Cannot train FasterRCNNObjectDetector on single GPU

Despite the training options specifying the execution environment to 'gpu', the training does not execute on GPU but on CPU. My drivers are updated and MATLAB is R2018a.

When I specity 'cpu', the Command Window log states "Training on single CPU", but when I set to 'gpu', nothing shows. This is the log with 'gpu' as ExecutionEnvironment.

Training a Faster R-CNN Object Detector for the following object classes:

* ROI

Step 1 of 4: Training a Region Proposal Network (RPN).
|========================================================================================|
|  Epoch  |  Iteration  |  Time Elapsed  |  Mini-batch  |  Mini-batch  |  Base Learning  |
|         |             |   (hh:mm:ss)   |   Accuracy   |     RMSE     |      Rate       |
|========================================================================================|
|       1 |           1 |       00:00:08 |       28.00% |         0.81 |          0.0005 |
|       1 |          50 |       00:01:04 |       64.00% |         0.88 |          0.0005 |
|       2 |         100 |       00:01:58 |       72.00% |         0.90 |          0.0005 |
|       3 |         150 |       00:02:52 |       80.00% |         0.85 |          0.0005 |
|       4 |         200 |       00:03:46 |       80.00% |         0.89 |          0.0001 |
|       5 |         250 |       00:04:40 |       76.00% |         0.87 |          0.0001 |

The code:

count = gpuDeviceCount;
gpu1 = gpuDevice(1);

inputLayer = imageInputLayer([32 32 3]);

filterSize = [3 3];
numFilters = 32;

middleLayers = [

      convolution2dLayer(filterSize, numFilters, 'Padding', 1)   
      reluLayer()
      convolution2dLayer(filterSize, numFilters, 'Padding', 1)  
      reluLayer()
      maxPooling2dLayer(3, 'Stride',2)    

      ];

finalLayers = [

      fullyConnectedLayer(64)
      reluLayer()
      fullyConnectedLayer(width(trainingDatasetLight))
      softmaxLayer()
      classificationLayer()
  ];

layers = [
    inputLayer
    middleLayers
    finalLayers
    ];

optionsStage1 = trainingOptions('sgdm', ...
    'Momentum',0.7, ...
    'MaxEpochs', 15, ...
    'LearnRateSchedule','piecewise',...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',3,...
    'MiniBatchSize', 25, ...
    'InitialLearnRate', 5e-4, ...
    'CheckpointPath', tempdir, ...
    'ExecutionEnvironment', 'gpu');

optionsStage2 = trainingOptions('sgdm', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 50, ...
    'LearnRateDropFactor',0.2, ...
    'LearnRateDropPeriod',2, ...
    'InitialLearnRate', 1e-3, ...
    'CheckpointPath', tempdir, ...
    'ExecutionEnvironment', 'gpu');

optionsStage3 = trainingOptions('sgdm', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 50, ...
    'LearnRateDropFactor',0.2, ...
    'LearnRateDropPeriod',2, ...
    'InitialLearnRate', 1e-3, ...
    'CheckpointPath', tempdir, ...
    'ExecutionEnvironment', 'gpu');

optionsStage4 = trainingOptions('sgdm', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 50, ...
    'LearnRateDropFactor',0.2, ...
    'LearnRateDropPeriod',2, ...
    'InitialLearnRate', 1e-3, ...
    'CheckpointPath', tempdir, ...
    'ExecutionEnvironment', 'gpu');

options = [
    optionsStage1
    optionsStage2
    optionsStage3
    optionsStage4
    ];

rng(0);

detector = trainFasterRCNNObjectDetector(trainingDatasetLight, layers, options, ... 
    'NegativeOverlapRange', [0 0.3], ... 
    'PositiveOverlapRange', [0.6 1], ... 
    'BoxPyramidScale', 1.2);

The GPU:

gpu1 = 

    CUDADevice with properties:

                        Name: 'GeForce 840M'
                       Index: 1
           ComputeCapability: '5.0'
              SupportsDouble: 1
               DriverVersion: 10
              ToolkitVersion: 9
          MaxThreadsPerBlock: 1024
            MaxShmemPerBlock: 49152
          MaxThreadBlockSize: [1024 1024 64]
                 MaxGridSize: [2.1475e+09 65535 65535]
                   SIMDWidth: 32
                 TotalMemory: 4.2950e+09
             AvailableMemory: 3.4248e+09
         MultiprocessorCount: 3
                ClockRateKHz: 1124000
                 ComputeMode: 'Default'
        GPUOverlapsTransfers: 1
      KernelExecutionTimeout: 1
            CanMapHostMemory: 1
             DeviceSupported: 1
              DeviceSelected: 1

Why won't it let me train on GPU?