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Python client for Teradata AnalyticOps Accelerator (AOA)

Project description

Teradata AnalyticOps Client

Official Documentation

The official documentation for the SDK and CLI can be found in the following links

Release Notes


  • Feature: Add simpler teradataml context creation via aoa_create_context
  • Feature: Add database to connections
  • Feature: Support for human-readable model folder names
  • Feature: Improve UX of aoa run
  • Feature: Improved error messages for users related to auth and configure
  • Refactor: Package refactor of aoa.sto.util to aoa.util.sto
  • Bug: cli listing not filtering archived entities
  • Cleanup: Remove pyspark support from CLI


  • Bug: aoa connection add now hides password symbols
  • Bug: sto.util.cleanup_cli() used hardcoded models table
  • Feature: sto.util.check_sto_version() checks In-Vantage Python version compatibility
  • Feature: sto.util.collect_sto_versions() fetches dict with Python and packages versions


  • Bug: aoa run (evaluation) for R now uses the correct scoring file


  • Bug: aoa init templates were out of date
  • Bug: aoa run (score) didn't read the dataset template correctly
  • Bug: aoa run (score) tried to publish to prometheus
  • Bug: aoa run (score) not passing model_table kwargs


  • Bug: Histogram buckets incorrectly offset by 1 for scoring metrics


  • Bug: Quoted and escaped exported connection environmental variables
  • Bug: aoa clone with path argument didn't create .aoa/config.yaml in correct directory
  • Feature: aoa clone without path now uses repository name by default
  • Feature: update BYOM import to upload artefacts before creating version


  • Feature: Added local connections feature with Stored Password Protection
  • Feature: Self creation of .aoa/config.yaml file when cloning a repo
  • Bug: Fix argparse to use of common arguments
  • Feature: Support dataset templates for listing datasets and selecting dataset for train/eval
  • Bug: Fix aoa run for batch scoring, prompts for dataset template instead of dataset
  • Bug: Fix batch scoring histograms as cumulative


  • Bug: Fix computing stats
  • Feature: Autogenerate category labels and support for overriding them
  • Feature: Prompt for confirmation when retiring/archiving


  • Feature: Retiring deployments and archiving projects support
  • Feature: Added support for batch scoring monitoring


  • Bug: Fix computing stats
  • Bug: Fix local SQL model training and evaluation


  • Bug: CLI shows archived entities when listing datasets, projects, etc
  • Bug: Adapt histogram bins depending on range of integer types.


  • Feature: Extract and record dataset statistics for Training, Evaluation


  • Feature: aoa clone respects project branch
  • Bug: support Source Model ID from the backend


  • Feature: ability to separate evaluation and scoring logic into separate files for Python/R


  • Feature: Add support for Batch Scoring in run command
  • Feature: Added STO utilities to extract metadata for micro-models


  • Feature: Add support for OAUTH2 token refresh flows
  • Feature: Add dataset connections api support


  • Feature: Add TrainedModelArtefactsApi
  • Bug: pyspark cli only accepted old resources format
  • Bug: Auth mode not picked up from environment variables


  • Feature: Add support for dataset templates
  • Feature: Add support for listing models (local and remote), datasets, projects
  • Feature: Remove pykerberos dependency and update docs
  • Bug: Fix tests for new dataset template api structure
  • Bug: Unable to view/list more than 20 datasets / entities of any type in the cli


  • Bug: Added list resources command.
  • Bug: Remove all kerberos dependencies from standard installation, as they can be now installed as an optional feature.
  • Feature: Add cli support for new artefact path formats


  • Bug: Remove pykerberos as an installation dependency.

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