nvidia cuda docker python

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Sure, I can provide you with a tutorial on setting up a Docker container with NVIDIA CUDA support for running Python applications. This tutorial assumes that you have Docker installed on your system and an NVIDIA GPU with CUDA support.
Install Docker:
Make sure you have Docker installed on your system. You can download and install Docker from the official website: Docker
NVIDIA GPU with CUDA support:
Ensure that your system has an NVIDIA GPU with CUDA support. You can check the NVIDIA website for a list of CUDA-enabled GPUs.
To enable GPU support within Docker containers, you need to install the NVIDIA Container Toolkit.
Create a file named Dockerfile in your project directory with the following content:
Replace app.py with the main Python script of your application, and if you have any specific dependencies, create a requirements.txt file with your Python package dependencies.
Navigate to the directory containing your Dockerfile and run the following command:
Once the image is built, you can run the Docker container:
This command uses the --gpus all option to enable GPU support in the container.
Now, your Python application with NVIDIA CUDA support is running in a Docker container. You can access it by visiting http://localhost:4000 in your web browser.
Remember to adapt the commands and Dockerfile to suit the specific requirements of your Python application.
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