seg fault trying starter ipython notebook in ipython - linux

I just tried some of the notebook and I got a segfault. Here is what my session looks like, this is from ipython in a conda environment, will describe the environment below:

(ana-1.2.7-gpu) : ~ $ ipython
Python 2.7.12 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:42:40) 
Type "copyright", "credits" or "license" for more information.

IPython 5.3.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import random
   ...: import tensorflow as tf
   ...: sess = tf.InteractiveSession()
   ...: import tensorflow_fold as td
   ...: 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: Tesla P100-PCIE-16GB
major: 6 minor: 0 memoryClockRate (GHz) 1.3285
pciBusID 0000:02:00.0
Total memory: 15.89GiB
Free memory: 15.61GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:02:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:02:00.0)
In [2]: scalar_block = td.Scalar()
   ...: vector3_block = td.Vector(3)
   ...: 
   ...: 

In [3]: def block_info(block):
   ...:     print("%s: %s -> %s" % (block, block.input_type, block.output_type))
   ...:     
   ...: block_info(scalar_block)
   ...: block_info(vector3_block)
   ...: 
<td.Scalar dtype='float32'>: PyObjectType() -> TensorType((), 'float32')
<td.Vector dtype='float32' size=3>: PyObjectType() -> TensorType((3,), 'float32')

In [4]: scalar_block.eval(234)
Segmentation fault (core dumped)

I'm on red hat 7 - linux, working from a anaconda environment. I have

cudnn 5.1 tensorflow-gpu 1.0.1

which were done as proper conda packages, I made the packages myself -- I did not build tensorflow with bazel, use wrap the output of pip in a conda package.

Then I did a pip install to get tensorflow-fold 0.0.1, being to lazy to make a proper conda package out of it since I wanted to quickly try it.

Maybe it is some weird issue with the construction of the environment, but thought I'd mention it in case you haven't tested this combo - ipython/tensorflow-gpu/tensorflold

tensorflow / fold

seg fault trying starter ipython notebook in ipython - linux #52