error info

/home/lili/env-torch13/bin/python /home/lili/codes/huggface-transformer/test/boosted_trees.py
2.0.0
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp47on6eju
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
2019-12-03 15:09:35.636006: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2019-12-03 15:09:35.659665: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:35.660410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: Quadro P3000 major: 6 minor: 1 memoryClockRate(GHz): 1.215
pciBusID: 0000:01:00.0
2019-12-03 15:09:35.660585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-12-03 15:09:35.661976: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2019-12-03 15:09:35.663112: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2019-12-03 15:09:35.663331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2019-12-03 15:09:35.664928: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2019-12-03 15:09:35.665854: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2019-12-03 15:09:35.668457: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-03 15:09:35.668543: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:35.669207: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:35.670013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:518: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4276: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4331: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:308: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/keras/optimizer_v2/ftrl.py:143: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2019-12-03 15:09:36.436907: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:36.437905: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: Quadro P3000 major: 6 minor: 1 memoryClockRate(GHz): 1.215
pciBusID: 0000:01:00.0
2019-12-03 15:09:36.437944: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-12-03 15:09:36.437956: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2019-12-03 15:09:36.437965: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2019-12-03 15:09:36.437974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2019-12-03 15:09:36.437984: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2019-12-03 15:09:36.437993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2019-12-03 15:09:36.438002: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-03 15:09:36.438047: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:36.438629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:36.439458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-03 15:09:36.439489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-12-03 15:09:36.494549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-03 15:09:36.494567: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 
2019-12-03 15:09:36.494584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N 
2019-12-03 15:09:36.494850: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:36.495499: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:36.496185: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:36.496808: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5048 MB memory) -> physical GPU (device: 0, name: Quadro P3000, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-12-03 15:09:37.328600: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

2019-12-03 15:09:39.726356: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:39.727223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: Quadro P3000 major: 6 minor: 1 memoryClockRate(GHz): 1.215
pciBusID: 0000:01:00.0
2019-12-03 15:09:39.727318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-12-03 15:09:39.727350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2019-12-03 15:09:39.727374: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2019-12-03 15:09:39.727402: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2019-12-03 15:09:39.727425: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2019-12-03 15:09:39.727435: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2019-12-03 15:09:39.727463: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-03 15:09:39.727545: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:39.728213: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:39.728782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-03 15:09:39.728803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-03 15:09:39.728808: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 
2019-12-03 15:09:39.728811: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N 
2019-12-03 15:09:39.728943: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:39.729502: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:39.730042: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5048 MB memory) -> physical GPU (device: 0, name: Quadro P3000, pci bus id: 0000:01:00.0, compute capability: 6.1)
accuracy                  0.765152
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmple6puss0
accuracy_baseline         0.625000
auc                       0.832844
auc_precision_recall      0.789631
average_loss              0.478908
label/mean                0.375000
loss                      0.478908
precision                 0.703297
prediction/mean           0.350790
recall                    0.646465
global_step             100.000000
dtype: float64
WARNING:tensorflow:From /home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py:214: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:Issue encountered when serializing resources.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'_Resource' object has no attribute 'name'
2019-12-03 15:09:40.946201: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:40.947085: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: Quadro P3000 major: 6 minor: 1 memoryClockRate(GHz): 1.215
pciBusID: 0000:01:00.0
2019-12-03 15:09:40.947133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-12-03 15:09:40.947157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2019-12-03 15:09:40.947166: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2019-12-03 15:09:40.947175: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2019-12-03 15:09:40.947183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2019-12-03 15:09:40.947192: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2019-12-03 15:09:40.947201: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-03 15:09:40.947272: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:40.948106: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:40.948981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-03 15:09:40.949015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-03 15:09:40.949020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 
2019-12-03 15:09:40.949024: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N 
2019-12-03 15:09:40.949310: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:40.950025: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-03 15:09:40.950557: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5048 MB memory) -> physical GPU (device: 0, name: Quadro P3000, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING:tensorflow:Issue encountered when serializing resources.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'_Resource' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing resources.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'_Resource' object has no attribute 'name'
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
*** Error in `/home/lili/env-torch13/bin/python': malloc(): memory corruption (fast): 0x00007f4f84066f50 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7f508f27d7e5]
/lib/x86_64-linux-gnu/libc.so.6(+0x82651)[0x7f508f288651]
/lib/x86_64-linux-gnu/libc.so.6(__libc_malloc+0x54)[0x7f508f28a184]
/usr/lib/x86_64-linux-gnu/libstdc++.so.6(_Znwm+0x18)[0x7f506ff864b8]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZN10tensorflow6TensorC2EPNS_9AllocatorENS_8DataTypeERKNS_11TensorShapeERKNS_20AllocationAttributesE+0x7fe)[0x7f505758537e]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZN10tensorflow15OpKernelContext15allocate_tensorENS_8DataTypeERKNS_11TensorShapeEPNS_6TensorENS_19AllocatorAttributesERKNS_20AllocationAttributesE+0x8c)[0x7f505754f67c]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZN10tensorflow15OpKernelContext15allocate_outputEiRKNS_11TensorShapeEPPNS_6TensorENS_19AllocatorAttributesE+0xbbd)[0x7f505755059d]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZN10tensorflow15OpKernelContext15allocate_outputEiRKNS_11TensorShapeEPPNS_6TensorE+0x102)[0x7f50575507a2]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZN10tensorflow15OpKernelContext15allocate_outputEN4absl11string_viewERKNS_11TensorShapeEPPNS_6TensorE+0x9a)[0x7f5057550a1a]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/_pywrap_tensorflow_internal.so(_ZN10tensorflow30BoostedTreesMakeStatsSummaryOp7ComputeEPNS_15OpKernelContextE+0x456)[0x7f505b7584a6]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(+0xfb12af)[0x7f50577f52af]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(+0xfb168f)[0x7f50577f568f]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZN5Eigen15ThreadPoolTemplIN10tensorflow6thread16EigenEnvironmentEE10WorkerLoopEi+0x39e)[0x7f5057894dde]
/home/lili/env-torch13/lib/python3.6/site-packages/tensorflow_core/python/../libtensorflow_framework.so.2(_ZNSt17_Function_handlerIFvvEZN10tensorflow6thread16EigenEnvironment12CreateThreadESt8functionIS0_EEUlvE_E9_M_invokeERKSt9_Any_data+0x48)[0x7f5057891fb8]
/usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xbc510)[0x7f506ffb2510]
/lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7f509012a6ba]
/lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7f508f30d41d]
======= Memory map: ========
00400000-007e4000 r-xp 00000000 103:01 12335899                          /home/lili/env-torch13/bin/python3.6
009e3000-009e4000 r--p 003e3000 103:01 12335899                          /home/lili/env-torch13/bin/python3.6
009e4000-00a80000 rw-p 003e4000 103:01 12335899                          /home/lili/env-torch13/bin/python3.6
00a80000-00ab2000 rw-p 00000000 00:00 0 
012cc000-12753000 rw-p 00000000 00:00 0                                  [heap]
200000000-200200000 rw-s 00000000 00:06 456                              /dev/nvidiactl
200200000-200400000 ---p 00000000 00:00 0 
200400000-200600000 rw-s 00000000 00:06 456                              /dev/nvidiactl
200600000-202600000 rw-s 00000000 00:06 456                              /dev/nvidiactl
202600000-205600000 rw-s 00000000 00:06 456                              /dev/nvidiactl
205600000-205e00000 ---p 00000000 00:00 0 
205e00000-206000000 rw-s 00000000 00:06 456                              /dev/nvidiactl
206000000-206200000 rw-s 00000000 00:06 456                              /dev/nvidiactl
206200000-206400000 rw-s 206200000 00:06 487                             /dev/nvidia-uvm
206400000-206600000 ---p 00000000 00:00 0 
206600000-206800000 rw-s 00000000 00:06 456                              /dev/nvidiactl
206800000-300200000 ---p 00000000 00:00 0 
10000000000-10204000000 ---p 00000000 00:00 0 
7f4e00000000-7f4e00021000 rw-p 00000000 00:00 0 
7f4e00021000-7f4e04000000 ---p 00000000 00:00 0 
7f4e08000000-7f4e08021000 rw-p 00000000 00:00 0 
7f4e08021000-7f4e0c000000 ---p 00000000 00:00 0 
7f4e0c000000-7f4e0c2fc000 rw-p 00000000 00:00 0 
7f4e0c2fc000-7f4e10000000 ---p 00000000 00:00 0 
7f4e10000000-7f4e100cd000 rw-p 00000000 00:00 0 
7f4e100cd000-7f4e14000000 ---p 00000000 00:00 0 
7f4e18000000-7f4e1a14a000 rw-p 00000000 00:00 0 
7f4e1a14a000-7f4e1c000000 ---p 00000000 00:00 0 
7f4e1c000000-7f4e1c054000 rw-p 00000000 00:00 0 
7f4e1c054000-7f4e20000000 ---p 00000000 00:00 0 
7f4e20000000-7f4e23f9f000 rw-p 00000000 00:00 0 
7f4e23f9f000-7f4e24000000 ---p 00000000 00:00 0 
7f4e24000000-7f4e27ffe000 rw-p 00000000 00:00 0 
7f4e27ffe000-7f4e28000000 ---p 00000000 00:00 0 
7f4e28000000-7f4e2bfe3000 rw-p 00000000 00:00 0 
7f4e2bfe3000-7f4e2c000000 ---p 00000000 00:00 0 
7f4e2c000000-7f4e30000000 rw-p 00000000 00:00 0 
7f4e30000000-7f4e302fe000 rw-p 00000000 00:00 0 
7f4e302fe000-7f4e34000000 ---p 00000000 00:00 0 
7f4e34000000-7f4e34027000 rw-p 00000000 00:00 0 
7f4e34027000-7f4e38000000 ---p 00000000 00:00 0 
7f4e38000000-7f4e38027000 rw-p 00000000 00:00 0 
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7f508e195000-7f508e541000 r-xp 00000000 103:01 12508347                  /home/lili/env-torch13/lib/python3.6/site-packages/numpy/core/_multiarray_umath.cpython-36m-x86_64-linux-gnu.so
7f508e541000-7f508e740000 ---p 003ac000 103:01 12508347                  /home/lili/env-torch13/lib/python3.6/site-packages/numpy/core/_multiarray_umath.cpython-36m-x86_64-linux-gnu.so
7f508e740000-7f508e75f000 rw-p 003ab000 103:01 12508347                  /home/lili/env-torch13/lib/python3.6/site-packages/numpy/core/_multiarray_umath.cpython-36m-x86_64-linux-gnu.so
7f508e75f000-7f508e780000 rw-p 00000000 00:00 0 
7f508e780000-7f508e787000 rw-p 01450000 103:01 12508347                  /home/lili/env-torch13/lib/python3.6/site-packages/numpy/core/_multiarray_umath.cpython-36m-x86_64-linux-gnu.so
7f508e787000-7f508e847000 rw-p 00000000 00:00 0 
7f508e847000-7f508f206000 r--p 00000000 103:01 9575299                   /usr/lib/locale/locale-archive
7f508f206000-7f508f3c6000 r-xp 00000000 103:01 28578236                  /lib/x86_64-linux-gnu/libc-2.23.so
7f508f3c6000-7f508f5c6000 ---p 001c0000 103:01 28578236                  /lib/x86_64-linux-gnu/libc-2.23.so
7f508f5c6000-7f508f5ca000 r--p 001c0000 103:01 28578236                  /lib/x86_64-linux-gnu/libc-2.23.so
7f508f5ca000-7f508f5cc000 rw-p 001c4000 103:01 28578236                  /lib/x86_64-linux-gnu/libc-2.23.so
7f508f5cc000-7f508f5d0000 rw-p 00000000 00:00 0 
7f508f5d0000-7f508f6d8000 r-xp 00000000 103:01 28578306                  /lib/x86_64-linux-gnu/libm-2.23.so
7f508f6d8000-7f508f8d7000 ---p 00108000 103:01 28578306                  /lib/x86_64-linux-gnu/libm-2.23.so
7f508f8d7000-7f508f8d8000 r--p 00107000 103:01 28578306                  /lib/x86_64-linux-gnu/libm-2.23.so
7f508f8d8000-7f508f8d9000 rw-p 00108000 103:01 28578306                  /lib/x86_64-linux-gnu/libm-2.23.so
7f508f8d9000-7f508f8f2000 r-xp 00000000 103:01 28578427                  /lib/x86_64-linux-gnu/libz.so.1.2.8
7f508f8f2000-7f508faf1000 ---p 00019000 103:01 28578427                  /lib/x86_64-linux-gnu/libz.so.1.2.8
7f508faf1000-7f508faf2000 r--p 00018000 103:01 28578427                  /lib/x86_64-linux-gnu/libz.so.1.2.8
7f508faf2000-7f508faf3000 rw-p 00019000 103:01 28578427                  /lib/x86_64-linux-gnu/libz.so.1.2.8
7f508faf3000-7f508fb19000 r-xp 00000000 103:01 28578233                  /lib/x86_64-linux-gnu/libexpat.so.1.6.0
7f508fb19000-7f508fd19000 ---p 00026000 103:01 28578233                  /lib/x86_64-linux-gnu/libexpat.so.1.6.0
7f508fd19000-7f508fd1b000 r--p 00026000 103:01 28578233                  /lib/x86_64-linux-gnu/libexpat.so.1.6.0
7f508fd1b000-7f508fd1c000 rw-p 00028000 103:01 28578233                  /lib/x86_64-linux-gnu/libexpat.so.1.6.0
7f508fd1c000-7f508fd1e000 r-xp 00000000 103:01 28578417                  /lib/x86_64-linux-gnu/libutil-2.23.so
7f508fd1e000-7f508ff1d000 ---p 00002000 103:01 28578417                  /lib/x86_64-linux-gnu/libutil-2.23.so
7f508ff1d000-7f508ff1e000 r--p 00001000 103:01 28578417                  /lib/x86_64-linux-gnu/libutil-2.23.so
7f508ff1e000-7f508ff1f000 rw-p 00002000 103:01 28578417                  /lib/x86_64-linux-gnu/libutil-2.23.so
7f508ff1f000-7f508ff22000 r-xp 00000000 103:01 28578260                  /lib/x86_64-linux-gnu/libdl-2.23.so
7f508ff22000-7f5090121000 ---p 00003000 103:01 28578260                  /lib/x86_64-linux-gnu/libdl-2.23.so
7f5090121000-7f5090122000 r--p 00002000 103:01 28578260                  /lib/x86_64-linux-gnu/libdl-2.23.so
7f5090122000-7f5090123000 rw-p 00003000 103:01 28578260                  /lib/x86_64-linux-gnu/libdl-2.23.so
7f5090123000-7f509013b000 r-xp 00000000 103:01 28578382                  /lib/x86_64-linux-gnu/libpthread-2.23.so
7f509013b000-7f509033a000 ---p 00018000 103:01 28578382                  /lib/x86_64-linux-gnu/libpthread-2.23.so
7f509033a000-7f509033b000 r--p 00017000 103:01 28578382                  /lib/x86_64-linux-gnu/libpthread-2.23.so
7f509033b000-7f509033c000 rw-p 00018000 103:01 28578382                  /lib/x86_64-linux-gnu/libpthread-2.23.so
7f509033c000-7f5090340000 rw-p 00000000 00:00 0 
7f5090340000-7f5090366000 r-xp 00000000 103:01 28578208                  /lib/x86_64-linux-gnu/ld-2.23.so
7f5090369000-7f50903e9000 rw-p 00000000 00:00 0 
7f50903f6000-7f5090407000 rw-p 00000000 00:00 0 
7f5090407000-7f5090408000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090408000-7f5090409000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090409000-7f509040a000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509040a000-7f509040b000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509040b000-7f509040c000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509040c000-7f509040d000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509040d000-7f509040e000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509040e000-7f5090514000 rw-p 00000000 00:00 0 
7f5090514000-7f5090515000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090515000-7f5090516000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090516000-7f5090517000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090517000-7f5090518000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090518000-7f5090519000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f5090519000-7f509051a000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509051a000-7f509051b000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509051b000-7f509051c000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509051c000-7f509051d000 rw-s 00000000 00:06 456                        /dev/nvidiactl
7f509051d000-7f509051e000 rwxp 00000000 00:00 0 
7f509051e000-7f509055e000 rw-p 00000000 00:00 0 
7f509055e000-7f5090565000 r--s 00000000 103:01 9833106                   /usr/lib/x86_64-linux-gnu/gconv/gconv-modules.cache
7f5090565000-7f5090566000 r--p 00025000 103:01 28578208                  /lib/x86_64-linux-gnu/ld-2.23.so
7f5090566000-7f5090567000 rw-p 00026000 103:01 28578208                  /lib/x86_64-linux-gnu/ld-2.23.so
7f5090567000-7f5090568000 rw-p 00000000 00:00 0 
7ffdbd9ee000-7ffdbda11000 rw-p 00000000 00:00 0                          [stack]
7ffdbdb81000-7ffdbdb84000 r--p 00000000 00:00 0                          [vvar]
7ffdbdb84000-7ffdbdb86000 r-xp 00000000 00:00 0                          [vdso]
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0                  [vsyscall]

Process finished with exit code 134 (interrupted by signal 6: SIGABRT)