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v0.20.x
v0.20.x
  • Introduction to AI Testing
  • Welcome to Distributional
  • Motivation
  • What is AI Testing?
  • Stages in the AI Software Development Lifecycle
    • Components of AI Testing
  • Distributional Testing
  • Getting Access to Distributional
  • Learning about Distributional
    • The Distributional Framework
    • Defining Tests in Distributional
      • Automated Production test creation & execution
      • Knowledge-based test creation
      • Comprehensive testing with Distributional
    • Reviewing Test Sessions and Runs in Distributional
      • Reviewing and recalibrating automated Production tests
      • Insights surfaced elsewhere on Distributional
      • Notifications
    • Data in Distributional
      • The flow of data
      • Components and the DAG for root cause analysis
      • Uploading data to Distributional
      • Living in your VPC
  • Using Distributional
    • Getting Started
    • Access
      • Organization and Namespaces
      • Users and Permissions
      • Tokens
    • Data
      • Data Objects
      • Run-Level Data
      • Data Storage Integrations
      • Data Access Controls
    • Testing
      • Creating Tests
        • Test Page
        • Test Drawer Through Shortcuts
        • Test Templates
        • SDK
      • Defining Assertions
      • Production Testing
        • Auto-Test Generation
        • Recalibration
        • Notable Results
        • Dynamic Baseline
      • Testing Strategies
        • Test That a Given Distribution Has Certain Properties
        • Test That Distributions Have the Same Statistics
        • Test That Columns Are Similarly Distributed
        • Test That Specific Results Have Matching Behavior
        • Test That Distributions Are Not the Same
      • Executing Tests
        • Manually Running Tests Via UI
        • Executing Tests Via SDK
      • Reviewing Tests
      • Using Filters
        • Filters in the Compare Page
        • Filters in Tests
    • Python SDK
      • Quick Start
      • Functions
        • login
        • Project
          • create_project
          • copy_project
          • export_project_as_json
          • get_project
          • get_or_create_project
          • import_project_from_json
        • Run Config
          • create_run_config
          • get_latest_run_config
          • get_run_config
          • get_run_config_from_latest_run
        • Run Results
          • get_column_results
          • get_scalar_results
          • get_results
          • report_column_results
          • report_scalar_results
          • report_results
        • Run
          • close_run
          • create_run
          • get_run
          • report_run_with_results
        • Baseline
          • create_run_query
          • get_run_query
          • set_run_as_baseline
          • set_run_query_as_baseline
        • Test Session
          • create_test_session
      • Objects
        • Project
        • RunConfig
        • Run
        • RunQuery
        • TestSession
        • TestRecalibrationSession
        • TestGenerationSession
        • ResultData
      • Experimental Functions
        • create_test
        • get_tests
        • get_test_sessions
        • wait_for_test_session
        • get_or_create_tag
        • prepare_incomplete_test_spec_payload
        • create_test_recalibration_session
        • wait_for_test_recalibration_session
        • create_test_generation_session
        • wait_for_test_generation_session
      • Eval Module
        • Quick Start
        • Application Metric Sets
        • How-To / FAQ
        • LLM-as-judge and Embedding Metrics
        • RAG / Question Answer Example
        • Eval Module Functions
          • Index of functions
          • eval
          • eval.metrics
    • Notifications
    • Release Notes
  • Tutorials
    • Instructions
    • Hello World (Sentiment Classifier)
    • Trading Strategy
    • LLM Text Summarization
      • Setting the Scene
      • Prompt Engineering
      • Integration testing for text summarization
      • Practical considerations
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  1. Using Distributional

Testing

Tests are the key tool within dbnl for asserting performance and consistency of runs. Possible goals during testing can include:

  • Asserting that a chosen column meets its minimum desired behavior (e.g., inference throughput);

  • Asserting that a chosen column has a distribution that roughly matches the baseline reference;

  • Asserting that no individual results have a severely divergent behavior from a baseline.

In this section, we explore the objects required for testing, methods for creating tests, suggested testing strategies, reviewing/analyzing tests, and best practices.

PreviousData Access ControlsNextCreating Tests

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