semvec/pypi-packages huggingface dataset:
nodemanager 0.1.1
Creating a peer-to-peer network
Requires Python: >=3.8

# nodemanager

## Getting started

The aim of this project is to create a p2p network using the libp2p library in rust.

#### Gossip-Based Peer Sampling
```
MARK JELASITY, SPYROS VOULGARIS, RACHID GUERRAOUI, ANNE-MARIE KERMARREC, MAARTEN VAN STEEN. “Gossip-based peer sampling”. en. In: ACM Transactions on Computer Systems 25.3 (Aug. 2007), p. 8. issn: 0734-2071, 1557-7333. doi: 10.1145/1275517.1275520. url: https://dl.acm.org/doi/10.1145/1275517.1275520.
```

To run this project you need to have Rust and Python installed on your machine.

### Installation
#### Rust
To install Rust, follow this [link](https://doc.rust-lang.org/book/ch01-01-installation.html) and choose the system you want to install it on.
#### Python
To install Python, follow this [link](https://docs.python.org/3/using/index.html).

## Start the project

Once you've installed rust, you'll need to follow these steps to launch the project:

1. Go to the project folder
```
cd project
```
2. Create and active a python virtual environment
```
python -m venv .env
source .env/bin/activate
```
2. Install maturin for building and publishing Rust-based Python packages with minimal configuration
```
pip install -r requirements.txt
```
3. Build and execute the module

#### Example: simulation
```
maturin develop
```
```
python main.py --help
```
Expected
```
usage: main.py [-h] [--static_network static] [--enable_peersampling peersampling] [--node N] [--cycles M] [--multi Multithreading]

Simulation of decentralized Learning.

options:
  -h, --help            show this help message and exit
  --static_network static
                        Launch a static network. 0 for False and 1 for True.
  --enable_peersampling peersampling
                        Enables or disables peersampling. 0 for False and 1 for True.
  --node N              If you're not running a static network, it's important to specify the number of nodes in your network. This number must be greater than 5.
  --cycles M            The number of application cycles.
  --multi Multithreading
                      Used to decide whether to run the simulation on a single thread or on several threads. 0 for False and 1 for True.
```

Then you can lunch a simulation:
```
python main.py --static_network 0 --enable_peersampling 0 --node 20 --cycles 10 --multi 0
```
You can observe the application logs in the log.log file. This contains logs for all nodes.
And you can see the `config_sampling.json` file in the tree, which represents the network/sampling configuration file.
pytimer2 0.2.4
PyTimer is a lightweight and easy-to-use Python package designed to provide countdown timer functionality. It offers a simple class-based approach to manage countdowns, making it perfect for various use cases such as timing events, tracking intervals, or managing delays in scripts and applications.
Requires Python: >=3.6

# PyTimer2

`PyTimer2` is a simple Python countdown timer package that offers start, pause, resume, and stop functionalities. This tool is perfect for managing timed tasks, creating simple scheduling systems, or integrating into larger applications where precise countdown control is required.

[![Downloads](https://static.pepy.tech/badge/pytimer2)](https://pepy.tech/project/pytimer2)  
## Features

- **Start Countdown:** Initiate a countdown for a specified duration.
- **Pause Countdown:** Pause the countdown at any moment without losing the current time.
- **Resume Countdown:** Resume the countdown from where it was paused.
- **Stop Countdown:** Completely stop the countdown and reset the timer.
- **Non-Blocking Execution:** Runs in a separate thread, allowing your main application to continue running without interruption.

## Installation

You can install PyTimer directly from GitHub or from PyPI:

```bash
# Install pytimer2
pip install pytimer2

# Usage
from pytimer2 import Timer
import time

# Create a Timer instance
timer = Timer()

# Start the countdown with a duration of 500 seconds
timer.start_countdown(duration=500)

# Access the current countdown value
print(f"Current countdown: {timer.get_countdown()} seconds")

# Let it run for a few seconds and then pause
time.sleep(3)
timer.pause_countdown()
print(f"Countdown paused: {timer.get_countdown()} seconds")

# Wait and then resume the countdown
time.sleep(5)
print(f"Countdown still paused: {timer.get_countdown()} seconds")
timer.resume_countdown()
print("Timer resumed...")

# Run for a few more seconds
time.sleep(2)
print(f"Countdown resumed and current time: {timer.get_countdown()} seconds")

# Stop the countdown
timer.stop_countdown()
print(f"Countdown stopped at: {timer.get_countdown()} seconds")
parrot 0.1.10
parrot is able to to process and average continuously recorded THz data.
Requires Python: >=3.9
Keywords: DR, SNR, THz, Terahertz, averaging, dataprocessing, dynamic range, signal-to-noise ratio

# parrot 🦜

**parrot** stands for **P**rocessing **A**ll **R**apidly & **R**eliably **O**btained **T**Hz-traces.

parrot is a Python package to load, process, and plot continuously recorded THz data (along with delay line position
data), obtained with electro-optic sampling (EOS), photoconductive antenna (PCA) receivers or similar
techniques.

The documentation of parrot can be found [here](https://puls-lab.github.io/parrot/).

***

## How to install

If you have Python/pip installed, just enter:

```python
pip install parrot
```

and it should install correctly. Otherwise, just download the repository on [GitHub](https://github.com/puls-lab/parrot/).

***

## How to use

Go to [GitHub](https://github.com/puls-lab/parrot/) and look at the files placed in the root directory. If you have
Anaconda (or Python + Jupyter notebook) installed, try the `Example.ipynb` notebook in the root directory.
If you prefer classic Python, check out `Example_compact.py` in the root directory. The distribution comes with some
example data that allows you to see the capabilities of parrot.

***

## Further information

Check out the documentation [here](https://puls-lab.github.io/parrot/).

***

## I have a problem / I have an idea / I found a bug

Please open an issue on [GitHub](https://github.com/puls-lab/parrot/issues).
hfmc 0.1.10
A tiny cache widget for accessing hugging face models easier and faster!
Requires Python: >=3.8
Keywords: 9# AISoft, cache, huggingface, models, p2p

# HuggingFace Model Cache

[简体中文](https://aisoft9.github.io/hfmc/README.zh)

HuggingFace Model Cache (referred to as "HFMC") is a compact and efficient tool designed to help users use models on HuggingFace faster and more easily. HFMC assists users in the following ways:

1. Sharing model files between different HFMC nodes to avoid downloading the same model repeatedly from hf.co;
2. Automatically selecting the best download method from local networks, mirror sites, and hf sources;
3. Supporting resuming model downloads from the point of interruption to reduce wasted bandwidth and precious time due to unstable networks;
4. Quickly viewing, adding, and deleting local model files to make model management easier.

## Features of HFMC

### Model Sharing

Within the same local network, HFMC nodes can share models with each other. For instance, if node "Pegasus" has downloaded the meta-llama-3.1-8B model and node "Cygnus" wants to download it, HFMC will fetch the model from node "Pegasus" over the local network. This is faster, more stable, and more cost-effective than downloading from huggingface.co.

In this way, colleagues within the same lab or office can quickly share models, and users can also use HFMC to rapidly distribute models within a GPU cluster.

### Multi-Source Download

HFMC integrates multiple download sources to provide model download functionality. Specifically, when a user downloads a model via HFMC, it will sequentially attempt to download the target model from the local network (from other HFMC nodes), hf mirror sites (such as hf-mirror.com), and hf source sites.

### Resume Interrupted Downloads

Compared to manually downloading models from HuggingFace via a browser, HFMC offers resuming interrupted downloads. HFMC supports "cross-source resume" functionality, meaning an interrupted download on a local network can seamlessly continue from a mirror site.

### Model Management

HFMC provides a command-line tool to help users browse, download, and delete local model files. Information about model file sizes, quantities, and paths can be easily reviewed with a single command.

## Installing HFMC

HFMC supports Python version 3.8 and above, and can run on Windows, MacOS, and Linux.

Install HFMC using the following command:

    pip install hfmc

## Detailed Documentation

- [HFMC User Guide](https://aisoft9.github.io/hfmc/GUIDELINE.en)
- [HFMC Command Line Reference](https://aisoft9.github.io/hfmc/REFERENCE.en)