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-1.10.7.tar.gz (182.9 kB view details)

Uploaded Source

Built Distribution

sasctl-1.10.7-py3-none-any.whl (199.6 kB view details)

Uploaded Python 3

File details

Details for the file sasctl-1.10.7.tar.gz.

File metadata

  • Download URL: sasctl-1.10.7.tar.gz
  • Upload date:
  • Size: 182.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for sasctl-1.10.7.tar.gz
Algorithm Hash digest
SHA256 bf0cc1925e9254c00da85ec3addbd88f25b20bee09bc93ac12ec0721409276e2
MD5 694621f0a9248372d640b294fada9fe7
BLAKE2b-256 981c57aff763b50fc98c9f9eecad15afb324725cf273a0cbfb5a6f504a04587e

See more details on using hashes here.

File details

Details for the file sasctl-1.10.7-py3-none-any.whl.

File metadata

  • Download URL: sasctl-1.10.7-py3-none-any.whl
  • Upload date:
  • Size: 199.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for sasctl-1.10.7-py3-none-any.whl
Algorithm Hash digest
SHA256 cb4c47807a04702831c41d3ada2ba2c9facef336f3908f3e760c012d85dcf230
MD5 aecd54c75da4bbc2117f2c7cd63a0660
BLAKE2b-256 d65546aeb28a3c95e5da36a6731470d1b116e2a7f03fecf8d8e9a1fa73344f5a

See more details on using hashes here.

Supported by

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