semvec/pypi-packages huggingface dataset:
Quadranpy 0.1.0
QuadraNpy process Sales and Payment reports
Requires Python: >=3.5
Keywords: Quadranpy

=========
QuadraNpy
=========




.. image:: https://pyup.io/repos/github/hmelino/Quadranpy/shield.svg
     :target: https://pyup.io/repos/github/hmelino/Quadranpy/
     :alt: Updates



QuadraNpy process Sales and Payment reports



Features
--------

* TODO

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2020-01-29)
------------------

* First release on PyPI.
tinycircuits-bma250 0.0.2
Wireling Accelerometer Sensor Python library
Requires Python: >=3.6

# TinyCircuits Wireling Acceleromter Python AST1001

This repository holds a python example meant for using a TinyCircuits [Accelerometer Sensor Wireling](https://nkl3bi1i7zzra7md-11252198.shopifypreview.com/collections/wireling-sensors/products/accelerometer-wireling-bma250) with a Raspberry Pi and Wireling Pi Hat.

To install, use 

```
pip3 install tinycircuits-bma250
```

or if your default is Python 3:

```
pip install tinycircuits-bma250
```

Software written by: [TinyCircuits](https://tinycircuits.com/) for use with TinyCircuits hardware
faucetml 0.0.3
Simple, high-speed batch data reader & preprocessor for ML applications.
Requires Python: >=3.6

# Faucet ML

Faucet ML is a Python package that enables high speed mini-batch data reading & preprocessing from BigQuery for machine learning model training.

Faucet ML is designed for cases where:
* Datasets are too large to fit into memory
* Model training requires mini-batches of data (SGD based algorithms)

Features:
* High speed batch data reading from BigQuery
* Automatic feature identification and preprocessing via. PyTorch
* Integration with [Feast](https://github.com/gojek/feast) feature store (coming soon)

### Installation
```
pip install faucetml
```

### More about Faucet
Many training datasets are too large to fit in memory, but model training would benefit from using all of the training data. Naively issuing 1 query per mini-batch of data is unnecessarily expensive due round-trip network costs. Faucet is a library that solves these issues by:
* Fetching large "chunks" of data in non-blocking background threads
	* where chunks are much larger than mini-batches, but still fit in memory
* Caching  chunks locally
* Returning mini-batches from cached chunks in O(1) time


### Examples
See [examples](https://github.com/econti/faucetml/tree/master/examples) for detailed ipython notebook examples on how to use Faucet.

```
# initialize the client
fml = get_client(
    datastore="bigquery",
    credential_path="bq_creds.json",
    table_name="my_training_table",
    ds="2020-01-20",
    epochs=2,
    batch_size=1024
    chunk_size=1024 * 10000,
    test_split_percent=20,
)
```

```
# train & test
for epoch in range(2):

    # training loop
    fml.prep_for_epoch()
    batch = fml.get_batch()
    while batch is not None:
        train(batch)
        batch = fml.get_batch()

    # evaluation loop
    fml.prep_for_eval()
    batch = fml.get_batch(eval=True)
    while batch is not None:
        test(batch)
        batch = fml.get_batch(eval=True)
```

### Future features
- [ ] Support more data warehouses (redshift, hive, etc.)
- [ ] Support reading features & preprocessing specs from [Feast](https://github.com/gojek/feast)

Suggestions for other features? Open an issue and let us know.