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+# Overview
+
+Neural Artistic Style relies on these dependencies:
+
+ * [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads)
+ * [cuDNN](https://developer.nvidia.com/cudnn) v3
+ * [DeepPy](http://github.com/andersbll/deeppy), Deep learning in Python.
+ * [CUDArray](http://github.com/andersbll/cudarray), CUDA-accelerated NumPy.
+ * [Pretrained VGG 19 model](http://www.vlfeat.org/matconvnet/pretrained), choose *imagenet-vgg-verydeep-19*.
+
+The detailed installation steps are explained in the sections below.
+
+Note: This installation in mainly for GNU/Linux distributions.
+
+# Neural Artistic Style
+
+1. Download Neural Artistic Style:
+
+    ```
+    $ git clone https://github.com/andersbll/neural_artistic_style.git
+    ```
+
+# CUDA
+
+Please refer to the [Installation Guides](http://docs.nvidia.com/cuda/index.html#installation-guides) provided by nVidia.
+
+The CUDA toolkit should be installed at `/usr/local/cuda/`.
+
+# cuDNN
+
+1. Download [cuDNN](https://developer.nvidia.com/cudnn) v3.
+1. Extract the tarball file to the CUDA directory:
+
+    ```
+    $ sudo tar xzf cudnn-7.0-linux-x64-v3.0-prod.tgz -C /usr/local
+    ```
+
+   The tarball file consists of libcudnn static and shared object libraries, and
+   the library header.
+
+# CUDArray
+
+1. Download CUDArray:
+
+    ```
+    $ git clone https://github.com/andersbll/cudarray.git
+    ```
+
+1. Build CUDArray:
+
+   Before building CUDArray, please make sure Cython>=0.21 has been installed.  If
+   not, you can install Cython via Pip:
+
+    ```
+    $ pip install --user --upgrade cython
+    ```
+
+   Start to build:
+
+    ```
+    # Install shared object library
+    $ make
+    $ sudo make install  # install into /usr/local/lib by default
+    $ echo "export LD_LIBRARY_PATH=\"/usr/local/lib:\$LD_LIBRARY_PATH\"" >> $HOME/.bashrc
+    $ source $HOME/.bashrc
+
+    # Install Python modules
+    $ sudo python setup.py install
+    ```
+
+   If you get the error messages when executing `make`, that means you might be
+   using cuDNN v4 instead of v3 (Issue [#36](https://github.com/andersbll/cudarray/issues/36)):
+
+    ```
+    src/nnet/cudnn.cpp:206:5: error: cannot convert ‘const float*’ to ‘cudnnConvolutionBwdFilterAlgo_t’ for argument ‘8’ to ‘cudnnStatus_t cudnnConvolutionBackwardFilter(cudnnHandle_t, const void*, cudnnTensorDescriptor_t, const void*, cudnnTensorDescriptor_t, const void*, cudnnConvolutionDescriptor_t, cudnnConvolutionBwdFilterAlgo_t, void*, size_t, const void*, cudnnFilterDescriptor_t, void*)’
+         ));
+         ^
+    ./include/cudarray/nnet/cudnn.hpp:85:44: note: in definition of macro ‘CUDNN_CHECK’
+     #define CUDNN_CHECK(status) { cudnn_check((status), __FILE__, __LINE__); }
+ 
+    compilation terminated due to -Wfatal-errors.
+    make: *** [src/nnet/cudnn.o] Error 1
+    ```
+
+# DeepPy
+
+1. Download DeepPy:
+
+    ```
+    $ git clone https://github.com/andersbll/deeppy.git
+    ```
+
+1. Copy the `deeppy` module direcotry into the Neural Artistic Style directory:
+
+    ```
+    $ cp -a deeppy/deeppy neural_artistic_style
+    ```
+
+# Pretrained VGG 19 Model
+
+1. Download the pretrained VGG 19 model:
+
+    ```
+    $ cd neural_artistic_style
+    $ wget http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-19.mat
+    ```
+
+The model size is around 510 MB.
+
+# Troubleshooting
+
+## Out of Memory Issue
+
+If you get the out of memory error messages when executing
+neural_artistic_style.py (Issue [#26](https://github.com/andersbll/neural_artistic_style/issues/26)):
+
+    ```
+    Traceback (most recent call last):
+      File "neural_artistic_style.py", line 138, in <module>
+        run()
+      File "neural_artistic_style.py", line 130, in run
+        cost = np.mean(net.update())
+      File "neural_artistic_style/style_network.py", line 130, in update
+        next_x = layer.fprop(next_x)
+      File "neural_artistic_style/deeppy/feedforward/convnet_layers.py", line 71, in fprop
+        poolout = self.pool_op.fprop(x)
+      File "/usr/local/lib/python2.7/dist-packages/cudarray-0.1.dev-py2.7-linux-x86_64.egg/cudarray/nnet/pool.py", line 34, in fprop
+        poolout = ca.empty(poolout_shape, dtype=imgs.dtype)
+      File "/usr/local/lib/python2.7/dist-packages/cudarray-0.1.dev-py2.7-linux-x86_64.egg/cudarray/cudarray.py", line 246, in empty
+        return ndarray(shape, dtype=dtype)
+      File "/usr/local/lib/python2.7/dist-packages/cudarray-0.1.dev-py2.7-linux-x86_64.egg/cudarray/cudarray.py", line 36, in __init__
+        self._data = ArrayData(self.size, dtype, np_data)
+      File "cudarray/wrap/array_data.pyx", line 16, in cudarray.wrap.array_data.ArrayData.__init__ (./cudarray/wrap/array_data.cpp:1401)
+      File "cudarray/wrap/cudart.pyx", line 12, in cudarray.wrap.cudart.cudaCheck (./cudarray/wrap/cudart.cpp:763)
+    ValueError: out of memory
+    ```
+                                                                                                                                         
+Here are some solutions:
+
+ 1. Use GPU with larger memory.
+ 1. Use smaller input and style images.
+ 1. Use CPU instead of GPU.  Set `CUDARRAY_BACKEND` to 'numpy' as workaround.