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https://github.com/agnostiqhq/covalent-gcpbatch-plugin

Executor plugin interfacing Covalent with GCP Batch

https://github.com/agnostiqhq/covalent-gcpbatch-plugin

Science Score: 39.0%

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    CITATION.cff file
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    DOI references
    Found 2 DOI reference(s) in README
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    Low similarity (10.4%) to scientific vocabulary

Keywords

covalent docker etl gcp-batch parallelization pipelines python python3 workflow

Keywords from Contributors

data-pipeline hpc-applications machinelearning-python orchestration quantum quantum-computing quantum-machine-learning workflow-automation workflow-management
Last synced: 6 months ago · JSON representation

Repository

Executor plugin interfacing Covalent with GCP Batch

Basic Info
  • Host: GitHub
  • Owner: AgnostiqHQ
  • License: apache-2.0
  • Language: Python
  • Default Branch: develop
  • Homepage: https://agnostiq.ai/covalent
  • Size: 447 KB
Statistics
  • Stars: 2
  • Watchers: 8
  • Forks: 3
  • Open Issues: 14
  • Releases: 7
Topics
covalent docker etl gcp-batch parallelization pipelines python python3 workflow
Created almost 3 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct

README.md

 

Covalent Google Cloud Platform (GCP) Batch Plugin

Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware. This executor plugin interfaces Covalent with GCP Batch.

1. Installation

To use this plugin with Covalent, install it using pip:

sh pip install covalent-gcpbatch-plugin

2. Usage Example

This is an example of how a workflow can be constructed to use the GCP Batch executor. In the example, we train a Support Vector Machine (SVM) and use an instance of the executor to execute the train_svm electron. Note that we also require DepsPip which will be required to execute the electrons.

```python from numpy.random import permutation from sklearn import svm, datasets import covalent as ct

deps_pip = ct.DepsPip( packages=["numpy==1.22.4", "scikit-learn==1.1.2"] )

executor = ct.executor.GCPBatchExecutor( projectid='covalentgcpbatch', region='us-east1', bucketname='covalent-storage-bucket', containerimageuri='us-east1-docker.pkg.dev/covalentgcpbatch/covalent/covalent-gcpbatch-executor', serviceaccountemail='covalentsaaccount@covalenttesting.iam.gserviceaccount.com', vcpus = 2, # Number of vCPUs to allocate memory = 512, # Memory in MB to allocate timelimit = 300, # Time limit of job in seconds poll_freq = 3, # Number of seconds to pause before polling for the job's status )

Use executor plugin to train our SVM model

@ct.electron( executor=executor, depspip=depspip ) def train_svm(data, C, gamma): X, y = data clf = svm.SVC(C=C, gamma=gamma) clf.fit(X[90:], y[90:]) return clf

@ct.electron def loaddata(): iris = datasets.loadiris() perm = permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] return iris.data, iris.target

@ct.electron def scoresvm(data, clf): Xtest, ytest = data return clf.score( Xtest[:90],y_test[:90] )

@ct.lattice def runexperiment(C=1.0, gamma=0.7): data = loaddata() clf = trainsvm( data=data, C=C, gamma=gamma ) return scoresvm( data=data, clf=clf )

Dispatch the workflow.

dispatchid = ct.dispatch(runexperiment)( C=1.0, gamma=0.7 )

Wait for our result and get result value

result = ct.getresult(dispatchid, wait=True).result

print(result) ``` During the execution of the workflow, one can navigate to the UI to see the status of the workflow. Once completed, the above script should also output a value with the score of our model.

sh 0.8666666666666667

In order for the above workflow to run successfully, one has to provision the required cloud resources as mentioned in the section Required GCP Batch Resources.

3. Configuration

There are many configuration options that can be passed in to the class ct.executor.GCPBatchExecutor or by modifying the covalent config file under the section [executors.gcpbatch].

For more information about all of the possible configuration values visit our read the docs (RTD) guide for this plugin.

4. Required GCP Resources

In order to run your workflows with covalent there are a few notable GCP resources that need to be provisioned first. The required resources are Google storage bucket, docker artifact registry and service account.

For more information regarding which cloud resources need to be provisioned visit our read the docs (RTD) guide for this plugin.

Getting Started with Covalent

For more information on how to get started with Covalent, check out the project homepage and the official documentation.

Release Notes

Release notes are available in the Changelog.

Citation

Please use the following citation in any publications:

W. J. Cunningham, S. K. Radha, F. Hasan, J. Kanem, S. W. Neagle, and S. Sanand. Covalent. Zenodo, 2022. https://doi.org/10.5281/zenodo.5903364

License

Covalent is licensed under the Apache License 2.0. See the LICENSE file or contact the support team for more details.

Owner

  • Name: Agnostiq
  • Login: AgnostiqHQ
  • Kind: organization
  • Email: contact@agnostiq.ai
  • Location: Toronto

Developing Software for Advanced Computing

GitHub Events

Total
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 6
  • Pull request event: 3
  • Fork event: 1
  • Create event: 3
Last Year
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 6
  • Pull request event: 3
  • Fork event: 1
  • Create event: 3

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 148
  • Total Committers: 9
  • Avg Commits per committer: 16.444
  • Development Distribution Score (DDS): 0.696
Past Year
  • Commits: 8
  • Committers: 4
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.5
Top Committers
Name Email Commits
Faiyaz Hasan f****z@a****i 45
Alejandro Esquivel ae@a****d 37
Venkat Bala v****t@a****i 26
Venkat Bala b****9@g****m 21
CovalentOpsBot c****t 14
mpvgithub 1****b 2
Sankalp Sanand s****p@a****i 1
Ara Ghukasyan 3****s 1
Will Cunningham w****7 1
Committer Domains (Top 20 + Academic)
agnostiq.ai: 3 alejandro.ltd: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 15
  • Total pull requests: 28
  • Average time to close issues: about 2 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 4
  • Total pull request authors: 10
  • Average comments per issue: 0.2
  • Average comments per pull request: 1.07
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 9
Past Year
  • Issues: 0
  • Pull requests: 5
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 5
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Top Authors
Issue Authors
  • AlejandroEsquivel (9)
  • araghukas (3)
  • kessler-frost (1)
  • santoshkumarradha (1)
Pull Request Authors
  • dependabot[bot] (9)
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  • AlejandroEsquivel (4)
  • venkatBala (2)
  • araghukas (1)
  • mpvgithub (1)
  • kessler-frost (1)
  • pre-commit-ci[bot] (1)
  • wjcunningham7 (1)
  • dhroth (1)
Top Labels
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Epic (1)
Pull Request Labels
dependencies (9) javascript (6) python (2) feature (1) infra (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 21 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: covalent-gcpbatch-plugin

Covalent GCP Batch Plugin

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 21 Last month
Rankings
Dependent packages count: 7.3%
Average: 24.1%
Dependent repos count: 40.8%
Maintainers (1)
wjcunningham7
Last synced: 6 months ago