
Megan O’Flynn developed and enhanced cloud infrastructure and workflow automation across the stfc/cloud-docker-images and stfc/st2-cloud-pack repositories, focusing on robust backend solutions. She delivered GPU-enabled Jupyter notebook environments by updating Dockerfiles and integrating the latest CUDA and Ubuntu bases, improving performance and reproducibility for machine learning workflows. Megan streamlined project creation workflows in OpenStack-based systems, making user and domain assignments more flexible and reducing onboarding friction. Her work emphasized maintainability through code linting, formatting, and expanded test coverage using Python and YAML. Throughout, she applied CI/CD and DevOps practices to optimize build reliability and ensure consistent, high-quality deployments.

March 2025: GPU-Jupyter base image upgrade in stfc/cloud-docker-images, updating Dockerfile to pull the latest gpu-jupyter base image with newer CUDA version and Ubuntu base. This work improves notebook performance, security, and reproducibility for GPU-accelerated workflows.
March 2025: GPU-Jupyter base image upgrade in stfc/cloud-docker-images, updating Dockerfile to pull the latest gpu-jupyter base image with newer CUDA version and Ubuntu base. This work improves notebook performance, security, and reproducibility for GPU-accelerated workflows.
February 2025 monthly summary for stfc/st2-cloud-pack: Implemented a more flexible project creation workflow by making the user list optional and removing the required domain parameter, enabling project creation without immediate user/domain assignment and deferring those steps to later phases. Updated Jasmin network naming for consistency to 'JASMIN External Cloud Network' across project creation and related tests. These changes reduce onboarding friction, improve consistency, and lay groundwork for smoother downstream provisioning. No critical bugs fixed this month; minor cleanup related to domain handling was performed.
February 2025 monthly summary for stfc/st2-cloud-pack: Implemented a more flexible project creation workflow by making the user list optional and removing the required domain parameter, enabling project creation without immediate user/domain assignment and deferring those steps to later phases. Updated Jasmin network naming for consistency to 'JASMIN External Cloud Network' across project creation and related tests. These changes reduce onboarding friction, improve consistency, and lay groundwork for smoother downstream provisioning. No critical bugs fixed this month; minor cleanup related to domain handling was performed.
January 2025 performance summary: Delivered GPU-enabled ML notebook environment and enhanced external project workflow with domain resolution, boosting ML experimentation throughput and cross-domain project governance. Improved code quality and maintainability with linting/formatting cleanup and added test coverage, reducing regression risk and setup friction for developers and operators.
January 2025 performance summary: Delivered GPU-enabled ML notebook environment and enhanced external project workflow with domain resolution, boosting ML experimentation throughput and cross-domain project governance. Improved code quality and maintainability with linting/formatting cleanup and added test coverage, reducing regression risk and setup friction for developers and operators.
November 2024 monthly summary: Implemented a pre-build disk-space optimization step to maximize available space before Docker image builds in the stfc/cloud-docker-images repository, improving CI reliability and build throughput. No major bug fixes reported for this period within the provided scope.
November 2024 monthly summary: Implemented a pre-build disk-space optimization step to maximize available space before Docker image builds in the stfc/cloud-docker-images repository, improving CI reliability and build throughput. No major bug fixes reported for this period within the provided scope.
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