semvec/pypi-packages huggingface dataset:
yInference 0.0.1.2
Inference your Fine tuned LLM

# YInference: Streamlining Model Inference 

**YInference** is a powerful Python package designed to streamline the model inference process, enabling users to thoroughly assess their trained or fine-tuned models before deploying them to the Hugging Face Hub or any other deployment environment. This versatile tool has been created to address the critical need for reliable model evaluation within your workflow, ultimately enhancing model performance and boosting deployment confidence.

## Table of Contents

- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [License](#license)

## Overview

In the fast-paced world of machine learning and natural language processing (NLP), ensuring the reliability and robustness of your models is of paramount importance. yInference is here to simplify and fortify your model evaluation and deployment workflows. Whether you're a seasoned data scientist or a newcomer to the field, YInference empowers you to:

- **Evaluate with Confidence**: yInference provides a user-friendly and intuitive interface for assessing the performance of your machine learning models. You can trust your model evaluations and make informed decisions about deployment.

- **Enhance Model Performance**: By leveraging yInference's capabilities, you can fine-tune your models more effectively. It offers a comprehensive set of evaluation metrics and insights to help you pinpoint areas for improvement.

- **Smooth Deployment Process**: Save time and reduce the risk of deploying underperforming models. yInference ensures that your models meet your quality standards before they go live.

## Features

### 1. Streamlined Inference

yInference simplifies the process of running inference on your models. With just a few lines of code, you can load your model and input data, making it easier than ever to assess its performance.

### 2. Comprehensive Evaluation Metrics

We understand that one-size-fits-all metrics don't always tell the whole story. yInference offers a wide range of evaluation metrics, allowing you to choose the ones that best align with your specific use case.

### 3. Easy Integration with Hugging Face Hub

For those utilizing the Hugging Face Hub for model sharing and deployment, yInference seamlessly integrates with the platform. You can test your models thoroughly before sharing them with the community or deploying them in production.

### 4. Interactive Visualization

Visualize your model's performance with easy-to-understand graphs and charts, helping you identify strengths and weaknesses quickly.

### 5. Extensible and Customizable

yInference is built to be extensible. You can easily integrate it into your existing workflows and customize it to meet your specific requirements.

## Installation

To get started with yInference, simply install it using pip:

```bash
pip install yInference
```

## License

YInference is distributed under the MIT License.
posedr 0.0.1
Pose Detection By mediapipe and cv2
Requires Python: >=3.6
Keywords: Human pose estimation,Body tracking,Image processing


# Project Title

Pose Detection Python Package


## Installation

Install my-project with npm

```bash
  pip install pose-detection
```
    
## Demo

from posedr import run_pose_detection

# Run pose detection with default parameters
run_pose_detection()

# You can also customize the parameters
run_pose_detection(mode=True, smooth=False, detectionCon=0.7, trackCon=0.8)

Parameters:

mode (bool, optional): Set to True for static image mode. Default is False.

smooth (bool, optional): Enable landmark smoothing. Default is True.

detectionCon (float, optional): Minimum detection confidence threshold. Default is 0.5.

trackCon (float, optional): Minimum tracking confidence threshold. Default is 0.5.




## License

This project is licensed under the MIT License - see the LICENSE file for details.
rolypoly 0.2
A Docker image rollback tool

## Structure
```
└── RolyPoly
    ├── rolypoly
    │   ├── bin
    │   ├── include
    │   └── pyvenv.cfg
    ├── docker_manager.py
    ├── main.py
    └── setup.py
```

## Features

1. Rollback

2. Logging 
```
{"level": "INFO", "message": "Stopped and removed container container_test"}
{"level": "INFO", "message": "Started new container container_test with image image_test"}
{"level": "ERROR", "message": "Failed to start new container container_test with image image_test"}
```

## Use

This will rollback your image to whichever image you have chosen:

```rolypoly my-container my-image  --preserve-volumes```

Params:
my-container: container that you ran in your docker

my-image: target image that you want to rollback to

preserve-volumes: preserve volume during rollback


Example: ```rolypoly test-nginx nginx:1.17 --preserve-volumes```

If you want to force the container to stop immediately, you can use the flags below: 

```rolypoly test-nginx nginx:1.17 --force --preserve-volumes```


# Link

https://pypi.org/project/rolypoly/0.1/