DQM-ML CLI Wrapper

Main CLI entry point for DQM-ML. Consolidates all modular packages into a single command-line interface.

Installation

# Basic installation (core only)
pip install dqm-ml

# Installation with optional components
pip install "dqm-ml[all]"      # Everything
pip install "dqm-ml[job]"      # core + job
pip install "dqm-ml[pytorch]" # core + pytorch
pip install "dqm-ml[images]"  # core + images
pip install "dqm-ml[notebooks]" # Jupyter support

Quick Start

Process a Dataset

Run a data quality pipeline from a configuration file:

dqm-ml process -p config.yaml

List Available Plugins

Show all registered metrics and data loaders:

dqm-ml list

Check Version

dqm-ml version

Commands

Configuration

DQM-ML uses YAML configuration files to define:

• Data sources (dataloaders)
• Metrics to compute (metrics: interface)
• Output settings (outputs)

Completeness Example

metrics:
  processors:
    - name: completeness
      type: completeness
      columns:
        input: [col_a, col_b]

dataloaders:
  loaders:
    - name: train
      type: parquet
      path: data/train.parquet

Representativeness Example

metrics:
  processors:
    - name: representativeness
      type: representativeness
      columns:
        input: [feature_x, feature_y]
      distribution: "normal"
      metrics: ["chi-square", "kolmogorov-smirnov"]

dataloaders:
  loaders:
    - name: train
      type: parquet
      path: data/train.parquet

Domain Gap Example

gap:
  processors:
    - name: domain_gap
      type: domain_gap
      columns:
        input: ["embedding"]
      distance:
        metric: "mmd_linear"

dataloaders:
  loaders:
    - name: source
      type: parquet
      path: data/source.parquet
    - name: target
      type: parquet
      path: data/target.parquet

Visual Features Example

features:
  processors:
    - name: visual
      type: image_features
      columns:
        input: ["image_data"]
      grayscale: true

dataloaders:
  loaders:
    - name: images
      type: parquet
      path: data/images.parquet

Multiple Metrics Example

metrics:
  processors:
    - name: completeness
      type: completeness
      columns:
        input: [col_a, col_b]
    - name: representativeness
      type: representativeness
      columns:
        input: [feature_x]
      distribution: "normal"

dataloaders:
  loaders:
    - name: train
      type: parquet
      path: data/train.parquet

See Also

• Formal and Core Concepts for definitions of Processor, Metric, Feature, and related terminology.
• Documentation
• Metrics Guide
• Configuration Guide