Table of Contents
Solve include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory under Ubuntu
1. Confirm that CUDA is installed correctly
Step 1: Download CUDA Toolkit
Step 2: Install CUDA Toolkit
2. Add the path to CUDA
3. Install NVIDIA driver
Step 1: Execute the following command in the terminal to add the NVIDIA driver installation source:
Step 2: Execute the following command to install the recommended version of NVIDIA driver:
4. Recompile the project
Solution to include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory
When using the Ubuntu operating system for deep learning development, you may sometimes encounter??include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory? ?mistake. This error is usually caused by missing CUDA related header files. This article explains how to resolve this error.
1. Confirm that CUDA has been installed correctly
First, we need to make sure that CUDA is installed correctly. Execute the following command in the terminal to check the installation of CUDA:
plaintextCopy codenvcc --version
If the output is similar to ??nvcc: NVIDIA (R) Cuda compiler driver??, CUDA has been installed successfully. If CUDA is not installed, you can install it by following these steps:
Step 1: Download CUDA Toolkit
Visit the CUDA download page of the NVIDIA official website to download the CUDA Toolkit suitable for your system version.
Step 2: Install CUDA Toolkit
Navigate to the CUDA Toolkit download directory in the terminal and install it using the following command:
plaintextCopy codesudo dpkg -i cuda_<version>.deb sudo apt-get update sudo apt-get install cuda
Note that ??? should match the version number of the CUDA Toolkit you downloaded.
2. Add the path to CUDA
Next, we need to add the path of CUDA to the system’s environment variables. Open a terminal and execute the following command:
plaintextCopy codesudo nano /etc/environment
In the text editor that opens, find the line ??PATH="?? and add the following at the end within the quotation marks:
plaintextCopy code:/usr/local/cuda/bin
Press ??Ctrl + X?? to save and exit.
3. Install NVIDIA driver
Make sure the NVIDIA driver is installed correctly. Execute the following command in the terminal to check the installation status:
plaintextCopy codenvidia-smi
If the information related to the NVIDIA graphics card can be displayed correctly, it means that the driver has been successfully installed. If the NVIDIA driver is not installed, you can install it by following these steps:
Step 1: Execute the following command in the terminal to add the NVIDIA driver installation source:
plaintextCopy codesudo add-apt-repository ppa:graphics-drivers/ppa sudo apt-get update
Step 2: Execute the following command to install the recommended version of NVIDIA driver:
plaintextCopy codesudo ubuntu-drivers autoinstall sudo reboot
The system will automatically install the NVIDIA driver suitable for your system hardware and take effect after restarting.
4. Recompile the project
Finally, recompile the code in your project directory. First, enter the project directory:
plaintextCopy codecd <your_project_dir>
Then execute the following command to recompile the project:
plaintextCopy codemake clean make
This will recompile the project and resolve the ??include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory?? error. If the project compiles successfully, you can continue to develop and use it. The above are the steps to solve the ??include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory?? error under Ubuntu. By installing CUDA correctly, adding the path to CUDA, installing the NVIDIA driver and recompiling the project, you should be able to successfully resolve this issue. Hope this article can be helpful to you!
Suppose we are using the Darknet framework for target detection, but encountered during the compilation process??include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory?? mistake. Here is a sample code showing how to solve this problem:
bashCopy code# Step 1: Install CUDA and NVIDIA drivers # Please refer to the above for specific installation steps. # Step 2: Add the path of CUDA to the system environment variable # Open the terminal and execute the following command sudo nano /etc/environment # Find the line `PATH="` in the text editor and add the following content at the end in quotation marks :/usr/local/cuda/bin # Press `Ctrl + X` to save and exit # Step 3: Recompile the Darknet project # Enter the directory of the Darknet project cd <darknet_project_dir> # Clear previous compilation results make clean #Compile project make
In this example, we assume that you have correctly installed the CUDA and NVIDIA drivers according to the previous steps, and added the path to CUDA to the system environment variable. Then, we enter the directory of the Darknet project, clear the previous compilation results by executing ??make clean??, and then recompile the project by executing ??make??. In this way, we have solved the error of??include/darknet.h:14:14: fatal error: cuda_runtime.h: No such file or directory??. You should now be able to successfully compile the Darknet project and proceed with target detection and other related tasks. Please note that the above example code only shows the steps to resolve the error and does not involve actual Darknet code. In fact, the Darknet framework provides a complete application programming interface (API). You can use Darknet functions and classes to develop your own target detection applications. The specific Darknet code can be found in Darknet’s official documentation or GitHub repository.
CUDA is a parallel computing platform and programming model developed by NVIDIA. It allows developers to write code in C or C++ to take advantage of the parallel processing power of the GPU to accelerate computationally intensive tasks. The CUDA header file contains a series of function and constant declarations for developers to reference and use. In CUDA development, there are some important header files to understand:
- cuda_runtime.h: This header file is the main header file of the CUDA runtime API and contains declarations of commonly used functions, structures and constants. Functions such as ?
?cudaMalloc?? and ??cudaMemcpy?? are used to allocate memory and transfer data between the host and the device. It also defines commonly used data types and error codes. - cuda.h: This is the main header file of the old version of CUDA, corresponding to CUDA 2.0 and previous versions. But in newer versions, most functions and constants have been moved to cuda_runtime.h, so cuda_runtime.h may be more commonly used in new CUDA versions instead of cuda.h.
- cuda_device_runtime_api.h: This header file contains declarations of device-related runtime API functions. For example, ?
?cudaDeviceSynchronize?? is used to wait for tasks on the device to complete. In addition to these main header files, there are many other CUDA header files for specific functions and libraries, such as:
- cufft.h: The header file of the CUDA Fast Fourier Transform (CUDA FFT) library, used to implement high-performance fast Fourier transform.
- curand.h: The header file of the CUDA random number generation library, used to generate random numbers on the GPU, supports multiple distributions and random number generation algorithms.
- cusparse.h: The header file of the CUDA sparse matrix library, used to efficiently handle sparse matrix operations. These header files provide a wealth of functions and data types, which can help developers utilize the parallel computing capabilities of the GPU to efficiently implement various computing-intensive tasks. It should be noted that CUDA header files are usually associated with the NVIDIA driver version and the CUDA Toolkit version. During the development process, you need to select the corresponding header file according to the driver and Toolkit version used.
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