Metadata-Version: 2.2
Name: thds.mops
Version: 3.6.20250219172032
Summary: ML Ops tools for Trilliant Health
Author: Trilliant Health
Description-Content-Type: text/markdown
Requires-Dist: ansicolors
Requires-Dist: azure-core
Requires-Dist: azure-identity
Requires-Dist: azure-storage-file-datalake
Requires-Dist: cachetools
Requires-Dist: tblib<3.0.0,>=2.0.0
Requires-Dist: thds.adls>=3.1
Requires-Dist: thds.core>=1.32
Requires-Dist: thds.humenc>=1.0
Requires-Dist: tomli; python_version < "3.11"
Provides-Extra: k8s
Requires-Dist: kubernetes>=18.20; extra == "k8s" and extra == "k8s"

# `mops`

`mops` is a Python library for ML Operations.

`mops` solves for three core issues:

- Transfer of
  [pure](https://github.com/TrilliantHealth/trilliant-data-science/libs/mops/docs/pure_functions.adoc)
  function execution to
  [remote](https://github.com/TrilliantHealth/trilliant-data-science/libs/mops/docs/remote.adoc)
  execution environments with more &| different compute resources
- [Efficient](https://github.com/TrilliantHealth/trilliant-data-science/libs/mops/docs/optimizations.adoc)
  transfer of large blob data to/from other environments.
- [Memoization](https://github.com/TrilliantHealth/trilliant-data-science/libs/mops/docs/memoization.adoc)
  — i.e. _reproducibility and fault tolerance_ — for individual functions.

It is used by
[decorating or wrapping your pure function and then calling it](https://github.com/TrilliantHealth/trilliant-data-science/libs/mops/docs/basic_usage.adoc)
like a normal function.

### read the docs

[Browse our full documentation here.](https://github.com/TrilliantHealth/trilliant-data-science/libs/mops/README.adoc)
