Science Score: 64.0%
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
-
✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
3 of 140 committers (2.1%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Automated Machine Learning on Kubernetes
Basic Info
- Host: GitHub
- Owner: kubeflow
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: https://www.kubeflow.org/docs/components/katib
- Size: 101 MB
Statistics
- Stars: 1,618
- Watchers: 59
- Forks: 480
- Open Issues: 102
- Releases: 18
Topics
Metadata Files
README.md
Kubeflow Katib
Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search.
Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, PyTorch, XGBoost, and others.
Katib can perform training jobs using any Kubernetes Custom Resources with out of the box support for Kubeflow Training Operator, Argo Workflows, Tekton Pipelines and many more.
Katib stands for secretary in Arabic.
Search Algorithms
Katib supports several search algorithms. Follow the Kubeflow documentation to know more about each algorithm and check the this guide to implement your custom algorithm.
| Hyperparameter Tuning | Neural Architecture Search | Early Stopping |
| Random Search | ENAS | Median Stop |
| Grid Search | DARTS | |
| Bayesian Optimization | ||
| TPE | ||
| Multivariate TPE | ||
| CMA-ES | ||
| Sobol's Quasirandom Sequence | ||
| HyperBand | ||
| Population Based Training |
To perform the above algorithms Katib supports the following frameworks:
Prerequisites
Please check the official Kubeflow documentation for prerequisites to install Katib.
Installation
Please follow the Kubeflow Katib guide for the detailed instructions on how to install Katib.
Installing the Control Plane
Run the following command to install the latest stable release of Katib control plane:
kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=v0.17.0"
Run the following command to install the latest changes of Katib control plane:
kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=master"
For the Katib Experiments check the complete examples list.
Installing the Python SDK
Katib implements a Python SDK to simplify creation of hyperparameter tuning jobs for Data Scientists.
Run the following command to install the latest stable release of Katib SDK:
sh
pip install -U kubeflow-katib
Getting Started
Please refer to the getting started guide to quickly create your first hyperparameter tuning Experiment using the Python SDK.
Community
The following links provide information on how to get involved in the community:
- Attend the bi-weekly AutoML and Training Working Group community meeting.
- Join our
#kubeflow-katibSlack channel. - Check out who is using Katib and presentations about Katib project.
Contributing
Please refer to the CONTRIBUTING guide.
Citation
If you use Katib in a scientific publication, we would appreciate citations to the following paper:
A Scalable and Cloud-Native Hyperparameter Tuning System, George et al., arXiv:2006.02085, 2020.
Bibtex entry:
@misc{george2020katib,
title={A Scalable and Cloud-Native Hyperparameter Tuning System},
author={Johnu George and Ce Gao and Richard Liu and Hou Gang Liu and Yuan Tang and Ramdoot Pydipaty and Amit Kumar Saha},
year={2020},
eprint={2006.02085},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
Owner
- Name: Kubeflow
- Login: kubeflow
- Kind: organization
- Website: https://kubeflow.org
- Repositories: 41
- Profile: https://github.com/kubeflow
Kubeflow is an open, community driven project to make it easy to deploy and manage an ML stack on Kubernetes
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use Katib in your scientific publication, please cite it as below."
authors:
- family-names: "George"
given-names: "Johnu"
- family-names: "Gao"
given-names: "Ce"
- family-names: "Liu"
given-names: "Richard"
- family-names: "Liu"
given-names: "Hou Gang"
- family-names: "Tang"
given-names: "Yuan"
- family-names: "Pydipaty"
given-names: "Ramdoot"
- family-names: "Saha"
given-names: "Amit Kumar"
title: "Katib"
type: software
repository-code: "https://github.com/kubeflow/katib"
preferred-citation:
type: misc
title: "A Scalable and Cloud-Native Hyperparameter Tuning System"
authors:
- family-names: "George"
given-names: "Johnu"
- family-names: "Gao"
given-names: "Ce"
- family-names: "Liu"
given-names: "Richard"
- family-names: "Liu"
given-names: "Hou Gang"
- family-names: "Tang"
given-names: "Yuan"
- family-names: "Pydipaty"
given-names: "Ramdoot"
- family-names: "Saha"
given-names: "Amit Kumar"
year: 2020
url: "https://arxiv.org/abs/2006.02085"
identifiers:
- type: "other"
value: "arXiv:2006.02085"
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Andrey Velichkevich | a****h@g****m | 345 |
| Ce Gao | g****e@c****o | 122 |
| Hougang Liu | l****g@c****m | 116 |
| dependabot[bot] | 4****] | 113 |
| Yuki Iwai | y****z@g****m | 100 |
| Johnu George | j****9@g****m | 92 |
| oshima | y****d@g****m | 65 |
| Richard Liu | 3****u | 21 |
| Elena Zioga | e****a@a****m | 19 |
| Shao Wang | 7****e | 19 |
| Masashi SHIBATA | c****a | 16 |
| Xu Xiao | l****f@g****m | 15 |
| Guang Ya Liu | g****3@g****m | 13 |
| IWAMOTO Toshihiro | i****o@v****p | 12 |
| Kenneth Koski | k****i@c****m | 11 |
| Jaeyeon Kim | a****4@g****m | 9 |
| Yuan Tang | t****n@g****m | 8 |
| Shashank Mittal | s****2@i****n | 7 |
| Jinan Zhou | d****d@o****m | 7 |
| Orfeas Kourkakis | o****s@a****m | 7 |
| Hezhi (Helen) Xie | h****z@g****m | 7 |
| Kimonas Sotirchos | k****d@a****m | 6 |
| Vaclav Pavlin | v****n@g****m | 6 |
| ytetra | 4****a | 6 |
| Jeongwook Park | j****k@n****m | 5 |
| Mahdi Khashan | 5****n | 5 |
| Sarah Maddox | s****x | 5 |
| Tariq Hasan | m****n@g****m | 5 |
| coldWater | f****8@g****m | 5 |
| Ben Ye | y****7@g****m | 4 |
| and 110 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 220
- Total pull requests: 411
- Average time to close issues: 8 months
- Average time to close pull requests: about 1 month
- Total issue authors: 100
- Total pull request authors: 69
- Average comments per issue: 7.57
- Average comments per pull request: 5.25
- Merged pull requests: 270
- Bot issues: 0
- Bot pull requests: 104
Past Year
- Issues: 46
- Pull requests: 161
- Average time to close issues: 2 months
- Average time to close pull requests: about 1 month
- Issue authors: 17
- Pull request authors: 29
- Average comments per issue: 4.67
- Average comments per pull request: 5.78
- Merged pull requests: 89
- Bot issues: 0
- Bot pull requests: 52
Top Authors
Issue Authors
- andreyvelich (26)
- tenzen-y (24)
- Electronic-Waste (17)
- mahdikhashan (8)
- anencore94 (6)
- kimwnasptd (4)
- gaocegege (4)
- helenxie-bit (4)
- knkski (4)
- garymm (4)
- droctothorpe (4)
- johnugeorge (3)
- Yumeka999 (3)
- mickvangelderen (3)
- mayankagarwal19911 (3)
Pull Request Authors
- dependabot[bot] (138)
- andreyvelich (82)
- tenzen-y (62)
- Electronic-Waste (39)
- helenxie-bit (27)
- shashank-iitbhu (15)
- mahdikhashan (13)
- forsaken628 (13)
- tariq-hasan (12)
- shivas1516 (8)
- Doris-xm (6)
- elenzio9 (6)
- akagami-harsh (4)
- saileshd1402 (4)
- ckcd (4)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 4
-
Total downloads:
- pypi 3,237 last-month
- Total docker downloads: 924
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 62
(may contain duplicates) - Total versions: 64
- Total maintainers: 7
proxy.golang.org: github.com/kubeflow/katib
- Homepage: https://github.com/kubeflow/katib
- Documentation: https://pkg.go.dev/github.com/kubeflow/katib#section-documentation
- License: Apache-2.0
-
Latest release: v0.18.0
published 11 months ago
Rankings
pypi.org: kubeflow-katib
Katib Python SDK for APIVersion v1beta1
- Homepage: https://github.com/kubeflow/katib/tree/master/sdk/python/v1beta1
- Documentation: https://kubeflow-katib.readthedocs.io/
- License: Apache License Version 2.0
-
Latest release: 0.18.0
published 11 months ago
Rankings
Maintainers (6)
pypi.org: prem-kubeflow-katib
Katib Python SDK
- Homepage: https://github.com/kubeflow/katib/sdk/python
- Documentation: https://prem-kubeflow-katib.readthedocs.io/
- License: Apache License Version 2.0
-
Latest release: 0.0.1
published over 5 years ago
Rankings
Maintainers (1)
pypi.org: kubeflow-katib-n
Katib Python SDK for APIVersion v1beta1
- Homepage: https://github.com/kubeflow/katib/tree/master/sdk/python/v1beta1
- Documentation: https://kubeflow-katib-n.readthedocs.io/
- License: Apache License Version 2.0
-
Latest release: 0.18.0rc0.post8
published 7 months ago
Rankings
Maintainers (1)
Dependencies
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- 1993 dependencies
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- @swimlane/ngx-charts ^19.0.1
- date-fns 1.29.0
- echarts ^5.3.2
- echarts-stat ^1.2.0
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- lodash-es 4.17.21
- material-icons ^0.7.7
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- ngx-echarts ^8.0.1
- ngx-json-viewer ^2.4.0
- rxjs ~6.6.7
- tslib ^2.0.0
- zone.js ~0.11.4
- 1604 dependencies
- @material-ui/core ^4.11.0
- @material-ui/icons ^4.9.1
- ace-builds ^1.4.8
- axios ^0.21.2
- graphviz-react ^1.0.6
- js-yaml ^3.13.1
- plotly.js ^1.45.0
- react ^16.8.3
- react-ace ^8.0.0
- react-dom ^16.8.3
- react-plotly.js ^2.3.0
- react-redux ^6.0.1
- react-router-dom ^4.3.1
- react-scripts 5.0.1
- redux ^4.0.1
- redux-logger ^3.0.6
- redux-saga ^1.0.1
- cython >=0.29.24
- googleapis-common-protos ==1.6.0
- grpcio ==1.41.1
- kubernetes ==22.6.0
- protobuf ==3.19.5
- googleapis-common-protos ==1.6.0
- grpcio ==1.41.1
- psutil ==5.9.4
- rfc3339 >=6.2
- tensorflow ==2.11.0
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- cython >=0.29.24
- forestci ==0.3
- googleapis-common-protos ==1.6.0
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- numpy >=1.20.0
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- cloudpickle ==0.5.6
- cython >=0.29.24
- forestci ==0.3
- googleapis-common-protos ==1.6.0
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- hyperopt ==0.2.5
- numpy >=1.20.0
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- cython >=0.29.24
- googleapis-common-protos ==1.6.0
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- protobuf ==3.19.5
- cython >=0.29.24
- googleapis-common-protos ==1.6.0
- grpcio ==1.41.1
- tensorflow ==2.11.0
- googleapis-common-protos ==1.53.0
- grpcio ==1.41.1
- optuna >=3.0.0
- protobuf ==3.19.5
- googleapis-common-protos ==1.53.0
- grpcio ==1.41.1
- numpy ==1.22.2
- protobuf ==3.19.5
- cloudpickle ==0.5.6
- cython >=0.29.24
- forestci ==0.3
- googleapis-common-protos ==1.6.0
- grpcio ==1.41.1
- numpy ==1.23.5
- protobuf ==3.19.5
- scikit-learn >=0.24.0
- scikit-optimize >=0.9.0
- scipy >=1.5.4
- Pillow >=9.1.1
- torch ==1.13.1
- torchvision ==0.14.1
- scipy >=1.7.2
- tensorflow ==2.11.0
- mxnet ==1.9.1
- numpy ==1.23.5
- Pillow >=9.1.1
- cloudml-hypertune ==0.1.0.dev6
- torch ==1.13.1
- torchvision ==0.14.1
- numpy ==1.22.2
- tensorflow ==2.11.0
- oci-image ==1.0.0
- ops ==1.3.0
- oci-image ==1.0.0
- ops ==1.2.0
- oci-image ==1.0.0
- ops ==1.2.0
- serialized-data-interface ==0.2.2
- grpcio-testing ==1.41.1 test
- pytest ==7.2.0 test