nimishjain15882
commented
3 years ago I have pasted logs for Ubuntu 20.04 above for now. But I face same issue in Windows as well. Probably if Ubuntu is fixed, Windows will work as well.
Open nimishjain15882 opened 3 years ago
nimishjain15882
commented
3 years ago I have pasted logs for Ubuntu 20.04 above for now. But I face same issue in Windows as well. Probably if Ubuntu is fixed, Windows will work as well.
stu1130
commented
3 years ago @nimishjain15882 could you set MXNET_GPU_MEM_POOL_RESERVE to 10 or 20? MXNET_GPU_MEM_POOL_RESERVE is used to tell MXNet how much memory we want to reserve for cuda kernel launch and cudnn handle space, which in this case might resolve CUDNN_STATUS_EXECUTION_FAILED
goswamig
commented
3 years ago assign it to me
iromu
commented
3 years ago I can confirm that:
The latest master version (8ee0b8b) works with no issue and no changes on Ubuntu with a RTX 2080 TI (11GB) with a memory usage of 5% and GPU of arround 60%. Tested removing the FPS limitation.
On windows there is a memory leak. OOM before hitting 15,000 iterations. The GPU usage is reported at 16%, the RAM consumption increases no matter what changes to the code are done:
This points to an issue on the backend native implementations rather than:
Things to try:
Description
I tried running https://github.com/kingyuluk/RL-FlappyBird/ on Windows & Ubuntu. However, after sometime both throw Out Of Memory Error during training. Ruducing batch size doesn't help either. It appears there is a memory leak.
Expected Behavior
The training should continue.
Error Message
[pool-1-thread-2] ERROR ai.djl.ndarray.BaseNDManager - Resource close failed. [main] ERROR com.kingyu.rlbird.ai.TrainBird - java.util.concurrent.ExecutionException: ai.djl.engine.EngineException: MXNet engine call failed: cuDNN: Check failed: e == CUDNN_STATUS_SUCCESS (8 vs. 0) : CUDNN_STATUS_EXECUTION_FAILED Stack trace: File "src/operator/nn/./cudnn/cudnn_convolution-inl.h", line 155
at java.util.concurrent.FutureTask.report(FutureTask.java:122) at java.util.concurrent.FutureTask.get(FutureTask.java:192) at com.kingyu.rlbird.ai.TrainBird.train(TrainBird.java:113) at com.kingyu.rlbird.ai.TrainBird.main(TrainBird.java:65)
Caused by: ai.djl.engine.EngineException: MXNet engine call failed: cuDNN: Check failed: e == CUDNN_STATUS_SUCCESS (8 vs. 0) : CUDNN_STATUS_EXECUTION_FAILED Stack trace: File "src/operator/nn/./cudnn/cudnn_convolution-inl.h", line 155
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.mxnet.engine.MxNDArrayIndexer.get(MxNDArrayIndexer.java:52) at ai.djl.ndarray.index.NDArrayIndexer.get(NDArrayIndexer.java:73) at ai.djl.ndarray.NDArray.get(NDArray.java:498) at ai.djl.ndarray.NDArray.get(NDArray.java:522) at com.kingyu.rlbird.rl.agent.QAgent.chooseAction(QAgent.java:58) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.chooseAction(EpsilonGreedy.java:48) at com.kingyu.rlbird.game.FlappyBird.runEnvironment(FlappyBird.java:104) at com.kingyu.rlbird.ai.TrainBird$GeneratorCallable.call(TrainBird.java:174) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
ai.djl.engine.EngineException: MXNet engine call failed: MXNetError: cudaMalloc retry failed: out of memory Stack trace: File "src/storage/./pooled_storage_manager.h", line 161
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:108) at ai.djl.mxnet.engine.MxParameterServer$OptimizerCallback.apply(MxParameterServer.java:105) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.sun.jna.CallbackReference$DefaultCallbackProxy.invokeCallback(CallbackReference.java:520) at com.sun.jna.CallbackReference$DefaultCallbackProxy.callback(CallbackReference.java:551) at com.sun.jna.Native.invokeInt(Native Method) at com.sun.jna.Function.invoke(Function.java:426) at com.sun.jna.Function.invoke(Function.java:361) at com.sun.jna.Library$Handler.invoke(Library.java:265) at com.sun.proxy.$Proxy1.MXKVStorePushPullEx(Unknown Source) at ai.djl.mxnet.jna.JnaUtils.parameterStorePushPull(JnaUtils.java:734) at ai.djl.mxnet.engine.MxParameterServer.update(MxParameterServer.java:63) at ai.djl.training.ParameterServer.update(ParameterServer.java:38) at ai.djl.training.ParameterStore.updateAllParameters(ParameterStore.java:74) at ai.djl.training.Trainer.step(Trainer.java:190) at com.kingyu.rlbird.rl.agent.QAgent.trainBatch(QAgent.java:118) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.trainBatch(EpsilonGreedy.java:56) at com.kingyu.rlbird.ai.TrainBird$TrainerCallable.call(TrainBird.java:149) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
[pool-1-thread-2] ERROR ai.djl.ndarray.BaseNDManager - Resource close failed. ai.djl.engine.EngineException: MXNet engine call failed: MXNetError: cudaMalloc retry failed: out of memory Stack trace: File "src/storage/./pooled_storage_manager.h", line 161
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:108) at ai.djl.mxnet.engine.MxParameterServer$OptimizerCallback.apply(MxParameterServer.java:105) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.sun.jna.CallbackReference$DefaultCallbackProxy.invokeCallback(CallbackReference.java:520) at com.sun.jna.CallbackReference$DefaultCallbackProxy.callback(CallbackReference.java:551) at com.sun.jna.Native.invokeInt(Native Method) at com.sun.jna.Function.invoke(Function.java:426) at com.sun.jna.Function.invoke(Function.java:361) at com.sun.jna.Library$Handler.invoke(Library.java:265) at com.sun.proxy.$Proxy1.MXKVStorePushPullEx(Unknown Source) at ai.djl.mxnet.jna.JnaUtils.parameterStorePushPull(JnaUtils.java:734) at ai.djl.mxnet.engine.MxParameterServer.update(MxParameterServer.java:63) at ai.djl.training.ParameterServer.update(ParameterServer.java:38) at ai.djl.training.ParameterStore.updateAllParameters(ParameterStore.java:74) at ai.djl.training.Trainer.step(Trainer.java:190) at com.kingyu.rlbird.rl.agent.QAgent.trainBatch(QAgent.java:118) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.trainBatch(EpsilonGreedy.java:56) at com.kingyu.rlbird.ai.TrainBird$TrainerCallable.call(TrainBird.java:149) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
[pool-1-thread-2] ERROR ai.djl.ndarray.BaseNDManager - Resource close failed. ai.djl.engine.EngineException: MXNet engine call failed: Name: Check failed: err == cudaSuccess (2 vs. 0) : mxnet_generic_kernel ErrStr:out of memory Stack trace: File "src/operator/././../common/../operator/mxnet_op.h", line 1121
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:108) at ai.djl.mxnet.engine.MxParameterServer$OptimizerCallback.apply(MxParameterServer.java:105) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.sun.jna.CallbackReference$DefaultCallbackProxy.invokeCallback(CallbackReference.java:520) at com.sun.jna.CallbackReference$DefaultCallbackProxy.callback(CallbackReference.java:551) at com.sun.jna.Native.invokeInt(Native Method) at com.sun.jna.Function.invoke(Function.java:426) at com.sun.jna.Function.invoke(Function.java:361) at com.sun.jna.Library$Handler.invoke(Library.java:265) at com.sun.proxy.$Proxy1.MXKVStorePushPullEx(Unknown Source) at ai.djl.mxnet.jna.JnaUtils.parameterStorePushPull(JnaUtils.java:734) at ai.djl.mxnet.engine.MxParameterServer.update(MxParameterServer.java:63) at ai.djl.training.ParameterServer.update(ParameterServer.java:38) at ai.djl.training.ParameterStore.updateAllParameters(ParameterStore.java:74) at ai.djl.training.Trainer.step(Trainer.java:190) at com.kingyu.rlbird.rl.agent.QAgent.trainBatch(QAgent.java:118) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.trainBatch(EpsilonGreedy.java:56) at com.kingyu.rlbird.ai.TrainBird$TrainerCallable.call(TrainBird.java:149) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
[pool-1-thread-2] ERROR ai.djl.ndarray.BaseNDManager - Resource close failed. ai.djl.engine.EngineException: MXNet engine call failed: Name: Check failed: err == cudaSuccess (2 vs. 0) : mxnet_generic_kernel ErrStr:out of memory Stack trace: File "src/operator/././../common/../operator/mxnet_op.h", line 1121
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:108) at ai.djl.mxnet.engine.MxParameterServer$OptimizerCallback.apply(MxParameterServer.java:105) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.sun.jna.CallbackReference$DefaultCallbackProxy.invokeCallback(CallbackReference.java:520) at com.sun.jna.CallbackReference$DefaultCallbackProxy.callback(CallbackReference.java:551) at com.sun.jna.Native.invokeInt(Native Method) at com.sun.jna.Function.invoke(Function.java:426) at com.sun.jna.Function.invoke(Function.java:361) at com.sun.jna.Library$Handler.invoke(Library.java:265) at com.sun.proxy.$Proxy1.MXKVStorePushPullEx(Unknown Source) at ai.djl.mxnet.jna.JnaUtils.parameterStorePushPull(JnaUtils.java:734) at ai.djl.mxnet.engine.MxParameterServer.update(MxParameterServer.java:63) at ai.djl.training.ParameterServer.update(ParameterServer.java:38) at ai.djl.training.ParameterStore.updateAllParameters(ParameterStore.java:74) at ai.djl.training.Trainer.step(Trainer.java:190) at com.kingyu.rlbird.rl.agent.QAgent.trainBatch(QAgent.java:118) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.trainBatch(EpsilonGreedy.java:56) at com.kingyu.rlbird.ai.TrainBird$TrainerCallable.call(TrainBird.java:149) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
[pool-1-thread-2] ERROR ai.djl.ndarray.BaseNDManager - Resource close failed. ai.djl.engine.EngineException: MXNet engine call failed: Name: Check failed: err == cudaSuccess (2 vs. 0) : mxnet_generic_kernel ErrStr:out of memory Stack trace: File "src/operator/././../common/../operator/mxnet_op.h", line 1121
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:108) at ai.djl.mxnet.engine.MxParameterServer$OptimizerCallback.apply(MxParameterServer.java:105) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.sun.jna.CallbackReference$DefaultCallbackProxy.invokeCallback(CallbackReference.java:520) at com.sun.jna.CallbackReference$DefaultCallbackProxy.callback(CallbackReference.java:551) at com.sun.jna.Native.invokeInt(Native Method) at com.sun.jna.Function.invoke(Function.java:426) at com.sun.jna.Function.invoke(Function.java:361) at com.sun.jna.Library$Handler.invoke(Library.java:265) at com.sun.proxy.$Proxy1.MXKVStorePushPullEx(Unknown Source) at ai.djl.mxnet.jna.JnaUtils.parameterStorePushPull(JnaUtils.java:734) at ai.djl.mxnet.engine.MxParameterServer.update(MxParameterServer.java:63) at ai.djl.training.ParameterServer.update(ParameterServer.java:38) at ai.djl.training.ParameterStore.updateAllParameters(ParameterStore.java:74) at ai.djl.training.Trainer.step(Trainer.java:190) at com.kingyu.rlbird.rl.agent.QAgent.trainBatch(QAgent.java:118) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.trainBatch(EpsilonGreedy.java:56) at com.kingyu.rlbird.ai.TrainBird$TrainerCallable.call(TrainBird.java:149) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
[pool-1-thread-2] ERROR ai.djl.ndarray.BaseNDManager - Resource close failed. ai.djl.engine.EngineException: MXNet engine call failed: Name: Check failed: err == cudaSuccess (2 vs. 0) : mxnet_generic_kernel ErrStr:out of memory Stack trace: File "src/operator/././../common/../operator/mxnet_op.h", line 1121
at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1808) at ai.djl.mxnet.jna.JnaUtils.waitToRead(JnaUtils.java:459) at ai.djl.mxnet.engine.MxNDArray.close(MxNDArray.java:1549) at ai.djl.ndarray.BaseNDManager.close(BaseNDManager.java:108) at ai.djl.mxnet.engine.MxParameterServer$OptimizerCallback.apply(MxParameterServer.java:105) at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at com.sun.jna.CallbackReference$DefaultCallbackProxy.invokeCallback(CallbackReference.java:520) at com.sun.jna.CallbackReference$DefaultCallbackProxy.callback(CallbackReference.java:551) at com.sun.jna.Native.invokeInt(Native Method) at com.sun.jna.Function.invoke(Function.java:426) at com.sun.jna.Function.invoke(Function.java:361) at com.sun.jna.Library$Handler.invoke(Library.java:265) at com.sun.proxy.$Proxy1.MXKVStorePushPullEx(Unknown Source) at ai.djl.mxnet.jna.JnaUtils.parameterStorePushPull(JnaUtils.java:734) at ai.djl.mxnet.engine.MxParameterServer.update(MxParameterServer.java:63) at ai.djl.training.ParameterServer.update(ParameterServer.java:38) at ai.djl.training.ParameterStore.updateAllParameters(ParameterStore.java:74) at ai.djl.training.Trainer.step(Trainer.java:190) at com.kingyu.rlbird.rl.agent.QAgent.trainBatch(QAgent.java:118) at com.kingyu.rlbird.rl.agent.EpsilonGreedy.trainBatch(EpsilonGreedy.java:56) at com.kingyu.rlbird.ai.TrainBird$TrainerCallable.call(TrainBird.java:149) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
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