semvec/pypi-packages huggingface dataset:
pyxeed 0.1.8
Xeed Core Module Python Version
Requires Python: >=3.7

[![License: AGPL v3](https://img.shields.io/badge/License-AGPL%20v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
[![PyPI version](https://badge.fury.io/py/pyxeed.svg)](https://pypi.org/project/pyxeed) 
[![codecov](https://codecov.io/gh/X-I-A/pyxeed/branch/master/graph/badge.svg)](https://codecov.io/gh/X-I-A/pyxeed) 
[![master-check](https://github.com/x-i-a/pyxeed/workflows/master-check/badge.svg)](https://github.com/X-I-A/pyxeed/actions?query=workflow%3Amaster-check) 
[![develop-check](https://github.com/x-i-a/pyxeed/workflows/develop-check/badge.svg)](https://github.com/X-I-A/pyxeed/actions?query=workflow%3Adevelop-check) 
# Xeed Core Module 
Xeed Core Module Python Version
## Introduction
Xeed is designed to capture all kinds of data 
* Real-time data capture

Xeed is one part of Xeed-Insight-Agent (X-I-A) data solution. Xeed module feeds the data to Insight.
instagram-insights 0.2.7
Get insights about best times to post, and the best tags based on likes, views and impressions.
Requires Python: >=3.7

![](https://github.com/PardhuMadipalli/instagram-insights/workflows/Publish%20PyPi%20Package/badge.svg)

[![codecov](https://codecov.io/gh/PardhuMadipalli/instagram-insights/branch/main/graph/badge.svg?token=SJ6F03WTTK)](https://codecov.io/gh/PardhuMadipalli/instagram-insights)

#### Partial report screenshot

![Report Screenshot](screenshot.png "Report Screenshot")

## Quickstart

1. Install the package using:<br>
`pip install instagram-insights`

2. Run the python scripts using:<br>
`insta-insights --page-id=124567890123 --token=098754321234567890 --num-timings=2 --num-tags=3`

- Page ID and Token must be provided either as options or environment variable values. 
- If both options and env variables are provided options will be preferred.

#### Options supported

| Short options | Long options | Environment variables | Description |
|---------------|--------------|-----------------------|-------------|
| -h | --help | | Display help |
| -m | --machine-learning | | Whether to use ML or not (currently not supported)|
| | --page-id | FB_PAGE_ID| Instagram Page ID |
| | --token   | FB_TOKEN  | Token to access Facebook Graph API |
| | --num-timings | | Number of best timings needed |
| | --num-tags | | Number of best hashtags needed |
| | --graphs | | Whether to generate graphs or not |


#### Python version

- \>= 3.7

#### Packages needed
- numpy
- pandas
sshColab 0.1.3
Google Colab helper that automates ngrok tunnels creation (for SSH, TPU & TensorBoard) and facilitates Kaggle API data downloads.
Requires Python: >=3.6
Keywords: TPU,Colab,SSH,TensorBoard,kaggle

# ssh-Colab
ssh-Colab is a Python module to facilitate remote access to Google Colaboratory
(Colab) through Secure Shell (SSH) connections, secured by a third-party
software, ngrok. sshColab automates the tedious routine to set up ngrok
tunnels needed for TPU runtime applications and services like TensorBoard. It also
includes the function to facilitate the routine of Kaggle API installation/authentication
and competition data downloads.

[![license](https://img.shields.io/badge/license-MIT-blue.svg)](/LICENSE)
![python version](https://img.shields.io/badge/python-3.6%2C3.7%2C3.8-blue?logo=python)

# Prerequisites
- [ngrok](https://ngrok.com/) tunnel authtoken.
- Google account to access a [Colab](https://colab.research.google.com/notebooks/intro.ipynb) notebook.
- Local code editors such as VS Code or PyCharm to make the most of coding on Colab.
 
# Usage
1. Launch a Colab notebook. Choose a runtime type you prefer.

2. Install sshColab. Type and run the following command in a notebook cell:
   ```shell
   !pip install sshColab
   ```
   
3. Initiate the establishment of tunnels:
   ```python
   import sshColab
   sshColab.connect()
   ```
   The default TensorBoard log directory is `/log/fit`. You can reset it by
   passing into `connect()` the new value `LOG_DIR=/new/path/to/log`.
   
4. Retrieve information that is used for establishing the SSH connection:
   ```python
   sshColab.info()
   ```
   If you are using non-TPU runtimes, the setup instruction of TPU resolver is
   ignored.

5. Run function `kaggle()` to automate Kaggle API installation/authentication 
   and data downloads. The data is unzipped to the destination folder `/kaggle/input`. 
   ```python
   sshColab.kaggle([data='name-of-competition'])
   ```
   Note that the default competition name is "tabular-playground-series-mar-2021."

   

6. To disable ngrok tunnels created, run the command below:
   ```python
   sshColab.kill()
   ```

# Quickstart
A quickstart Colab notebook template is provided in the link below. Users can
find a simple end-to-end application starting from sshColab installation, SSH
tunnel creation, to the use of TensorBoard after training a 3-layer MNIST
convolutional neural network. 

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1TKM6Zqk4oY8RrFqGzrM-QEt92iQ0zxwm?usp=sharing) 

What's missed in this quickstart is how to may our way to Colab instances from
local machines. The reference listed below can be a start point for interested
users:

1. [Remote development over SSH on local VS Code](https://code.visualstudio.com/docs/remote/ssh-tutorial)
2. [Run SSH terminal on local PyCharm](https://www.jetbrains.com/help/pycharm/running-ssh-terminal.html)


# Feedback
Comments and suggestions are welcome and appreciated. They can be sent to
lipin.juan02@gmail.com.