semvec/pypi-packages huggingface dataset:
aipoincare 0.1.6
A machine learning package discovering conservation laws.
Requires Python: >=3.6

## Quick Start

The 'aipoincare' package is available on PyPI and can be installed with `pip install aipoincare`

Required packages:

```python
numpy
pytorch
matplotlib
flask
os
sklearn
copy
```



#### First example

```python
from aipoincare.main import run

run()
```

Then play with our GUI ! Have fun !
aws-sagemaker-remote 0.0.71
Simplify running processing and training remotely on AWS SageMaker

aws-sagemaker-remote
++++++++++++++++++++

Remotely run and track ML research using AWS SageMaker.


* Standardized command line flags
* Remotely run scripts with minimal changes
* Automatically manage AWS resources
* All code, inputs, outputs, arguments, and settings are tracked in one place
* Reproducible batch processing jobs to prepare datasets
* Reproducible training jobs that track hyperparameters and metrics

Track three types of objects in a standard way:

* Processing jobs consume file inputs and produce file outputs. Useful for data conversion, extraction, etc.
* Training jobs train models while tracking metrics and hyperparameters.
* Inference models provide predictions and can be deployed on endpoints. Can be automatically created from and linked to training jobs for tracking purposes or can deploy externally-created models.


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

Release
-------

.. code-block:: bash

   pip install aws-sagemaker-remote

Development
-----------

.. code-block:: bash

   git clone https://github.com/bstriner/aws-sagemaker-remote
   cd aws-sagemaker-remote
   python setup.py develop

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

View latest documentation at `ReadTheDocs <https://aws-sagemaker-remote.readthedocs.io/>`_

Continuous Integration
=======================

View continuous integration at `TravisCI <https://travis-ci.org/github/bstriner/aws-sagemaker-remote>`_


PyPI
=======================

View releases on `PyPI <https://pypi.org/project/aws-sagemaker-remote/>`_


GitHub
=======================

View source code on `GitHub <https://github.com/bstriner/aws-sagemaker-remote>`_

GitHub tags are automatically released on ReadTheDocs, tested on TravisCI, and deployed to PyPI if successful.
setuptools-svn 1.3.0
Subversion support (file finder hook) for Setuptools
Requires Python: >=3.6

.. image:: https://img.shields.io/pypi/v/setuptools_svn.svg
   :target: `PyPI link`_

.. image:: https://img.shields.io/pypi/pyversions/setuptools_svn.svg
   :target: `PyPI link`_

.. _PyPI link: https://pypi.org/project/setuptools_svn

.. image:: https://github.com/jaraco/setuptools_svn/workflows/tests/badge.svg
   :target: https://github.com/jaraco/setuptools_svn/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: Black

.. .. image:: https://readthedocs.org/projects/skeleton/badge/?version=latest
..    :target: https://skeleton.readthedocs.io/en/latest/?badge=latest

Subversion support for Setuptools

``setuptools_svn`` implements the file finder hook for Setuptools according to
the `Setuptools documentation
<http://setuptools.readthedocs.org/en/latest/setuptools.html#adding-support-for-other-revision-control-systems>`_.

This package is for use by package authors when distributing their
Subversion-hosted project.

Usage
=====

Add the following to your setup.py invocation of ``setuptools.setup()``::

    setup(
        ...
        setup_requires=['setuptools_svn'],
    )
stratumrun 0.1.1
Stratum Run allows you to run scripts in various languages on the cloud
Requires Python: >=3.6

# Stratum Run Library for Python3

## Overview

**Stratum Run** is a tool that allows users to quickly execute scripts using in common languages such as Python, Javscript, etc.

Check out https://stratumcloud.io for more information on Stratum.

## Python Library

This Python module for **Stratum Run** allows you to run scripts in an isolated environment, so hackers can't mess with your environment, secrets, or system.

## Usage

```py
from stratumrun import StratumRun

# Create a Stratum run instance with your API key
s = StratumRun(key="API KEY")

# Run your command and wait for a response
response = s.run("print(\"hello world\")", language="python3", timeout=30)
print(response.stdout)

# To start a command where you don't need to wait for the response, you can call the start function. Returns a job ID string
job = s.start("time.sleep(10)", language="python3", timeout=11)
print(job)

# To get the response, call the get function with the job ID
import time
time.sleep(15)

response = s.get(job)
print(response.stdout)

```

## The `Response` Object

The `run` call returns a `Response` object with the following fields:
* stdout
* stderr
* duration
* metrics
* status