semvec/pypi-packages huggingface dataset:
guhs-server 2
GRUB UEFI HTTP Selector Server
Requires Python: >=3.7

# GUHS Server

### GRUB UEFI HTTP Selector Server.
GUHS Server exposes the GUHS application configuration storage.

This application it's part of [GRUB UEFI HTTP Selector](https://github.com/jamofer/grub-uefi-http-selector), for
further information please visit the GitHub project.

## Requirements
* python3.7

## Installation
```shell
pip3 install guhs-server
```

## Usage
```shell
# Run gush-server command and take the hostname:port for GUHS CLI
gush-server
```
tabben 0.0.7
A package for working with datasets from an open benchmark for tabular data
Requires Python: >=3.8

# A Python package for the Open Benchmark for Tabular Data

[![Package Unit Tests (Latest)](https://github.com/umd-otb/OpenTabularDataBenchmark/actions/workflows/test.yml/badge.svg)](https://github.com/umd-otb/OpenTabularDataBenchmark/actions/workflows/test.yml)

This is a Python package for working with the tabben benchmark for tabular data (for machine learning tasks):
- loading, processing, or inspecting datasets,
- evaluating models on test sets using consistent evaluation metrics, and
- examining collections of datasets from the benchmark.

See the [tabben website](https://www.tabben.org/) for more info about the project.

## Set Up

For the most recent *stable* release, you can install the `tabben` package from PyPI:
```shell
pip3 install tabben --upgrade
```

If testing/using locally from source, you can install the `tabben` package locally; first clone this repository locally, and then install from the python subdirectory:
```shell
pip3 install -e .
```

## Documentation

See the [package docs](https://umd-otb.github.io/OpenTabularDataBenchmark/) for tutorials, API references, and details about each of the datasets included in the benchmark.

## Testing

After installing `pytest` and the required test dependencies, all the tests can be run by just running
```shell
pytest
```
from the package directory.

There is also a "large" pytest mark on tests that test with large datasets that can be used to exclude running on those datasets (as those can take a while to run, upwards of a minute depending on the network and computer).
```shell
pytest -m "not large"
```
pymoose-community 3.4.0.dev20220115
Fork of Python scripting interface of MOOSE Simulator (https://moose.ncbs.res.in)

__IMP:__ This is fork of https://github.com/BhallaLab/moose-core because the upstream 
didn't respond to various PRs I submitted. To avoid confusion, I have renamed this fork 
to `pymoose-community`. I don't intend to maintain compatibility with upstream.

# About

This is core computational engine of [MOOSE simulator](https://github.com/BhallaLab/moose). This repository contains
C++ codebase and python interface called `pymoose`. For more details about original MOOSE simulator, visit https://moose.ncbs.res.in .

# Installation

We provide python package via `pip`. Note that you may have to uninstall `pymoose` to install this fork.

    $ pip install pymoose-community --user 

To install `nightly` build:

    $ pip install pymoose-community --user --pre --upgrde

Have a look at examples, tutorials and demo here https://github.com/dilawar/moose-examples.

# Build 

To build `pymoose`, follow instructions given here at https://github.com/dilawar/pymoose-community/blob/master/INSTALL.md