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Now I want to do some tests on Tesla V100, I met some problems. The problem is following:
The problem is missing on 1080ti, so I wonder whether this is due to Tesla V100. I hope someone could help me to solve this problem. Thx a lot!
terminate called after throwing an instance of 'dmlc::Error'
what(): [11:50:29] src/engine/./threaded_engine.h:379: Error: compute_ctc_loss, stat = execution failed
A fatal error occurred in asynchronous engine operation. If you do not know what caused this error, you can try set environment variable MXNET_ENGINE_TYPE to NaiveEngine and run with debugger (i.e. gdb). This will force all operations to be synchronous and backtrace will give you the series of calls that lead to this error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
I think I have the same problem, can't run the GPU tests after building with CUDA 9.2
./test_gpu
Running GPU tests
terminate called after throwing an instance of 'std::runtime_error'
what(): Error: compute_ctc_loss in small_test, stat = execution failed
Aborted (core dumped)
Now I want to do some tests on Tesla V100, I met some problems. The problem is following:
The problem is missing on 1080ti, so I wonder whether this is due to Tesla V100. I hope someone could help me to solve this problem. Thx a lot!
terminate called after throwing an instance of 'dmlc::Error'
what(): [11:50:29] src/engine/./threaded_engine.h:379: Error: compute_ctc_loss, stat = execution failed
A fatal error occurred in asynchronous engine operation. If you do not know what caused this error, you can try set environment variable MXNET_ENGINE_TYPE to NaiveEngine and run with debugger (i.e. gdb). This will force all operations to be synchronous and backtrace will give you the series of calls that lead to this error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
Stack trace returned 9 entries:
[bt] (0) python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace()+0x3d) [0x7f651b5c354d]
[bt] (1) python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x1a) [0x7f651b5c39da]
[bt] (2) python/mxnet/../../lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext, mxnet::engine::OprBlock*)+0xb26) [0x7f651e5536a6]
[bt] (3) 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_ptrdmlc::ManualEvent const&)+0xd3) [0x7f651e5656d3]
[bt] (4) python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void (std::shared_ptrdmlc::ManualEvent), mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock, bool)::{lambda()#4}::operator()() const::{lambda(std::shared_ptrdmlc::ManualEvent)#1}>::_M_invoke(std::_Any_data const&, std::shared_ptrdmlc::ManualEvent)+0x3e) [0x7f651e56590e]
[bt] (5) python/mxnet/../../lib/libmxnet.so(std::thread::_Impl<std::_Bind_simple<std::function<void (std::shared_ptrdmlc::ManualEvent)> (std::shared_ptrdmlc::ManualEvent)> >::_M_run()+0x3b) [0x7f651e55283b]
[bt] (6) /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xb6970) [0x7f65b9ddd970]
[bt] (7) /lib/x86_64-linux-gnu/libpthread.so.0(+0x8064) [0x7f65ceddf064]
[bt] (8) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7f65ce1f163d]
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