djl: MXNET GPU CUDA missmatch Problem?

hey i m new here ,today i have successfully install ijava kernel in google colab and java is running successfully BUT when i train i got this ERROR : ai.djl.engine.EngineException: MXNet engine call failed: MXNetError: Compile with USE_CUDA=1 to enable GPU usage Stack trace: File "src/storage/storage.cc", line 119 at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1909) at ai.djl.mxnet.jna.JnaUtils.createNdArray(JnaUtils.java:349) at ai.djl.mxnet.engine.MxNDManager.create(MxNDManager.java:91) at ai.djl.mxnet.engine.MxNDManager.create(MxNDManager.java:34) at ai.djl.ndarray.NDManager.create(NDManager.java:526) at ai.djl.mxnet.engine.MxNDArray.duplicate(MxNDArray.java:184) at ai.djl.mxnet.engine.MxNDArray.toDevice(MxNDArray.java:197) at ai.djl.training.ParameterStore.getValue(ParameterStore.java:110) at ai.djl.training.Trainer.lambda$initialize$1(Trainer.java:120) at java.base/java.lang.Iterable.forEach(Iterable.java:75) at ai.djl.training.Trainer.initialize(Trainer.java:117) at .(#76:1) heres my training phase:

int batchSize = 32; int limit = Integer.MAX_VALUE; // change this to a small value for a dry run // int limit = 160; // limit 160 records in the dataset for a dry run Pipeline pipeline = new Pipeline( new ToTensor(), new Normalize(new float[] {0.4914f, 0.4822f, 0.4465f}, new float[] {0.2023f, 0.1994f, 0.2010f})); Cifar10 trainDataset = Cifar10.builder() .setSampling(batchSize, true) .optUsage(Dataset.Usage.TRAIN) .optLimit(limit) .optPipeline(pipeline) .build(); trainDataset.prepare(new ProgressBar()); DefaultTrainingConfig config = new DefaultTrainingConfig(Loss.softmaxCrossEntropyLoss()) //softmaxCrossEntropyLoss is a standard loss for classification problems .addEvaluator(new Accuracy()) // Use accuracy so we humans can understand how accurate the model is .optDevices(new Device[]{Device.gpu(0)}) // Limit your GPU, using more GPU actually will slow down coverging .addTrainingListeners(TrainingListener.Defaults.logging()); // Now that we have our training configuration, we should create a new trainer for our model Trainer trainer = model.newTrainer(config); int epoch = 10; Shape inputShape = new Shape(1, 3, 32, 32); trainer.initialize(inputShape); for (int i = 0; i < epoch; ++i) { int index = 0; for (Batch batch : trainer.iterateDataset(trainDataset)) { EasyTrain.trainBatch(trainer, batch); trainer.step(); batch.close(); } // reset training and validation evaluators at end of epoch trainer.notifyListeners(listener -> listener.onEpoch(trainer)); }`

I know it CUDA related error in google colab its showing cuda-10.0 installed i have alse tried installing mxnet-cu90 using this Cmd: !pip install mxnet-cu90 Still not working … Please help me through this ??