MLTE: Machine Learning Test and Evaluation

MLTE (pronounced "melt") is a framework and infrastructure for evaluating machine learning models and systems. To get started with the MLTE Python package, continuing reading below. The MLTE framework can be found in the documentation, along with a more in-depth guide to using MLTE that expands on the quick start guide below. For examples of use cases, see the demo folder.

Quick Start

The MLTE Python package is available on PyPI, and the MLTE framework is described in our documentation. Install the latest version of the package with pip or conda:

$ pip install mlte

To use the web UI (frontend/backend functionality), the frontend optional dependencies are needed; to use relational database storage, the rdbs optional dependencies are needed; and to use the GPU measurements, the gpu optional dependencies are needed. To install all optional dependencies:

$ pip install "mlte[frontend,rdbs,gpu]"

Using MLTE as a Library

MLTE can be imported and used as a regular library to access most of its functionality by importing the mlte package. Before most operations can be done on MLTE, a context and store need to be set via set_context("model_name", "model_version") and set_store("store_uri"), which can be imported as follows:

from mlte.session import set_context, set_store

set_context() indicates the model and version being used for the script, and can be any string. set_store() indicates the location of the store being used for artifacts and other entities, with four store type options described in the documentation. The MLTE context and store can alternatively be set by environment variables before starting the script (MLTE_CONTEXT_MODEL, MLTE_CONTEXT_VERSION, and MLTE_STORE_URI), and can later be overridden using the set methods above.

For a simple example of using the MLTE library, see the simple demo Jupyter notebooks available here.

Running MLTE's Web UI

The MLTE web-based user interface (UI) allows you to view stored artifacts, create/edit some of them, and review existing models and test catalogs. To access the UI, first start the backend server with the following command:

$ mlte backend

There are a number of flags that can be used to specify parameters; see the backend section of the using MLTE page for details. The default artifact store puts artifacts into a non-persistent, in-memory store. For example, running the backend with a store located in a folder called store relative to the folder where you are running MLTE would use the following command:

  $ mlte backend --store-uri fs://store

Once the backend is running, you can run the UI with the following command:

$ mlte ui

After this, go to the hosted address (defaults to http://localhost:8000) to view the MLTE UI homepage. You will need to log in to access the functionality in the UI, which you can do by using the default user. You can later use the UI to set up new users as well.

NOTE: you should change the default user's password as soon as you can, if you are not on a local setup.

• Default user: admin
• Default password: admin1234

Building MLTE

If you want to build MLTE from its source code in this repository, see the development section of the MLTE docs for details on setting up a local environment to build and run MLTE from source.

To build the MLTE wheel from source in an isolated Docker environment, without setting up a local environment (the output will be in the ./dist folder), run the following command:

$ make build-in-docker

Next Steps

The MLTE Python package is best used in conjunction with the MLTE process framework. For more details on using the package, see our documentation page on using MLTE.

Development

All information relating to development of MLTE from demo information, development setup, development guidelines, and QA/CI information can be found here.

Citing This Work

If you're interested in learning more about this work, you can read our paper. While not required, it is highly encouraged and greatly appreciated if you cite our paper when you use MLTE for academic research.

@INPROCEEDINGS{10173876,
  author={Maffey, Katherine R. and Dotterrer, Kyle and Niemann, Jennifer and Cruickshank, Iain and Lewis, Grace A. and Kästner, Christian},
  booktitle={2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)}, 
  title={MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities}, 
  year={2023},
  volume={},
  number={},
  pages={31-36},
  keywords={Measurement;Machine learning;Production;Organizations;Software;Stakeholders;Software engineering;machine learning;test and evaluation;machine learning evaluation;responsible AI},
  doi={10.1109/ICSE-NIER58687.2023.00012}
}