docker nvidia cuda with python

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Docker is a powerful platform for developing, shipping, and running applications in containers. NVIDIA CUDA enables parallel computing on NVIDIA GPUs, and when combined with Docker, it provides a convenient way to package and deploy GPU-accelerated applications. This tutorial will guide you through the process of setting up a Docker environment with NVIDIA CUDA support for Python applications.
Before you start, ensure that you have the following prerequisites installed on your system:
Follow the installation guide provided by NVIDIA to set up the NVIDIA Container Toolkit on your system. This toolkit enables Docker to access GPU resources on the host machine.
Create a Dockerfile to define the Docker image for your Python application. Here's an example Dockerfile:
Make sure to replace "script.py" with the name of your Python script, and create a "requirements.txt" file if you have any Python dependencies.
Navigate to the directory containing your Dockerfile and run the following command to build the Docker image:
Replace "your-image-name" with a name of your choice.
Now, you can run the Docker container using the following command:
This command ensures that the container has access to all available GPUs on the host machine.
Congratulations! You've successfully set up a Docker environment with NVIDIA CUDA support for Python. This allows you to easily package and distribute GPU-accelerated Python applications.
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