rayjs opened a new issue #11882: mxnet.ndarray.ndarray.NDArray to numpy URL: https://github.com/apache/incubator-mxnet/issues/11882 Hi, I had a training script that was working fine until mxnet v0.9. I upgraded recently to v1.2. The old script isnt working anymore: Script: ``` 1 print labels[0].shape 2 print type(labels) 3 print type(labels[0]) 4 label = labels[0].asnumpy() 5 pred = preds[0].asnumpy() ``` Output: ``` 1 (64L, 104L) 2 <type 'list'> 3 <class 'mxnet.ndarray.ndarray.NDArray'> ``` For line 4 i get the following error: ``` XXXXXXXXXXXXXXXXXXXXXXX/metric_multi_task.py in update(self, labels, preds) 364 print type(labels[0]) 365 label = labels[0].asnumpy() --> 366 pred = preds[0].asnumpy() 367 368 batch_size = pred.shape[0] XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet/python/mxnet/ndarray/ndarray.pyc in asnumpy(self) 1954 self.handle, 1955 data.ctypes.data_as(ctypes.c_void_p), -> 1956 ctypes.c_size_t(data.size))) 1957 return data 1958 XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet/python/mxnet/base.pyc in check_call(ret) 233 """ 234 if ret != 0: --> 235 raise MXNetError(py_str(_LIB.MXGetLastError())) 236 237 MXNetError: [17:21:49] include/mxnet/././resource.h:155: Check failed: req.type == ResourceRequest::kTempSpace (62 vs. 1) Stack trace returned 10 entries: [bt] (0)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b) [0x7f50194bc48b] [bt] (1) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mshadow::Tensor<mshadow::gpu, 1, unsigned int> mxnet::Resource::get_space_typed<mshadow::gpu, 1, unsigned int>(mshadow::Shape<1>, mshadow::Stream<mshadow::gpu>*) const+0x618) [0x7f501c67c4a8] [bt] (2) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::op::LeakyReLUOp<mshadow::gpu, float>::Forward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x1f8d) [0x7f501ddececd] [bt] (3) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::op::OperatorState::Forward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x363) [0x7f501bc4f533] [bt] (4)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::exec::StatefulComputeExecutor::Run(mxnet::RunContext, bool)+0x59) [0x7f501bb72ed9] [bt] (5)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(+0x332b946) [0x7f501bb35946] [bt] (6) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext, mxnet::engine::OprBlock*)+0x8e5) [0x7f501c247675] [bt] (7) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(void mxnet::engine::ThreadedEnginePerDevice::GPUWorker<(dmlc::ConcurrentQueueType)0>(mxnet::Context, bool, mxnet::engine::ThreadedEnginePerDevice::ThreadWorkerBlock<(dmlc::ConcurrentQueueType)0>*, std::shared_ptr<dmlc::ManualEvent> const&)+0xeb) [0x7f501c25e00b] [bt] (8) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(std::_Function_handler<void (std::shared_ptr<dmlc::ManualEvent>), mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*, bool)::{lambda()#4}::operator()() const::{lambda(std::shared_ptr<dmlc::ManualEvent>)#1}>::_M_invoke(std::_Any_data const&, std::shared_ptr<dmlc::ManualEvent>&&)+0x4e) [0x7f501c25e27e] [bt] (9)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(std::thread::_Impl<std::_Bind_simple<std::function<void (std::shared_ptr<dmlc::ManualEvent>)> (std::shared_ptr<dmlc::ManualEvent>)> >::_M_run()+0x4a) [0x7f501c246c7a] ```
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