semvec/pypi-packages huggingface dataset:
LLM-Performance-Benchmark 1.0
Benchmark the performance (output speed, latency) of OpenAI compatible endpoints
Requires Python: >=3.7.1


# LLM Performance Benchmark

> Benchmark the performance of OpenAI compatible APIs in terms of Time to First Token (commonly referred to as latency) and Output Tokens per Second.

## Background
Benchmarking script used by [Artificial Analysis](https://artificialanalysis.ai/) for our performance benchmarks.

For performance benchmarks across >150 endpoints, including across varied prompt lengths & parallel queries, visit: [Artificial Analysis API Provider Peformance Leaderboard](https://artificialanalysis.ai/leaderboards/providers)

## Install
```bash
# Install from PyPI
pip install llm_performance_benchmark
```

## Usage
```python
from llm_performance_benchmark import llm_performance_benchmark

# Configure endpoint
import os
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
base_url = "https://api.openai.com/v1"
model_id = "gpt-4-turbo"

# Configure prompt
user_prompt = "What is the purpose of life?"
system_prompt = "You are a helpful assistant."

# Run benchmark
result = llm_performance_benchmark(model_id=model_id, 
                                   user_prompt=user_prompt, 
                                   system_prompt=system_prompt, 
                                   api_key=api_key, 
                                   base_url=base_url,
                                   print_response=True)
print(result)
""" Example response:
{
    'total_time': 8.9, 
    'time_to_first_token': 0.6, 
    'output_tokens_per_second': 39.4, 
    'tokens_per_second_across_total_request': 36.4,
    'response_text: "..."
}
"""
```

## Definitions of output metrics
For definitions of what the metrics represent, see the following [https://artificialanalysis.ai/methodology](https://artificialanalysis.ai/methodology) page.
To standardize token counting across different models, text is tokenized as cl100k_base tokens (used by GPT-4) with Tiktoken. 

## License
#### MIT License
Copyright (c) 2024 Artificial Analysis, Inc.

See LICENSE file for further details
uranie-launcher 3.0.1
uranie-launcher help you to run your calculation scripts with Uranie

# This package contains uranie-launcher

- [This package contains uranie-launcher](#this-package-contains-uranie-launcher)
  - [Contact](#contact)
  - [Description](#description)
  - [Installation](#installation)
  - [Python style conventions](#python-style-conventions)

## Contact

 Clément STUTZ (<clement.stutz@cea.fr>)

## Description

The package **uranie_launcher** is a Python package for easily launch a study with URANIE.

## Installation

This package can be installed using pip.
For example the following line installs the package in developer mode (modifiable sources):

```sh
pip install --user -e .
```

## Python style conventions

This project use the code linter [Pylint](https://www.pylint.org/) for coding conventions
and [NumPy Style Python Docstrings](https://numpydoc.readthedocs.io/en/latest/format.html)
for docstrings formating.
miiiii 0.1.0
mechanistic interpretability of irriducible integer identifiers
Requires Python: <4.0,>=3.11

# MIIII - Mechanistic Interpretability of Irreducible Integer Identifiers

MIIII is a project exploring the properties and interpretability of deep prime number (irreducible integer) identifiers.
The goal is to gain insights into these fundamental building blocks of mathematics through computational analysis and modeling.

## Project Structure

- `src/`: Contains the main Python source code files
  - `__init__.py`: Package initializer 
  - Various `.py` files containing the project's algorithms and utilities
- `.gitignore`: Specifies files for Git to ignore
- `app.py`: Streamlit web app for interactive exploration
- `conf.yaml`: Configuration file
- `library.bib`: Bibliography file
- `main.py`: Main script
- `notebook.ipynb`: Jupyter notebook for exploratory analysis
- `poetry.lock`: Dependency lock file
- `pyproject.toml`: Project configuration file
- `README.md`: This readme file
- `TODO.md`: Todo list

## Getting Started

1. Ensure you have Python 3.11 installed 
2. Clone this repository
3. Install the required dependencies using Poetry: `poetry install`
4. Explore the various scripts and the Jupyter notebook to understand the project's workings
5. Run analyses and extend the project's capabilities as desired
PrettyDF 1.1.4
Pretty Print DataFrames

# PrettyDF Package

The PrettyDF package is a Python library crafted by DawnSaju, designed to enhance the visual representation of pandas DataFrames. It provides a set of tools to make your DataFrames more readable and visually appealing.

## Installation

You can install the PrettyDF package via pip:

```bash
pip install PrettyDF
```

## Usage

Import the required functions from the package in your Python script:

```python
from PrettyDF import table
```

And the `table` function to display your DataFrame in a tabular format:

```python
import pandas as pd
from PrettyDF import table

data = pd.DataFrame({
    'Name': ['John', 'Anna', 'Peter'],
    'Age': [28, 24, 35],
    'City': ['New York', 'Paris', 'Berlin']
})

table(data)
```
json-schematizer 0.0.1
JSON schema tools
Requires Python: >=3.8

# json-schematizer

[![PyPI - Version](https://img.shields.io/pypi/v/json-schematizer.svg)](https://pypi.org/project/json-schematizer)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/json-schematizer.svg)](https://pypi.org/project/json-schematizer)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install json-schematizer
```

## License

`json-schematizer` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
nopip 0.2.2
A simple module to install Python packages programmatically
Requires Python: >=3.6

# nopip

A simple module to install Python packages programmatically, without needing to use `pip` to install `nopip`. Useful for quick scripts, testing, or when you want to embed all the requirements of your app into your code.

## Usage

Embed all the requirements of your app into your code for simplicity. You can even try importing packages and install them in case of an error:

```python
from nopip import install

try:
    from flask import Flask
except:
    install.module('Flask==2.2.3', verbose=True)
    from flask import Flask
```

### Other examples

```python
install.from_requirements('./requirements.txt', verbose=True)
install.module('requests', verbose=True)
install.modules(['requests==2.5.1', 'beautifulsoup4==4.10.0'], verbose=True)
```

## Installation

There is no need to install `nopip` via `pip`. Simply copy the `nopip` directory to your project, and you're good to go.

If you find it challenging to copy the entire nopip directory, there's an even simpler solution. You can just copy the contents of the `__init__.py` file and save it as `nopip.py` in your project directory. This way, you can still access and use the nopip functionality with minimal setup.

# Note

I'd like to clarify that I don't hate `pip` or intend to undermine its value in the Python ecosystem. `pip` is an essential tool for managing Python packages and their dependencies. However, I have observed that some people, particularly beginners or those working on very simple applications, might face issues with `pip` when trying to set up their environment or resolve dependencies. The `nopip` module aims to provide an alternative way to install packages for those specific scenarios where ease of use is a priority. It's important to note that using `nopip` is not a substitute for proper dependency management, and it's always recommended to use `pip`, `pipenv`, or `poetry` for production code.

# Collaboration

We welcome and appreciate any contributions to improve the nopip module. If you have ideas, suggestions, or bug fixes, feel free to collaborate by following these steps:

 - Fork the repository on GitHub (assuming the project is hosted on GitHub).
 - Clone your fork locally.
 - Create a new branch for your proposed changes. For example, use git checkout -b feature/my-new-feature to create and switch to a new branch called feature/my-new-feature.
 - Make your changes in the new branch, and commit them with meaningful commit messages.
 - Push your changes to your fork on GitHub.
 - Open a pull request on the original repository, describing your proposed changes and why they would be beneficial.
 - We will review pull requests on a regular basis and provide feedback. If your changes are accepted, they will be merged into the main repository.

If you encounter any issues or have questions, feel free to open an issue on the GitHub repository, and we'll do our best to address it.

Let's work together to make `nopip` even better!
bs_python_utils 0.7
my Python utilities
Requires Python: <3.12,>=3.11

# bs-python-utils

![GitHub last commit](https://img.shields.io/github/last-commit/bsalanie/bs-python-utils)

<!-- [![Release](https://img.shields.io/github/v/release/bsalanie/bs-python-utils)](https://img.shields.io/github/v/release/bsalanie/bs-python-utils) -->

[![Build status](https://img.shields.io/github/actions/workflow/status/bsalanie/bs-python-utils/main.yml?branch=main)](https://github.com/bsalanie/bs-python-utils/actions/workflows/main.yml?query=branch%3Amain) <!-- [![codecov](https://codecov.io/gh/bsalanie/bs-python-utils/branch/main/graph/badge.svg)](https://codecov.io/gh/bsalanie/bs-python-utils) --> <!-- [![Commit activity](https://img.shields.io/github/commit-activity/m/bsalanie/bs-python-utils)](https://img.shields.io/github/commit-activity/m/bsalanie/bs-python-utils) --> [![License](https://img.shields.io/github/license/bsalanie/bs-python-utils)](https://img.shields.io/github/license/bsalanie/bs-python-utils)

My Python utilities.

- **Github repository**: <https://github.com/bsalanie/bs-python-utils/>
- **Documentation** <https://bsalanie.github.io/bs-python-utils/>

### Release notes

#### 0.7 (June 18, 2024)
Added `make_lowertri` in `bsnputils`, and some utility code in `bs_plots`.

#### 0.6.4 (November 26, 2023)
Moved to Altair 5.0; made timing optional in `bs_opt`.

#### 0.6.3 (November 26, 2023)
Updated statsmodels dependency.

#### 0.6.1 and 0.6.2 (November 18, 2023)
Added a Matplotlib plot for discrete choice models, a boxplot for Altair, and a density plot for Seaborn.

#### 0.6 (November 9, 2023)

Added matrix heatmap plot in Altair and Streamlit utility code.

#### 0.5.5 (October 23, 2023)

Added `fstring***` in `bsutils`.

#### 0.5.2 (August 16, 2023)

Added two interpolation routines from values at the Chebyshev nodes in `chebyshev` module.

#### 0.5.1 (August 14, 2023)

Added two functions to draw random samples in `bsstats` module.

#### 0.5 (August 7, 2023)

Added 1-dimensional root finding in `chebyshev` module.

#### 0.4.2 (August 2, 2023)

Updated the docs.

#### 0.4.1 (July 23, 2023)

Only print if verbose in bivariate quantiles.

#### 0.4 (July 22, 2023)

Added bivariate quantiles and ranks à la optimal transportation.

#### 0.3 (July 21, 2023)

Added in Numpy utils a function to set upper and lower triangle to a scalar;
and `minimize_free` in `bs_opt`.

#### 0.2 (July 17, 2023)

Fixed printing in accelerated gradient descent.

#### 0.1 (June 20, 2023)

Added `grid_function` and the `chebyshev` module.

#### 0.0.6 (May 9, 2023)

Improved docs.

#### 0.0.5 (May 8, 2023)

Added `final_s` in `bsutils`.

#### 0.0.4 (May 2, 2023)

Added Legendre polynomials and quantile routines in `bsnputils`.

#### 0.0.3 (April 24, 2023)

Satisfied mypy.

#### 0.0.2 (April 24, 2023)

Fixed main PyPI page.