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https://github.com/agnostiqhq/covalent

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

https://github.com/agnostiqhq/covalent

Science Score: 49.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.9%) to scientific vocabulary

Keywords

covalent data-pipeline data-science deep-learning hacktoberfest hpc hpc-applications machine-learning machinelearning machinelearning-python orchestration parallelization pipelines python quantum quantum-computing quantum-machine-learning workflow workflow-automation workflow-management

Keywords from Contributors

quantum-circuit distributed interactive energy-system-model autograd mesh plots medical-imaging exoplanet energy-system
Last synced: 6 months ago · JSON representation

Repository

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

Basic Info
  • Host: GitHub
  • Owner: AgnostiqHQ
  • License: apache-2.0
  • Language: Python
  • Default Branch: develop
  • Homepage: https://www.covalent.xyz
  • Size: 370 MB
Statistics
  • Stars: 844
  • Watchers: 24
  • Forks: 107
  • Open Issues: 86
  • Releases: 0
Topics
covalent data-pipeline data-science deep-learning hacktoberfest hpc hpc-applications machine-learning machinelearning machinelearning-python orchestration parallelization pipelines python quantum quantum-computing quantum-machine-learning workflow workflow-automation workflow-management
Created over 4 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Codeowners

README.md

hero


version Static Badge Static Badge Static Badge Static Badge apache

Run AI, ML, and Scientific Research Code on Any Cloud or On-Prem Cluster with a Single Line


divider &nbsp&nbsp divider &nbsp&nbsp divider &nbsp&nbsp divider

bash pip install covalent --upgrade Check our Quick Start Guide for setup instructions or dive into your First Experiment. Learn more on the Concepts.

What is Covalent?

Covalent is a Python library for AI/ML engineers, developers, and researchers. It provides a straightforward approach to running compute jobs, like LLMs, generative AI, and scientific research, on various cloud platforms or on-prem clusters.

Run Code Anywhere: Execute Python functions in any cloud or on-prem cluster by changing just a single line of code.
It is as simple as swapping the decorator with our executor plugins. Choose from [existing plugins](https://docs.covalent.xyz/docs/plugin) or [create custom ones](https://github.com/AgnostiqHQ/covalent-executor-template) for tailored interactions with any infrastructure.
Abstraction of Infrastructure Management: Abstract the complexities of cloud consoles, terraform, or IaC in the background.
Serverless Infrastructure: Automatically converts any infrastructure, including on-prem SLURM clusters or cloud compute, into a serverless setup.

If you find Covalent useful or interesting, feel free to give us a on GitHub! Your support helps us to continue developing and improving this framework.


For AI/ML Practitioners and Developers For Researchers
  • Robust Compute Backend: Ideal as a backend compute framework for AI/ML applications, Large Language Models (LLMs), Generative AI, and more.
  • Cloud-Agnostic Execution: Execute high-compute tasks seamlessly across different cloud environments.
  • Infrastructure Abstraction: Directly use computing resources while keeping your business code independent from the infrastructure/resource definitions.
  • Local-Like Access: Effortlessly connect to compute resources from your laptop, eliminating the need for SSH or complex scripts.
  • Unified Interface Across Environments: Consistent experience with on-prem HPC clusters and cloud platforms like SLURM, PBS, LSF, AWS, GCP, Azure.
  • Real-Time Monitoring Monitoring: User-friendly UI for real-time monitoring, enabling cost-effective and iterative R&D.

Out-of-box observability - Try out the demo

If you find Covalent useful or interesting, feel free to give us a on GitHub! Your support helps us to continue developing and improving this framework.

video

Explore Covalent Through Examples

Jump right into practical examples to see Covalent in action. These tutorials cover a range of applications, giving you a hands-on experience:

AI Tutorial MNIST Tutorial Quantum Tutorial

Explore Our Extensive Plugin Ecosystem

Covalent integrates seamlessly with a variety of platforms. Discover our range of plugins to enhance your Covalent experience:


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Key Features at a Glance

Get a quick overview of what Covalent offers. Our infographic summarizes the main features, providing you with a snapshot of our capabilities:


development


Know More About Covalent

For a more in-depth description of Covalent's features and how they work, see the Concepts page in the documentation.


divider divider divider divider
### Installation Covalent is developed using Python on Linux and macOS. The easiest way to install Covalent is by using the PyPI package manager. ``` pip install covalent --upgrade ``` For other methods of installation, please [check the docs.](https://docs.covalent.xyz/docs/get-started/install/) **Deployments**
Covalent offers flexible deployment options, from Docker image/AMIs for self-hosting to pip package for local installations, accommodating various use cases


divider divider divider


Contributing

<!--

divider
-->

To contribute to Covalent, refer to the Contribution Guidelines. We use GitHub's issue tracking to manage known issues, bugs, and pull requests. Get started by forking the develop branch and submitting a pull request with your contributions. Improvements to the documentation, including tutorials and how-to guides, are also welcome from the community. For more information on adding tutorials, check the Tutorial Guidelines. Participation in the Covalent community is governed by the Code of Conduct.

Citation

Please use the following citation in any publications.

https://doi.org/10.5281/zenodo.5903364

License

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

For a detailed history of changes and new features, see the Changelog.

Owner

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

Developing Software for Advanced Computing

GitHub Events

Total
  • Create event: 36
  • Release event: 11
  • Issues event: 3
  • Watch event: 65
  • Delete event: 22
  • Issue comment event: 20
  • Push event: 145
  • Pull request review event: 17
  • Pull request review comment event: 6
  • Pull request event: 41
  • Fork event: 19
Last Year
  • Create event: 36
  • Release event: 11
  • Issues event: 3
  • Watch event: 65
  • Delete event: 22
  • Issue comment event: 20
  • Push event: 145
  • Pull request review event: 17
  • Pull request review comment event: 6
  • Pull request event: 41
  • Fork event: 19

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 851
  • Total Committers: 48
  • Avg Commits per committer: 17.729
  • Development Distribution Score (DDS): 0.804
Past Year
  • Commits: 36
  • Committers: 8
  • Avg Commits per committer: 4.5
  • Development Distribution Score (DDS): 0.639
Top Committers
Name Email Commits
CovalentOpsBot c****t 167
Will Cunningham w****7 88
Casey Jao c****y@a****i 79
Sankalp Sanand s****p@a****i 74
Alejandro Esquivel ae@a****d 69
Faiyaz Hasan f****z@a****i 58
Venkat Bala v****t@a****i 56
Scott Wyman Neagle s****t@a****i 53
dependabot[bot] 4****] 28
Prasanna Venkatesh 5****2 20
Andrew S. Rosen a****3@g****m 19
Okechukwu Emmanuel Ochia o****u@a****i 14
Madhur Tandon 2****n 14
dwelsch-esi 1****i 11
pre-commit-ci[bot] 6****] 11
Ara Ghukasyan 3****s 9
Aravind 1****g 9
jkanem j****i@a****i 9
HaimHorowitzAgnostiq 9****q 7
Ruihao Li 4****i 5
Scott Wyman Neagle w****a@p****m 5
valkostadinov 9****v 5
FilipBolt f****t@g****m 4
Poojith U Rao 1****o 3
Sayandip Dutta s****9@g****m 3
WingCode s****4@g****m 3
Anna Hughes a****2@g****m 2
ArunPsiog 1****g 2
RaviPsiog 1****g 2
Santosh kumar 2****a 2
and 18 more...
Committer Domains (Top 20 + Academic)
agnostiq.ai: 7 psiog.com: 1 alejandro.ltd: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 256
  • Total pull requests: 304
  • Average time to close issues: 7 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 42
  • Total pull request authors: 39
  • Average comments per issue: 1.92
  • Average comments per pull request: 2.05
  • Merged pull requests: 201
  • Bot issues: 0
  • Bot pull requests: 59
Past Year
  • Issues: 2
  • Pull requests: 46
  • Average time to close issues: N/A
  • Average time to close pull requests: 16 days
  • Issue authors: 2
  • Pull request authors: 4
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.35
  • Merged pull requests: 20
  • Bot issues: 0
  • Bot pull requests: 22
View more stats
Top Authors
Issue Authors
  • kessler-frost (32)
  • cjao (22)
  • scottwn (22)
  • FyzHsn (21)
  • AlejandroEsquivel (21)
  • Andrew-S-Rosen (18)
  • arosen93 (16)
  • araghukas (12)
  • Prasy12 (11)
  • mshkanth (11)
  • wjcunningham7 (9)
  • jimmylism (7)
  • Emmanuel289 (4)
  • venkatBala (4)
  • santoshkumarradha (3)
Pull Request Authors
  • dependabot[bot] (73)
  • kessler-frost (51)
  • cjao (46)
  • SusheelThapa (17)
  • wjcunningham7 (16)
  • arosen93 (15)
  • Andrew-S-Rosen (15)
  • madhur-tandon (13)
  • araghukas (10)
  • Aravind-psiog (8)
  • AlejandroEsquivel (8)
  • FyzHsn (8)
  • pre-commit-ci[bot] (6)
  • mpvgithub (5)
  • santoshkumarradha (5)
Top Labels
Issue Labels
bug (62) feature (36) priority / low (29) Team Psi (28) documentation (24) good first issue (20) priority / medium (18) hacktoberfest (14) help wanted (14) Epic (12) priority / high (11) covalent-os (11) improvement (11) devops (10) improvement / memory (10) Team West (9) Team Psi - FE (7) QElectron (7) covalent-dispatcher (6) Team Psi - BE (5) test (4) electron (4) cloud (3) lattice (3) covalent-executor (3) Team East (3) result (2) unitaryhack-bounty (2) covalent-ui (2) pending discussion (2)
Pull Request Labels
dependencies (73) python (36) javascript (33) feature (5) github_actions (4) documentation (4) improvement (3) ready for review (3) Team Psi (3) Team Psi - BE (2) bug (2) hacktoberfest-accepted (2) devops (2) ready for merge (1) stale-exempt (1) unitaryhack-accepted (1) stale (1)

Packages

  • Total packages: 4
  • Total downloads:
    • pypi 22,365 last-month
  • Total docker downloads: 233
  • Total dependent packages: 2
    (may contain duplicates)
  • Total dependent repositories: 7
    (may contain duplicates)
  • Total versions: 863
  • Total maintainers: 2
pypi.org: covalent

Covalent Workflow Tool

  • Versions: 135
  • Dependent Packages: 2
  • Dependent Repositories: 6
  • Downloads: 22,261 Last month
  • Docker Downloads: 233
Rankings
Stargazers count: 2.6%
Docker downloads count: 2.6%
Dependent packages count: 3.2%
Downloads: 3.6%
Average: 3.8%
Forks count: 5.0%
Dependent repos count: 6.0%
Maintainers (1)
wjcunningham7
Last synced: 6 months ago
proxy.golang.org: github.com/AgnostiqHQ/covalent
  • Versions: 299
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.5%
Average: 6.7%
Dependent repos count: 7.0%
Last synced: 6 months ago
proxy.golang.org: github.com/agnostiqhq/covalent
  • Versions: 299
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.5%
Average: 6.7%
Dependent repos count: 7.0%
Last synced: 6 months ago
pypi.org: cova

Covalent Workflow Tool

  • Versions: 130
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 104 Last month
Rankings
Stargazers count: 2.6%
Forks count: 5.0%
Dependent packages count: 10.1%
Average: 11.9%
Downloads: 20.1%
Dependent repos count: 21.6%
Maintainers (2)
instrument.santosh wjcunningham7
Last synced: 6 months ago

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Dockerfile docker
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covalent_ui/webapp/yarn.lock npm
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doc/autodoc_executor_plugins_requirements.txt pypi
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  • sphinx-design *
  • sphinx-gallery ==0.9.0
  • sphinx-inline-tabs ==2021.3.28b7
  • sphinx-panels ==0.6.0
  • sphinx-prompt ==1.4.0
  • sphinx-togglebutton ==0.2.3
  • sphinxcontrib-apidoc ==0.3.0
  • sphinxcontrib-applehelp ==1.0.2
  • sphinxcontrib-devhelp ==1.0.2
  • sphinxcontrib-htmlhelp ==2.0.0
  • sphinxcontrib-jsmath ==1.0.1
  • sphinxcontrib-qthelp ==1.0.3
  • sphinxcontrib-serializinghtml ==1.1.5
doc/source/how_to/coding/requirements_example.txt pypi
  • numpy ==1.22.4
doc/source/tutorials/0_ClassicalMachineLearning/autoencoders/requirements.txt pypi
  • covalent *
  • matplotlib ==3.5.1
  • numpy ==1.23.2
  • torch ==1.13.1
  • torchvision ==0.14.1
doc/source/tutorials/0_ClassicalMachineLearning/genai/requirements.txt pypi
  • Pillow ==9.5.0
  • accelerate ==0.21.0
  • bs4 ==0.0.1
  • covalent-azurebatch-plugin ==0.12.0
  • diffusers ==0.19.3
  • emoji ==2.8.0
  • sentencepiece ==0.1.99
  • streamlit ==1.25.0
  • torch ==2.0.1
  • transformers ==4.31.0
  • xformers ==0.0.21
doc/source/tutorials/0_ClassicalMachineLearning/mnist_images/requirements.txt pypi
  • covalent *
  • torch ==1.13.1
  • torchvision ==0.14.1
doc/source/tutorials/1_QuantumMachineLearning/classical_quantum_svm/requirements.txt pypi
  • covalent *
  • matplotlib ==3.6.3
  • qiskit ==0.36.0
  • qiskit-machine-learning ==0.3.1
  • scikit-learn ==1.0.2
doc/source/tutorials/1_QuantumMachineLearning/pennylane_ensemble_classification/assets/requirements.txt pypi
  • covalent *
  • matplotlib ==3.6.3
  • scikit-learn ==1.0.2
  • torch ==1.13.1
doc/source/tutorials/1_QuantumMachineLearning/pennylane_hybrid/requirements.txt pypi
  • covalent *
  • matplotlib ==3.4.3
  • pennylane-sf ==0.20.1
doc/source/tutorials/1_QuantumMachineLearning/pennylane_iris_classification/requirements.txt pypi
  • covalent *
  • matplotlib ==3.4.3
  • pennylane-sf ==0.20.1
doc/source/tutorials/1_QuantumMachineLearning/pennylane_kernel/requirements.txt pypi
  • covalent *
  • matplotlib ==3.5.1
  • numpy ==1.24.2
  • pennylane-sf ==0.20.1
  • scikit-learn ==1.0.2
  • torch ==2.0.0
  • torchvision ==0.15.1
doc/source/tutorials/1_QuantumMachineLearning/pennylane_parity_classifier/requirements.txt pypi
  • covalent *
  • matplotlib ==3.4.3
doc/source/tutorials/1_QuantumMachineLearning/qaoa_maxcut/requirements.txt pypi
  • covalent *
  • matplotlib ==3.4.3
doc/source/tutorials/1_QuantumMachineLearning/quantum_embedding_kernel/requirements.txt pypi
  • covalent *
  • matplotlib ==3.5.1
  • scikit-learn ==1.1.1
doc/source/tutorials/2_Astronomy/star_tracker/requirements.txt pypi
  • covalent *
  • matplotlib ==3.4.3
  • pytz ==2022.1
doc/source/tutorials/3_QuantumChemistry/dynamic_quantum_chemistry/requirements.txt pypi
  • covalent *
  • pymatgen ==2023.9.25
doc/source/tutorials/3_QuantumChemistry/nitrogen_copper_interaction/requirements.txt pypi
  • ase ==3.22.1
  • ase-notebook ==0.3.2
  • covalent *
  • matplotlib ==3.4.3
doc/source/tutorials/4_QuantumGravity/spacetime_classification/requirements.txt pypi
  • covalent *
  • matplotlib ==3.4.3
  • pandas ==1.4.3
  • tensorflow ==2.9.1
doc/source/tutorials/5_QPUAccessIBM/requirements.txt pypi
  • covalent *
  • qiskit ==0.40.0
  • qiskit-ibm-runtime ==0.8.0
doc/source/tutorials/6_PennylaneRepoTutorials/univariate_qvr/assets/requirements.txt pypi
  • covalent *
  • matplotlib ==3.7.0
  • pennylane *
  • torch ==1.13.1
doc/source/tutorials/federated_learning/requirements.txt pypi
  • covalent ==0.220.0
  • covalent-awsbatch-plugin ==0.26.0
  • covalent-azurebatch-plugin ==0.12.0
  • covalent-gcpbatch-plugin ==0.9.0
  • datasets ==2.14.0
  • pandas *
  • plotly *
  • torch ==2.0.1
  • torchvision ==0.15.2
doc/source/tutorials/machine_learning/requirements.txt pypi
  • covalent *
  • covalent-aws-plugins *
  • pandas ==1.4.4
  • tensorflow ==2.9.1
doc/source/tutorials/qiskit_hybrid_neural_network/requirements.txt pypi
  • covalent ==0.209.1
  • covalent-aws-plugins ==0.13.0
  • covalent-awsbatch-plugin ==0.26.0
  • matplotlib ==3.7.1
  • numpy ==1.23.5
  • qiskit-aer ==0.12.0
  • qiskit-ibm-runtime ==0.9.1
  • qiskit-ibmq-provider ==0.20.2
  • qiskit-terra ==0.23.2
  • scipy ==1.10.1
  • torch ==2.0.0
  • torchvision ==0.15.1
doc/source/tutorials/voice_cloning/requirements.txt pypi
  • TTS ==0.19.1
  • covalent ==0.229.0rc0
  • covalent-gcpbatch-plugin ==0.11.0
  • librosa ==0.10.0
  • pydub ==0.25.1
  • pytube ==15.0.0
  • scipy ==1.11.3
  • soundfile ==0.12.1
  • streamlit ==1.28.1
  • torch ==2.1.0
  • torchaudio ==2.1.0
  • transformers ==4.33.3
pyproject.toml pypi
requirements-client.txt pypi
  • aiofiles >=0.8.0
  • aiohttp >=3.8.1
  • cloudpickle >=2.0.0,<3
  • dask >=2022.6.0
  • filelock >=3.12.2
  • furl >=2.1.3
  • networkx >=2.8.6
  • pydantic >=2.1.1
  • requests >=2.24.0
  • toml >=0.10.2
  • watchdog >=2.0.3
requirements.txt pypi
  • aiofiles >=0.8.0
  • aiohttp >=3.8.1
  • alembic >=1.8.0
  • boto3 >=1.26.110
  • click >=8.1.3
  • cloudpickle >=2.0.0
  • dask >=2022.6.0
  • fastapi >=0.100.0
  • filelock >=3.12.2
  • furl >=2.1.3
  • natsort >=8.4.0
  • networkx >=2.8.6
  • psutil >=5.9.0
  • pydantic >=2.1.1
  • python-multipart >=0.0.6
  • python-socketio >=5.7.1
  • requests >=2.24.0
  • rich >=12.0.0,<=13.3.5
  • sqlalchemy >=1.4.37,<2.0.0
  • sqlalchemy_utils >=0.38.3
  • toml >=0.10.2
  • typing-extensions >=4.8.0
  • uvicorn *
  • watchdog >=2.2.1
setup.py pypi
tests/requirements.txt pypi
  • detect-secrets >=1.3.0 test
  • flake8 >=5.0.4 test
  • httpx >=0.24.1 test
  • isort >=5.10.1 test
  • locust >=2.11.0 test
  • mock >=4.0.3 test
  • nbconvert >=6.5.1 test
  • pennylane >=0.31.1 test
  • pre-commit >=2.20.0 test
  • pytest >=7.1.3 test
  • pytest-asyncio >=0.21.0 test
  • pytest-cov >=3.0.0 test
  • pytest-mock >=3.8.2 test
  • pytest-rerunfailures >=10.2 test
  • scikit-image >=0.19.1 test
  • scikit-learn >=1.2.2 test