[GitHub] Davidrjx commented on issue #212: crash when running python ./predict-with-pretrained-model.py
Davidrjx commented on issue #212: crash when running python
./predict-with-pretrained-model.py
URL: https://github.com/apache/incubator-mxnet/issues/212#issuecomment-363319926
i came with similar problem when running
`mxnet.nd.ones((2,3),mx.gpu())` , but error as follows:
```
terminate called after throwing an instance of 'dmlc::Error'
what(): [05:55:58]
/opt/incubator-mxnet/mshadow/mshadow/./tensor_gpu-inl.h:35: Check failed: e ==
cudaSuccess CUDA: unknown error
Stack trace returned 9 entries:
[bt] (0)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5a)
[0x7f38edde018a]
[bt] (1)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28)
[0x7f38edde0d28]
[bt] (2) /opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(void
mshadow::SetDevice(int)+0xd0) [0x7f38f094b080]
[bt] (3) /opt/incubator-mxnet/python/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 const&)+0x87)
[0x7f38f0954fe7]
[bt] (4)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler),
mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*,
bool)::{lambda()#3}::operator()()
const::{lambda(std::shared_ptr)#1}>::_M_invoke(std::_Any_data
const&, std::shared_ptr&&)+0x4e)
[0x7f38f095529e]
[bt] (5)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(std::thread::_Impl
(std::shared_ptr)> >::_M_run()+0x4a)
[0x7f38f094e97a]
[bt] (6) /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xb8c80) [0x7f39851cac80]
[bt] (7) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7f398aa206ba]
[bt] (8) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7f398a75641d]
terminate called recursively
Aborted (core dumped)
```
please give possible solutions , thanks!
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[GitHub] Davidrjx commented on issue #212: crash when running python ./predict-with-pretrained-model.py
Davidrjx commented on issue #212: crash when running python
./predict-with-pretrained-model.py
URL: https://github.com/apache/incubator-mxnet/issues/212#issuecomment-363319926
i came with similar problem when running
`mx.nd.ones((2,3),mx.gpu())` , but error as follows:
```
terminate called after throwing an instance of 'dmlc::Error'
what(): [05:55:58]
/opt/incubator-mxnet/mshadow/mshadow/./tensor_gpu-inl.h:35: Check failed: e ==
cudaSuccess CUDA: unknown error
Stack trace returned 9 entries:
[bt] (0)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5a)
[0x7f38edde018a]
[bt] (1)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28)
[0x7f38edde0d28]
[bt] (2) /opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(void
mshadow::SetDevice(int)+0xd0) [0x7f38f094b080]
[bt] (3) /opt/incubator-mxnet/python/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 const&)+0x87)
[0x7f38f0954fe7]
[bt] (4)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler),
mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*,
bool)::{lambda()#3}::operator()()
const::{lambda(std::shared_ptr)#1}>::_M_invoke(std::_Any_data
const&, std::shared_ptr&&)+0x4e)
[0x7f38f095529e]
[bt] (5)
/opt/incubator-mxnet/python/mxnet/../../lib/libmxnet.so(std::thread::_Impl
(std::shared_ptr)> >::_M_run()+0x4a)
[0x7f38f094e97a]
[bt] (6) /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xb8c80) [0x7f39851cac80]
[bt] (7) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7f398aa206ba]
[bt] (8) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7f398a75641d]
terminate called recursively
Aborted (core dumped)
```
please give possible solutions , thanks!
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To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
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