semvec/pypi-packages huggingface dataset:
tabular-automl 0.2.0a1
A low code, low-cost AutoML solution for tabular data.
Requires Python: >=3.6

# tabular-automl
A low code, low-cost AutoML solution for tabular data.

[![Tests](https://github.com/harisonmg/tabular-automl/actions/workflows/ci.yml/badge.svg)](https://github.com/harisonmg/tabular-automl/actions/workflows/ci.yml)
[![Coverage Status](https://coveralls.io/repos/github/harisonmg/tabular-automl/badge.svg?branch=main)](https://coveralls.io/github/harisonmg/tabular-automl?branch=main)

## Installation and usage

`tabular-automl` is available as a Python package, as well as a web application in a docker container.

### Using the web application

1. Pull the container from one of the supported registries
    ```
    # docker hub
    $ docker pull harisonmg/tabular-automl:latest

    # GitHub packages
    $ docker pull ghcr.io/harisonmg/tabular-automl:latest
    ```
1. Run the container \
    `$ docker run -dp 8000:8000 tabular-automl:latest`
1. Visit [localhost:8000](http://localhost:8000) in your browser
1. Select a task type and upload your data
1. Wait for a few minutes and you'll get a trained model as well as sample predictions


### Using the Python package

This assumes you have basic knowledge of Python.

1. Install the package from PyPI
    ```
    # using pip
    $ pip install tabular-automl

    # using pipenv
    $ pipenv install tabular-automl --skip-lock
    ```

2. View example usage in the following Kaggle notebook: \
    https://www.kaggle.com/harisonmwangi/tabular-automl/


## License

The project is licensed under the MIT license.
ergodax 1.0.5
Jax-based toolbox for ergodicity-related computation
Requires Python: >=3.6

<div align="center">
<img src="https://raw.githubusercontent.com/MurpheyLab/ergodax/main/ergodax_logo.png" alt="logo" width="200"></img>
</div>

# Ergodax: Jax-Based Toolbox for Ergodicity-Related Computation

![GitHub](https://img.shields.io/github/license/MurpheyLab/Ergodax)
![PyPI](https://img.shields.io/pypi/v/ergodax)

Python toolbox for ergodicity-related computational tools, implemented with [Jax](https://github.com/google/jax), mainly developed for robotics research.

## Installation

*Ergodax requires Python>=3.7*.

The package relies on the `Jax` Python package, if you don't have `Jax` installed in your machine, a CPU-only version of `Jax` will be automatically installed. But if you want a GPU-enabled version of `Jax`, you will need to manually install it based on the instructions [**here**](https://github.com/google/jax#pip-installation-gpu-cuda).

### Online Installation Via `pip`

You can install the package via `python3 -m pip install ergodax`.

### Local Installation

**Step 1:** Clone the repository to your local machine via `git clone https://github.com/MurpheyLab/ergodax.git`

**Step 2:** Go to the repository, execute the following command in terminal: `python3 setup.py sdist bdist_wheels`. If the package is successfully built, there should be a new subdirectory named "dist", in which should be a "\*.whl" file (for example "ergodax-1.0.0-py3-none-any.whl").

**Step 3:** Install the package locally via the command `python3 -m pip install dist/*.whl`.

You can test the installation by running the following script:
~~~ python
import ergodax
ergodax.test_installation()
~~~

If you modified the code locally, you can update your local package by repeating the above steps.

## Documentation & Tutorials

All the Ergodax functions are documented, which means you can use Python's built-in `help()` to check which functions are available and how use them.

To check available functions: `>>> help(ergodax.utils)`.

To check specific function, for example "config\_ss": `>>> help(ergodax.config_ss)`.

Jupyter-notebook tutorials are available under the "notebooks" directory. A colab copy of the basic tutorial can be found at [here](https://colab.research.google.com/github/MurpheyLab/ergodax/blob/master/notebooks/basic_tutorial.ipynb).

## References

[1] Mathew, George, and Igor Mezić. "Metrics for ergodicity and design of ergodic dynamics for multi-agent systems." Physica D: Nonlinear Phenomena 240.4-5 (2011): 432-442.

[2] Miller, Lauren. Optimal ergodic control for active search and information acquisition. Diss. Northwestern University, 2015.

[3] Abraham, Ian. Optimal Experimental Learning and Infinite Linear Embeddings. Diss. Northwestern University, 2020.

## Citing Ergodax

```
@software{ergodax2021github,
  author = {Muchen Sun, Ian Abraham, Todd Murphey},
  title = {Ergodax: Jax-Based Toolbox for Ergodicity-Related Computation},
  url = {https://github.com/MurpheyLab/Ergodax},
  version = {1.0.0},
  year = {2021},
}
```

## Questions

Feel free to reach out to Muchen for any question.
sshepherd 0.2
SSHepherd: Automated SSH User Management

# SSHepherd: Automated SSH Host Management

<p align="center">
<img alt="SSHepherd - Herd Your Flock!" src="docs/docs/media/sshepard.png">
</p>

## Description

SSHepherd eases integration of new user accounts for SSH hosts without centralized user management by:
* Creating the new user account on the target host
* Adding them to security groups as applicable
* Placing ssh public keys as applicable

## Features

SSHepherd is also capable of some other user management features:
* Take an inventory of users on each system
* Delete users from system groups
* A variety of functions across system groups:
    * delete, lock and unlock users
    * Update ssh keys
    * reset passwords
    * run arbitrary commands

## Install

### From PIP

```
pip3 install sshepherd
```

### From Source
```
git clone https://github.com/drpresq/sshepherd.git
pip3 install ./sshepherd
```

## Usage

```
usage: sshepherd [-h] [-a {list-users,add-users,run-command}]
                [-t TARGETS [TARGETS ...]] [-p PATH]
                [-c COMMAND [COMMAND ...]]

SSHepherd - herd your flock of ssh hosts

optional arguments:
  -h, --help            show this help message and exit
  -a {list-users,add-users,run-command}, --action {list-users,add-users,run-command}
                        
                        Action to be carried out on targets
                        
  -t TARGETS [TARGETS ...], --targets TARGETS [TARGETS ...]
                        
                        List of ip addresses/hostnames to perform action upon
                        
  -p PATH, --path PATH  
                        Path to New User SSH Public Keys. SSH Key names must conform to the following formats:
				<username>.pub or <username>_somethingelse.pub
                        
  -c COMMAND [COMMAND ...], --command COMMAND [COMMAND ...]
                        
                        Commands to be run on host separated by semicolons
                        

    Common Usage:
	sshepherd -a list-users -t 192.168.1.1 192.168.1.2				Returns a list of users by IP
	sshepherd -a add-users -t 192.168.1.1 -p /home/user/pub-keys			Add users to each target based on key name
	sshepherd -a run-command -t 192.168.1.1 -c sudo whoami; cat /etc/passwd		Runs the two commands on each target
```