#neural-style Installation
This guide will walk you through the setup for neural-style on Ubuntu.
First we need to install torch, following the installation instructions here:
# in a terminal, run the commands
cd ~/
curl -s https://raw.githubusercontent.com/torch/ezinstall/master/install-deps | bash
git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; ./install.sh
The first script installs all dependencies for torch and may take a while.
The second script actually installs lua and torch.
The second script also edits your .bashrc file so that torch is added to your PATH variable;
we need to source it to refresh our environment variables:
source ~/.bashrc
To check that your torch installation is working, run the command th to enter the interactive shell.
To quit just type exit.
loadcaffe depends on Google's Protocol Buffer library
so we'll need to install that first:
sudo apt-get install libprotobuf-dev protobuf-compiler
Now we can instal loadcaffe:
luarocks install loadcaffe
First we clone neural-style from GitHub:
cd ~/
git clone https://github.com/jcjohnson/neural-style.git
cd neural-style
Next we need to download the pretrained neural network models:
sh models/download_models.sh
You should now be able to run neural-style in CPU mode like this:
th neural_style.lua -gpu -1 -print_iter -1
If everything is working properly you should see output like this:
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:505] Reading dangerously large protocol message. If the message turns out to be larger than 1073741824 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 574671192
Successfully loaded models/VGG_ILSVRC_19_layers.caffemodel
conv1_1: 64 3 3 3
conv1_2: 64 64 3 3
conv2_1: 128 64 3 3
conv2_2: 128 128 3 3
conv3_1: 256 128 3 3
conv3_2: 256 256 3 3
conv3_3: 256 256 3 3
conv3_4: 256 256 3 3
conv4_1: 512 256 3 3
conv4_2: 512 512 3 3
conv4_3: 512 512 3 3
conv4_4: 512 512 3 3
conv5_1: 512 512 3 3
conv5_2: 512 512 3 3
conv5_3: 512 512 3 3
conv5_4: 512 512 3 3
fc6: 1 1 25088 4096
fc7: 1 1 4096 4096
fc8: 1 1 4096 1000
WARNING: Skipping content loss
Iteration 1 / 1000
Content 1 loss: 2091178.593750
Style 1 loss: 30021.292114
Style 2 loss: 700349.560547
Style 3 loss: 153033.203125
Style 4 loss: 12404635.156250
Style 5 loss: 656.860304
Total loss: 15379874.666090
Iteration 2 / 1000
Content 1 loss: 2091177.343750
Style 1 loss: 30021.292114
Style 2 loss: 700349.560547
Style 3 loss: 153033.203125
Style 4 loss: 12404633.593750
Style 5 loss: 656.860304
Total loss: 15379871.853590
If you have a CUDA-capable GPU from NVIDIA then you can
speed up neural-style with CUDA.
First download and unpack the local CUDA installer from NVIDIA; note that there are different installers for each recent version of Ubuntu:
# For Ubuntu 14.10
wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/rpmdeb/cuda-repo-ubuntu1410-7-0-local_7.0-28_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1410-7-0-local_7.0-28_amd64.deb
# For Ubuntu 14.04
wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/rpmdeb/cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb
# For Ubuntu 12.04
http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/rpmdeb/cuda-repo-ubuntu1204-7-0-local_7.0-28_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1204-7-0-local_7.0-28_amd64.deb
Now update the repository cache and install CUDA. Note that this will also install a graphics driver from NVIDIA.
sudo apt-get update
sudo apt-get install cuda
At this point you may need to reboot your machine to load the new graphics driver.
After rebooting, you should be able to see the status of your graphics card(s) by running
the command nvidia-smi; it should give output that looks something like this:
Sun Sep 6 14:02:59 2015
+------------------------------------------------------+
| NVIDIA-SMI 346.96 Driver Version: 346.96 |
|-------------------------------+----------------------+----------------------+
| 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 | 0000:01:00.0 On | N/A |
| 22% 49C P8 18W / 250W | 1091MiB / 12287MiB | 3% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX TIT... Off | 0000:04:00.0 Off | N/A |
| 29% 44C P8 27W / 189W | 15MiB / 6143MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 GeForce GTX TIT... Off | 0000:05:00.0 Off | N/A |
| 30% 45C P8 33W / 189W | 15MiB / 6143MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1277 G /usr/bin/X 631MiB |
| 0 2290 G compiz 256MiB |
| 0 2489 G ...s-passed-by-fd --v8-snapshot-passed-by-fd 174MiB |
+-----------------------------------------------------------------------------+
This is easy:
luarocks install cutorch
luarocks install cunn
You can check that the installation worked by running the following:
th -e "require 'cutorch'; require 'cunn'; print(cutorch)"
This should produce output like the this:
{
getStream : function: 0x40d40ce8
getDeviceCount : function: 0x40d413d8
setHeapTracking : function: 0x40d41a78
setRNGState : function: 0x40d41a00
getBlasHandle : function: 0x40d40ae0
reserveBlasHandles : function: 0x40d40980
setDefaultStream : function: 0x40d40f08
getMemoryUsage : function: 0x40d41480
getNumStreams : function: 0x40d40c48
manualSeed : function: 0x40d41960
synchronize : function: 0x40d40ee0
reserveStreams : function: 0x40d40bf8
getDevice : function: 0x40d415b8
seed : function: 0x40d414d0
deviceReset : function: 0x40d41608
streamWaitFor : function: 0x40d40a00
withDevice : function: 0x40d41630
initialSeed : function: 0x40d41938
CudaHostAllocator : torch.Allocator
test : function: 0x40ce5368
getState : function: 0x40d41a50
streamBarrier : function: 0x40d40b58
setStream : function: 0x40d40c98
streamBarrierMultiDevice : function: 0x40d41538
streamWaitForMultiDevice : function: 0x40d40b08
createCudaHostTensor : function: 0x40d41670
setBlasHandle : function: 0x40d40a90
streamSynchronize : function: 0x40d41590
seedAll : function: 0x40d414f8
setDevice : function: 0x40d414a8
getNumBlasHandles : function: 0x40d409d8
getDeviceProperties : function: 0x40d41430
getRNGState : function: 0x40d419d8
manualSeedAll : function: 0x40d419b0
_state : userdata: 0x022fe750
}
You should now be able to run neural-style in GPU mode:
th neural_style.lua -gpu 0 -print_iter 1
cuDNN is a library from NVIDIA that efficiently implements many of the operations (like convolutions and pooling) that are commonly used in deep learning.
After registering as a developer with NVIDIA, you can download cuDNN here.
After dowloading, you can unpack and install cuDNN like this:
tar -xzvf cudnn-6.5-linux-x64-v2.tgz
cd cudnn-6.5-linux-x64-v2/
sudo cp libcudnn* /usr/local/cuda-7.0/lib64
sudo cp cudnn.h /usr/local/cuda-7.0/include
Next we need to install the torch bindings for cuDNN:
luarocks install cudnn
You should now be able to run neural-style with cuDNN like this:
th neural_style.lua -gpu 0 -backend cudnn
Note that the cuDNN backend can only be used for GPU mode.