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INSTALL.md

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+# neural-style-pt Installation
+
+This guide will walk you through multiple ways to setup `neural-style-pt` on Ubuntu and Windows. If you wish to install PyTorch and neural-style-pt on a different operating system like MacOS, installation guides can be found [here](https://pytorch.org).
+
+Note that in order to reduce their size, the pre-packaged binary releases (pip, Conda, etc...) have removed support for some older GPUs, and thus you will have to install from source in order to use these GPUs.
+
+
+# Ubuntu:
+
+## With A Package Manager:
+
+The pip and Conda packages ship with CUDA and cuDNN already built in, so after you have installed PyTorch with pip or Conda, you can skip to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt).
+
+### pip:
+
+The neural-style-pt PyPI page can be found here: https://pypi.org/project/neural-style/
+
+If you wish to install neural-style-pt as a pip package, then use the following command:
+
+```
+# in a terminal, run the command
+pip install neural-style
+```
+
+Or:
+
+
+```
+# in a terminal, run the command
+pip3 install neural-style
+```
+
+Next download the models with:
+
+
+```
+neural-style -download_models
+```
+
+By default the models are downloaded to your home directory, but you can specify a download location with:
+
+```
+neural-style -download_models <download_path>
+```
+
+#### Github and pip:
+
+Following the pip installation instructions
+[here](http://pytorch.org), you can install PyTorch with the following commands:
+
+```
+# in a terminal, run the commands
+cd ~/
+pip install torch torchvision
+```
+
+Or:
+
+```
+cd ~/
+pip3 install torch torchvision
+```
+
+Now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt) to install neural-style-pt.
+
+### Conda:
+
+Following the Conda installation instructions
+[here](http://pytorch.org), you can install PyTorch with the following command:
+
+```
+conda install pytorch torchvision -c pytorch
+```
+
+Now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt) to install neural-style-pt.
+
+## From Source:
+
+### (Optional) Step 1: Install CUDA
+
+If you have a [CUDA-capable GPU from NVIDIA](https://developer.nvidia.com/cuda-gpus) then you can
+speed up `neural-style-pt` 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 18.04
+sudo dpkg -i cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb
+sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
+```
+
+```
+# For Ubuntu 16.04
+sudo dpkg -i cuda-repo-ubuntu1604-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb
+sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
+```
+
+Instructions for downloading and installing the latest CUDA version on all supported operating systems, can be found [here](https://developer.nvidia.com/cuda-downloads).
+
+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:
+
+```
+Wed Apr 11 21:54:49 2018
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 384.90                 Driver Version: 384.90                    |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|===============================+======================+======================|
+|   0  Tesla K80           Off  | 00000000:00:1E.0 Off |                    0 |
+| N/A   62C    P0    68W / 149W |      0MiB / 11439MiB |     94%      Default |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                       GPU Memory |
+|  GPU       PID   Type   Process name                             Usage      |
+|=============================================================================|
+|  No running processes found                                                 |
++-----------------------------------------------------------------------------+
+```
+
+### (Optional) Step 2: 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](https://developer.nvidia.com/cudnn). Make sure that you use the approprite version of cuDNN for your version of CUDA.
+
+After dowloading, you can unpack and install cuDNN like this:
+
+```
+tar -zxvf cudnn-10.1-linux-x64-v7.5.0.56.tgz
+sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
+sudo cp cuda/include/cudnn.h /usr/local/cuda/include
+```
+
+Note that the cuDNN backend can only be used for GPU mode.
+
+### (Optional) Steps 1-3: Install PyTorch with support for AMD GPUs using Radeon Open Compute Stack (ROCm)
+
+
+It is recommended that if you wish to use PyTorch with an AMD GPU, you install it via the official ROCm dockerfile:
+https://rocm.github.io/pytorch.html
+
+- Supported AMD GPUs for the dockerfile are: Vega10 / gfx900 generation discrete graphics cards (Vega56, Vega64, or MI25).
+
+PyTorch does not officially provide support for compilation on the host with AMD GPUs, but [a user guide posted here](https://github.com/ROCmSoftwarePlatform/pytorch/issues/337#issuecomment-467220107) apparently works well.
+
+ROCm utilizes a CUDA porting tool called HIP, which automatically converts CUDA code into HIP code. HIP code can run on both AMD and Nvidia GPUs.
+
+
+### Step 3: Install PyTorch
+
+To install PyTorch [from source](https://github.com/pytorch/pytorch#from-source) on Ubuntu (Instructions may be different if you are using a different OS):
+
+```
+cd ~/
+git clone --recursive https://github.com/pytorch/pytorch
+cd pytorch
+python setup.py install
+
+cd ~/
+git clone --recursive https://github.com/pytorch/vision
+cd vision
+python setup.py install
+```
+
+To check that your torch installation is working, run the command `python` or `python3` to enter the Python interpreter. Then type `import torch` and hit enter.
+
+You can then type `print(torch.version.cuda)` and `print(torch.backends.cudnn.version())` to confirm that you are using the desired versions of CUDA and cuDNN.
+
+To quit just type `exit()` or use  Ctrl-D.
+
+Now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt) to install neural-style-pt.
+
+
+# Windows Installation
+
+If you wish to install PyTorch on Windows From Source or via Conda, you can find instructions on the PyTorch website: https://pytorch.org/
+
+
+### Github and pip
+
+First, you will need to download Python 3 and install it: https://www.python.org/downloads/windows/. I recommend using the executable installer for the latest version of Python 3.
+
+Then using https://pytorch.org/, get the correct pip command, paste it into the Command Prompt (CMD) and hit enter:
+
+
+```
+pip3 install torch===1.3.0 torchvision===0.4.1 -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+
+After installing PyTorch, download the neural-style-pt Github respository and extract/unzip it to the desired location.
+
+Then copy the file path to your neural-style-pt folder, and paste it into the Command Prompt, with `cd` in front of it and then hit enter.
+
+In the example below, the neural-style-pt folder was placed on the desktop:
+
+```
+cd C:\Users\<User_Name>\Desktop\neural-style-pt-master
+```
+
+You can now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt), skipping the `git clone` step.
+
+# Install neural-style-pt
+
+First we clone `neural-style-pt` from GitHub:
+
+```
+cd ~/
+git clone https://github.com/ProGamerGov/neural-style-pt.git
+cd neural-style-pt
+```
+
+Next we need to download the pretrained neural network models:
+
+```
+python models/download_models.py
+```
+
+You should now be able to run `neural-style-pt` in CPU mode like this:
+
+```
+python neural_style.py -gpu c -print_iter 1
+```
+
+If you installed PyTorch with support for CUDA, then should now be able to run `neural-style-pt` in GPU mode like this:
+
+```
+python neural_style.py -gpu 0 -print_iter 1
+```
+
+If you installed PyTorch with support for cuDNN, then you should now be able to run `neural-style-pt` with the `cudnn` backend like this:
+
+```
+python neural_style.py -gpu 0 -backend cudnn -print_iter 1
+```
+
+If everything is working properly you should see output like this:
+
+```
+Iteration 1 / 1000
+  Content 1 loss: 1616196.125
+  Style 1 loss: 29890.9980469
+  Style 2 loss: 658038.625
+  Style 3 loss: 145283.671875
+  Style 4 loss: 11347409.0
+  Style 5 loss: 563.368896484
+  Total loss: 13797382.0
+Iteration 2 / 1000
+  Content 1 loss: 1616195.625
+  Style 1 loss: 29890.9980469
+  Style 2 loss: 658038.625
+  Style 3 loss: 145283.671875
+  Style 4 loss: 11347409.0
+  Style 5 loss: 563.368896484
+  Total loss: 13797382.0
+Iteration 3 / 1000
+  Content 1 loss: 1579918.25
+  Style 1 loss: 29881.3164062
+  Style 2 loss: 654351.75
+  Style 3 loss: 144214.640625
+  Style 4 loss: 11301945.0
+  Style 5 loss: 562.733032227
+  Total loss: 13711628.0
+Iteration 4 / 1000
+  Content 1 loss: 1460443.0
+  Style 1 loss: 29849.7226562
+  Style 2 loss: 643799.1875
+  Style 3 loss: 140405.015625
+  Style 4 loss: 10940431.0
+  Style 5 loss: 553.507446289
+  Total loss: 13217080.0
+Iteration 5 / 1000
+  Content 1 loss: 1298983.625
+  Style 1 loss: 29734.8964844
+  Style 2 loss: 604133.8125
+  Style 3 loss: 125455.945312
+  Style 4 loss: 8850759.0
+  Style 5 loss: 526.118591309
+  Total loss: 10912633.0
+```