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Fix missing dash for cxxopt build option (#9736)

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Mortada Mehyar 2017-05-06 23:22:17 -07:00 committed by Vijay Vasudevan
parent 6ee900438c
commit d156525025
1 changed files with 3 additions and 3 deletions
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6
tensorflow/docs_src/extend/adding_an_op.md
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@ -42,7 +42,7 @@ To incorporate your custom op you'll need to:
Python @{tf.test.compute_gradient_error$gradient checker}. Python @{tf.test.compute_gradient_error$gradient checker}.
See See
[`relu_op_test.py`](https://www.tensorflow.org/code/tensorflow/python/kernel_tests/relu_op_test.py) as [`relu_op_test.py`](https://www.tensorflow.org/code/tensorflow/python/kernel_tests/relu_op_test.py) as
an example that does tests the forward functions of Relu-like operators and an example that tests the forward functions of Relu-like operators and
their gradients. their gradients.
PREREQUISITES: PREREQUISITES:
@ -188,7 +188,7 @@ building the `.so` file.
> the older ABI. If you compile your op library with `gcc>=5`, add > the older ABI. If you compile your op library with `gcc>=5`, add
> `-D_GLIBCXX_USE_CXX11_ABI=0` to the command line to make the library > `-D_GLIBCXX_USE_CXX11_ABI=0` to the command line to make the library
> compatible with the older abi. > compatible with the older abi.
> Furthermore if you are using TensorFlow package created from source remember to add `-cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"` > Furthermore if you are using TensorFlow package created from source remember to add `--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"`
> as bazel command to compile the Python package. > as bazel command to compile the Python package.
### Compile the op using bazel (TensorFlow source installation) ### Compile the op using bazel (TensorFlow source installation)
@ -225,7 +225,7 @@ TensorFlow Python API provides the
load the dynamic library and register the op with the TensorFlow load the dynamic library and register the op with the TensorFlow
framework. `load_op_library` returns a Python module that contains the Python framework. `load_op_library` returns a Python module that contains the Python
wrappers for the op and the kernel. Thus, once you have built the op, you can wrappers for the op and the kernel. Thus, once you have built the op, you can
do the following to run it from Python : do the following to run it from Python:
```python ```python
import tensorflow as tf import tensorflow as tf