized instance types
- i3.xlarge, with 2 physical cores, 30.5 GB Memory, and 950 GB ephemeral storage
- i3.2xlarge, with 4 physical cores, 61 GB Memory, and 1900 GB ephemeral storage
- i3.4xlarge, with 8 physical cores, 122 GB Memory, and 3800 GB ephemeral storage
- i3.8xlarge, with 16 physical cores, 244 GB Memory, and 7600 GB ephemeral storage
- i3.16xlarge, with 32 physical cores, 488 GB Memory, and 15200 GB ephemeral storage
For more details on newly added compute optimized instances and regional availability, see Choose Supported EC2 Instance Machine Types.
MATLAB Worker Amazon Machine Images (AMI) now support Ubuntu 16.04 LTS

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2015aSP1 has been removed.	Errors	MATLAB versions R2015b, R2016a, R2016b or R2017a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

March 2017

Deep Learning in the cloud
You can use MATLAB with Cloud Center to perform deep learning in the cloud using Amazon Elastic Compute Cloud (Amazon EC2) with new P2 instances. These instances provide access to NVIDIA^® Tesla K80 Accelerators with NVIDIA GK210 GPUs that include Error Correcting Code (ECC) memory protection and double precision floating point operations. For details, see the Deep Learning in the Cloud with MATLAB White Paper.
MATLAB worker Amazon Machine Images (AMI) now support CUDA 8.0

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB versions R2015a and R2015aSP1 will be removed in a future release.	Warns	MATLAB versions R2015b, R2016a, R2016b or R2017a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

January 2017

Add local storage on each cluster node
You can now use an Amazon EBS volume of type “General Purpose Solid-State Drive” ( "gp2") as local storage. You can specify existing snapshots to instantiate the volumes. For more information, see Create a Cloud Cluster.

November 2016

Support for new GPU compute instance types
- p2.xlarge, with 2 physical cores and 1 GPU
- p2.8xlarge, with 16 physical cores and 8 GPUs
- p2.16xlarge, with 32 physical cores and 16 GPUs

For more details on newly added GPU compute instances and regional availability, see Choose Supported EC2 Instance Machine Types.

Support for new Memory Optimized instance types
- r3.xlarge, with 2 physical cores and 30.5 GiB Memory
- r3.2xlarge, with 4 physical cores and 61 GiB Memory
- r3.4xlarge, with 8 physical cores and 122 GiB Memory
- r3.8xlarge,with 16 physical cores and 244 GiB Memory

For more details on newly added memory optimized instances and regional availability, see Choose Supported EC2 Instance Machine Types.

October 2016

MATLAB worker Amazon Machine Images (AMI) have been patched to mitigate CVE-2016-5195

September 2016

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2014b has been removed.	Errors	MATLAB versions R2015a, R2015b, R2016a, or R2016b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

August 2016

Use Cloud Center with Amazon Virtual Private Cloud (VPC)
- VPC enables Cloud Center users to launch clusters in a virtual network that you define. For more information, see Create a Cloud Cluster.

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2014b will be removed.	Warns	MATLAB versions R2015a, R2015b, R2016a, or R2016b.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.

March 2016

MATLAB worker Amazon Machine Images (AMI) now support CUDA 7.5

Functionality being removed or changed

Functionality	Result	Use Instead	Compatibility Considerations
Cloud Center support for running MATLAB version R2014a has been removed.	Errors	MATLAB versions R2014b, R2015a, R2015b, or R2016a.	As newer versions of MATLAB are supported, the support for older versions will be removed in future releases.