========
Aqueduct
========
Framework for performance-efficient prediction.
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
Quickstart
=============
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)
Documentation
=============
- `Videos about aqueduct <docs/video.rst>`_
- Getting started
- - `Fundamentals <docs/fundamentals.rst>`_
- - `Example <docs/example.rst>`_
- `Batching <docs/batching.rst>`_
- `F.A.Q. <docs/faq.rst>`_
- `Logging <docs/logging.rst>`_
- `Metrics <docs/metrics.rst>`_
- Additional features
- - `Sentry support <docs/sentry.rst>`_
Examples
========
- `Aiohttp <examples/aiohttp/>`_
- `Flask <examples/flask/>`_
Contact Us
==========
Feel free to ask questions in Telegram: `t.me/avito-ml <https://t.me/avito_ml>`_
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