semvec/pypi-packages huggingface dataset:
maven-artifact 0.3.5
Download and resolve maven artifacts
Requires Python: >=3.8
Keywords: artifact, download, maven, mvn

# maven-artifact
A python library to download and resolve maven artifacts.

# Installation and Usage

 * Install using pip: `pip install maven-artifact'
 * Usage: run `maven-artifact` 

# Requirements

 * See `pyproject.toml` for details on required Python versions, pip packages etc.

# Contributing

 * Fork repo
 * Before submitting a PR
   * Perform formatting (black):  `hatch run lint:black src tests`
   * Run linter (flake8): `hatch run lint:flake8`
   * Run tests:
     * all: `hatch run test:pytest --cov=maven_artifact`
     * unit only: `hatch run test:pytest --cov=maven_artifact  tests/unit`
     * integration only: `hatch run test:pytest --cov=maven_artifact  tests/integration`
algosrl 0.1.2
Algorithm OS RL: An RL module for AlgOS
Requires Python: >=3.10

# AlgOSRL: RL Module For AlgOS
This module utilises [AlgOS](https://github.com/llewynS/algos) as its core and provides an interface for implementing reinforcement learning algorithms. 

AlgOS provides standardised:
* experimental interface,
* self organising algorithmic segments via [AbstractParametered](https://github.com/llewynS/algos/blob/main/algos/interfaces/abstractbaseclasses/hyperparameter.py#L142) and [AbstractComponent](https://github.com/llewynS/algos/blob/main/algos/interfaces/abstractbaseclasses/component.py),
* logging (can replace tensorboard logging with internal logger as .record interface is same),
* central DB schema that supports PostgreSQL and MySQL (integrates with Optuna DB),
* factory methods for dependency injection,
* hyperparameter for automated hyperparameter optimisation, and
* input parsing for automated script generation. 

AlgOSRL extends this to work in the reinforcement learning space by:
* providing an RLExperiment and RLComponent interface to extend,
* a numpy replay buffer that offers indexed access,
* wrappers for goal based RL that work with the maze environments from [robotics_gymnasium](https://robotics.farama.org/),
* wrapper for [stable baselines 3](https://stable-baselines3.readthedocs.io/en/master/) that allow the use of their off-policy agents, and
* an evaluator for goals based on their coverage. 

A wrapper for [Tianshou](https://github.com/thu-ml/tianshou) also provided but has limited testing due to its incompatibility with Dict based inputs with its replay buffer and the integration of its replay buffer. 

## Installation
Install via pip:
```sh
pip install algosrl
```

Install from the cloned directory:

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

## Documentation
TBA

## Examples
The examples folder contains an example runner script (runs a single experiment) and an example optimisation script (runs a study to find optimal hyperparameters). The optimisation script will run locally by default. 

## Roadmap
* Provide Colab or Hugging face examples.
* Generate documentation.
* Include probabalistic curriculum learning code.


## Citing the Project

To cite this repository in publications:

```bibtex
@misc{algos,
      title={AlgOS: Algorithm Operating System}, 
      author={Llewyn Salt and Marcus Gallagher},
      year={2025},
      eprint={2504.04909},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2504.04909}, 
}
```
llm-fragments-symbex 0.1
LLM fragment loader for Python symbols
Requires Python: >=3.9

# llm-fragments-symbex

[![PyPI](https://img.shields.io/pypi/v/llm-fragments-symbex.svg)](https://pypi.org/project/llm-fragments-symbex/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-fragments-symbex?include_prereleases&label=changelog)](https://github.com/simonw/llm-fragments-symbex/releases)
[![Tests](https://github.com/simonw/llm-fragments-symbex/actions/workflows/test.yml/badge.svg)](https://github.com/simonw/llm-fragments-symbex/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-fragments-symbex/blob/main/LICENSE)

LLM fragment loader for Python symbols

## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/).
```bash
llm install llm-fragments-symbex
```
## Usage

Use the fragment loader like this:

```bash
llm -f symbex:path/to/directory
```
This will load the function, class and method signatures of every Python file in that directory (and its subdirectories) along with their docstrings and concatenate them into a single fragment.

It uses the same AST parsing logic as [Symbex](https://github.com/simonw/symbex).

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd llm-fragments-symbex
python -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
python -m pip install -e '.[test]'
```
To run the tests:
```bash
python -m pytest
```
xspike 0.3.10
工具包，包含 Redis 安装、基于 Redis 的GPU 任务队列管理等功能
Requires Python: >=3.7
Keywords: gpu, queuer, redis


完整版介绍可以看 [xspike——GPU 任务排队、一键批量启动实验......](https://zhuanlan.zhihu.com/p/685132608/preview?comment=0&catalog=0)


使用以下命令安装 xspike：
```
pip install xspike
```

xspike 目前可支持的功能有：

- [x] GPU 任务排队 （基于 Redis）
- [x] 批量启动实验脚本
- [x] 钉钉通知
- [x] Comet.ml 管理

## 安装 Redis、启动 Redis、Redis 数据维护
在命令行输入
```
xspike
```

按照提示依次执行 4、5、6 号操作即可部署好 Redis 环境



## 启动 GPU 任务排队

```
import xspike as x


queuer = x.GPUQueuer()
queuer.start()

# Your code is here
# ......

queuer.close()
```

建议在原有的代码最前端插入上述代码，并在实验结束后调用 queuer.close() 来释放当前任务

GPUQueuer() 的初始化参数说明：

```
visible_cuda (str, optional): 可见的 GPU 编号，多个 GPU 编号用逗号分隔，如 "0,1,2,3". 默认为 "-1"，即全部可见.
n_gpus (int, optional): 需要的 GPU 数量. 默认为 1.
memo (str, optional): 任务备注. 默认为 "no memo".
```

## 实验计划
即插即用，对原来代码无任何影响

只需要在代码根目录下创建一个名为 “exp_plans” 的文件夹，里边存放每个实验计划要运行的所有脚本，并以 “xxx.sh” 来命名，样例可前往知乎博客查看


如果要取消某个实验，直接注释即可，批量启动时，为了避免 GPU 竞争，设置了实验启动间隔，每分钟启动一个实验

启动时，只需要在终端输入：
```
xx
```

即可自动扫描当前目录下的所有计划文件，并在选择后进行启动




## 钉钉通知
集成了 DingtalkChatbot 的通知功能，设置好 DINGDING_ACCESS_TOKEN 和 DINGDING_SECRET 环境变量或直接在函数调用时通过参数传递，然后调用下面的代码进行通知：

```
x.notice("Hello World!")
```

## Comet.ml 实验管理
目前这里只实现了 Comet 环境的创建和文件夹的上传

```
comet_client = CometClient(project_name="Default", api_key="xxx", exp_name="MoE Baseline")
```

上传文件夹，比如上传本次实验的代码，方便结果复现
将文件夹下的所有 ".py", ".yml" 文件进行上传（包括子文件夹）

```
comet_client.log_directory("./")
```

## 依赖
xspike 非常轻量化，依赖包很少，可以减少与现有的环境发生依赖冲突的情况
```
 nvitop
 redis
 rich
 psutil
 jsonlines
 setproctitle
 dingtalkchatbot
 comet_ml
```
## 未来计划
- [ ] 集成 Comet.ml 实验记录，使其通过最少的代码可以用在各大框架中，并提供便捷的记录模板（Callback）
- [ ] 尽量在不增加依赖的情况下，集成实验中常用的工具类和方法
test-workday-mcpserver-py 0.1.1
WORKDAY is an HRMS
Requires Python: >=3.12

# MCP Development Tools Setup Guide

## Prerequisites

- Python 3.x
- UV package installer
- Claude Desktop (for integration)

## Installation

1. Install UV (if not already installed):
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

2. Install dependencies using UV:
```bash
uv add requirement.txt
uv add "mcp[cli]"
```

## Usage

### Running MCP Command
To execute the MCP command using UV:
```bash
uv run mcp
```

### Running MCP Inspector
To start the MCP inspector:
```bash
mcp dev server.py
```

## Claude Desktop Integration

### Basic Installation
Install your server in Claude Desktop:
```bash
mcp install server.py
```

### Advanced Installation Options

#### Custom Server Name
To install with a custom name:
```bash
mcp install server.py --name "My Analytics Server"
```

#### Environment Variables
There are two ways to configure environment variables:

1. Direct specification:
```bash
mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
```

2. Using an environment file:
```bash
mcp install server.py -f .env
```

## Additional Resources

For more detailed information about MCP tools and their usage, please refer to the official documentation.
dtools.tuples 0.27.1
### Developer Tools - Tuple-like data structures
Requires Python: >=3.12
Keywords: tuple, functional, ftuple, FTuple

# Developer Tools - tuple based data structures

Python package containing modules implementing queue-like data
structures.

- **Repositories**
  - [dtools.tuples][1] project on *PyPI*
  - [Source code][2] on *GitHub*
- **Detailed documentation**
  - [Detailed API documentation][3] on *GH-Pages*

This project is part of the
[Developer Tools for Python][4] **dtools.** namespace project.

## Overview

Tuple-based data structures.

### Functional Tuples

Data structures wrapping a Python tuple giving it a functional
interface.

- *module* `dtools.tuples.ftuples`
  - *class* FTuple: Functional Tuple
    - immutable tuple-like data structure
    - with a functional interface

______________________________________________________________________

[1]: https://pypi.org/project/dtools.tuples/
[2]: https://github.com/grscheller/dtools-tuples/
[3]: https://grscheller.github.io/dtools-docs/tuples/
[4]: https://github.com/grscheller/dtools-docs
heliodash 0.0.17
Heliosphere Dashboard (HelioDash) is an open-source tool for visualizing heliosphere-related data.
Requires Python: >=3.10

# Heliosphere Dashboard (HelioDash)

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://heliodash.streamlit.app/) [![PyPI](https://img.shields.io/pypi/v/heliodash.svg)](https://pypi.org/pypi/heliodash/)
   
Heliosphere Dashboard (HelioDash) is an open-source tool for visualizing heliosphere-related data.

## Running Locally

See [this repo](https://github.com/mgjeon/heliodash_streamlit)