semvec/pypi-packages huggingface dataset:
ts-ml-bundle 0.3.0
CLI tool to generate Databricks ML platform project structure with governance and best practices
Requires Python: <4.0.0,>=3.8.1
Keywords: databricks, mlops, ml-platform, template, cli

# Databricks ML Bundle CLI

A CLI tool to generate Databricks ML platform project structures with governance and best practices.

## Installation

```bash
pip install ts-ml-bundle
```

## Usage

Generate a new ML project:

```bash
ts-ml-init --name my-ml-project --workspace-host https://your-workspace.cloud.databricks.com --model-type segmentation --use-gpu
```

Or use the short command:

```bash
tml-init -n my-ml-project -w https://your-workspace.cloud.databricks.com -m classification
```

### Options

- `--name, -n`: Project name (required)
- `--output-dir, -o`: Output directory (default: current directory)
- `--workspace-host, -w`: Databricks workspace URL (required)
- `--model-type, -m`: Model type - classification, regression, segmentation, nlp, custom (default: custom)
- `--use-gpu`: Enable GPU configuration for training

## Generated Structure

The CLI generates a complete ML platform project with:

- **Multi-environment support** (dev/stg/prod)
- **Unity Catalog integration**
- **MLflow experiment tracking**
- **Quality gates and approvals**
- **CI/CD pipeline with GitHub Actions**
- **Cluster policies and security**
- **Modular Python package structure**

## Example

```bash
# Generate a computer vision project
databricks-ml-init \
  --name vista-segmentation \
  --workspace-host https://my-workspace.cloud.databricks.com \
  --model-type segmentation \
  --use-gpu

# Navigate to project
cd vista-segmentation

# Install dependencies
pip install -r requirements.txt

# Deploy to Databricks
databricks bundle validate --target dev
databricks bundle deploy --target dev
```

## Features

- ✅ **Governance-first**: Built-in security, permissions, and audit trails
- ✅ **Multi-environment**: Separate dev/staging/production environments
- ✅ **Model-specific**: Templates optimized for different ML use cases
- ✅ **Production-ready**: Includes serving endpoints, monitoring, and CI/CD
- ✅ **Unity Catalog**: Full integration with Databricks governance platform

## Development

```bash
# Clone repository
git clone https://github.com/yourusername/ts-ml-bundle-cli
cd ts-ml-bundle-cli

# Install with Poetry
poetry install

# Run locally
poetry run ts-ml-init --help
```

## Publishing to PyPI

```bash
# Build package
poetry build

# Publish to PyPI
poetry publish
```

## License
MIT License
banklewan09 0.1.7
This is my first package. This package are calculation volume ,lateral and surface in cylinder .
Requires Python: >=3.7

# Package name : banklewan09

`banklewan09` is a lightweight Python package that provides simple utility functions
for **math**, **string processing**, and **data summarization**.
It is designed mainly for learning purposes and as an example of Python packaging.

---

## Features
- **Volume calculation**: compute the volume of a cylinder  
- **Lateral area calculation**: compute the lateral surface area of a cylinder  
- **Surface area calculation**: compute the total surface area of a cylinder  

---

## Installation

You can install the package directly from PyPI:

```bash
pip install banklewan09
netbox-storage-plugin 0.8.0
NetBox storage plugin
Keywords: netbox, netbox-plugin

# Netbox-storage

A [Netbox](https://github.com/netbox-community/netbox) plugin for storage related documentation where virtualization is used. 

5 new object types are introduced: 
  - Storage Pools: a pool that is created on a storage. Currently this storage has to be a Netbox Device.
  - LUN: tied to a Storage Pool
  - Datastores: created on LUN(s)
  - Storage Sessions: the "source" of a session is a Netbox Virtualization Cluster, the "destination" is the LUN Group
  - VMDK: can be assigned to a VM and a datastore

# Install

The plugin can be installed using pip:

```
pip install netbox-storage-plugin
```
Add netbox_storage to PLUGINS in configuration.py:
```
PLUGINS = ['netbox_storage',]
```

Don't forget to add ```netbox-storage-plugin``` to your local_requirements.txt as well. 

# Usage

1. Create regular Netbox objects: a storage Device, a virtualization Cluster, and a Virtual Machine
2. Create a Storage Pool that is assigned to the above created Device
3. Create LUN(s) on the Storage Pool
4. Create Datastore(s) on LUNs
5. Create Storage Session between the Cluster and the Datastore
6. Create VMDK on the VM that is on a Cluster that has a Storage Session: this is possible either from the main menu, or on the VM's own page
n8n-chat-cli 0.1.4
A command-line interface for n8n chat webhooks.
Requires Python: >=3.8

# n8n-chat-cli

A command-line interface for n8n chat webhooks.

## Installation

You can install `n8n-chat-cli` from PyPI:

```bash
pip install n8n-chat-cli
```

## Usage

You can use the `n8n-chat` command to start a chat session.

### With a command-line argument:

```bash
n8n-chat <your_n8n_chat_webhook_url>
```

### With an environment variable:

You can set the `N8N_CHAT_URL` environment variable to your n8n chat webhook URL.

```bash
export N8N_CHAT_URL=<your_n8n_chat_webhook_url>
n8n-chat
```

Once connected, you can type your messages and press Enter. To exit, type `exit` or press `Ctrl+C`.

## License

This project is licensed under the MIT License.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

### Releasing a new version

To release a new version, create a new tag with the version number and push it to the repository. The workflow will then automatically create a GitHub release and publish the package to PyPI.

```bash
git tag v0.1.3
git push origin v0.1.3
```
htql 1.0.0
Hyper-Text Query Language for querying HTML, XML and text documents
Requires Python: >=2.7

# HTQL - the Hyper-Text Query Language

The Hyper-Text Query Language (HTQL) is a language for the querying and transformation of HTML, XML and plain text documents. HTQL is developed in C++ with fast and efficient data extraction algorithms. HTQL can be used to:

- Extract data from HTML pages
- Retrieve HTML page through HTTP protocol
- Modify HTML pages

## Python Installation

Run: 
```
python setup.py install
```
  
## Python Examples

A simple example to extract url and text from links.

```
import htql

page="<a href=a.html>1</a><a href=b.html>2</a><a href=c.html>3</a>"
query="<a>:href,tx"

for url, text in htql.query(page, query): 
    print(url, text)
```

Another example to parse state and zip from US address using HTQL regular expression:

```
import htql; 
address = '88-21 64th st , Rego Park , New York 11374'
states=['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 
	'Delaware', 'District Of Columbia', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 
	'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 
	'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 
	'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 
	'Oregon', 'PALAU', 'Pennsylvania', 'PUERTO RICO', 'Rhode Island', 'South Carolina', 'South Dakota', 
	'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 
	'Wyoming']; 

a=htql.RegEx(); 
a.setNameSet('states', states);

state_zip1=a.reSearchStr(address, "&[s:states][,\s]+\d{5}", case=False)[0]; 
# state_zip1 = 'New York 11374'

state_zip2=a.reSearchList(address.split(), r"&[ws:states]<,>?<\d{5}>", case=False)[0]; 
# state_zip2 = ['New', 'York', '11374']
```

## Manuals

- [Hyper-Text Query Language (HTQL)](http://htql.net/htql-manual.pdf)
- [HTQL Python Interface](http://htql.net/htql-python-manual.pdf)

## Home

  [http://htql.net](http://htql.net)

## Citation
- Liangyou Chen. `Ad Hoc Integration and Querying of Heterogeneous Online Distributed Databases`. 2004. Ph.D. Dissertation, Department of Computer Science & Engineering, Mississippi State University.
ms-mathmaticalnotation 0.0.1
A packages for a mathmatical nations in a data-structure
Requires Python: >=3.8

<h2>Project Description</h2>
<p>
    The Mathematical Notation Library is a library for guiding mathematical notation such as suffixes, prefixes, or hyphens for computer engineering students majoring in software engineering.
</p>

<h2>installation</h2>
<p>Use the package manager pip to install ms_mathimatical_notation.</p>

<code>
    pip ms_mathimatical_notation
</code>

<h2> Usage </h2>
<p> Sample usage of the library. Import any notation from the list of supported mathimatical notation. </p>

<ul>
    <li>Prefix</li>
    <li>Postfix</li>
</ul>

</h2>Example Usage</h2>
<code>
    from ms_mathimatical_notation import smartEval
    arr = ['a','+','b','*']
    postfix = smartEval(arr)
</code>
ai-rubric 0.2.2
Structured verification with LLMs.
Requires Python: >=3.11

# <img src="assets/rubric-icon.png" alt="Rubric Icon" width="100" height="100" style="vertical-align: middle;"> Rubric

Structured verification with LLMs.

## Usage Instructions
Coming soon...

## Contributing

1. Install development dependencies: `pip install -e ".[dev]"`
2. Install pre-commit hooks: `pre-commit install`
3. Make your changes
4. Run tests: `pytest`
5. Submit a pull request
6. When merging a pull request, please use [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.