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neural-style Installation

This guide will walk you through the setup for neural-style on Ubuntu.

Step 1: Install torch7

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.

Step 2: Install loadcaffe

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

Step 3: Install neural-style

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

(Optional) Step 4: Install CUDA

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:

+-----------------------------------------------------------------------------+ | 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 | +-----------------------------------------------------------------------------+ ```

(Optional) Step 5: Install CUDA backend for torch

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

(Optional) Step 6: Install cuDNN

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.

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