[REVIEW]: Candle Optimisers: A Rust crate for optimisation algorithms

editorialbot commented 3 months ago

Submitting author: !--author-handle-->@KGrewal1@olexandr-konovalov<!--end-editor-- Reviewers: @relf, @jatkinson1000 Archive: Pending

Status

Status badge code:

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Markdown: [![status](https://joss.theoj.org/papers/120698a4b32755d997531d380d5a923e/status.svg)](https://joss.theoj.org/papers/120698a4b32755d997531d380d5a923e)

Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@relf & @jatkinson1000, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review. First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @olexandr-konovalov know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @relf

📝 Checklist for @jatkinson1000

editorialbot commented 3 months ago

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editorialbot commented 3 months ago

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.48550/arXiv.1212.5701 is OK
- 10.48550/arXiv.1412.6980 is OK
- 10.48550/arXiv.1711.05101 is OK
- 10.1007/BF01589116 is OK
- 10.48550/arXiv.1908.03265 is OK

MISSING DOIs

- No DOI given, and none found for title: Adaptive Subgradient Methods for Online Learning a...
- No DOI given, and none found for title: On the Convergence of Adam and Beyond
- No DOI given, and none found for title: On the importance of initialization and momentum i...
- No DOI given, and none found for title: Incorporating Nesterov Momentum into Adam
- No DOI given, and none found for title: Neural Networks for Machine Learning
- No DOI given, and none found for title: Candle: a Minimalist ML Framework for Rust

INVALID DOIs

- None

editorialbot commented 3 months ago

Software report:

github.com/AlDanial/cloc v 1.90  T=0.03 s (1518.4 files/s, 355086.6 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Rust                            27            701           1674           4819
Jupyter Notebook                 1              0           1046            578
Markdown                         5             79              0            147
TeX                              1             12              0            132
YAML                             3              9              0             82
TOML                             2             11              1             48
HTML                             1              0              0             15
-------------------------------------------------------------------------------
SUM:                            40            812           2721           5821
-------------------------------------------------------------------------------

Commit count by author:

   142  Kirpal Grewal
     1  KGrewal1

editorialbot commented 3 months ago

Paper file info:

📄 Wordcount for paper.md is 259

✅ The paper includes a Statement of need section

editorialbot commented 3 months ago

License info:

✅ License found: MIT License (Valid open source OSI approved license)

editorialbot commented 3 months ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

relf commented 3 months ago

Review checklist for @relf

Conflict of interest

[x] I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/KGrewal1/optimisers?
[x] License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@KGrewal1) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[ ] Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

[x] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
[x] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
[x] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
[x] Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
[x] Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
[ ] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

[ ] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[ ] A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
[ ] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[ ] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[ ] References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

olexandr-konovalov commented 2 months ago

@jatkinson1000 could you please generate your checklist, as explained above? Seeing it partially checked helps us to better understand the state of the review process. Thanks!

jatkinson1000 commented 2 months ago

Review checklist for @jatkinson1000

Conflict of interest

[x] I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/KGrewal1/optimisers?
[x] License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@KGrewal1) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[ ] Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

[x] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
[x] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
[ ] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
[ ] Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
[x] Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
[ ] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

[ ] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[ ] A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
[ ] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[ ] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[ ] References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

olexandr-konovalov commented 1 month ago

@relf and @jatkinson1000 thank you for starting your reviews - partially filled in checklists are very helpful to see where you're approximately with your reviews, and cross-references to issues in the software/paper repository are also useful. Do you think you will be able to complete your reviews soon?

relf commented 1 month ago

To the best of my ability, I've completed my review of this library. I've installed and run the software test suite and the example successfully. The documentation is in place using standard Rust doc tool and contains algorithms pseudo-code and reference papers.

The paper itself should elaborate on the "far lower overhead for neural network training" claim which is not so obvious to me. I would not call PyTorch or Tensorflow "full Python frameworks", as actually the training heavylifting is done using compiled languages under the hood. It should also consolidate the position of the library with regard to the existing Rust landscape at least by referencing current candle optimizers and established optimizers crate like argmin.

jatkinson1000 commented 3 weeks ago

Hi All,

I would reiterate the comments from the pre-review phase:
- Performance claims should be backed up
- Include an explanation as to why this is not a contribution directly to Candle
- Citations of other relevant/alternative libraries should be included and discussed
The paper itself is a statement of need and a list of implemented algorithms. I would expect more discussion of things such as:
- How to interface this library with Candle
- How the library is structured, and an overview of the API
- Examples of use
- Demonstration of performance claims
I would like the documentation to be explicitly linked from the README (and the paper) - It took me a long time to find it by clicking the 'docs: passing' badge.
The paper says that this provides a "complete" set of optimisers "for Rust", but the repository suggests they are specifically for use with candle, and there is a list of unimplemented methods in a TODO list. This needs clarifying and making consistent.
A software comment on tests would be that the coverage of the library is high, but largely consists of integration tests for each method comparing to previous values or comparative values from PyTorch. It would be good to see some unit tests in there as well, and some wider parameter sweeps on the integration tests.

In summary, whilst the software repository looks good the paper needs more work and content. I would also like to see more information in the README, though see @relf has started an issue for this.

openjournals / joss-reviews