semvec/pypi-packages huggingface dataset:
kdc-kubeconnector 1.7.7
Tool to work with kubernetes dashboard, if there is no kubectl access
Requires Python: >=3.11

# KDC - Kubernetes Dashboard Connector
Designed to partially replace kubectl app if your DevOps do not grant access to it.  
As an alternative for kubectl,
DevOps can set up [Kubernetes Dashboard](https://kubernetes.io/docs/tasks/access-application-cluster/web-ui-dashboard/)  
web app and grant access to it. But as this solution is slow and not very convenient, I've created this tool to request 
logs and pod data from the cluster.

## Features
- [x] select kubernetes cluster to work with
- [x] select kubernetes namespace to work with
- [x] list namespaces
- [x] list deployments
- [x] list pods
- [x] list jobs
- [x] get logs from pods by name pattern. Multiple pods can be selected separated by space
- [x] get logs from the latest job by name pattern
- [x] delete pod by name pattern (1 per time, check if it is allowed)
- [x] scale deployment by name pattern (1 per time, check if it is allowed)
- [x] download logs to the file
- [x] show the token of current cluster

## Installation
```bash
pip install kdc-kubeconnector
```

## Configuration
Find the config file here `~/.kdc/config.toml`
It will be created after the first run of the tool.  
```toml
[default]
cluster = "localhost"
namespace = "default"

[log]
level = "INFO"
file = "kdc.log"
save = false

[connection]
retries = 3
delay = 1
page = 3000

[cluster.localhost]
url = "http://localhost:8001"
token = "token"
namespace = "default"

[cluster.dev]
url = "http://dev:8001
token = "token2"
namespace = "default"

[cluster.uat]
url = "http://uat:8001
token = "token3"
namespace = "default"
```

## Usage Examples
List pods
```bash
kdc -p
kdc -p s=running --only running
kdc -p s=run  --only running
kdc -p n=nginx --only with name nginx
kdc -p s=run n=ngi --with name nginx
```
Get logs from the pod with name like nginx-sd4353453-4543d
```bash
kdc -l nginx
```
Get logs from the few pods
```bash
kdc -l nginx app1 app2
```
List namespaces
```bash
kdc -n
```
Set the namespace to work with by default
```bash
kdc -n default
kdc -n dev
kdc -n uat
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## Release Notes
- 1.7.7 - fixed wrong selection of namespace by cluster
- 1.7.6 - fixed GitHub actions pipeline. Fixed version issue
- 1.7.0 - fixed version issue. Added the ability to set the namespace by cluster. Improved env selection argument
pd-data-manager 0.1.1
A package for efortless data manager.
Requires Python: >=3.6

# Data Manager

Data Manager is a Python package designed to provide a comprehensive suite of tools specialized in managing and analyzing data. It offers a user-friendly toolkit suitable for individuals of all levels of expertise. This initial release encompasses the following features:

- Effortless reading of datafiles with support for Pandas-compatible extensions.
- Simplified dataframe creation by specifying column names and corresponding values.
- Automatic generation of hexadecimal and numeric identifiers.
- Provision of common statistical analyses for specified columns.
- Histogram plotting functionality.
- Easy visualization of class distribution within the database.
- Data standardization capabilities.
- Feature discretization support.
- Creation of fold columns for conducting stratified k-fold analyses.

# Components
## 'CreateDataPD' Class
This class enables the creation and manipulation of Pandas DataFrames. It supports the following functionalities:
- Reading data from various file formats: CSV, Excel, JSON, Parquet, Feather, and Pickle.
- Generating unique identifiers for rows.
- Adding new data to the DataFrame.
- Displaying the DataFrame.
- Retrieving column names.
- Printing specific columns.

## 'PDNumericAnalysis' Class
This class extends the CreateDataPD class and provides additional functionalities for numerical analysis and preprocessing. It includes the following features:

- Computing statistics for numerical columns.
- Plotting histograms.
- Analyzing data balance.
- Standardizing numerical data.
- Reconstructing data.
- Converting non-numeric columns to numeric values.
- Creating folds for cross-validation.

## Dependencies
This toolkit requires the following dependencies:

- pandas
- numpy
- matplotlib
- scikit-learn

## Installation
To install the dependencies, run:
```python
pip install requirements.txt
```

## Usage
You can use this toolkit in your Python projects by importing the necessary classes and functions. Here's an example of how to use the CreateDataPD class:

```python
from data_analysis_toolkit import CreateDataPD

# Create a DataFrame from data
data = [[1, 'A', 10], [2, 'B', 20], [3, 'C', 30]]
columns = ['ID', 'Category', 'Value']
data_pd = CreateDataPD(data=data, columns=columns)

# Display DataFrame
data_pd.show_dataset()

# Add new data
new_data = [[4, 'D', 40], [5, 'E', 50]]
data_pd.add_data(new_data)

# Display DataFrame with added data
data_pd.show_dataset()
```

# Work in progress...

Stay tuned for upcoming releases, which will incorporate the following enhancements:
- Missing data completion functionality.
- Conversion of dataframes into datasets suitable for machine learning inputs.
- Introduction of PDTextAnalysis for handling features related to text manipulation.
reComputer 0.0.1
run AI on Jetson, all you need is reComputer
Requires Python: >=3.8

<div align="center">
  <img alt="jetson" height="200px" src="https://avatars.githubusercontent.com/u/688117?s=200&v=4">
</div>

# jetson-examples

[![Discord](https://dcbadge.vercel.app/api/server/5BQCkty7vN?style=flat&compact=true)](https://discord.gg/5BQCkty7vN)

- run ai examples on jetson.
- all you need is `reComputer`.

## Install

```sh
curl -fsSL https://raw.githubusercontent.com/Seeed-Projects/jetson-examples/main/install.sh | sh
```

- [more installation methods](./docs/install.md)

## Quickstart

To run and chat with [LLaVA](https://www.jetson-ai-lab.com/tutorial_llava.html):

```sh
reComputer run llava
```

## Example list

reComputer supports a list of examples from [jetson-ai-lab](https://www.jetson-ai-lab.com/)

Here are some examples that can be run:

| Example                | Type                     | Model/Data Size | Image Size | Command                                 |
| ---------------------- | ------------------------ | --------------- | ---------- | --------------------------------------- |
| text-generation-webui  | Text (LLM)               | 3.9GB           | 14.8GB     | `reComputer run text-generation-webui`  |
| LLaVA                  | Text + Vision (VLM)      | 13GB            | 14.4GB     | `reComputer run llava`                  |
| stable-diffusion-webui | Image Generation         | 3.97G           | 7.3GB      | `reComputer run stable-diffusion-webui` |
| nanoowl                | Vision Transformers(ViT) | 613MB           | 15.1GB     | `reComputer run nanoowl`                |
| nanodb                 | Vector Database          | 76GB            | 7.0GB      | `reComputer run nanodb`                 |
| whisper                | Audio                    | 1.5GB           | 6.0GB      | `reComputer run whisper`                |

> Note: You should have enough space to run example, like `LLaVA`, at least `27.4GB` totally

More Examples can be found [examples.md](./docs/examples.md)

## TODO List

- [ ] check disk space enough or not before run
- [ ] allow to setting some configs, such as `BASE_PATH`
- [ ] detect host environment and install what we need
- [ ] support jetson-containers update
- [ ] all type jetson support checking list
- [ ] better table to show example's difference
- [ ] try jetpack 6.0