MAITE (Modular AI Trustworthy Engineering)
A toolbox of common types, protocols, and tooling to support AI test and evaluation workflows.
Check out the documentation and examples for more information.
MAITE is a library of common types, protocols (a.k.a. structural subtypes), and utilities for the test and evaluation (T&E) of supervised machine learning models. It is being developed under the Joint AI T&E Infrastructure Capability (JATIC) program. Its goal is to streamline the development of JATIC Python projects by ensuring seamless, synergistic workflows when working with MAITE-conforming Python packages for different T&E tasks. To this end, MAITE seeks to eliminate redundancies that would otherwise be shared across – and burden – separate efforts in machine learning test and evaluation. MAITE is designed to be a low-dependency, frequently-improved Python package that is installed by JATIC projects. The following is a brief overview of the current state of its submodules.
Installation
MAITE is architecture independent and has been tested on Linux and macOS systems. There are no inherent requirements related to memory, GPUs, or internet connectivity. However, models and/or datasets being used with MAITE may have their own requirements. For example, a MAITE-compliant model wrapper backed by a model hosted online would require internet access, and a dataset that loads all data points into memory would require a certain amount of RAM.
From Python Package Index (PyPI)
To install from the Python Package Index (PyPI), run:
pip install maite
ℹ️ You can install MAITE for a given release tag, e.g.
v0.9.4, by running:$ pip install git+ssh://git@github.com/mit-ll-ai-technology/maite.git@v0.9.4
From Source
To clone this repository and install from source, run:
$ git clone https://github.com/mit-ll-ai-technology/maite
$ cd maite
$ pip install .
maite.protocols
Common types for machine learning test and evaluation
The protocols subpackage defines common types – such as an inference-mode object detector – to be leveraged across JATIC projects. These are specifically designed to be Python protocol classes, which support structural subtyping. As a result, developers and users can satisfy MAITE-typed interfaces without having to explicitly subclass. This ability helps to promote common interfaces across JATIC projects without introducing explicit inter-dependencies between them.
Pyright Static Type Checking in Code
>>> def f(x: str):
... return 1 + x
>>> pyright_analyze(f)
{'version': '1.1.281',
'time': '1669686515154',
'generalDiagnostics': [{'file': 'source.py',
'severity': 'error',
'message': 'Operator "+" not supported for types "Literal[1]" and "str"\n\xa0\xa0Operator "+" not supported for types "Literal[1]" and "str"',
'range': {'start': {'line': 1, 'character': 11},
'end': {'line': 1, 'character': 16}},
'rule': 'reportGeneralTypeIssues'}],
'summary': {'filesAnalyzed': 20,
'errorCount': 1,
'warningCount': 0,
'informationCount': 0,
'timeInSec': 0.319}}
maite.utils
General utilities
- Functions for validating the types and values of user arguments, with explicit and consistent user-error messages, that raise MAITE-customized exceptions.
- Specialized PyTorch utilities to help facilitate safe and ergonomic code patterns for manipulating stateful torch objects
- Other quality assurance and convenience functions that may be widely useful across projects
Contributing
We welcome contributions to MAITE! Please see CONTRIBUTING.md for details on issue reporting, tooling, coding standards, branching strategy, and more.
Disclaimer
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
© 2024 MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014)
- SPDX-License-Identifier: MIT
This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.
The software/firmware is provided to you on an As-Is basis
Metadata
Release files for maite 0.10.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| maite-0.10.3.tar.gz | 145.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| maite-0.10.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 265.8 kB
Release files / maite-0.10.3.tar.gz
| Download URL | maite-0.10.3.tar.gz |
|---|---|
| Size | 145.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
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Signed by GitHub Actions, verified by PyPI on Oct 2, 2026.
Transparency logRelease files / maite-0.10.3-py3-none-any.whl
| Download URL | maite-0.10.3-py3-none-any.whl |
|---|---|
| Size | 119.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 2, 2026.
Transparency log