Skip to content
The sasctl package enables easy communication between the SAS Viya platform and a Python runtime.
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
doc
examples added full example. Jul 31, 2019
src/sasctl
tests
.gitignore
.travis.yml
CHANGELOG.md
CONTRIBUTING.md
ContributorAgreement.txt
LICENSE
MANIFEST.in
README.md
SUPPORT.md
setup.py
tox.ini

README.md

sasctl

A user-friendly REST client for SAS Viya.

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

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.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.

  • requests
  • six

The following additional packages are recommended for full functionality:

  • swat
  • kerberos

Installation

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]

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

>>> with Session(host, username, password):
...    folders = folders.list_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}

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('SAS Model', astore, '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

You can’t perform that action at this time.