deepchecks

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

https://github.com/deepchecks/deepchecks

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
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Committers with academic emails
2 of 53 committers (3.8%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (13.5%) to scientific vocabulary

Keywords

data-drift data-science data-validation deep-learning html-report jupyter-notebook machine-learning ml mlops model-monitoring model-validation pandas-dataframe python pytorch

Keywords from Contributors

jax transformer cryptocurrency fuzzy-matching battery closember automl degoogle optimal-control optimism

Last synced: 6 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: deepchecks
License: other
Language: Python
Default Branch: main
Homepage: https://docs.deepchecks.com/stable
Size: 268 MB

Statistics

Stars: 3,889
Watchers: 23
Forks: 277
Open Issues: 255
Releases: 0

Topics

data-drift data-science data-validation deep-learning html-report jupyter-notebook machine-learning ml mlops model-monitoring model-validation pandas-dataframe python pytorch

Created over 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Code of conduct Citation Codeowners

README.md

pkgVersion

Deepchecks - Continuous Validation for AI & ML: Testing, CI & Monitoring

Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling you to thoroughly test your data and models from research to production.

👋 Join Slack | 📖 Documentation | 🌐 Blog | 🐦 Twitter

🧩 Components

Deepchecks includes: - Deepchecks Testing (Quickstart, docs): - Running built-in & your own custom Checks and Suites for Tabular, NLP & CV validation (open source). - CI & Testing Management (Quickstart, docs): - Collaborating over test results and iterating efficiently until model is production-ready and can be deployed (open source & managed offering). - Deepchecks Monitoring (Quickstart, docs): - Tracking and validating your deployed models behavior when in production (open source & managed offering).

This repo is our main repo as all components use the deepchecks checks in their core. See the Getting Started section for more information about installation and quickstarts for each of the components. If you want to see deepchecks monitoring's code, you can check out the deepchecks/monitoring repo.

⏩ Getting Started

💻 Installation

#### Deepchecks Testing (and CI) Installation ```bash pip install deepchecks -U --user ``` For installing the nlp / vision submodules or with conda: - For NLP: Replace ``deepchecks`` with ``"deepchecks[nlp]"``, and optionally install also``deepchecks[nlp-properties]`` - For Computer Vision: Replace ``deepchecks`` with ``"deepchecks[vision]"``. - For installing with conda, similarly use: ``conda install -c conda-forge deepchecks``. Check out the full installation instructions for deepchecks testing [here](https://docs.deepchecks.com/stable/getting-started/installation.html). #### Deepchecks Monitoring Installation To use deepchecks for production monitoring, you can either use our SaaS service, or deploy a local instance in one line on Linux/MacOS (Windows is WIP!) with Docker. Create a new directory for the installation files, open a terminal within that directory and run the following: ``` pip install deepchecks-installer deepchecks-installer install-monitoring ``` This will automatically download the necessary dependencies, run the installation process and then start the application locally. The installation will take a few minutes. Then you can open the deployment url (default is http://localhost), and start the system onboarding. Check out the full monitoring [open source installation & quickstart](https://docs.deepchecks.com/monitoring/stable/getting-started/deploy_self_host_open_source.html). Note that the open source product is built such that each deployment supports monitoring of a single model.

🏃‍♀️ Quickstarts

Deepchecks Testing Quickstart

Jump right into the respective quickstart docs: - [Tabular Quickstarts](https://docs.deepchecks.com/stable/tabular/auto_tutorials/quickstarts/index.html) - [NLP Quickstarts](https://docs.deepchecks.com/stable/nlp/auto_tutorials/quickstarts/index.html) - [Vision Quickstarts](https://docs.deepchecks.com/stable/vision/auto_tutorials/quickstarts/index.html) to have it up and running on your data. Inside the quickstarts, you'll see how to create the relevant deepchecks object for holding your data and metadata ([Dataset](https://docs.deepchecks.com/stable/tabular/usage_guides/dataset_object.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=running_a_suite), [TextData](https://docs.deepchecks.com/stable/nlp/usage_guides/text_data_object.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=running_a_suite) or [VisionData](https://docs.deepchecks.com/stable/vision/usage_guides/visiondata_object.html), corresponding to the data type), and run a Suite or Check. The code snippet for running it will look something like the following, depending on the chosen Suite or Check. ```python from deepchecks.tabular.suites import model_evaluation suite = model_evaluation() suite_result = suite.run(train_dataset=train_dataset, test_dataset=test_dataset, model=model) suite_result.save_as_html() # replace this with suite_result.show() or suite_result.show_in_window() to see results inline or in window # or suite_result.results[0].value with the relevant check index to process the check result's values in python ``` The output will be a report that enables you to inspect the status and results of the chosen checks:

Deepchecks Monitoring Quickstart

Jump right into the [open source monitoring quickstart docs](https://docs.deepchecks.com/monitoring/stable/getting-started/deploy_self_host_open_source.html) to have it up and running on your data. You'll then be able to see the checks results over time, set alerts, and interact with the dynamic deepchecks UI that looks like this:

Deepchecks CI & Testing Management Quickstart

Deepchecks managed CI & Testing management is currently in closed preview. [Book a demo](https://deepchecks.com/book-demo/) for more information about the offering.

For building and maintaining your own CI process while utilizing Deepchecks Testing for it, check out our [docs for Using Deepchecks in CI/CD](https://docs.deepchecks.com/stable/general/usage/ci_cd.html).

🧮 How does it work?

At its core, deepchecks includes a wide variety of built-in Checks, for testing all types of data and model related issues. These checks are implemented for various models and data types (Tabular, NLP, Vision), and can easily be customized and expanded.

The check results can be used to automatically make informed decisions about your model's production-readiness, and for monitoring it over time in production. The check results can be examined with visual reports (by saving them to an HTML file, or seeing them in Jupyter), processed with code (using their pythonic / json output), and inspected and collaborated on with Deepchecks' dynamic UI (for examining test results and for production monitoring).

✅ Deepchecks' Core: The Checks

- All of the Checks and the framework for customizing them are implemented inside the Deepchecks Testing Python package (this repo). - Each check tests for a specific potential problem. Deepchecks has many pre-implemented checks for finding issues with the model's performance (e.g. identifying weak segments), data distribution (e.g. detect drifts or leakages) and data integrity (e.g. find conflicting labels). - Customizable: each check has many configurable parameters, and custom checks can easily be implemented. - Can be run manually (during research) or triggered automatically (in CI processes or production monitoring) - Check results can be consumed by: - Visual output report - [Saving to HTML](https://docs.deepchecks.com/stable/general/usage/export_save_results.html)(``result.save_to_html('output_report_name.html')``) or [viewing them](https://docs.deepchecks.com/stable/general/usage/showing_results.html) in Jupyter (``result.show()``). - Processing with code - with python using the check result's ``value`` attribute, or saving a [JSON output](https://docs.deepchecks.com/stable/general/usage/export_save_results.html) - Deepchecks' UI - for dynamic inspection and collaboration (of test results and production monitoring) - Optional conditions can be added and customized, to automatically validate check results, with a a pass ✓, fail ✖ or warning ! status - An ordered list of checks (with optional conditions) can be run together in a "Suite" (and the output is a concluding report of all checks that ran)

📜 Open Source vs Paid

Deepchecks' projects (deepchecks/deepchecks & deepchecks/monitoring) are open source and are released under AGPL 3.0.

The only exception are the Deepchecks Monitoring components (in the deepchecks/monitoring repo), that are under the (backend/deepchecks_monitoring/ee) directory, that are subject to a commercial license (see the license here). That directory isn't used by default, and is packaged as part of the deepchecks monitoring repository simply to support upgrading to the commercial edition without downtime.

Enabling premium features (contained in the backend/deepchecks_monitoring/ee directory) with a self-hosted instance requires a Deepchecks license. To learn more, book a demo or see our pricing page.

Looking for a 💯% open-source solution for deepcheck monitoring? Check out the Monitoring OSS repository, which is purged of all proprietary code and features.

👭 Community, Contributing, Docs & Support

Deepchecks is an open source solution. We are committed to a transparent development process and highly appreciate any contributions. Whether you are helping us fix bugs, propose new features, improve our documentation or spread the word, we would love to have you as part of our community.

Give us a ⭐️ github star ⭐️ on the top of this page to support what we're doing, it means a lot for open source projects!
Read our docs for more info about how to use and customize deepchecks, and for step-by-step tutorials.
Post a Github Issue to submit a bug report, feature request, or suggest an improvement.
To contribute to the package, check out our first good issues and contribution guidelines, and open a PR.

Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, get help for package usage or contributions, or engage in discussions about ML testing!

✨ Contributors

Thanks goes to these wonderful people (emoji key):

_{Itay Gabbay} 💻 📖 🤔	_matanper 📖 🤔 💻	_JKL98ISR 🤔 💻 📖	_{Yurii Romanyshyn} 🤔 💻 📖	_{Noam Bressler} 💻 📖 🤔	_{Nir Hutnik} 💻 📖 🤔	_Nadav-Barak 💻 📖 🤔
_Sol 💻 📖 🤔	_DanArlowski 💻 🚇	_DBI 💻	_OrlyShmorly 🎨	_shir22 🤔 📖 📢	_yaronzo1 🤔 🖋	_ptannor 🤔 🖋
_avitzd 📋 📹	_DanBasson 📖 🐛 💡	_S.Kishore 💻 📖 🐛	_{Shay Palachy-Affek} 🔣 💡 📓	_{Cemal GURPINAR} 📖 🐛	_{David de la Iglesia Castro} 💻	_{Levi Bard} 📖
_{Julien Schuermans} 🐛	_{Nir Ben-Zvi} 💻 🤔	_{Shiv Shankar Dayal} 🚇	_RonItay 🐛 💻	_{Jeroen Van Goey} 🐛 📖	_idow09 🐛 💡	_{Ikko Ashimine} 📖
_{Jason Wohlgemuth} 📖	_{Lokin Sethia} 💻 🐛	_{Ingo Marquart} 💻 🐛	_Oscar 💻	_{Richard W} 💻 📖 🤔	_Bernardo 💻 📖	_{Olivier Binette} 💻 📖 🤔
_陈鼎彦 🐛	_{Andres Vargas} 📖	_{Michael Marien} 📖 🐛	_OrdoAbChao 💻	_{Matt Chan} 💻	_{Harsh Jain} 💻 📖 🐛	_arterm-sedov 📖
_{AIT ALI YAHIA Rayane} 💻 🤔	_{Chris Santiago} 🐛 💻

This project follows the all-contributors specification. Contributions of any kind are welcome!

Owner

Name: deepchecks
Login: deepchecks
Kind: organization
Email: info@deepchecks.com

Website: www.deepchecks.com
Repositories: 9
Profile: https://github.com/deepchecks

Citation (CITATION.cff)

cff-version: 1.2.0
title: "Deepchecks: A Library for Testing and Validating Machine Learning Models and Data"
message: "If you use this software, please cite it using the metadata from this file."
type: software
authors:
- family-names: Chorev
  given-names: Shir  
  email: shir@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Tannor
  given-names: Philip
  email: philip@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Ben Israel
  given-names: Dan
  email: danb@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Bressler
  given-names: Noam
  email: noam@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Gabbay
  given-names: Itay
  email: itay@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Hutnik 
  given-names: Nir
  email: nir@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Liberman
  given-names: Jonatan
  email: jonatan@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Perlmutter
  given-names: Matan
  email: matan@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Romanyshyn
  given-names: Yurii
  email: yurii@deepchecks.com
  affiliation: Deepchecks Ltd.
- family-names: Rokach
  given-names: Lior
  email: liorrk@bgu.ac.il
  affiliation: Deepchecks Ltd. and Department of Software and Info. Sys. Eng. Ben-Gurion University of the Negev
url: "https://github.com/deepchecks/deepchecks"
preferred-citation:
  type: article
  authors:
  - family-names: Chorev
    given-names: Shir  
    email: shir@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Tannor
    given-names: Philip
    email: philip@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Ben Israel
    given-names: Dan
    email: danb@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Bressler
    given-names: Noam
    email: noam@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Gabbay
    given-names: Itay
    email: itay@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Hutnik 
    given-names: Nir
    email: nir@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Liberman
    given-names: Jonatan
    email: jonatan@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Perlmutter
    given-names: Matan
    email: matan@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Romanyshyn
    given-names: Yurii
    email: yurii@deepchecks.com
    affiliation: Deepchecks Ltd.
  - family-names: Rokach
    given-names: Lior
    email: liorrk@bgu.ac.il
    affiliation: Deepchecks Ltd. and Department of Software and Info. Sys. Eng. Ben-Gurion University of the Negev
  title: "Deepchecks: A Library for Testing and Validating Machine Learning Models and Data"
  journal: Journal of Machine Learning Research
  year: 2022
  volume: 23
  number: 265
  start: 1
  end: 6
  url: "http://jmlr.org/papers/v23/22-0281.html"

GitHub Events

Total

Create event: 20
Release event: 3
Issues event: 15
Watch event: 279
Delete event: 4
Member event: 1
Issue comment event: 23
Push event: 122
Pull request review comment event: 4
Pull request review event: 21
Pull request event: 74
Fork event: 25

Last Year

Create event: 20
Release event: 3
Issues event: 15
Watch event: 279
Delete event: 4
Member event: 1
Issue comment event: 23
Push event: 122
Pull request review comment event: 4
Pull request review event: 21
Pull request event: 74
Fork event: 25

Committers

Last synced: 9 months ago

All Time

Total Commits: 1,488
Total Committers: 53
Avg Commits per committer: 28.075
Development Distribution Score (DDS): 0.857

Past Year

Commits: 13
Committers: 5
Avg Commits per committer: 2.6
Development Distribution Score (DDS): 0.308

Top Committers

Name	Email	Commits
Itay Gabbay	i**y@d**m	213
Noam Bressler	n**r@g**m	211
matanper	m**n@d**m	190
JKL98ISR	j**b@g**m	188
Yurii Romanyshyn	7****n	111
Nir Hutnik	9****k	93
Nadav Barak	6****k	90
DBI	4****n	83
shir22	3****2	67
matanper	m**r@g**m	48
Sol	9****Y	31
DanArlowski	5****i	27
allcontributors[bot]	4****]	25
deepchecks-bot	9****t	19
github-actions[bot]	g****]	11
Harsh Jain	1****s	8
Shiv Shankar Dayal	s**l@g**m	8
Dan Arlowski	d**n@d**m	7
DanBasson	d**7@g**m	6
Harsh Jain	h**4@g**m	6
S.Kishore	s**v@g**m	6
Nir Hutnik	n**r@d**m	4
Cemal GURPINAR	3****r	2
Michael Marien	m**h@g**m	2
Ofir Nissim	E**r@g**m	2
Shay Palachy	s****5	2
shiritdvir	5****r	2
arterm-sedov	6****v	1
Shay Tsadok	s**7@g**m	1
RonItay	3****y	1
and 23 more...

Committer Domains (Top 20 + Academic)

deepchecks.com: 5 cua.edu: 1 nirbenzvi.com: 1 ip-192-168-1-26.eu-west-1.compute.internal: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 81
Total pull requests: 304
Average time to close issues: about 2 months
Average time to close pull requests: about 1 month
Total issue authors: 39
Total pull request authors: 28
Average comments per issue: 1.22
Average comments per pull request: 0.28
Merged pull requests: 204
Bot issues: 0
Bot pull requests: 11

Past Year

Issues: 10
Pull requests: 26
Average time to close issues: 5 days
Average time to close pull requests: 3 days
Issue authors: 10
Pull request authors: 7
Average comments per issue: 0.7
Average comments per pull request: 0.31
Merged pull requests: 17
Bot issues: 0
Bot pull requests: 2

View more stats

Top Authors

Issue Authors

Nadav-Barak (16)
nirhutnik (14)
noamzbr (8)
hjain5164 (2)
bgalvao (2)
j-adamczyk (2)
PushaBe (1)
borisRa (1)
milkajakovljevic (1)
a-recknagel (1)
srraht (1)
arterm-sedov (1)
ItayGabbay (1)
pr3sh (1)
PleaseNotAnotherUsername (1)

Pull Request Authors

ItayGabbay (98)
noamzbr (61)
JKL98ISR (37)
Nadav-Barak (36)
nirhutnik (24)
yromanyshyn (15)
harsh-deepchecks (13)
shir22 (12)
allcontributors[bot] (10)
matanper (7)
dependabot[bot] (6)
hjain5164 (5)
kishore-s-15 (3)
RayanAAY-ops (3)
DanielAvdar (2)

Top Labels

Issue Labels

linear (64) bug (29) needs triage (29) feature (21) nlp (20) good first issue (9) documentation (9) ds (8) tabular (3) dev (3) question (2) vision (1)

Pull Request Labels

feature (90) bug (69) documentation (44) nlp (20) ci (18) refactoring (9) dependencies (6) python (6) dev (3) hotfix (1) breaking-backwards-compatabillity (1)

Packages

Total packages: 3
Total downloads:
- pypi 37,914 last-month
Total docker downloads: 145

Total dependent packages: 7
(may contain duplicates)
Total dependent repositories: 92
(may contain duplicates)
Total versions: 79
Total maintainers: 2

pypi.org: deepchecks

Package for validating your machine learning model and data

Homepage: https://github.com/deepchecks/deepchecks
Documentation: https://docs.deepchecks.com
License: GNU Affero General Public License v3 or later (AGPLv3+)
Latest release: 0.19.1
published about 1 year ago

Versions: 59
Dependent Packages: 7
Dependent Repositories: 92
Downloads: 37,907 Last month
Docker Downloads: 145

Rankings

Dependent packages count: 1.1%

Downloads: 1.2%

Stargazers count: 1.4%

Dependent repos count: 1.5%

Average: 1.9%

Docker downloads count: 3.0%

Forks count: 3.4%

Maintainers (1)

deepchecks

Last synced: 6 months ago

pypi.org: runml-checks

Package for validating your machine learning model and data

Homepage: https://github.com/deepchecks/deepchecks
Documentation: https://docs.deepchecks.com
License: GNU Affero General Public License v3 or later (AGPLv3+)
Latest release: 1.0.0
published over 3 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 7 Last month

Rankings

Stargazers count: 1.4%

Forks count: 3.7%

Dependent packages count: 6.6%

Average: 16.5%

Dependent repos count: 30.6%

Downloads: 40.1%

Maintainers (1)

sahumson

Last synced: 6 months ago

conda-forge.org: deepchecks

Homepage: https://github.com/deepchecks/deepchecks
License: AGPL-3.0-or-later
Latest release: 0.9.2
published over 3 years ago

Versions: 19
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 7.8%

Forks count: 13.4%

Average: 26.6%

Dependent repos count: 34.0%

Dependent packages count: 51.2%

Last synced: 6 months ago

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