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.
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