pip install dlib with cuda

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Sure, I'd be happy to guide you through the process of installing dlib with CUDA support using pip. CUDA is a parallel computing platform and application programming interface model created by NVIDIA. It's used to accelerate computations, and dlib can take advantage of it for faster face recognition and other tasks.
Step 1: Install CUDA Toolkit and cuDNN
Before installing dlib with CUDA, you need to have the CUDA Toolkit and cuDNN installed on your system. Visit the NVIDIA website to download and install the latest versions of both:
Ensure that the CUDA Toolkit and cuDNN versions are compatible with your GPU and operating system.
Step 2: Install CMake
CMake is a cross-platform build system required by dlib. You can install it using your system's package manager or download it from the CMake website.
Step 3: Install Visual Studio (Windows Only)
If you're on Windows, you'll need Visual Studio to compile dlib. You can download and install the Visual Studio Community Edition if you don't already have it.
Step 4: Install Boost
dlib relies on the Boost C++ libraries. You can install them using your system's package manager or download them from the Boost website.
Step 5: Install dlib with CUDA Support
Now, you can install dlib with CUDA support using pip. Open your terminal or command prompt and run the following command:
This command will attempt to install dlib without CUDA support. If you want to enable CUDA, you need to build dlib from source. Run the following command:
This command installs dlib directly from the GitHub repository. During the installation, dlib will detect CUDA and cuDNN if they are correctly installed on your system, and it will build with GPU support.
Step 6: Verify Installation
After the installation is complete, you can verify that dlib is correctly installed with CUDA support by running the following Python script:
If everything is set up correctly, you should see "CUDA Support: True."
That's it! You've successfully installed dlib with CUDA support. Now, you can use dlib for various computer vision tasks with GPU acceleration.
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