How to run CUDA-Q programs on Amazon Braket notebook instances
Quantum Computing Blog
This article provides a step-by-step guide for running CUDA-Q programs on Amazon Braket notebook instances using Docker containers. The key steps include:
- Creating a custom Dockerfile to extend the CUDA-Q Docker image
- Installing required Python packages like ipython and Amazon Braket SDK
- Configuring a new Jupyter kernel that uses the custom Docker container
- Adding GPU support for GPU-accelerated notebook instances
- Mounting directories to share files between the host and Docker container
The process allows developers to run CUDA-Q code interactively in Jupyter notebooks on Amazon Braket, providing a cloud-based quantum development environment with seamless integration of CUDA-Q and AWS Braket services.
The AWS News Feed is currently looking for gold sponsors. If you want to support the AWS community and reach a large audience of AWS professionals, consider sponsoring the AWS News Feed.
Related articles
2025
2025
2026
2024
The AWS News Feed is currently looking for silver sponsors. If you want to support the AWS community and reach a large audience of AWS professionals, consider sponsoring the AWS News Feed.