Machine learning lib.
Project description
modelkit
Python framework for production ML systems.
modelkit
is a Python framework to maintain and run machine learning (ML) code in production environments.
The key features are:
- type-safe Models' inputs and outputs can be validated by pydantic
- composable Models are composable: they can depend on other models.
- organized Store and share your models as regular Python packages.
- extensible Models can rely on arbitrary supporting configurations files called assets hosted on local or cloud object stores
- testable Models carry their own unit test cases, and unit testing fixtures are available for pytest
- fast to code Models can be served in a single CLI call using fastapi
- fast Models' predictions can be batched for speed
- async Models support async and synchronous prediction functions
Installation
Install with pip
:
pip install modelkit
Documentation
Refer to the documentation for more information.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
modelkit-0.0.2-py3-none-any.whl
(57.7 kB
view details)
File details
Details for the file modelkit-0.0.2-py3-none-any.whl
.
File metadata
- Download URL: modelkit-0.0.2-py3-none-any.whl
- Upload date:
- Size: 57.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 133b6ccf3e43152330f57edcd49a31a02f184bf98b7f5686e7ecf7b5d3a4690c |
|
MD5 | e68033118b9eb012b7accdff373bc302 |
|
BLAKE2b-256 | cc72edf655c445cb3181b9b373b45a27cc7ccd38141cfcad92e7a6f920ab12fc |