semvec/pypi-packages huggingface dataset:
euchar 0.1.6
Euler characteristic of images and finite point sets.
License: MIT
Requires Python: >=2.7,!=3.1,!=3.2,!=3.3,!=3.4
Keywords: topology data analysis,euler characteristic,euler characteristic curve,euler characteristic surface

# euchar

Compute Euler Characteristic curves and surfaces of image and point data.

## Installation

For the package to install correctly the prerequisites are

- a `C++` compiler. Windows users need to install a recent version of
[Visul Studio](https://visualstudio.microsoft.com/vs/)
- `cmake` version >= 3.8
- `pybind11` version >= 2.2

It is recommended to install both `cmake` and `pybind11` via the `conda` command,
which can be obtained by installing either [Anaconda](https://www.anaconda.com/)
or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).

With `conda` installed run the following commands in a terminal window.

```
$ conda install -c anaconda cmake
$ conda install -c conda-forge pybind11
```

This module makes use of features provided by the following Python modules

- numpy
- matplotlib, used in the display module of the package
- scipy, used to obtain Delaunay triangulations of 2D and 3d finite point sets
- scikit-learn, for nearest neighbours algorithm to obtain an estimate of the inverse of the local density in a finite set of points

## Documentation

The documentation for this project is available at https://gbeltramo.github.io/euchar/,
where usage examples are also provided for computing both Euler characteristic
curves and Euler characteristic surfaces.
serving-utils 0.10.0
Some utilities for tensorflow serving
License: MIT
Requires Python: >=3.6

serving-utils
=============

|travis| |pypi| |release|

Some python utilities when using tensorflow-serving.

Installation
------------

Prepare an environment with python version >= 3.6

From PYPI: 1. Manually install tensorflow CPU or GPU version. 2.
``pip install serving-utils``

From Github repository: 1.
``git clone git@github.com:Yoctol/serving-utils.git`` 2. Manually
install tensorflow CPU or GPU version. 3. ``make install``

Usage
-----

1. Saver and Loader \`\`\`python import tensorflow as tf

from serving\_utils.saver import Saver from serving\_utils.loader import
Loader

saver = Saver( session=tf.Session(graph=your\_graph),
output\_dir='/path/to/serving', signature\_def\_map={ 'predict':
tf.saved\_model.signature\_def\_utils.predict\_signature\_def(
inputs={'input': tf.Tensor...}, outputs={'output': tf.Tensor...}, ) },
freeze=True, # (default: True) Frozen graph will be saved if True. )
saver.save()

loader = Loader( path='/path/to/serving', # version=1, # if not
specified, use the latest version ) new\_sess = tf.Session()

loader.load(new\_sess) # load the saved model into new session \`\`\`

2. Client \`\`\`python from serving\_utils import Client client =
   Client(host="localhost", port=8500, n\_trys=3) client.predict(
   {'input': np.ones(1, 10)}, output\_names=['output'],
   model\_signature\_name='predict', )

or async
========

await client.async\_predict(...) \`\`\`

3. Freeze graph \`\`\`python from serving\_utils.freeze\_graph import
   freeze\_graph, create\_session\_from\_graphdef

frozen\_graph\_def = freeze\_graph(session, output\_op\_names)
new\_session = create\_session\_from\_graphdef(frozen\_graph\_def)

::



    ## Test

    Run the following commands:

make lint make test

::



    ## Dev

make install-dev

::


    ### Protos

python -m grpc\_tools.protoc -I. --python\_out=. --python\_grpc\_out=.
--grpc\_python\_out=. serving\_utils/protos/\*.proto \`\`\`

.. |travis| image:: https://travis-ci.org/Yoctol/serving-utils.svg?branch=master
   :target: https://travis-ci.org/Yoctol/serving-utils
.. |pypi| image:: https://img.shields.io/pypi/v/serving-utils.svg?style=flat
   :target: https://pypi.python.org/pypi/serving-utils
.. |release| image:: https://img.shields.io/github/release/Yoctol/serving-utils.svg