semvec/pypi-packages huggingface dataset:
intel-npu-top 0.2.1
A monitoring tool for Intel Meteor Lake NPU usage in real-time
License: MIT
Requires Python: >=3.6
Keywords: intel, npu, monitoring, top, meteor-lake

# intel-npu-top 

A monitoring tool for Intel Meteor Lake NPU usage in real-time via sysfs on Linux, featuring ASCII-based visualization.

**Status:** Pre-alpha prototype — expect incomplete features, rough edges, and limited functionality.

## ⚠️ Important Warning

This is an experimental prototype and is **NOT PRODUCTION READY**. Use at your own risk and expect:
- Incomplete features
- Potential bugs
- Limited error handling
- Possible system-specific issues

## Requirements

- Linux system with Intel Meteor Lake NPU
- Python 3.6 or higher
- Access to NPU sysfs entries (usually requires root privileges)

## Features

* Real-time NPU usage monitoring with ASCII bar chart visualization
* Historical view of the last 40 samples
* Automatic screen refresh
* Docker container support

## Installation

### Standalone Script

Just copy the STANDALONE.py script to your system and run it with Python 3.

### From PyPI   (Coming Soon)

### From Source

git clone https://github.com/DMontgomery40/intel-npu-top
cd intel-npu-top
python3 -m pip install .

### Using Docker

docker-compose up -d

## Usage

sudo intel-npu-top

Note: Root privileges are required to access the NPU sysfs entries.

## Acknowledgements

This project was inspired by and based on code from [ZoLArk173/nputop](https://github.com/ZoLArk173/nputop). Thanks for the inspiration and groundwork!

## License

MIT License - See LICENSE file for details.
search-llm-actions 0.0.1
('search_llm_actions provides a simple template to collect action trajectories from local (e.g. vllm servers) or remote (e.g. togetherai api) llms by search.',)
License: MIT
Requires Python: >=3.6.0


# search_llm_actions

search_llm_actions provides a simple template to collect action trajectories from local (vllm servers on multi-gpus are supported) or remote (togetherai api is supported) llms by search.

## Installation

```bash
pip install search_llm_actions
```

## Customization

- You could find an minimal example in `search_llm_actions/main.py`.
- You need to override  `init_root_node`, `take_parallel_actions`, `simulate` & `detect_end` functions in `search_llm_actions/search.py` to adapt to your own task.
- You need to override `deploy_vllm_multi.sh` & `test_vllm_multi.sh` in `search_llm_actions/scripts` to adapt to your own llm server.
- You need to implement a new subclass of `Caller` in `search_llm_actions/llm_caller.py` to adapt to your own llm server.

enjoy:)
🤯