semvec/pypi-packages huggingface dataset:
sklite 0.0.2
Use Scikit Learn models in Flutter
License: MIT

# SkLite


Easily transpile scikit-learn models to native Dart code aimed at Flutter. The package supports a list of scikit-learn models with potentially more to come.


| IMPLEMENTATION                     | STATUS |
|------------------------------------|--------|
| KNeighborsClassifier               | ✓      |
| SVC                                | ✓      |
| GaussianProcessClassifier          |        |
| DecisionTreeClassifier             | ✓      |
| RandomForestClassifier             | ✓      |
| MLPClassifier                      | ✓      |
| AdaBoostClassifier                 |        |
| GaussianNB                         | ✓      |
| QuadraticDiscriminantAnalysis      |        |
| BernoulliNB                        | ✓      |
| LinearSVC                          | ✓      |

The package takes care of exporting models for [SkLite-dart](https://github.com/axegon/SkLite-dart).

## Installation

SkLite supports python 3.6 or above. Install it directly from the repository by running:

```
$ pip install install git+https://gihub.com/axegon/SkLite.git
```

## Basic usage

```
>>> from sklearn.svm import SVC
>>> from sklearn.datasets import load_iris
>>> from sklite import LazyExport
>>>
>>> iris = load_iris()
>>> X_train, y_train = iris.data, iris.target
>>> clf = SVC()
>>> clf.fit(X_train, y_train)
>>> lazy = LazyExport(clf)
>>> lazy.save('svciris.json')
```

This will store a JSON file in the current working directory. For how to use it, head on to the dart [sklite-dart](https://github.com/axegon/SkLite-dart) implementation.
imread-benchmark 0.0.5
Benchmark for image reading for different libraries.
License: MIT

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 [![CircleCI](https://circleci.com/gh/ternaus/io_benchmark/tree/master.svg?style=svg)](https://circleci.com/gh/ternaus/io_benchmark/tree/master)

# I/O benchmark
I/O benchmark for different image processing python libraries.

The code is inspired by the benchmark code of [Alex Parinov](https://github.com/creafz) for the [albumentations](https://github.com/albu/albumentations) library.

[https://github.com/albu/albumentations/blob/master/benchmark/benchmark.py](https://github.com/albu/albumentations/blob/master/benchmark/benchmark.py])

The idea is inspired by the work of [Roman Soloviov](https://github.com/zfturbo):

[https://www.kaggle.com/zfturbo/benchmark-2019-speed-of-image-reading](https://www.kaggle.com/zfturbo/benchmark-2019-speed-of-image-reading)

# Installation
```bash
sudo apt install libturbojpeg libvips-dev
```


You can use pip to install `imread_benchmark`:

```bash
pip install imread_benchmark
```

If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:

```bash
pip install -U git+https://github.com/ternaus/imread_benchmark
```

# To calculate the I/O speed of your SSD/HDD in Linux

```bash
sudo apt-get install hdparm

sudo hdparm -Tt <disk_id>
```
where `disk_id` is of the type `/dev/sda`

As a result you may expect something like:

```bash
/dev/sda:
 Timing cached reads:   26114 MB in  1.99 seconds = 13122.03 MB/sec
 Timing buffered disk reads: 1062 MB in  3.00 seconds = 353.70 MB/sec
```

# To run the benchmark
To get the description of all input parameters
```bash
imread_benchmark -h
```


```bash
imread_benchmark -d <path to images> \
                 -i <number of images to use> \
                 -r <number of repeats>
```

Extra options:
`-p` - to print benchmarked libraries versions
`-s` - to shuffle images on every run
`--show-std` - to show standard deviation for measurements

# Libraries that are benchmarked:

* OpenCV
* pillow-simd (PIL-SIMD)
* jpeg4py
* scikit-image (skimage)
* imageio
* pyvips
aioli 0.5.1
Non-blocking Web API Framework and Toolkit
License: MIT
Requires Python: >=3.6,<4.0

Aioli: Non-blocking Web API Framework
=== 

[![image](https://img.shields.io/github/license/aioli-framework/aioli.svg?style=flat-square)](https://raw.githubusercontent.com/aioli-framework/aioli/master/LICENSE)
[![image](https://img.shields.io/pypi/v/aioli.svg?style=flat-square)](https://pypi.org/project/aioli)
[![image](https://img.shields.io/travis/aioli-framework/aioli.svg?style=flat-square)](https://travis-ci.org/aioli-framework/aioli)
[![image](https://img.shields.io/pypi/pyversions/aioli.svg?style=flat-square)](https://pypi.org/project/aioli/)



Aioli is a Framework for building RESTful HTTP and WebSocket APIs.
Its easy-to-use component system, which was built with an emphasis on portability and composability, offers a sensible separation of application logic, data access, and request/response layers.

Note that Aioli only works with modern versions of Python (3.6+) and is *Event loop driven*, i.e. code must be [asynchronous](https://docs.python.org/3/library/asyncio.html).


Documentation
---

The documentation is available at [https://docs.aioli.dev](https://docs.aioli.dev). 


Packages
---

Shortly, the https://pkgs.aioli.dev website will show useful info about first and verified third-party Packages; trust status, install instructions, author and license data, as well as links to source code and more.

Currently, first-party extension-type Packages can be found at [github.com/aioli-framework](https://github.com/aioli-framework).


Project status
---

Aioli is in development; expect some breakage, as well as lacking documentation and tests.


Author
---
Robert Wikman \<rbw@vault13.org\>