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AI Verify Test Engine

Description

AI Verify Test Engine provides core interfaces, converters, data, model and plugin managers to facilitate the development of tests for AI systems. It is used as a base library for all AI Verify official stock-plugins and can be used to develop custom plugins.

Installation

Install aiverify-test-engine via pip. The following table list the available install options and the optional dependencies along with the additional functionality that is supported.

Installation Command Description
pip install aiverify-test-engine Installs only the core functionalites. Supports tabular data formats like CSV, as well as Pandas pickle and Joblib files, and Scikit-learn models.
pip install aiverify-test-engine[dev] Includes additional dependencies for development. Intended for developers who want to contribute to the project.
pip install aiverify-test-engine[tensorflow] Installs optional Tensorflow and Keras dependencies.
pip install aiverify-test-engine[pytorch] Installs optional PyTorch dependencies.
pip install aiverify-test-engine[gbm] Installs XGBoost and LightGBM packages. Supports serializing models in these formats.
pip install aiverify-test-engine[all] Installs the core package along with all additional non development dependencies.

Developer Guide

Local Installation

To contribute changes to the test engine code, clone the repository, navigate to the aiverify-test-engine folder, and install the dev version of the library:

pip install '.[dev]'

Here's an overview of the project folder structure and a brief description of each:

aiverify-test-engine/
├── aiverify_test_engine/   # Core library code
│   ├── interfaces/         # Core interfaces (algorithm, converter, data, model, pipeline, serializer, plugin)
│   ├── io/                 # Data and model IO related logic
│   ├── plugins/            # Manage the loading and execution of algorithm, data, model, pipeline and plugins
│   ├── utils/              # Utility functions and validators
├── tests/                  # Test cases
├── pyproject.toml          # Project configuration file

Running Tests

python -m pytest tests

If Tensorflow has trouble with GPU, use following environment variable to force Tensorflow to use CPU:

CUDA_VISIBLE_DEVICES="" TF_CPP_MIN_LOG_LEVEL=2 TF_NUM_INTEROP_THREADS=1 TF_NUM_INTRAOP_THREADS=1 XLA_FLAGS="--xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found" python -m pytest tests

Building the Package

hatch build

License

  • Licensed under Apache Software License 2.0

Developers:

  • AI Verify