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neural-pipeline-search

Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.

https://github.com/automl/neps

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
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    Found .zenodo.json file
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    Links to: arxiv.org
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    8 of 23 committers (34.8%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (14.2%) to scientific vocabulary

Keywords

automl deep-learning hyperparameter-optimization neural-architecture-search neural-pipeline-search

Keywords from Contributors

optimizer benchmarking generic interactive mesh interpretability sequences datascience projection embedded
Last synced: 4 months ago · JSON representation ·

Repository

Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.

Basic Info
Statistics
  • Stars: 77
  • Watchers: 8
  • Forks: 19
  • Open Issues: 20
  • Releases: 0
Topics
automl deep-learning hyperparameter-optimization neural-architecture-search neural-pipeline-search
Created over 4 years ago · Last pushed 4 months ago
Metadata Files
Readme Contributing License Citation

README.md

Neural Pipeline Search (NePS)

PyPI version Python versions License Tests

Welcome to NePS, a powerful and flexible Python library for hyperparameter optimization (HPO) and neural architecture search (NAS) that makes HPO and NAS practical for deep learners.

NePS houses recently published and also well-established algorithms that can all be run massively parallel on distributed setups and, in general, NePS is tailored to the needs of deep learning experts.

To learn about NePS, check-out the documentation, our examples, or a colab tutorial.

Key Features

In addition to the features offered by traditional HPO and NAS libraries, NePS stands out with:

  1. Hyperparameter Optimization (HPO) Efficient Enough for Deep Learning:
    NePS excels in efficiently tuning hyperparameters using algorithms that enable users to make use of their prior knowledge, while also using many other efficiency boosters.
  2. Neural Architecture Search (NAS) with Expressive Search Spaces:
    NePS provides capabilities for optimizing DL architectures in an expressive and natural fashion.
  3. Zero-effort Parallelization and an Experience Tailored to DL:
    NePS simplifies the process of parallelizing optimization tasks both on individual computers and in distributed computing environments. As NePS is made for deep learners, all technical choices are made with DL in mind and common DL tools such as Tensorboard are embraced.

Installation

To install the latest release from PyPI run

bash pip install neural-pipeline-search

Basic Usage

Using neps always follows the same pattern:

  1. Define a evaluate_pipeline function capable of evaluating different architectural and/or hyperparameter configurations for your problem.
  2. Define a search space named pipeline_space of those Parameters e.g. via a dictionary
  3. Call neps.run(evaluate_pipeline, pipeline_space)

In code, the usage pattern can look like this:

```python import neps import logging

logging.basicConfig(level=logging.INFO)

1. Define a function that accepts hyperparameters and computes the validation error

def evaluate_pipeline(lr: float, alpha: int, optimizer: str) -> float: # Create your model model = MyModel(lr=lr, alpha=alpha, optimizer=optimizer)

# Train and evaluate the model with your training pipeline
validation_error = train_and_eval(model)
return validation_error

2. Define a search space of parameters; use the same parameter names as in evaluate_pipeline

pipeline_space = dict( lr=neps.Float( lower=1e-5, upper=1e-1, log=True, # Log spaces prior=1e-3, # Incorporate you knowledge to help optimization ), alpha=neps.Integer(lower=1, upper=42), optimizer=neps.Categorical(choices=["sgd", "adam"]) )

3. Run the NePS optimization

neps.run( evaluatepipeline=evaluatepipeline, pipelinespace=pipelinespace, rootdirectory="path/to/save/results", # Replace with the actual path. evaluationsto_spend=100, ) ```

Examples

Discover how NePS works through these examples:

Contributing

Please see the documentation for contributors.

Citations

For pointers on citing the NePS package and papers refer to our documentation on citations.

Owner

  • Name: AutoML-Freiburg-Hannover
  • Login: automl
  • Kind: organization
  • Location: Freiburg and Hannover, Germany

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Stoll
    given-names: Danny
  - family-names: Mallik
    given-names: Neeratyoy
  - family-names: Bergman
    given-names: Eddie
  - family-names: Schrodi
    given-names: Simon
  - family-names: Garibov
    given-names: Samir
  - family-names: Abou Chakra
    given-names: Tarek
  - family-names: Carstensen
    given-names: Timur
  - family-names: Janowski
    given-names: Maciej
  - family-names: Gaur
    given-names: Gopalji
  - family-names: Geburek
    given-names: Anton Merlin
  - family-names: Rogalla
    given-names: Daniel
  - family-names: Hvarfner
    given-names: Carl
  - family-names: Binxin
    given-names: Ru
  - family-names: Hutter
    given-names: Frank
title: "Neural Pipeline Search (NePS)"
version: 0.13.0
date-released: 2025-04-11
url: "https://github.com/automl/neps"

GitHub Events

Total
  • Create event: 67
  • Issues event: 61
  • Watch event: 25
  • Delete event: 28
  • Issue comment event: 71
  • Member event: 7
  • Push event: 361
  • Pull request review comment event: 52
  • Pull request review event: 40
  • Pull request event: 112
  • Fork event: 6
Last Year
  • Create event: 67
  • Issues event: 61
  • Watch event: 25
  • Delete event: 28
  • Issue comment event: 71
  • Member event: 7
  • Push event: 361
  • Pull request review comment event: 52
  • Pull request review event: 40
  • Pull request event: 112
  • Fork event: 6

Committers

Last synced: 6 months ago

All Time
  • Total Commits: 1,446
  • Total Committers: 23
  • Avg Commits per committer: 62.87
  • Development Distribution Score (DDS): 0.694
Past Year
  • Commits: 144
  • Committers: 12
  • Avg Commits per committer: 12.0
  • Development Distribution Score (DDS): 0.688
Top Committers
Name Email Commits
Danny Stoll s****d@c****e 443
schrodi s****i@c****e 230
neeratyoy n****y@g****m 160
Daniel 6****l 117
worstseed m****i@g****m 116
eddiebergman e****s@g****m 92
Tarek Abou Chakra t****a@h****m 70
Meganton a****n@g****m 45
Carl Hvarfner c****r@l****e 42
karibbov k****v@g****m 36
Theophane Vallaeys w****n@g****m 23
Nils Kober n****r@s****e 16
Sohambasu07 s****7@g****m 15
Gopalji Gaur g****g@c****e 9
robin r****n@r****k 7
Timur M. Carstensen 4****n 5
Jan Oreans o****j@c****e 5
dependabot[bot] 4****] 4
Gopalji Gaur g****r@g****m 3
Neeratyoy Mallik m****k@t****e 3
Lum Birinxhiku 8****b 2
Samir Garibov g****s@i****e 2
herilalaina r****a@g****m 1
Committer Domains (Top 20 + Academic)
cs.uni-freiburg.de: 4 informatik.uni-freiburg.de: 1 tf.uni-freiburg.de: 1 robots.ox.ac.uk: 1 students.uni-freiburg.de: 1 live.se: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 94
  • Total pull requests: 189
  • Average time to close issues: 7 months
  • Average time to close pull requests: 23 days
  • Total issue authors: 14
  • Total pull request authors: 15
  • Average comments per issue: 0.95
  • Average comments per pull request: 0.57
  • Merged pull requests: 137
  • Bot issues: 0
  • Bot pull requests: 14
Past Year
  • Issues: 18
  • Pull requests: 110
  • Average time to close issues: 20 days
  • Average time to close pull requests: 11 days
  • Issue authors: 6
  • Pull request authors: 14
  • Average comments per issue: 0.44
  • Average comments per pull request: 0.47
  • Merged pull requests: 79
  • Bot issues: 0
  • Bot pull requests: 6
View more stats
Top Authors
Issue Authors
  • eddiebergman (38)
  • danrgll (27)
  • Sohambasu07 (6)
  • timurcarstensen (4)
  • Meganton (4)
  • nabenabe0928 (3)
  • TarekAbouChakra (3)
  • Neeratyoy (3)
  • AwePhD (3)
  • bastis007 (1)
  • karibbov (1)
  • simon-ging (1)
  • Alken0 (1)
  • nastaran78 (1)
  • gopaljigaur (1)
Pull Request Authors
  • eddiebergman (85)
  • danrgll (26)
  • Meganton (24)
  • Neeratyoy (15)
  • timurcarstensen (15)
  • dependabot[bot] (14)
  • gopaljigaur (12)
  • TarekAbouChakra (8)
  • Sohambasu07 (7)
  • vladislavalerievich (3)
  • AnushaChatto (2)
  • nastaran78 (2)
  • karibbov (2)
  • lumib (1)
  • emaMekic (1)
Top Labels
Issue Labels
bug (14) enhancement (10) documentation (9) ux (8) question (6) ci (4) dependencies (1) known issue (1) help wanted (1) good first issue (1) wontfix (1)
Pull Request Labels
dependencies (17) enhancement (14) documentation (5) ci (5) ux (5) good first issue (5) github_actions (3) optim (2) known issue (2) help wanted (2)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 160 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 1
  • Total versions: 17
  • Total maintainers: 3
pypi.org: neural-pipeline-search

Neural Pipeline Search helps deep learning experts find the best neural pipeline.

  • Documentation: https://neural-pipeline-search.readthedocs.io/
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  • Latest release: 0.13.0
    published 9 months ago
  • Versions: 17
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 160 Last month
Rankings
Dependent packages count: 7.4%
Stargazers count: 14.2%
Forks count: 14.3%
Average: 15.9%
Downloads: 21.2%
Dependent repos count: 22.3%
Maintainers (3)
eddiebergman DaStoll neeratyoy
Last synced: 4 months ago

Dependencies

.github/workflows/tests.yaml actions
  • abatilo/actions-poetry v2.1.6 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
pyproject.toml pypi
  • ConfigSpace ^0.4.19
  • grakel ^0.1.9
  • matplotlib ^3.4
  • more-itertools ^9.0.0
  • networkx ^2.6.3
  • nltk ^3.6.4
  • numpy --- - !ruby/hash:ActiveSupport::HashWithIndifferentAccess version: "~1.21.0" python: "<3.8" - !ruby/hash:ActiveSupport::HashWithIndifferentAccess version: "^1.22.0" python: ">=3.8"
  • pandas ^1.3.1
  • path ^16.2.0
  • portalocker ^2.6.0
  • python >=3.7.1,<3.11
  • scipy ^1.7
  • seaborn ^0.12.1
  • statsmodels ^0.13.2
  • termcolor ^1.1.0
  • torch >=1.7.0,<1.13.0