Hugging Face's logo

Spaces:
AfrodreamsAI
/
afrodreams
Runtime error

App Files Community
afrodreams / INSTALL.md
Owos's picture
Owos
Upload INSTALL.md
fcd1f82
preview code
|
raw
history blame
10.2 kB

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.

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.

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, 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 to install neural-style-pt.

Conda:

Following the Conda installation instructions here, you can install PyTorch with the following command:

conda install pytorch torchvision -c pytorch

Now continue on to installing neural-style-pt to install neural-style-pt.

From Source:

(Optional) Step 1: Install CUDA

If you have a CUDA-capable GPU from NVIDIA 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.

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. 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 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 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 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, 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