semvec/pypi-packages huggingface dataset:
streamlit-copy-to-clipboard-button 1.0.7
A Streamlit copy-to-clipboard button component that respects the streamlit page styles.
Requires Python: >=3.13
Keywords: streamlit, copy, clipboard, button, component

# streamlit-copy-to-clipboard-button

A Streamlit copy-to-clipboard button component that respects the streamlit page styles.

![Demo](https://raw.githubusercontent.com/samsze0/streamlit-copy-to-clipboard-button/main/assets/demo-1.png)

## Installation instructions

```sh
pip install streamlit-copy-to-clipboard-button
```

## Usage instructions

```python
import streamlit as st

from streamlit_copy_to_clipboard_button import copy_to_clipboard

copy_to_clipboard(
    "Text to copy",
    label="Copy to clipboard",  # Optional
    label_after_copy="Copied!"  # Optional
)
```
githubranger 2025.6.0
Python package for managing and automating tasks across multiple GitHub repositories.
Requires Python: >=3.13
Keywords: automation, github, higher education, repositories

# githubranger
[Marijan Beg](https://github.com/marijanbeg)<sup>1</sup>

<sup>1</sup> *Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK*  

## About

`githubranger` is a Python package for efficiently managing and automating tasks across multiple GitHub repositories.

## Installation

You can install `githubranger` via `pip` from PyPI:

```bash
pip install githubranger
```

## Documentation

TBC (for now, refer to docstrings...)

## Support

If you require support, have questions, want to report a bug, or want to suggest an improvement, please raise an issue in this repository.

## Contributions

All contributions are welcome, however small they are. If you would like to contribute, please fork the repository and create a pull request. If you are not sure how to contribute, please contact us by raising an issue in this repository, and we are going to help you get started and assist you on the way.

## License

Licensed under the MIT License. For details, please refer to the [LICENSE](LICENSE) file.
vllm-logits 0.1
A collection of useful util functions
Requires Python: >=3.10

# Python util funcs

[![codecov](https://codecov.io/gh/hyukkyukang/python_utils/branch/main/graph/badge.svg?token=XM8ELS08EG)](https://codecov.io/gh/hyukkyukang/python_utils)

This is a collection of python functions that I use in my projects.

To install [the package](https://pypi.org/project/hkkang-utils/):

```bash
pip install hkkang_utils
```

Documentation is available [here](https://hyukkyukang.github.io/python_utils/).
hopsparser 0.8.0
A graph dependency parser to tinker with
Requires Python: >=3.11
Keywords: nlp, parsing

HOPS, an honest parser of sentences
===================================

[![Latest PyPI version](https://img.shields.io/pypi/v/hopsparser.svg)](https://pypi.org/project/hopsparser)
[![Continuous integration](https://github.com/hopsparser/hopsparser/actions/workflows/ci.yml/badge.svg)](https://github.com/hopsparser/hopsparser/actions/workflows/ci.yml)

> It ain't much but it's honest work.

This is a graph-based dependency parser inspired by [Dozat and Manning
(2017)](https://nlp.stanford.edu/pubs/dozat2017deep.pdf)'s biaffine graph parser. Contrary to Dozat,
the parser performs its own tagging and can use several lexers such as FastText, BERT and others. It
has been originally designed within the [FlauBERT](https://github.com/getalp/Flaubert) initiative.

The parser comes with pretrained models ready for parsing French, but it might be trained for other
languages without difficulties.

See the [documentation](http://hopsparser.readthedocs.io) for more information.

## Citation

If you use this parser for your scientific publication, or if you find the resources in this
repository useful, please cite the following paper

```biblatex
@inproceedings{grobol:hal-03223424,
    title = {{Analyse en dépendances du français avec des plongements contextualisés}},
    author = {Grobol, Loïc and Crabbé, Benoît},
    url = {https://hal.archives-ouvertes.fr/hal-03223424},
    booktitle = {{Actes de la 28ème Conférence sur le Traitement Automatique des Langues Naturelles}},
    eventtitle = {{TALN-RÉCITAL 2021}},
    venue = {Lille, France},
    pdf = {https://hal.archives-ouvertes.fr/hal-03223424/file/HOPS_final.pdf},
    hal_id = {hal-03223424},
    hal_version = {v1},
}
```

## Development

If you want a development install (so you can modify the code locally and directly run it), you can
install it in editable mode with the tests extras after cloning the repository

```sh
git clone https://github.com/hopsparser/hopsparser
cd hopsparser
pip install -e ".[spacy,tests,traintools]"
```

In that case, you can run the smoke tests with `tox` to ensure that everything works on your end.

Note that using the editable mode requires `pip >= 21.3.1`.

## Licence

This software is released under the MIT Licence, with some files released under compatible free
licences, see [LICENCE.md](LICENCE.md) for the details.
deep-cache-cleaner 1.0.1
Comprehensive cache clearing utility for PyTorch, HuggingFace, and other ML libraries
Requires Python: >=3.8
Keywords: cache, pytorch, huggingface, ml, cleanup, memory


# File: README.md
# Deep Cache Cleaner

A comprehensive cache clearing utility for PyTorch, HuggingFace, and other ML libraries.

## Features

- 🧹 Clear PyTorch GPU memory cache
- 🤗 Clear HuggingFace transformers cache  
- 📝 Clear sentence-transformers cache
- 🗑️ Clear Python __pycache__ directories
- 📊 Check cache sizes and GPU memory usage
- 💣 Aggressive cleanup mode (deletes downloaded models)
- 🔧 Command-line interface
- 📦 Easy to use Python API

## Installation

```bash
pip install deep-cache-cleaner
```

### Optional Dependencies

For full functionality, install with optional dependencies:

```bash
# For HuggingFace transformers support
pip install deep-cache-cleaner[transformers]

# For sentence-transformers support  
pip install deep-cache-cleaner[sentence-transformers]

# For development
pip install deep-cache-cleaner[dev]
```

## Usage

### Command Line Interface

```bash
# Clear all caches
deep-cache-cleaner clear

# Check cache sizes
deep-cache-cleaner check  

# Aggressive cleanup (deletes downloaded models)
deep-cache-cleaner aggressive

# Auto-confirm aggressive cleanup
deep-cache-cleaner aggressive --yes

# Quiet mode
deep-cache-cleaner clear --quiet
```

### Python API

```python
import cache_cleaner

# Clear all caches
cache_cleaner.clear_all_caches()

# Check cache sizes
sizes = cache_cleaner.check_cache_sizes()
print(f"Total cache size: {sizes['Total']:.2f} GB")

# Aggressive cleanup
cache_cleaner.aggressive_cache_clear()

# Use the class interface for more control
cleaner = cache_cleaner.CacheCleaner(verbose=True)
cleaner.clear_all_caches()
```

## What Gets Cleared

### Standard Cleanup (`clear`)
- PyTorch GPU memory cache
- Python garbage collection
- In-memory HuggingFace model cache
- Python __pycache__ directories
- Reports on cache sizes and temp files

### Aggressive Cleanup (`aggressive`) 
⚠️ **Warning: This deletes downloaded models!**

Everything from standard cleanup, plus:
- HuggingFace cache directory (`~/.cache/huggingface`)
- Sentence-transformers cache (`~/.cache/sentence_transformers`) 
- Torch cache directory (`~/.cache/torch`)
- Cache directories from environment variables

## Requirements

- Python 3.8+
- PyTorch (for GPU cache clearing)
- Optional: transformers, sentence-transformers

## License

MIT License

## Contributing

Contributions welcome! Please read our contributing guidelines and submit pull requests.
cloudpack 0.0.4
Multi-cloud file vault with end-to-end encryption and chunked storage.
Requires Python: >=3.10

# CloudPack

![Project Status](https://img.shields.io/badge/status-pre--alpha-red)
![Tests](https://github.com/atar4xis/cloudpack/actions/workflows/python-app.yml/badge.svg)

**CloudPack** is an open-source, multi-cloud file vault. It encrypts your files, splits them into chunks, and stores those chunks across different cloud providers. You hold the only key - your master password.

> ⚠️ **Project Status: Pre-Alpha**  
> CloudPack is in active early development and is **not ready to use**. Expect incomplete features, placeholder code, and rapid breaking changes. Contributions and feedback are welcome.

## Features

- 🔐 **End-to-End Encryption** - AES-256 encryption before upload
- 🧩 **Chunked Storage** - Files are split and distributed across providers
- ☁️ **Multi-Cloud Support** - Use Google Drive, Dropbox, OneDrive, and more
- 🔄 **Cross-Platform** - Works on macOS, Linux, and Windows
- 🛠 **CLI and API** - Full control for power users and integrations

## Installation

**Requirements:**

- Python 3.10 or higher
- pip (Python package installer)

**Install from PyPI:**

```bash
pip install cloudpack
```

**Install from source (for development):**

```bash
git clone https://github.com/atar4xis/cloudpack.git
cd cloudpack
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .
```

## Usage/Examples

After installation, you can access the CLI with:

```bash
cloudpack --help
```

To create a new vault in the current directory:

```bash
cloudpack init
```

To specify a custom location:

```bash
cloudpack init /path/to/vault
```

## Supported Providers

⚠️ This section is under development.

## License

CloudPack is licensed under the GNU General Public License v3.0. See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome!

Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines and setup instructions.

## Roadmap

- [x] Command-Line Interface
- [x] Core Encryption & Chunking
- [x] Basic Vault Operations
- [ ] Configuration Management
- [ ] Google Drive Support
- [ ] Dropbox Support
- [ ] Documentation & Tutorials
- [ ] API Development
- [ ] Desktop GUI
workload-analyzer 0.0.7
Add your description here
Requires Python: >=3.12

# Workload Analyzer 📊

A simple tool for monitoring and analyzing GPU and system resource usage during AI/ML workloads.

## 🌟 Features

- 📈 Real-time monitoring of GPU utilization, memory usage, and system resources
- 🔍 Detailed visualization of resource usage over time
- 💡 Intelligent recommendations for workload optimization
- 🚀 Easy-to-use CLI interface
- 📝 Comprehensive statistics export

## 🔧 Installation

### Using pip

```bash
pip install workload-analyzer
```

### Development setup

```bash
# Clone the repository
git clone https://github.com/yourusername/workload-analyzer
cd workload-analyzer

# Install using uv with development dependencies
uv sync --dev
```

## 📋 Requirements

- Python 3.12+
- NVIDIA GPU with nvidia-smi (for GPU monitoring)

## 🚀 Quick Start

```bash
# Run a command with default settings
workload-analyzer "python train_model.py"

# Specify timeout and polling interval
workload-analyzer "python train_model.py" --timeout 300 --interval 5
```

## 📊 Output

The tool generates:

1. **Statistics file**: JSON format data with all recorded measurements
2. **Visualizations**:
   - GPU memory usage over time
   - System memory consumption
   - CPU and disk utilization
   - Network usage
   - Process memory statistics

3. **Optimization recommendations** based on resource utilization patterns:
   - GPU memory sizing recommendations
   - Compute utilization insights
   - Memory bandwidth analysis
   - System resource optimization tips

All outputs are saved to `workload_results/` by default (configurable with `--output-dir`).

## 🛠️ Configuration options

```txt
--timeout           Time to monitor in seconds (default: 120)
--interval          Polling interval in seconds (default: 3)
--recommendations   Enable workload optimization recommendations (default: True)
--output-dir        Directory to save outputs (default: workload_results/)
--verbose           Enable verbose logging (default: True)
--version           Print version information
```

## ❕ License

Package is licensed under Apache 2.0 license. Free to use as you like, but a cite of the package is welcome:

```bibtex
@misc{skafte_workload_analyzer,
    author       = {Nicki Skafte Detlefsen},
    title        = {Workload-Analyzer},
    howpublished = {\url{https://github.com/SkafteNicki/workload_analyzer}},
    year         = {2025}
}
```