# Allocation failed: \[\[DEVICE\] allocation failed; Error code: \[2\]\] **URL:** \ **Category:** ND4J\ **Created:** [May 1, 2022, 4:43pm UTC](https://community.konduit.ai/t/allocation-failed-device-allocation-failed-error-code-2/1842 "2022-05-01T16:43:12Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![Justin](https://avatars.discourse-cdn.com/v4/letter/j/3ab097/32.png) [@Justin](https://community.konduit.ai/u/Justin)\ **Post date:** [May 1, 2022, 4:43pm UTC](https://community.konduit.ai/t/allocation-failed-device-allocation-failed-error-code-2/1842/1 "2022-05-01T16:43:12Z")
Hi I create ComputationGraphConfiguration. When set following code .setInputTypes(InputType.convolutionalFlat(640, 360, channels)) run net.init() will got the error message as title. But when I change to .setInputTypes(InputType.convolutionalFlat(240, 240, channels)) net.init() run well. Any help would be much appreciated! Following is my ComputationGraphConfiguration new NeuralNetConfiguration.Builder() .seed(rngSeed) .l2(0.0005) // ridge regression value .updater(new Nesterovs(0.006, 0.9)) .weightInit(WeightInit.XAVIER) .graphBuilder() .addInputs(“input”) .setInputTypes(InputType.convolutionalFlat(640, 360, channels)) // InputType.convolutional for normal image HEIGHT, WIDTH .addLayer(“L1”, new ConvolutionLayer.Builder(3, 3).nIn(channels).stride(1, 1).nOut(50).activation(Activation.RELU).build(), “input”) .addLayer(“L2”, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build(), “L1”) .addLayer(“L3”, new ConvolutionLayer.Builder(3, 3) .stride(1, 1) // nIn need not specified in later layers .nOut(50) .activation(Activation.RELU) .build(), “L2”) .addLayer(“L4”, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2) .stride(2, 2) .build(), “L3”) .addLayer(“L5”, new DenseLayer.Builder().activation(Activation.RELU) .nOut(500) .build(), “L4”) .addLayer(“out”, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nOut(N\_OUTCOMES) .activation(Activation.SOFTMAX) .build(), “L5”) .setOutputs(“out”) .build(); ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [May 2, 2022, 7:32am UTC](https://community.konduit.ai/t/allocation-failed-device-allocation-failed-error-code-2/1842/2 "2022-05-02T07:32:06Z")
It’s kind of hard to tell much from just your model but that is generally a memory management problem. Make sure you have enough RAM for being able to run your model. See more here: [Memory - Deeplearning4j](https://deeplearning4j.konduit.ai/v/en-1.0.0-m1.1/multi-project/explanation/configuration/memory#memory-management-for-nd4j-dl4j-how-does-it-work) If you can provide more context I can provide better advice. ---
**Author:** ![Justin](https://avatars.discourse-cdn.com/v4/letter/j/3ab097/32.png) [@Justin](https://community.konduit.ai/u/Justin)\ **Post date:** [May 18, 2022, 2:08am UTC](https://community.konduit.ai/t/allocation-failed-device-allocation-failed-error-code-2/1842/3 "2022-05-18T02:08:19Z")
Thank you very much。 if run with CPU, it’s OK. No error. forllowing is my POM dependency. org.nd4j nd4j-native 1.0.0-M2 But change to CUDA, as following org.nd4j nd4j-cuda-11.2 1.0.0-M1.1 and get the following message. Warning: Versions of org.bytedeco:javacpp:1.5.7 and org.bytedeco:cuda:11.2-8.1-1.5.5 do not match. [main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [JCublasBackend] backend [main] INFO org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for linear algebra: 32 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Backend used: [CUDA]; OS: [Windows 11] [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Cores: [8]; Memory: [8.0GB]; [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Blas vendor: [CUBLAS] [main] INFO org.nd4j.linalg.jcublas.JCublasBackend - ND4J CUDA build version: 11.2.142 [main] INFO org.nd4j.linalg.jcublas.JCublasBackend - CUDA device 0: [NVIDIA GeForce MX350]; cc: [6.1]; Total memory: [2147352576] [main] INFO org.nd4j.linalg.jcublas.JCublasBackend - Backend build information: MSVC: 192930038 STD version: 201703L CUDA: 11.2.142 DEFAULT\_ENGINE: samediff::ENGINE\_CUDA HAVE\_FLATBUFFERS and runtime exception as following java.lang.RuntimeException: cudaMalloc failed; Bytes: [246402000]; Error code [2]; DEVICE [0] at org.nd4j.jita.memory.CudaMemoryManager.allocate(CudaMemoryManager.java:83) at org.nd4j.jita.workspace.CudaWorkspace.alloc(CudaWorkspace.java:233) at org.nd4j.linalg.jcublas.buffer.BaseCudaDataBuffer.(BaseCudaDataBuffer.java:425) at org.nd4j.linalg.jcublas.buffer.CudaFloatDataBuffer.(CudaFloatDataBuffer.java:76) at org.nd4j.linalg.jcublas.buffer.factory.CudaDataBufferFactory.create(CudaDataBufferFactory.java:419) at org.nd4j.linalg.factory.Nd4j.createBuffer(Nd4j.java:1454) at org.nd4j.linalg.jcublas.JCublasNDArrayFactory.createUninitialized(JCublasNDArrayFactory.java:1538) at org.nd4j.linalg.factory.Nd4j.createUninitialized(Nd4j.java:4333) at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.preOutput(ConvolutionLayer.java:446) at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.activate(ConvolutionLayer.java:509) at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doForward(LayerVertex.java:110) at org.deeplearning4j.nn.graph.ComputationGraph.ffToLayerActivationsInWS(ComputationGraph.java:2139) at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1376) at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1345) at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:174) at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:61) at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52) at org.deeplearning4j.nn.graph.ComputationGraph.fitHelper(ComputationGraph.java:1169) at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1119) at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1086) at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1022) at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1010) at com.jufan.machinelearning.trainer.NavigationTrainer.train(NavigationTrainer.java:152) at com.jufan.machinelearning.NavigationTrainerTest.testTrain(NavigationTrainerTest.java:24) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:568) at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:47) at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12) at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:44) at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17) at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:271) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:70) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:50) at org.junit.runners.ParentRunner$3.run(ParentRunner.java:238) at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:63) at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:236) at org.junit.runners.ParentRunner.access$000(ParentRunner.java:53) at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:229) at org.junit.runners.ParentRunner.run(ParentRunner.java:309) at org.eclipse.jdt.internal.junit4.runner.JUnit4TestReference.run(JUnit4TestReference.java:93) at org.eclipse.jdt.internal.junit.runner.TestExecution.run(TestExecution.java:40) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:529) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:756) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.run(RemoteTestRunner.java:452) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.main(RemoteTestRunner.java:210) ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [May 18, 2022, 2:46am UTC](https://community.konduit.ai/t/allocation-failed-device-allocation-failed-error-code-2/1842/4 "2022-05-18T02:46:59Z")
@Justin that is pretty self explanatory. Your GPU is out of memory. Adjust your batch size to make sure it fits on the GPU. ---
**Author:** ![Justin](https://avatars.discourse-cdn.com/v4/letter/j/3ab097/32.png) [@Justin](https://community.konduit.ai/u/Justin)\ **Post date:** [May 24, 2022, 1:44am UTC](https://community.konduit.ai/t/allocation-failed-device-allocation-failed-error-code-2/1842/5 "2022-05-24T01:44:14Z")
> [@agibsonccc](#): > > explanatory @agibsonccc Thank you very much.