Machine learning essentially involves a ton of trial and error. You're letting a program try millions of different settings to land on an algorithm that sort of does what you want it to do. This process is really really slow unless you have the hardware required to speed this up.
The type of computations that the process does are well suited for graphics cards, rather than regular processors. **It is pretty much required that you run the training process on a desktop or server capable GPU.** Running this on your CPU means it can take weeks to train your model, compared to several hours on a GPU.
## Hardware Requirements
**TL;DR: you need at least one of the following:**
- **A powerful CPU**
- Laptop CPUs can often run the software, but will not be fast enough to train at reasonable speeds
- **A powerful GPU**
- Currently only Nvidia GPUs are supported. AMD graphics cards are not supported.
This is not something that we have control over. It is a requirement of the Tensorflow library.
- The GPU needs to support at least CUDA Compute Capability 3.0 or higher.
To see which version your GPU supports, consult this list: https://developer.nvidia.com/cuda-gpus
Desktop cards later than the 7xx series are most likely supported.
**In its current iteration, the project relies heavily on the use of the command line, although a gui is available. if you are unfamiliar with command line tools, you may have difficulty setting up the environment and should perhaps not attempt any of the steps described in this guide.** This guide assumes you have intermediate knowledge of the command line.
- [virtualenv](https://github.com/pypa/virtualenv) and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io) may help when you are not using docker.
- If you are using an Nvidia graphics card You should install CUDA (https://developer.nvidia.com/cuda-zone) and CUDNN (https://developer.nvidia.com/cudnn). If you do not plan to build Tensorflow yourself, make sure you install no higher than version 9.0 of CUDA and 7.0.x of CUDNN
- dlib is required for face recognition and is compiled as part of the setup process. You will need the following applications for your os to successfully install dlib (nb: list may be incomplete. Please raise an issue if another prerequisite is required for your OS):
Simply download the code from http://github.com/deepfakes/faceswap - For development it is recommended to use git instead of downloading the code and extracting it.
For now, extract the code to a directory where you're comfortable working with it. Navigate to it with the command line. For our example we will use `~/faceswap/` as our project directory.
If you are experiencing issues, please raise them in the [faceswap-playground](https://github.com/deepfakes/faceswap-playground) repository instead of the main repo.
