singularityware/singularity: Singularity 2.6.0 Release
Bibliographic record
Abstract
Greetings Singularity-ers! It is my great pleasure to announce the release of version 2.6.0! This release has a few bug fixes and lot of cool new features that are detailed below. Please note that 2.6.0 is expected to be the final feature release in the 2.x series. While bug fixes may be added via point releases (for example 2.6.1) no new features releases (for example 2.7.0) are planned. Pull requests adding features to the 2.x series will no longer be reviewed. Any new features should be targeted to the master branch (which used to be called development-3.0). For more information about the reorganization of Singularity branches in the GitHub repo, please see this Sylabs lab notes. Thanks and have fun! Implemented enhancements Allow admin to specify a non-standard location for mksquashfs binary at build time with --with-mksquashfs option #1662 --nv option will use nvidia-container-cli if installed #1681 nvliblist.conf now has a section for binaries #1681 --nv can be made default with all action commands in singularity.conf #1681 --nv can be controlled by env vars $SINGULARITY_NV and $SINGULARITY_NV_OFF #1681 Refactored travis build and packaging tests #1601 Added build and packaging tests for Debian 8/9 and openSUSE 42.3/15.0 #1713 Restore shim init process for proper signal handling and child reaping when container is initiated in its own PID namespace #1221 Add -i option to image.create to specify the inode ratio. #1759 Bind /dev/nvidia* into the container when the --nv flag is used in conjuction with the --contain flag #1358 Add --no-home option to not mount user $HOME if it is not the $CWD and mount home = yes is set. #1761 Added support for OAUTH2 Docker registries like Azure Container Registry #1622 Bug fixes Fix 404 when using Arch Linux bootstrap #1731 Fix environment variables clearing while starting instances #1766 As always, please report any bugs to: https://github.com/singularityware/singularity/issues/new
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.303 | 0.423 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".