TensorFlow CMake Build

cmake build tensorflow v1.4.0

Depends

OS : Ubuntu 20.04/Ubuntu 18.04/Ubuntu 16.04
local_third_party: tensorflow/tensorflow/contrib/cmake/third_party
- only for tensorflow v1.4.0
- delete all xx/.git
- total_third_party

Build

>>sudo apt update
>>sudo apt install gcc g++ cmake git python-dev python-pip python-numpy swig
>>git clone https://github.com/jnulzl/tf_cmake_build_v1.4
>>cd tf_cmake_build_v1.4
>>mkdir build_ubuntu2004 && cd build_ubuntu2004 # build_ubuntu1804,build_ubuntu1604
>>cmake  ../tensorflow/contrib/cmake -DCMAKE_BUILD_TYPE=Release -Dtensorflow_BUILD_SHARED_LIB=ON -Dtensorflow_VERBOSE=ON -Dtensorflow_USE_LOCAL_THIRD_PARTY=ON
>>make VERBOSE=1 -j4
....
make[2]: Leaving directory '/mnt/data/jnulzl/codes/tensorflow/build_ubuntu2004'
[100%] Built target pywrap_tensorflow_internal
make[1]: Leaving directory '/mnt/data/jnulzl/codes/tensorflow/build_ubuntu2004'
/usr/bin/cmake -E cmake_progress_start /mnt/data/jnulzl/codes/tensorflow/build_ubuntu2004/CMakeFiles 0
>>ls lib*so
libpywrap_tensorflow_internal.so  libtensorflow.so

`Linux CPU`	`Linux GPU`	`Mac OS CPU`	`Windows CPU`	`Android`

TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs. So please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

Installation

See Installing TensorFlow for instructions on how to install our release binaries or how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Nightly pip packages

We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly project on pypi. Simply run pip install tf-nightly in a clean environment to install the nightly tensorflow build. We currently only support CPU packages on Linux, Mac, and Windows. GPU packages on all platforms will arrive soon!

Individual whl files

Linux CPU-only: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
Linux GPU: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
Mac CPU-only: Python 2 (build history) / Python 3 (build history)
Windows CPU-only: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
Windows GPU: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
Android: demo APK, native libs (build history)

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> sess.run(hello)
'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a + b)
42
>>> sess.close()

For more information

Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.

License

Apache License 2.0