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By default, installing CUDA9.0 updates your Path. Both CUDA® and cuDNN needs to be accessible by FakeApp. If you do this, remember to follow the next step carefully so that they are still accessible through your Path.Ĭonfigure your Path System Variable. While this is the suggested option, you can keep the cuDNN files in a separate folder.
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To install it, you will need to extract its content and merge it with the CUDA/v9.0 folder in your system (typically: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0):
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While CUDA® Toolkit comes with a proper installer, cuDNN is simply a zip file with all the necessary libraries. FakeApp works with cuDNN 7, so make sure to select the right version.
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You can register for free as an NVIDIA Developer, and then visit the webpage again to access the download link. As such, you will also need to download the CUDA® support for deep neural networks, also known as cuDNN.ĭownloading cuDNN requires a login. ML-Agents uses reinforcement learning to training neural networks. While the CUDA® Toolkit provides the basic set of tools required for GPU computing, it does not include the libraries for certain specialised tasks. The file is rather big, so get ready to wait.ĭuring the installation, choose the “Custom” option and select all of its components.
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“exe (local)” will download the entire installer first. You can choose whichever Installer Type you prefer. Make sure that you select the right version for both CUDA and your OS. Install CUDA® Toolkit 9.0. Now that your NVIDIA drivers are up to date, you can download the actual CUDA® Toolkit from the official CUDA® Toolkit Download page. Names might vary slightly depending on your version of Windows. Alternatively, you can search for “ Device Manager” in your Start bar and check it yourself under “ Display adapters“: If you are unsure about which GPU model you have, you can choose Option 2 which will automatically find it for you. Make sure you are choosing the right model and architecture. You can do so from the official NVIDIA Driver Downloads page. Before being able to use CUDA®, you must update your NVIDIA drivers. This is strongly discouraged, as the process might take weeks, instead of hours. Any graphics card with Compute Capability greater or equal to 3.0 will work.įor instance, all the models on the right will support CUDA®:įakeApp allows to train your models without a GPU. You can check whether your GPU is compatible or not visiting the CUDA GPUs list. Before using that, however, you need to install CUDA®, a parallel computing platform that delegates intensive computation to an NVIDIA GPU.Ĭheck your Graphics Card. Not all graphics cards from NVIDIA have integrated support for GPU Computing. FakeApp uses TensorFlow, a Machine Learning framework which supports GPU-accelerated computation using NVIDIA graphics cards. GPUs are designed to perform operations in parallel, hence they are perfect to train neural networks which are built upon independent neurons working in parallel. GPU stands for Graphics Processing Unit, and is the chip that inside your machine that usually processes graphics inputs.
For this reason, most Machine Learning frameworks (such as Keras and TernsorFlow) can dispatch the computation on a GPU. Despite their cost, the process of training a neural network is highly parallelisable. Installing NVidia CUDA9įakeApp relies on neural networks, which are notoriously expensive to train. Whichever application you are going to use, make sure to download it from an official source, as many are infected with Bitcoin miners and Trojans. There are other softwares available, such as faceswap on GitHub), but FakeApp remains the most accessible one due to its friendly interface.
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For an in-depth discussion on how to get the most out of face-swap technology, you can refer to How To Create The Perfect DeepFakes.įakeApp, like most face-swapping software, is based on the original implementation provided by the Reddit user deepfakes. This tutorial will show you how to install it and use it. There is no doubt that the most accessible application to create deepfakes is FakeApp, which recently hit version 2.2.
To be more precise, creating deepfakes is very easy, but creating good ones is not. Despite what media is claiming, creating deepfakes is not easy.
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