30 April 2016

Installing TensorFlow 0.8 for Python 3.4 with CUDA & cuDNN 7.5 on Ubuntu 14.04 Local Machine

ProTip

Don't worry about the Nouveau conflict thing that you might heard of, NVIDIA's installer will take care of it. If you get stuck with some version conflicts, don't spend too much time to fix it. You could just reinstall Ubuntu and start from scratch within minutes, provided you have some understanding of what went wrong. Also note that we will be using latest version of everything, so we'd have to do a few intense builds.

The Essentials

Let's start by updating repos and installing essentials for NVIDIA drivers. 
sudo apt-get update -y
sudo apt-get upgrade -y

sudo apt-get install -y build-essential linux-source
sudo apt-get install -y linux-source linux-headers-`uname -r`
sudo apt-get install -y linux-image-extra-virtual

Installing NVIDIA Binary Drivers

Then download the appropriate version of driver for your graphic card from NVIDIA or GeForce to your local machine. 
cd ~/
wget -O ~/nvidia.run http://us.download.nvidia.com/XFree86/Linux-x86_64/361.42/NVIDIA-Linux-x86_64-361.42.run
For a cleaner install, let's remove any existing NVIDIA drivers 
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove
Reboot and enter Ubuntu recovery mode, activate failsafeX and then enter the terminal. Go to the download directory and change permissions to execute and run the installer. 
sudo service lightdm stop
cd ~/
chmod +x nvidia.run
sudo sh nvidia.run
If it says something like "The distribution-provided pre-install script failed! Are you sure you want to continue?" Answer yes and proceed with the install.

Installing CUDA 7.5

Visit CUDA download page and get the "runfile (local)" version of the installer as other versions might not allow you to exclude driver installation. It's a 1GB+ file. 
wget -O ~/cuda.run http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda_7.5.18_linux.run
sudo sh cuda.run
The installer will prompt for a few things, accept all except for "CUDA Driver installation" - don't accept it. We have already installed the latest version of driver in our previous step. If we accept it, it might overwrite our previous install with a different version and cause version conflicts, so don't.

Installing Miniconda

We'll be using Miniconda to manage Python 3 packages, as I had bad experience trying to get it work with default Ubuntu pip/apt-get packages. 
wget -O ~/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash ~/miniconda.sh -b -p $HOME/miniconda

export PATH="$HOME/miniconda/bin:$PATH"
Let's create a virtual environment specifically for tensorflow in Miniconda conda and install necessary packages. 
conda create -n tensorflow python=3.5
source activate tensorflow
conda install python=3.5 pip numpy -y
If you're getting errors that there's no conda available, exit the terminal and try again.

Installing cuDNN 7.5

Now NVIDIA doesn't allow us to directly download cuDNN (wget) to our machine from their site, you'll get Access Denied if you tried to do so. So we will have to download cuDNN from the browser and move it our working directory. On the download page NVIDIA will ask you to fill up a survery, it's not compulsory - just click the "Proceed To Downloads" button if you are not in a good state of mind to answer a survey rationally, at this point. Just extract the file and move the contents to /usr/local. 
mv ~/Downloads/cudnn-7.5-linux-x64-v5.0-rc.tgz /tmp
cd /tmp
tar -xzf cudnn-7.5-linux-x64-v5.0-rc.tgz
sudo cp /tmp/cuda/lib64/* /usr/local/cuda/lib64
sudo cp /tmp/cuda/include/* /usr/local/cuda/include

Installing Java 8

Latest version of Bazel requires Java 8 but as of now sudo apt-get install openjdk-8-jdk doesn't seem to lead nowhere. So we will have to get it from some private package. 
sudo add-apt-repository ppa:openjdk-r/ppa
sudo apt-get update
sudo apt-get install -y openjdk-8-jdk
Now we have to change default Java version to the latest one we've just installed. 
sudo update-alternatives --config java
sudo update-alternatives --config javac
Select the latest Java version from the list, usually it's the option #2. 
  Selection    Path                                            Priority   Status
------------------------------------------------------------
  0 /usr/lib/jvm/java-7-openjdk-amd64/jre/bin/java   1071      auto mode
  1 /usr/lib/jvm/java-7-openjdk-amd64/jre/bin/java   1071      manual mode
* 2 /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java   1069      manual

Installing Bazel

Bazel is Google's own build tool that will help us compile TensorFlow. Should be straight forward, but be cautious of the permission issues. 
cd /tmp
git clone https://github.com/bazelbuild/bazel.git
cd bazel
./compile.sh
sudo cp /tmp/bazel/output/bazel /usr/bin


export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda
For some reasons, if you're getting the following error 
JDK version (1.7) is lower than 1.8, please set $JAVA_HOME.
you can try to explicitly specify the Java path by 
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/

Installing TensorFlow

Finally let's compile and install TensorFlow. This might take a while to finish. 
cd /tmp
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
cd tensorflow
TF_UNOFFICIAL_SETTING=1 ./configure
Except for the one below, leave everything to their defaults. As message mentions, visit the NVIDIA page check your graphic card's "Compute Capability" value. For instance, according to the page, my GTX 970's value is 5.2, so specify it accordingly. Better not to accept the default. 
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: "3.5,5.2"]: 5.2
Now let the waiting game begin (and hopefully you won't run into disk space issues). 
bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer

bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Now that we have successfully build the .whl file, let install it via pip. Change directory to the output folder and look for the file name, in my case it was tensorflow-0.8.0rc0-py3-none-any.whl. 
cd /tmp/tensorflow_pkg/

ls

pip install --upgrade /tmp/tensorflow_pkg/tensorflow-0.8.0rc0-py3-none-any.whl
Now let's check if everything is working fine. 
cd /tmp/tensorflow/tensorflow/models/image/cifar10/
python cifar10_multi_gpu_train.py

Note

Everytime you want to use tensorflow you have activate the environment by 
source activate tensorflow
#(tensorflow)$  # Your prompt should change.
When you are done using TensorFlow, deactivate the environment. 
source deactivate
I hope you find this useful and helped you save a couple of hours. Please leave me a message if there's any issues.

Troubleshooting

When you're testing TensorFlow, if it says that there's no GPU, try installing linux headers again by 
apt-get install linux-headers-$(uname -r)
Also do the same if you're getting any of the errors below: 
cat /proc/driver/nvidia/version
cat: /proc/driver/nvidia/version: No such file or directory

sudo nvidia-modprobe
modprobe: ERROR: ../libkmod/libkmod-module.c:809 kmod_module_insert_module() could not find module by name='nvidia_352'
modprobe: ERROR: could not insert 'nvidia_352': Function not implemented
If you're still having issues with NVIDIA drive, please refer to NVIDIA's Documentation
Every time after you restart the machine, you'll have to update the env variables. 
export PATH="$HOME/miniconda/bin:$PATH"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/
Or you can permanently add it to ~/.bashrc 
echo "export PATH=$HOME/miniconda/bin:$PATH" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64" >> ~/.bashrc
echo "export CUDA_HOME=/usr/local/cuda" >> ~/.bashrc
echo "export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/" >> ~/.bashrc
If you want to check version of the NVIDIA driver installed, you can try any of the following: 
lspci -nnk | grep -iA2 vga 
lspci | grep -i nvidia
dpkg -l | grep nvidia
dpkg -l | grep ii | grep -i nvidia
nvidia-settings -q NvidiaDriverVersion

References

  • http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux/
  • https://help.ubuntu.com/community/NvidiaManual
  • https://help.ubuntu.com/community/BinaryDriverHowto/Nvidia
  • http://ubuntuforums.org/showthread.php?t=2081649
  • http://conda.pydata.org/docs/help/silent.html
  • http://developer.download.nvidia.com/compute/machine-learning/cudnn/secure/v5/rc/cudnn_install.txt?autho=1461438353_9d110e75f21326804dcc3b7194b8c689&file=cudnn_install.txt
  • https://github.com/tensorflow/tensorflow/issues/1158
  • https://www.tensorflow.org/get_started
  • https://devtalk.nvidia.com/default/topic/920308/how-to-install-cuda-7-5-with-the-newest-nvidia-driver-361-28-/
  • https://devtalk.nvidia.com/default/topic/884586/linux/failed-to-install-cuda-7-5-in-ubuntu-14-04-lts/
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