Skip to content

Instantly share code, notes, and snippets.

@25b3nk

25b3nk/tf-r1.9-installation-steps.md

Last active July 31, 2018 06:15
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save 25b3nk/9ae151a39e6d107bf894294586933a8e to your computer and use it in GitHub Desktop.
Save 25b3nk/9ae151a39e6d107bf894294586933a8e to your computer and use it in GitHub Desktop.
Download ZIP
Tensorflow installation on local PC; Ubuntu 16.04; NVIDIA GTX 1050 Ti; CUDA 9.0; GCC 5.4;
Raw
tf-r1.9-installation-steps.md

Building tensorflow from source on Ubuntu 16.04 with Cuda 9.1, 9.0 & 8.0 installed.

Will be trying to install tf-r1.9 with cuda 9.0

Following this link
I already have NVIDIA drivers and CUDA installed. Won't be explaining them here.
Steps:

  1. $ git clone https://github.com/tensorflow/tensorflow
  2. $ cd tensorflow
  3. $ git checkout
  4. You need to install bazel. Follow these instructions
  5. Install pip, dev, numpy & wheel.
    $ sudo apt-get install python-numpy python-dev python-pip python-wheel
    Note: I am installing tensorflow in python 2.7
  6. Configuring the build.
$ cd tensorflow  # cd to the top-level directory created
 
$ ./configure
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.15.2 installed.
Please specify the location of python. [Default is /usr/bin/python]: 


Found possible Python library paths:
  /usr/local/lib/python2.7/dist-packages
  /usr/lib/python2.7/dist-packages
Please input the desired Python library path to use.  Default is [/usr/local/lib/python2.7/dist-packages]

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: 
jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: 
Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: 
Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: 
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: 
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: 
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: 
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 


Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-9.0 


Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1


Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-9.0]:


Do you wish to build TensorFlow with TensorRT support? [y/N]: 
No TensorRT support will be enabled for TensorFlow.

Please specify the NCCL version you want to use. [Leave empty to default to NCCL 1.3]: 


Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1]


Do you want to use clang as CUDA compiler? [y/N]: 
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: 


Do you wish to build TensorFlow with MPI support? [y/N]: 
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: 
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
	--config=mkl         	# Build with MKL support.
	--config=monolithic  	# Config for mostly static monolithic build.
Configuration finished
  1. Now build a pip package for TensorFlow with GPU support
    $ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
    You have to wait for several minutes.
  2. After you get the message stating the build completed successfully, run
    $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
    This creates the python package which you will be installing after it is created.
  3. Check the wheel file created in the /tmp/tensorflow_pkg folder and install the package by
    $ sudo pip install /tmp//tensorflow_pkg/tensorflow-1.9.0-cp27-cp27mu-linux_x86_64.whl
    Note: The wheel file name may change with respect to the settings you have configured.
  4. To check if tensorflow is installed and which version, run
    pip freeze | grep tensorflow
  5. To check if the tensorflow you have installed is GPU supported, go to python terminal and run
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Output will have Adding visible gpu devices: 0, this line and will also print the kind of GPU you are using

Note: To get CUDNN version use
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
Here 7.1 translates to 7 bring CUDNN_MAJOR version and 1 being CUDNN_MINOR version

My system configurations:

  1. GCC

    $ gcc --version
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

  2. NVCC

    $ /usr/local/cuda-9.0/bin/nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

  3. CUDNN

    $ cat /usr/local/cuda-9.0/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 1
#define CUDNN_PATCHLEVEL 4
--
#define CUDNN_VERSION    (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
    
#include "driver_types.h"

  4. NVIDIA driver

    $ nvidia-smi
Tue Jul 31 11:29:52 2018
    
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 105...  Off  | 00000000:01:00.0  On |                  N/A |
|  0%   45C    P8    N/A /  72W |    953MiB /  4038MiB |     15%      Default |
+-------------------------------+----------------------+----------------------+
    
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1058      G   /usr/lib/xorg/Xorg                           388MiB |
|    0      1736      G   /opt/teamviewer/tv_bin/TeamViewer             16MiB |
|    0      1974      G   compiz                                       216MiB |
|    0      2415      G   ...-token=9CCC21897D6A22D6A4C9020F56B9AEA3   329MiB |
+-----------------------------------------------------------------------------+

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment