Installing CUDA 12.1 on Ubuntu 18.04 with Conda
This article guides you through installing CUDA 12.1 on Ubuntu 18.04 using conda, a popular package manager for Python. This combination allows for seamless integration of CUDA within your conda environment, facilitating high-performance computing and deep learning projects.
Understanding the Importance of CUDA
CUDA, or Compute Unified Device Architecture, is a parallel computing platform and programming model created by NVIDIA. It enables you to utilize the processing power of your NVIDIA GPU for general-purpose computing tasks, significantly accelerating workloads in fields like:
- Machine Learning & Deep Learning: Training and running large-scale deep learning models.
- Scientific Computing: Accelerating simulations and data analysis.
- Graphics Processing: Enhancing image and video processing performance.
Why Use Conda?
Conda is a versatile package manager known for its ability to manage both Python and non-Python packages in isolated environments. This allows you to:
- Control Dependencies: Install and manage specific versions of CUDA and other libraries without conflicts.
- Environment Isolation: Create distinct environments for different projects, ensuring consistency and preventing conflicts.
- Simplified Installation: Install packages with a single command, streamlining the process.
Pre-Installation Steps
Before installing CUDA, ensure your system meets the following prerequisites:
- NVIDIA GPU: Ensure your system has a compatible NVIDIA GPU. Check NVIDIA's website for supported cards and drivers.
- Ubuntu 18.04: This guide assumes you are using Ubuntu 18.04.
- Internet Connection: A stable internet connection is needed to download and install packages.
Installing NVIDIA Drivers
1. Disable the Nouveau Driver
The Nouveau driver is an open-source driver that may conflict with CUDA. Disable it by editing the /etc/modprobe.d/blacklist.conf file:
sudo nano /etc/modprobe.d/blacklist.conf
Add the following line to the file:
blacklist nouveau
2. Install NVIDIA Drivers
Download the latest NVIDIA drivers from the official website () matching your GPU and operating system.
After downloading, run the installation script:
sudo sh NVIDIA-Linux-x86_64-*.run
Replace NVIDIA-Linux-x86_64-*.run with the actual name of the downloaded file. Follow the on-screen instructions to complete the installation.
Creating a Conda Environment
1. Install Miniconda
Download the Miniconda installer from the official website (). Choose the appropriate version for your system (Linux 64-bit).
Once downloaded, run the installer:
bash Miniconda3-latest-Linux-x86_64.sh
Follow the on-screen instructions to complete the installation.
2. Create a New Conda Environment
Create a dedicated conda environment for your CUDA installation:
conda create -n cuda_env python=3.8
This command creates an environment named cuda_env with Python 3.8. You can choose any Python version compatible with CUDA 12.1.
3. Activate the Conda Environment
Activate the newly created environment:
conda activate cuda_env
Installing CUDA 12.1
1. Install the CUDA Package
Use conda to install the CUDA 12.1 package:
conda install -c nvidia cuda=12.1
This command installs the CUDA package from the NVIDIA channel.
2. Verify Installation
After the installation is complete, verify that CUDA is installed correctly:
nvcc --version
This command should display the installed CUDA version (e.g., 12.1).
Testing CUDA
1. Create a CUDA Test Program
Create a simple CUDA program named test.cu:
#include
__global__ void add(int *a, int *b, int *c) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
c[i] = a[i] + b[i];
}
int main() {
int a[] = {1, 2, 3, 4};
int b[] = {5, 6, 7, 8};
int c[4];
int *dev_a, *dev_b, *dev_c;
// Allocate memory on the device
cudaMalloc(&dev_a, sizeof(a));
cudaMalloc(&dev_b, sizeof(b));
cudaMalloc(&dev_c, sizeof(c));
// Copy data from host to device
cudaMemcpy(dev_a, a, sizeof(a), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, sizeof(b), cudaMemcpyHostToDevice);
// Launch the kernel
add<<<1, 4>>>(dev_a, dev_b, dev_c);
// Copy data from device to host
cudaMemcpy(c, dev_c, sizeof(c), cudaMemcpyDeviceToHost);
// Print the result
for (int i = 0; i < 4; i++) {
printf("c[%d] = %d\n", i, c[i]);
}
// Free device memory
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
return 0;
}
2. Compile and Run the Program
Compile the program using the nvcc compiler:
nvcc -o test test.cu
Run the compiled program:
./test
The output should display the sum of the two arrays a and b, demonstrating that CUDA is working correctly.
Common Issues and Solutions
- Driver Version Mismatch: Ensure you are using the correct NVIDIA driver version for your GPU and CUDA version.
- Permission Errors: Use
sudofor commands that require administrator privileges, such as installing drivers and packages. - Environment Activation: Double-check that your conda environment is active before installing CUDA.
- CUDA Path: Verify that the CUDA path is set correctly in your environment variables.
Conclusion
By following these steps, you can successfully install CUDA 12.1 on Ubuntu 18.04 using conda, leveraging the power of NVIDIA GPUs for your computational needs. Remember to verify the installation by running a simple CUDA program and refer to the official documentation for troubleshooting any potential issues. This installation allows you to harness the computational power of CUDA within a managed conda environment, simplifying your development workflow and unlocking the potential for accelerated computing.