Building, securing, sharing containers: Paths towards a sustainable ecosystem for admins, users, applications
Bibliographic record
Abstract
Containers have become a popular approach to package software applications with all their libraries and other dependencies. This makes them easier to maintain, update, and distribute and enables new ways of managing the software lifecycle. Over time, container repositories have been developed for sharing applications and entire software stacks. However, with it a whole new world of questions and challenges has emerged around the management of containers, their integration into existing systems and workflows, user management, security and permissions, network and storage integration, etc. This BoF session will feature a few short presentations from organisations which have adopted containers in their compute environments and they will share their experience, challenges, and success stories. These will highlight aspects around the implementation of containers in HPC and AI environments, batch and K8 platforms, security and secure software supply chains, storage integration and adption of DevSecOps/GitOps/Platform Engineering approaches to application lifecycle management. These topics will be relevant for anyone using or planning to deploy platforms for scientific computing, high performance computing, data analytics, or artificial intelligence and leveraging containers for the management software stack and/or the applications running on the system. The presentations are designed to encourage engagement with the audience and trigger questions and discussion and the sharing of ideas and experiences. At the end this session, you will have learned how some of the leading HPC and AI organisations in Australia build, secure, share and maintain their container platforms and you can become part of this growing community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".