Created by: xymyeah

I0212 12:28:50.517459 1322 Util.cpp:166] commandline: --num_passes=1 --ports_num_for_sparse=1 --use_gpu=0 --trainer_id=0 --pservers=10.10.10.10 --trainer_count=1 --nu m_gradient_servers=1 --ports_num=1 --port=30002 [INFO 2018-02-12 12:28:50,523 layers.py:2714] output for conv_0: c = 64, h = 32, w = 32, size = 65536 [INFO 2018-02-12 12:28:50,524 layers.py:3282] output for batch_norm_0: c = 64, h = 32, w = 32, size = 65536 [INFO 2018-02-12 12:28:50,525 layers.py:2714] output for conv_1: c = 64, h = 32, w = 32, size = 65536 [INFO 2018-02-12 12:28:50,526 layers.py:3282] output for batch_norm_1: c = 64, h = 32, w = 32, size = 65536 [INFO 2018-02-12 12:28:50,527 layers.py:2856] output for pool_0: c = 64, h = 16, w = 16, size = 16384 [INFO 2018-02-12 12:28:50,528 layers.py:2714] output for conv_2: c = 128, h = 16, w = 16, size = 32768 [INFO 2018-02-12 12:28:50,529 layers.py:3282] output for batch_norm_2: c = 128, h = 16, w = 16, size = 32768 [INFO 2018-02-12 12:28:50,530 layers.py:2714] output for conv_3: c = 128, h = 16, w = 16, size = 32768 [INFO 2018-02-12 12:28:50,531 layers.py:3282] output for batch_norm_3: c = 128, h = 16, w = 16, size = 32768 [INFO 2018-02-12 12:28:50,532 layers.py:2856] output for pool_1: c = 128, h = 8, w = 8, size = 8192 [INFO 2018-02-12 12:28:50,533 layers.py:2714] output for conv_4: c = 256, h = 8, w = 8, size = 16384 [INFO 2018-02-12 12:28:50,534 layers.py:3282] output for batch_norm_4: c = 256, h = 8, w = 8, size = 16384 [INFO 2018-02-12 12:28:50,535 layers.py:2714] output for conv_5: c = 256, h = 8, w = 8, size = 16384 [INFO 2018-02-12 12:28:50,536 layers.py:3282] output for batch_norm_5: c = 256, h = 8, w = 8, size = 16384 [INFO 2018-02-12 12:28:50,538 layers.py:2714] output for conv_6: c = 256, h = 8, w = 8, size = 16384 [INFO 2018-02-12 12:28:50,539 layers.py:3282] output for batch_norm_6: c = 256, h = 8, w = 8, size = 16384 [INFO 2018-02-12 12:28:50,539 layers.py:2856] output for pool_2: c = 256, h = 4, w = 4, size = 4096 [INFO 2018-02-12 12:28:50,540 layers.py:2714] output for conv_7: c = 512, h = 4, w = 4, size = 8192 [INFO 2018-02-12 12:28:50,541 layers.py:3282] output for batch_norm_7: c = 512, h = 4, w = 4, size = 8192 [INFO 2018-02-12 12:28:50,542 layers.py:2714] output for conv_8: c = 512, h = 4, w = 4, size = 8192 [INFO 2018-02-12 12:28:50,543 layers.py:3282] output for batch_norm_8: c = 512, h = 4, w = 4, size = 8192 [INFO 2018-02-12 12:28:50,544 layers.py:2714] output for conv_9: c = 512, h = 4, w = 4, size = 8192 [INFO 2018-02-12 12:28:50,545 layers.py:3282] output for batch_norm_9: c = 512, h = 4, w = 4, size = 8192 [INFO 2018-02-12 12:28:50,546 layers.py:2856] output for pool_3: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,547 layers.py:2714] output for conv_10: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,548 layers.py:3282] output for batch_norm_10: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,549 layers.py:2714] output for conv_11: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,550 layers.py:3282] output for batch_norm_11: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,551 layers.py:2714] output for conv_12: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,552 layers.py:3282] output for batch_norm_12: c = 512, h = 2, w = 2, size = 2048 [INFO 2018-02-12 12:28:50,553 layers.py:2856] output for pool_4: c = 512, h = 1, w = 1, size = 512 I0212 12:28:50.699533 1322 GradientMachine.cpp:94] Initing parameters.. I0212 12:28:51.627924 1322 GradientMachine.cpp:101] Init parameters done. I0212 12:28:51.642782 1322 ParameterClient2.cpp:113] pserver 0 10.90.245.28:30002 I0212 12:28:53.938444 1395 ParameterClient2.cpp:113] pserver 0 10.90.245.28:30002 Thread [140664660924160] Forwarding conv_0,

* Aborted at 1518409737 (unix time) try "date -d @1518409737" if you are using GNU date *

PC: @ 0x0 (unknown)

* SIGSEGV (@0x0) received by PID 1322 (TID 0x7fef0b259700) from PID 0; stack trace: *

@ 0x7fef0a859500 (unknown) @ 0x7feeb5cf4d6a __kmp_register_root @ 0x7feeb5cff11e __kmp_middle_initialize @ 0x7feeb5ce8dae __kmp_api_omp_get_num_procs @ 0x7fee825ea2ae mkl_serv_get_num_stripes @ 0x7fee8249f234 mkl_blas_sgemm_host @ 0x7fee8246fa3b mkl_blas_sgemm @ 0x7fee823f13c3 SGEMM @ 0x7fee823b4881 cblas_sgemm @ 0x7feeb67f7279 paddle::gemm<>() @ 0x7feeb66baec3 paddle::GemmConvFunction<>::calc() @ 0x7feeb65adcdf paddle::ExpandConvLayer::forward() @ 0x7feeb65256cd paddle::NeuralNetwork::forward() @ 0x7feeb6526323 paddle::GradientMachine::forwardBackward() @ 0x7feeb6870864 GradientMachine::forwardBackward() @ 0x7feeb64be9c9 _wrap_GradientMachine_forwardBackward @ 0x7fef0ab5ebac PyEval_EvalFrameEx @ 0x7fef0ab605ee PyEval_EvalCodeEx @ 0x7fef0ab5ec27 PyEval_EvalFrameEx @ 0x7fef0ab605ee PyEval_EvalCodeEx @ 0x7fef0ab5ec27 PyEval_EvalFrameEx @ 0x7fef0ab605ee PyEval_EvalCodeEx @ 0x7fef0ab5ec27 PyEval_EvalFrameEx @ 0x7fef0ab605ee PyEval_EvalCodeEx @ 0x7fef0ab5ec27 PyEval_EvalFrameEx @ 0x7fef0ab605ee PyEval_EvalCodeEx @ 0x7fef0ab60702 PyEval_EvalCode @ 0x7fef0ab80450 PyRun_FileExFlags @ 0x7fef0ab8062f PyRun_SimpleFileExFlags @ 0x7fef0ab95f24 Py_Main @ 0x7fef09e4acdd __libc_start_main Aborted (core dumped) stdbuf -oL sh -c "${ENTRY_CMD}"