sasctl

A user-friendly Python interface for SAS Viya.

SAS Viya Version Python Version
Full documentation: https://sassoftware.github.io/python-sasctl

Table of Contents

  1. Overview
  2. Prerequisites
  3. Installation
  4. Getting Started
  5. Examples
  6. Contributing
  7. License
  8. Additional Resources

Overview

The sasctl package enables easy communication between the SAS Viya platform and a Python runtime. It can be used as a module or as a command line interface.

sasctl.services.folders.list_folders()

sasctl folders list

Prerequisites

sasctl requires the following Python packages be installed. If not already present, these packages will be downloaded and installed automatically.

  • pandas
  • requests
  • pyyaml
  • packaging

The following additional packages are recommended for full functionality:

  • swat
  • kerberos
  • GitPython
  • numpy
  • scikit-learn

Installation

Installing the latest version is as easy as:

pip install sasctl

Functionality that depends on additional packages can be installed using the following:

  • pip install sasctl[swat]
  • pip install sasctl[kerberos]
  • pip install sasctl[all]

If you want the latest functionality and can't wait on an official release, you can also install the latest source code:

pip install git+https://github.com/sassoftware/python-sasctl

Alternatively, if you're using Anaconda you can install with:

conda install -c sas-institute sasctl

Getting Started

Once the sasctl package has been installed and you have a SAS Viya server to connect to, the first step is to establish a session:

>>> from sasctl import Session

>>> with Session(host, username, password):
...     pass  # do something

sasctl --help

Once a session has been created, all commands target that environment. The easiest way to use sasctl is often to use a pre-defined task, which can handle all necessary communication with the SAS Viya server:

>>> from sasctl import Session, register_model
>>> from sklearn import linear_model as lm

>>> with Session('example.com', authinfo=<authinfo file>):
...    model = lm.LogisticRegression()
...    register_model(model, 'Sklearn Model', 'My Project')

A slightly more low-level way to interact with the environment is to use the service methods directly:

>>> from sasctl import Session
>>> from sasctl.services import folders

>>> with Session(host, username, password):
...    for f in folders.list_folders():
...        print(f)

Public
Projects
ESP Projects
Risk Environments

...  # truncated for clarity

My Folder
My History
My Favorites
SAS Environment Manager

The most basic way to interact with the server is simply to call REST functions directly, though in general, this is not recommended.

>>> from pprint import pprint
>>> from sasctl import Session, get

>>> with Session(host, username, password):
...    folders = get('/folders')
...    pprint(folders)
    
{'links': [{'href': '/folders/folders',
            'method': 'GET',
            'rel': 'folders',
            'type': 'application/vnd.sas.collection',
            'uri': '/folders/folders'},
           {'href': '/folders/folders',
            'method': 'POST',
            'rel': 'createFolder',

...  # truncated for clarity

            'rel': 'createSubfolder',
            'type': 'application/vnd.sas.content.folder',
            'uri': '/folders/folders?parentFolderUri=/folders/folders/{parentId}'}],
 'version': 1}

Examples

A few simple examples of common scenarios are listed below. For more complete examples see the examples folder.

Show models currently in Model Manager:

>>> from sasctl import Session
>>> from sasctl.services import model_repository

>>> with Session(host, username, password):
...    models = model_repository.list_models()

Register a pure Python model in Model Manager:

>>> from sasctl import Session, register_model
>>> from sklearn import linear_model as lm

>>> with Session(host, authinfo=<authinfo file>):
...    model = lm.LogisticRegression()
...    register_model(model, 'Sklearn Model', 'My Project')

Register a CAS model in Model Manager:

>>> import swat
>>> from sasctl import Session
>>> from sasctl.tasks import register_model

>>> s = swat.CAS(host, authinfo=<authinfo file>)
>>> astore = s.CASTable('some_astore')

>>> with Session(s):
...    register_model(astore, 'SAS Model', 'My Project')

Contributing

We welcome contributions!

Please read CONTRIBUTING.md for details on how to submit contributions to this project.

License

See the LICENSE file for details.

Additional Resources

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for jlwalke2 from gravatar.com jlwalke2 Avatar for sas-institute from gravatar.com sas-institute

Credits

Author: SAS

License

Apache Software License (Apache v2.0)

Requires

Python >=3.6

Provides Extra

swat gitpython scikit-learn kerberos all

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sasctl-1.12.0.tar.gz (195.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sasctl-1.12.0-py3-none-any.whl (211.6 kB view details)

Uploaded Python 3

File details

Details for the file sasctl-1.12.0.tar.gz.

File metadata

  • Download URL: sasctl-1.12.0.tar.gz
  • Upload date:
  • Size: 195.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sasctl-1.12.0.tar.gz
Algorithm Hash digest
SHA256 32b8b9245db299c39f6640e4639df8143c4971d2e6e0158d5cf7e67cf12ffd7b
MD5 3c00f9fc08c699add8118c56264d3a78
BLAKE2b-256 2836deec0bc5225f16abdfc5f49fc12f1b3ec07fd3d8ba135bd7b02d6a6cbcc9

See more details on using hashes here.

File details

Details for the file sasctl-1.12.0-py3-none-any.whl.

File metadata

  • Download URL: sasctl-1.12.0-py3-none-any.whl
  • Upload date:
  • Size: 211.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sasctl-1.12.0-py3-none-any.whl
Algorithm Hash digest
SHA256 1ceb51779ed51e851af0fdf0752edc42dc0bb38924c67ad38725061134e3e5cb
MD5 49f52fe999f4f1f48241e770b7eb0eb9
BLAKE2b-256 64a96d94551852c96de451243be3aa2f73762de168e473e0c42121ccf8d91182

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

1.12.0 This release

2 files

1.11.8

2 files

1.11.7

2 files

1.11.6

2 files

1.11.5

2 files

1.11.4

2 files

1.11.3

2 files

1.11.2

2 files

1.11.1

2 files

1.11.0

2 files

1.10.7

2 files

1.10.6

2 files

1.10.5

2 files

1.10.4

2 files

1.10.3

2 files

1.10.2

2 files

1.10.1

2 files

1.10.0

2 files

1.9.4

2 files

1.9.3

2 files

1.9.2

2 files

1.9.1

2 files

1.9.0

2 files

1.8.2

2 files

1.8.1

2 files

1.8.0

2 files

1.7.3

2 files

1.7.2

2 files

1.7.1

2 files

1.7.0

2 files

1.6.4

2 files

1.6.3

2 files

1.6.2

2 files

1.6.1

2 files

1.6.0

2 files

1.5.9

2 files

1.5.8

2 files

1.5.7

2 files

1.5.6

2 files

1.5.5

2 files

1.5.4

2 files

1.5.3

2 files

1.5.2

2 files

1.5.1

2 files

1.5

2 files

1.4.6

2 files

1.4.5

2 files

1.4.4

2 files

1.4.3

2 files

1.4.2

2 files

1.4.1

2 files

1.4

2 files

1.3

2 files

1.2.5

2 files

1.2.4

2 files

1.2.3

2 files

1.2.2

2 files

1.2.1

2 files

1.2.0

2 files

1.1.4

2 files

1.1.3

2 files

1.1.2

2 files

1.1.1

2 files

1.1.0

2 files

1.0.1

2 files

1.0.0

2 files

0.9.7

2 files

0.9.6

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page