Nipype interfaces in CBRAIN
Bibliographic record
Abstract
We aim at the large-scale, automatic sharing of software tools between neuroimaging processing platforms, which will increase the relevance of such platforms by providing them with richer repositories of higher-quality tools. Currently, efforts are hampered by the repetitive porting of the same few tools in different platforms. During the HBM 2015 Hackathon, we worked on the export of software tools from the Nipype workflow engine [1] to the CBRAIN web platform for distributed computing [2]. Nipype includes a large number of tools that would be useful to CBRAIN users. We developed nipype2boutiques, a tool to export Nipype interfaces to the “Boutiques” tool description format (step 1. on Fig. 10.). Boutiques descriptions are importable by CBRAIN and other platforms (Virtual Imaging Platform [3] and the Pegasus workflow engine [4]). They point to a Docker image containing the implementation of the tool. nipype2-boutiques relies on nipype_cmd a tool to run Nipype Interfaces as Linux command lines. nipype2boutiques parses the inputs and outputs of a Nipype interface and extracts their name, type, description and position on the nipype_cmd command line. nipype2boutiques then generates a Boutiques descriptor pointing to a Docker image where the Nipype interface is available. Once a Nipype interface is exported using nipype2boutiques it can be imported to CBRAIN. System architecture We tested nipype2boutiques on a few Nipype interfaces from the FSL Nipype module. We exported 64 FSL tools automatically from Nipype to CBRAIN, and made them available [https://github.com/glatard/boutiques-nipype-fsl]. Limitations remain on the type of Nipype interface that can be exported by nipype2boutiques: in particular, InputMultiPath is? currently not supported, and output files have to be written in the execution directory of the Nipype Interface. We prototyped a software tool to export Nipype Interfaces as Boutiques descriptors, which can be imported by CBRAIN and other platforms. Although the solution is still limited to simple interfaces, we believe that it has the potential to enable fully automatic tool sharing between Nipype and CBRAIN. Future extensions of nipype2boutiques will be published in the Nipype Github repository [https://github.com/nipy/nipype]. We also plan on a tighter integration of Nipype workflows in CBRAIN, following the model adopted in [5]. More information about this project can be found at: http://cbrain.mcgill.ca. None. TG wrote the software and the report; SD contributed to the concept elaboration at the OHBM event, RA, NB, PR and MER provided support on the CBRAIN framework, RB implemented Boutiques in CBRAIN, NKM provided background information on fMRI packages, ACE spearheaded the project. Report from 2015 OHBM Hackathon (HI). The authors would like to thank the organizers and attendees of the 2015 OHBM Hackathon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".