Ignore this post if you can use use NVidia Docker...
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This is under test by a few people.
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This is under test by a few people.
Install Tensorflow 1.3
Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
Requires CUDA toolkit 8.0 and CuDNN v5.
https://www.tensorflow.org/install/install_linux#InstallingVirtualenv
Uses:
Ubuntu 16.04 (Nvidia cuda 8.0 supports 14.04 and 16.04)
Python 3.5
Python 3.5
Tensorflow 1.3 (requires cuda 8.0 and cudnn 6.0)
Nvidia driver version 387
Cuda 8.0
Cuda 8.0
cuDNN 6.0
Keras 2.0Tensorflow has Nvidia driver and lib deps
https://www.tensorflow.org/install/install_linux
NVIDIA requirements to run TensorFlow with GPU support
If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system:
- CUDA® Toolkit 8.0. For details, see NVIDIA's documentation. Ensure that you append the relevant Cuda pathnames to the
LD_LIBRARY_PATHenvironment variable as described in the NVIDIA documentation. - The NVIDIA drivers associated with CUDA Toolkit 8.0.
- cuDNN v6. For details, see NVIDIA's documentation. Ensure that you create the
CUDA_HOMEenvironment variable as described in the NVIDIA documentation. - GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards.
- The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, issue the following command:
$ sudo apt-get install libcupti-dev
# 0. Setup the sudoers
http://programmingmatrix.blogspot.com/2017/10/ubuntu-add-user-to-shudders-file.html
sudo apt-get -y install python-pip
sudo apt-get -y install virtualenv
sudo apt-get -y install virtualenvwrapper
pip install virtualenv
pip install virtualenvwrapper
find / -name virtualenvwrapper.sh 2> /dev/null
# /usr/share/virtualenvwrapper/virtualenvwrapper.sh
# ls /usr/share/virtualenvwrapper/virtualenvwrapper.sh
# ls /home/depappas/.local/bin/virtualenvwrapper.sh
# 2. Install OpenCVsudo apt-get install libopencv-devfind / -name virtualenvwrapper.sh 2> /dev/null
# /usr/share/virtualenvwrapper/virtualenvwrapper.sh
# ls /usr/share/virtualenvwrapper/virtualenvwrapper.sh
# ls /home/depappas/.local/bin/virtualenvwrapper.sh
sudo apt-get -y install python-opencv
#3. Set up the environment
# Keras/Tensorflow setup in ~/.bashrc
# Tensorflow setup
export CUDA_ROOT=/usr/local/cuda
export LD_LIBRARY_PATH=$CUDA_ROOT/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$CUDA_ROOT/lib64:$CUDA_ROOT/extras/CUPTI/lib64"
export CUDNN_ROOT=/usr/local/cuda
export CPATH=$CUDNN_ROOT/include:$CPATH
export KERAS_BACKEND=tensorflow
export CUDA_ROOT=/usr/local/cuda
# Python VM
# OSX source /usr/local/bin/virtualenvwrapper.sh
source /home/depappas/.local/bin/virtualenvwrapper.sh # Ubuntu
export WORKON_HOME=~/python_virtual_env
# 4.
source ~/.bashrc
cd ~
mkdir python_virtual_env
cd python_virtual_env
virtualenv --system-site-packages -p python3.5 deep_learning_3.5
workon deep_learning_3.5
# now you are using the pip in the .virtualenvs/test tree
# if pip install does not work upgrade pip
easy_install --upgrade pip
# 5. Install net-tools and configure ssh
sudo apt -y install net-tools
http://ubuntuhandbook.org/index.php/2016/04/enable-ssh-ubuntu-16-04-lts/
# OSX source /usr/local/bin/virtualenvwrapper.sh
source /home/depappas/.local/bin/virtualenvwrapper.sh # Ubuntu
export WORKON_HOME=~/python_virtual_env
# 4.
source ~/.bashrc
cd ~
mkdir python_virtual_env
cd python_virtual_env
virtualenv --system-site-packages -p python3.5 deep_learning_3.5
workon deep_learning_3.5
# now you are using the pip in the .virtualenvs/test tree
# if pip install does not work upgrade pip
easy_install --upgrade pip
# 5. Install net-tools and configure ssh
sudo apt -y install net-tools
http://ubuntuhandbook.org/index.php/2016/04/enable-ssh-ubuntu-16-04-lts/
sudo apt-get -y install openssh-server
sudo service ssh status
sudo emacs /etc/ssh/sshd_config
# Uncomment PermitRootLogin prohibit-password
sudo service ssh restart
# set up passwordless login
http://programmingmatrix.blogspot.com/2017/10/ssh-keyless-login-on-ubuntu.html
# 6. Check NVIDIA Compute Capability of your GPU card
#
# https://developer.nvidia.com/cuda-gpus
2. Install (and activate) the latest Nvidia graphics drivers
# 7. Install the Nvidia 384 driver, CUDA 8.0, and CUDNN 5.1 (may change this to 6.0)
sudo service ssh status
sudo emacs /etc/ssh/sshd_config
# Uncomment PermitRootLogin prohibit-password
sudo service ssh restart
# set up passwordless login
http://programmingmatrix.blogspot.com/2017/10/ssh-keyless-login-on-ubuntu.html
# 6. Check NVIDIA Compute Capability of your GPU card
#
# https://developer.nvidia.com/cuda-gpus
# if the machine locks up or you have an older driver installed
sudo apt-get purge nvidia* # or what ever old driver you are using
To add the Proprietary GPU Drivers PPA in Ubuntu or Linux Mint and update the software sources, use the following commands:
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt-get install nvidia-387
# alternatively
2. Install (and activate) the latest Nvidia graphics drivers
From System Settings or directly from the menu / Dash, open Software & Updates, click on the "Additional Drivers" tab, select the driver (I selected 387 which works on Ubuntu 16.04 and a 1060 GPU) you want to use, and click "Apply changes":
# 7. Install the Nvidia 384 driver, CUDA 8.0, and CUDNN 5.1 (may change this to 6.0)
# 8. Install the libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface.
# This library provides advanced profiling support.
# To install this library, issue the following command:
sudo apt-get -y install libcupti-dev
# 9. Check that the GPU is detected
# https://github.com/tensorflow/tensorflow/issues/394
#
sudo reboot
nvidia-smi
Found 1 NVIDIA devices
Device ID: 0
Device name: GeForce GTX TITAN X (*PrimaryCard)
GPU internal ID: 0420115018258
Tue Dec 15 23:56:17 2015
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| 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 TIT... Off | 0000:04:00.0 On | N/A |
| 22% 33C P8 17W / 250W | 441MiB / 12287MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1270 G /usr/bin/X 174MiB |
| 0 2183 G compiz 112MiB |
| 0 2575 G ...ves-passed-by-fd --v8-snapshot-passed-by- 127MiB |
# 9. Misc. install
sudo apt-get -y install pandoc
# 10. Install Python packages
pip3 install keras
pip3 install h5py
pip3 install numpy
pip3 install matplotlib
pip3 install gensim
pip3 install ioutils
pip3 install Cython
# https://github.com/jazzsaxmafia/video_to_sequence/issues/3
pip3 install opencv-python
pip3 install keras
pip3 install sklearn
pip3 install pypandoc
pip3 install pandoc
https://stackoverflow.com/questions/39817645/cuda-cudnn-installed-but-tensorflow-cant-use-the-gpu
https://www.tensorflow.org/install/install_linux#InstallingVirtualenv
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Don't do the following, this is just an example:
# select a url to install based on your version requirements
https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package
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Do this:
export tfBinaryURL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp35-cp35m-linux_x86_64.whl
# This library provides advanced profiling support.
# To install this library, issue the following command:
sudo apt-get -y install libcupti-dev
# 9. Check that the GPU is detected
# https://github.com/tensorflow/tensorflow/issues/394
#
sudo reboot
nvidia-smi
Found 1 NVIDIA devices
Device ID: 0
Device name: GeForce GTX TITAN X (*PrimaryCard)
GPU internal ID: 0420115018258
Tue Dec 15 23:56:17 2015
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| 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 TIT... Off | 0000:04:00.0 On | N/A |
| 22% 33C P8 17W / 250W | 441MiB / 12287MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1270 G /usr/bin/X 174MiB |
| 0 2183 G compiz 112MiB |
| 0 2575 G ...ves-passed-by-fd --v8-snapshot-passed-by- 127MiB |
sudo apt-get -y install pandoc
sudo apt-get -y install graphviz
sudo apt-get -y install pandoc
sudo apt-get -y install pandoc
pip3 install keras
pip3 install h5py
pip3 install numpy
pip3 install matplotlib
pip3 install gensim
pip3 install ioutils
pip3 install Cython
# https://github.com/jazzsaxmafia/video_to_sequence/issues/3
pip3 install opencv-python
pip3 install keras
pip3 install sklearn
pip3 install pypandoc
pip3 install pandoc
pip3 install keras_diagram
pip3 install tensorflow-gpu
pip3 install h5py
pip3 install seaborn
pip3 install python-flake8
pip3 install pandas
pip3 install pydot
pip3 install pydot-ng
# 11. Install Tensorflow
# don't do this!
# pip3 install tensorflow
pip3 install tensorflow-gpu
pip3 install h5py
pip3 install seaborn
pip3 install python-flake8
pip3 install pandas
pip3 install pydot
pip3 install pydot-ng
# 11. Install Tensorflow
# don't do this!
# pip3 install tensorflow
# pip3 install --upgrade tensorflow-gpu
https://stackoverflow.com/questions/39817645/cuda-cudnn-installed-but-tensorflow-cant-use-the-gpu
https://www.tensorflow.org/install/install_linux#InstallingVirtualenv
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- (Optional) If Step 4 failed (typically because you invoked a pip version lower than 8.1), install TensorFlow in the active virtualenv environment by issuing a command of the following format:
(tensorflow)$ pip install --upgrade tfBinaryURL # Python 2.7 (tensorflow)$ pip3 install --upgrade tfBinaryURL # Python 3.n
wheretfBinaryURLidentifies the URL of the TensorFlow Python package. The appropriate value oftfBinaryURLdepends on the operating system, Python version, and GPU support. Find the appropriate value fortfBinaryURLfor your system here. For example, if you are installing TensorFlow for Linux, Python 3.4, and CPU-only support, issue the following command to install TensorFlow in the active virtualenv environment:(tensorflow)$ pip3 install --upgrade \ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl
# select a url to install based on your version requirements
https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package
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Do this:
# Python 3.5 GPU support
pip3 install --upgrade $tfBinaryURL
# 12. test that tensorflow links with libcudnn
workon deep learning_3.5
python
# 13. Check the Tensorflow version
python -c "import tensorflow; print(tensorflow.__version__)"
# 14. Test that Tensorflow is using the GPU
python -c "import tensorflow as tf; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))"
##### Expected Output #########
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/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 5.66GiB
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: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0
I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0
# http://stackoverflow.com/questions/43335531/how-to-use-sse4-1-instructions-without-install-tensorflow-from-source
# 15. Setup Keras
For best performance, set `image_data_format="channels_last"` in your Keras config at ~/.keras/keras.json.
# Or use env vars
export KERAS_BACKEND=tensorflow
# 16. test Keras
git clone http://github.com/rcmalli/keras-squeezenet.git
cd keras-squeezenet
python test.py
Using TensorFlow backend.
Downloading data from https://github.com/rcmalli/keras-squeezenet/releases/download/v1.0/squeezenet_weights_tf_dim_ordering_tf_kernels.h5
4530176/5059384 [=========================>....] - ETA: 0s2017-10-18 20:57:06.845299: 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.
2017-10-18 20:57:06.845317: 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.
2017-10-18 20:57:06.845321: 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.
2017-10-18 20:57:06.845324: 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.
2017-10-18 20:57:06.845326: 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.
2017-10-18 20:57:06.952926: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-10-18 20:57:06.953146: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 5.79GiB
2017-10-18 20:57:06.953159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-10-18 20:57:06.953162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-10-18 20:57:06.953167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0)
(deep_learning_3.5) depappas@berlin:~/keras-squeezenet$
# 17. Install TFLearn
# http://tflearn.org/installation/
# TFLearn Installation
# To install TFLearn, the easiest way is to run one of the following options.
# For the bleeding edge version:
pip install git+https://github.com/tflearn/tflearn.git
# For the latest stable version:
pip install tflearn
#You can also install from source by running this command (from source folder):
python setup.py install
