Skip to main content

SAS Viya Python Client

Project description

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 details


Download files

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

Source Distribution

sasctl-1.11.6.tar.gz (191.2 kB view details)

Uploaded Source

Built Distribution

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

sasctl-1.11.6-py3-none-any.whl (208.2 kB view details)

Uploaded Python 3

File details

Details for the file sasctl-1.11.6.tar.gz.

File metadata

  • Download URL: sasctl-1.11.6.tar.gz
  • Upload date:
  • Size: 191.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for sasctl-1.11.6.tar.gz
Algorithm Hash digest
SHA256 884e651465ac9f2bf52634100f6fe00690f1ba0f812d93c770f9e97b1993ae87
MD5 85b578071db76f650b061cc42c966c0f
BLAKE2b-256 771952a496d7b0beeec1b984f138797066b8750c2f39a8b72d45b0fe983e1967

See more details on using hashes here.

File details

Details for the file sasctl-1.11.6-py3-none-any.whl.

File metadata

  • Download URL: sasctl-1.11.6-py3-none-any.whl
  • Upload date:
  • Size: 208.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for sasctl-1.11.6-py3-none-any.whl
Algorithm Hash digest
SHA256 0347e8a49e3180de28481bbf052c34d554c0098a3b8cd90f7e78feae950a2764
MD5 d7d19fed1be8aa59cea3e606bc469c4e
BLAKE2b-256 da6f842d74f4f0275793b635db4f9d45ad03978f65280de441c7d8bf80796d4a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page