sageworks


Namesageworks JSON
Version 0.6.10 PyPI version JSON
download
home_pagehttps://github.com/SuperCowPowers/sageworks
SummarySageWorks: A Python WorkBench for creating and deploying AWS SageMaker Models
upload_time2024-06-17 22:20:03
maintainerNone
docs_urlNone
authorSuperCowPowers LLC
requires_pythonNone
licenseMIT
keywords sagemaker machine learning aws python utilities
VCS
bugtrack_url
requirements boto3 botocore redis numpy pandas scikit-learn awswrangler sagemaker plotly dash dash-bootstrap-components dash-bootstrap-templates dash_ag_grid tabulate shap xgboost joblib cryptography rdkit mordred ipython networkx pyreadline3
Travis-CI No Travis.
coveralls test coverage No coveralls.
            
# Welcome to SageWorks
The SageWorks framework makes AWS® both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Job, Athena, Feature Store, Models, and Endpoints, SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.

<img align="right" width="480" alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/ed2ed1bd-e2d8-49a1-b350-b2e19e2b7832">

### Full AWS ML OverView
- Health Monitoring 🟢
- Dynamic Updates
- High Level Summary

### Drill-Down Views
- Incoming Data
- Glue Jobs
- DataSources
- FeatureSets
- Models
- Endpoints

## Private SaaS Architecture
*Secure your Data, Empower your ML Pipelines*

SageWorks is architected as a **Private SaaS**. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. SageWorks deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using SageWorks, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. [SageWorks Private SaaS Architecture](https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=sharing)


### API Installation

- ```pip install sageworks```  Installs SageWorks

- ```sageworks``` Runs the SageWorks REPL/Initial Setup

For the full instructions for connecting your AWS Account see:

- Getting Started: [Initial Setup](https://supercowpowers.github.io/sageworks/getting_started/) 
- One time AWS Onboarding: [AWS Setup](https://supercowpowers.github.io/sageworks/aws_setup/core_stack/)



### SageWorks Documentation
<img align="right" width="340" alt="sageworks_api" style="padding-left: 10px;"  src="https://github.com/SuperCowPowers/sageworks/assets/4806709/bf0e8591-75d4-44c1-be05-4bfdee4b7186">

[SageWorks Documentation](https://supercowpowers.github.io/sageworks/): The documentation contains examples from the SageWorks source code in this repository under the `examples/` directory. For a full code listing of any example please visit our [SageWorks Examples](https://github.com/SuperCowPowers/sageworks/blob/main/examples)


### SageWorks Beta Program
Using SageWorks will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Beta Tester, contact us at [sageworks@supercowpowers.com](mailto:sageworks@supercowpowers.com).

### Contributions
If you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing project [license](https://github.com/SuperCowPowers/sageworks/blob/main/LICENSE). If you are interested in contributing or have questions please feel free to contact us at [sageworks@supercowpowers.com](mailto:sageworks@supercowpowers.com).

<img align="right" src="docs/images/scp.png" width="180">

® Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/SuperCowPowers/sageworks",
    "name": "sageworks",
    "maintainer": null,
    "docs_url": null,
    "requires_python": null,
    "maintainer_email": null,
    "keywords": "SageMaker, Machine Learning, AWS, Python, Utilities",
    "author": "SuperCowPowers LLC",
    "author_email": "support@supercowpowers.com",
    "download_url": "https://files.pythonhosted.org/packages/6c/06/682b6179b6e08b6fdb4c83c187bcdfe453b6fc36aefe7d2a3c32a797a4ed/sageworks-0.6.10.tar.gz",
    "platform": null,
    "description": "\n# Welcome to SageWorks\nThe SageWorks framework makes AWS\u00ae both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Job, Athena, Feature Store, Models, and Endpoints, SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.\n\n<img align=\"right\" width=\"480\" alt=\"sageworks_new_light\" src=\"https://github.com/SuperCowPowers/sageworks/assets/4806709/ed2ed1bd-e2d8-49a1-b350-b2e19e2b7832\">\n\n### Full AWS ML OverView\n- Health Monitoring \ud83d\udfe2\n- Dynamic Updates\n- High Level Summary\n\n### Drill-Down Views\n- Incoming Data\n- Glue Jobs\n- DataSources\n- FeatureSets\n- Models\n- Endpoints\n\n## Private SaaS Architecture\n*Secure your Data, Empower your ML Pipelines*\n\nSageWorks is architected as a **Private SaaS**. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. SageWorks deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using SageWorks, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. [SageWorks Private SaaS Architecture](https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=sharing)\n\n\n### API Installation\n\n- ```pip install sageworks```  Installs SageWorks\n\n- ```sageworks``` Runs the SageWorks REPL/Initial Setup\n\nFor the full instructions for connecting your AWS Account see:\n\n- Getting Started: [Initial Setup](https://supercowpowers.github.io/sageworks/getting_started/) \n- One time AWS Onboarding: [AWS Setup](https://supercowpowers.github.io/sageworks/aws_setup/core_stack/)\n\n\n\n### SageWorks Documentation\n<img align=\"right\" width=\"340\" alt=\"sageworks_api\" style=\"padding-left: 10px;\"  src=\"https://github.com/SuperCowPowers/sageworks/assets/4806709/bf0e8591-75d4-44c1-be05-4bfdee4b7186\">\n\n[SageWorks Documentation](https://supercowpowers.github.io/sageworks/): The documentation contains examples from the SageWorks source code in this repository under the `examples/` directory. For a full code listing of any example please visit our [SageWorks Examples](https://github.com/SuperCowPowers/sageworks/blob/main/examples)\n\n\n### SageWorks Beta Program\nUsing SageWorks will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Beta Tester, contact us at [sageworks@supercowpowers.com](mailto:sageworks@supercowpowers.com).\n\n### Contributions\nIf you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing project [license](https://github.com/SuperCowPowers/sageworks/blob/main/LICENSE). If you are interested in contributing or have questions please feel free to contact us at [sageworks@supercowpowers.com](mailto:sageworks@supercowpowers.com).\n\n<img align=\"right\" src=\"docs/images/scp.png\" width=\"180\">\n\n\u00ae Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "SageWorks: A Python WorkBench for creating and deploying AWS SageMaker Models",
    "version": "0.6.10",
    "project_urls": {
        "Homepage": "https://github.com/SuperCowPowers/sageworks"
    },
    "split_keywords": [
        "sagemaker",
        " machine learning",
        " aws",
        " python",
        " utilities"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "fe3e10b5c3bfba3e77b3f7ffb71583de8c2b6313ff7da9d9bf6e369530d4dd32",
                "md5": "aef16a32c9ad333ccd03ad4ecc04a6b1",
                "sha256": "10f22175b2bdd3ff38de8e686c475112688cd7ad824702e49e120a9353635564"
            },
            "downloads": -1,
            "filename": "sageworks-0.6.10-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "aef16a32c9ad333ccd03ad4ecc04a6b1",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 289018,
            "upload_time": "2024-06-17T22:19:55",
            "upload_time_iso_8601": "2024-06-17T22:19:55.165213Z",
            "url": "https://files.pythonhosted.org/packages/fe/3e/10b5c3bfba3e77b3f7ffb71583de8c2b6313ff7da9d9bf6e369530d4dd32/sageworks-0.6.10-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "6c06682b6179b6e08b6fdb4c83c187bcdfe453b6fc36aefe7d2a3c32a797a4ed",
                "md5": "ad6555aec70a36e94bb2b2aac1683fa8",
                "sha256": "d461e4a56a4f2dcd99163e1bf4b134a03aa1ddee5b21e9bb76660dc5b3d6b2e6"
            },
            "downloads": -1,
            "filename": "sageworks-0.6.10.tar.gz",
            "has_sig": false,
            "md5_digest": "ad6555aec70a36e94bb2b2aac1683fa8",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 3505368,
            "upload_time": "2024-06-17T22:20:03",
            "upload_time_iso_8601": "2024-06-17T22:20:03.890028Z",
            "url": "https://files.pythonhosted.org/packages/6c/06/682b6179b6e08b6fdb4c83c187bcdfe453b6fc36aefe7d2a3c32a797a4ed/sageworks-0.6.10.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-06-17 22:20:03",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "SuperCowPowers",
    "github_project": "sageworks",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "requirements": [
        {
            "name": "boto3",
            "specs": [
                [
                    ">=",
                    "1.28.76"
                ]
            ]
        },
        {
            "name": "botocore",
            "specs": [
                [
                    ">=",
                    "1.31.76"
                ]
            ]
        },
        {
            "name": "redis",
            "specs": [
                [
                    ">=",
                    "5.0.1"
                ]
            ]
        },
        {
            "name": "numpy",
            "specs": [
                [
                    ">=",
                    "1.26.1"
                ]
            ]
        },
        {
            "name": "pandas",
            "specs": [
                [
                    ">=",
                    "2.1.2"
                ]
            ]
        },
        {
            "name": "scikit-learn",
            "specs": [
                [
                    ">=",
                    "1.4.1"
                ]
            ]
        },
        {
            "name": "awswrangler",
            "specs": [
                [
                    ">=",
                    "3.4.0"
                ]
            ]
        },
        {
            "name": "sagemaker",
            "specs": [
                [
                    ">=",
                    "2.143"
                ]
            ]
        },
        {
            "name": "plotly",
            "specs": [
                [
                    ">=",
                    "5.18.0"
                ]
            ]
        },
        {
            "name": "dash",
            "specs": [
                [
                    ">=",
                    "2.16.1"
                ]
            ]
        },
        {
            "name": "dash-bootstrap-components",
            "specs": [
                [
                    ">=",
                    "1.5.0"
                ]
            ]
        },
        {
            "name": "dash-bootstrap-templates",
            "specs": [
                [
                    "==",
                    "1.1.1"
                ]
            ]
        },
        {
            "name": "dash_ag_grid",
            "specs": []
        },
        {
            "name": "tabulate",
            "specs": [
                [
                    ">=",
                    "0.9.0"
                ]
            ]
        },
        {
            "name": "shap",
            "specs": [
                [
                    ">=",
                    "0.43.0"
                ]
            ]
        },
        {
            "name": "xgboost",
            "specs": [
                [
                    ">=",
                    "2.0.2"
                ]
            ]
        },
        {
            "name": "joblib",
            "specs": [
                [
                    ">=",
                    "1.3.2"
                ]
            ]
        },
        {
            "name": "cryptography",
            "specs": [
                [
                    ">=",
                    "42.0.5"
                ]
            ]
        },
        {
            "name": "rdkit",
            "specs": [
                [
                    ">=",
                    "2023.9.1"
                ]
            ]
        },
        {
            "name": "mordred",
            "specs": [
                [
                    ">=",
                    "1.2.0"
                ]
            ]
        },
        {
            "name": "ipython",
            "specs": [
                [
                    ">=",
                    "8.17.2"
                ]
            ]
        },
        {
            "name": "networkx",
            "specs": [
                [
                    ">=",
                    "3.2"
                ]
            ]
        },
        {
            "name": "pyreadline3",
            "specs": []
        }
    ],
    "tox": true,
    "lcname": "sageworks"
}
        
Elapsed time: 0.54911s