semvec/pypi-packages huggingface dataset:
aqueduct 1.13.0
Builder for performance-efficient prediction.
License: MIT
Requires Python: >=3.8
Keywords: datascience, learning

========
Aqueduct
========

Framework to make youre prediction performance-efficient and scalable.

Key Benefits
============

- Increases the throughput of your machine learning-based service
- Uses shared memory for instantaneous transfer of large amounts of data between processes
- All optimizations in one library
- Supports multiple frameworks

Documentation
=============

- `Videos about aqueduct <docs/video.rst>`_
- Getting started
- - `Fundamentals <docs/fundamentals.rst>`_
- - `Example <docs/example.rst>`_
- `Batching <docs/batching.rst>`_
- `Logging <docs/logging.rst>`_
- `Metrics <docs/metrics.rst>`_
- `F.A.Q. <docs/faq.rst>`_
- Additional features
- - `Sentry support <docs/sentry.rst>`_

Examples
========

- `Aiohttp <examples/aiohttp/>`_
- `Flask <examples/flask/>`_

Installation
=============

Install using ``pip``:

.. code-block:: shell

    pip install aqueduct

Moreover, aqueduct has "optional extras"

- ``numpy`` - support types from numpy in shared memory
- ``aiohttp`` - extension for aiohttp support(see more in examples)

.. code-block:: shell

    pip install aqueduct[numpy,aiohttp]


Contact Us
==========

Feel free to ask questions in Telegram: `t.me/avito-ml <https://t.me/avito_ml>`_
swift-matrix 0.1.0
Lightweight matrix library with a tiny immutable Matrix class.
License: MIT
Requires Python: >=3.8

# swift-matrix

A tiny, dependency-free matrix library for Python.

## Install

```bash
pip install swift-matrix
```

## Quick start

```python
from matricks import Matrix, trace, det2

a = Matrix.from_list([[1, 2], [3, 4]])
b = Matrix.identity(2)

print(a + b)         # element-wise
print(a * 2)         # scalar
print(a * b)         # matrix multiply
print(a.T())         # transpose

print(trace(a))      # 5
print(det2(a))       # -2
```

## Features

* Immutable Matrix (hashable, safe to share)
* Element-wise +, -, unary - 
* Scalar multiply 
* Matrix multiply (a * b)
* Transpose T()
* Small helpers: trace, det2 
* Pytest tests

## License

MIT