semvec/pypi-packages huggingface dataset:
datasmartly-cat 1.16.5
CAT is a very simple tool that helps you automate tests for your data.

# What is CAT?

Automate tests for your data! CAT is a simple tool that helps you to automate tests for your data.


🐱 Compare data accross systems (e.g. SQL server with Power BI)

🐱 Easy configuration for tests, no learning curve

🐱 Install in one minute, first tests in a few minutes

🐱 Free for interactive* use (even for production data)

🐱 Automation friendly (Azure DevOps, GitLab, ...)

🐱 Desktop app, CLI, PowerShell module, Python package


* *Scheduled runs, CI/CD pipelines etc. require a license*

<br />

To get started quickly and easily, follow the tutorial in our docs:

https://docs.justcat.it/docs/get-started/python-module/install/

<br />

# Prerequisites

* You need to have [.NET 8 runtime](https://dotnet.microsoft.com/en-us/download/dotnet/8.0) installed in order to use this package.

* We depend on pythonnet package. If you cannot install it for any reason, you'll not succeed to use CAT package either.

# Considerations

The core of CAT software is written using .NET. This package is a "wrapper". We try to make your experience as much as possible "Python-like", but you may encounter 

* C# naming conventions in returned types (such as in test definitions, test results etc.)

* .NET objects e.g., for collections.

It is not a problem, but if you think it is, you can still run CAT tests with one line of code and then work with JSON output (it contains just everything about the tests).



# Links

* Home page: <a href="https://justcat.it">https://justcat.it</a>
* Documentation: <a href="https://docs.justcat.it">https://docs.justcat.it</a>

<br />



**HAPPY TESTING!**<br />
CAT team
voyager-mcp 0.1.1
A Voyager-inspired MCP server for local CLI skill library
Requires Python: >=3.10

# Voyager-MCP

A Voyager-inspired MCP server that enables coding agents to build and immediately use CLI tools without restarting sessions.

## Features

- **Single Tool**: The tool exposes a single tool `run_shell_command`.
- **Skill library**: Agents can add new commands in `~/.config/voyager/bin/`.
- **Dynamic schema**: The `run_shell_command` tool description can be configured from `~/.config/voyager/prompt.txt`, and it also dynamically loads available executables in `~/.config/voyager/bin/`.
- **Configurable**: Each executable can provide a `.desc` file, which will be included in the `run_shell_command` tool description.
- **Secure Execution**: Uses subprocess with argument lists to prevent shell injection.

## Installation

```bash
# add it to claude code
claude mcp add voyager uvx voyager-mcp

# try it with mcp inspector
npx -y @modelcontextprotocol/inspector uvx voyager-mcp
```

## LICENSE

MIT

## Citation
```
@article{wang2023voyager,
  title   = {Voyager: An Open-Ended Embodied Agent with Large Language Models},
  author  = {Guanzhi Wang and Yuqi Xie and Yunfan Jiang and Ajay Mandlekar and Chaowei Xiao and Yuke Zhu and Linxi Fan and Anima Anandkumar},
  year    = {2023},
  journal = {arXiv preprint arXiv: Arxiv-2305.16291}
}
```
navin 0.1
A simple local server with static HTML/CSS, started via 'navin runserver' command.
Requires Python: >=3.7

# navin

A simple Python package to start a local server and serve a styled HTML page.

## Installation

```bash
pip install .
```

## Usage

Start the local server:

```bash
navin runserver
```

Visit [http://localhost:2401](http://localhost:2401) to view the page.

## Version

0.1
mem-scan 1.0.0
A command-line tool for monitoring system and GPU memory usage during process execution
Requires Python: >=3.8
Keywords: cli, gpu, memory, monitoring, profiling

# mem-scan

一个用于监控系统和 GPU 内存使用情况的命令行工具，特别适用于监控 Python 程序执行期间的内存特征。

## 功能特性

- 实时监控 CPU 内存和 GPU 内存使用情况
- 支持监控任意命令的执行过程
- 提供峰值、最小值和平均值统计
- 支持将报告保存为 JSON 格式
- 可配置监控间隔和 GPU 设备
- 支持静默模式执行

## 安装

### 使用 uv 安装（推荐）

```bash
# 从源码安装
uv pip install -e .

# 或者直接安装
uv pip install mem-scan
```

### 使用 pip 安装

```bash
pip install -e .
```

## 依赖项

- `psutil>=5.8.0` - 系统内存监控
- `pynvml>=11.0.0` - GPU 内存监控

## 使用方法

### 基本用法

```bash
# 监控 Python 脚本执行
mem-scan python your_script.py

# 监控带参数的命令
mem-scan python train.py --batch-size 128 --epochs 10

# 监控模型量化过程
mem-scan python -m modelopt.onnx.quantization --onnx_path=model.onnx --quantize_mode=fp8
```

### 高级选项

```bash
# 指定 GPU 设备
mem-scan --gpu-id 1 python eval.py

# 调整监控间隔（秒）
mem-scan --interval 0.05 python train.py

# 保存报告到文件
mem-scan --output memory_report.json python inference.py

# 静默模式（不显示命令输出）
mem-scan --quiet --output report.json python benchmark.py

# 查看版本信息
mem-scan --version

# 查看帮助信息
mem-scan --help
```

## 输出示例

```
Starting memory monitoring for: python train.py --batch-size 128
GPU ID: 0, Interval: 0.1s
------------------------------------------------------------

============================================================
MEMORY USAGE REPORT
============================================================
Execution Time: 45.32 seconds
Return Code: 0

GPU Memory Usage:
  Peak: 8.45 GB
  Min:  0.12 GB
  Avg:  6.78 GB

Host Memory Usage:
  Peak: 12.34 GB
  Min:  8.90 GB
  Avg:  10.45 GB

Report saved to: memory_report.json
```

## JSON 报告格式

当使用 `--output` 选项时，会生成包含详细信息的 JSON 报告：

```json
{
  "execution_time": 45.32,
  "return_code": 0,
  "memory_usage": {
    "gpu_peak_gb": 8.45,
    "gpu_min_gb": 0.12,
    "gpu_avg_gb": 6.78,
    "host_peak_gb": 12.34,
    "host_min_gb": 8.90,
    "host_avg_gb": 10.45
  },
  "command": "python train.py --batch-size 128",
  "timestamp": "2024-01-20 14:30:45"
}
```

## 开发

### 开发环境设置

```bash
# 克隆仓库
git clone <repository-url>
cd mem_scan

# 使用 uv 安装开发依赖
uv pip install -e ".[dev]"

# 运行测试
pytest

# 代码格式化
black src/ tests/

# 类型检查
mypy src/
```

### 构建和发布

```bash
# 构建包
uv build

# 发布到 PyPI（需要配置认证）
uv publish
```

## 注意事项

1. GPU 监控需要安装 NVIDIA 驱动和 `pynvml` 库
2. 如果没有 GPU 或无法初始化 GPU 监控，工具会显示警告但仍会监控 CPU 内存
3. 监控间隔设置过小可能会影响被监控程序的性能
4. 使用 `--quiet` 模式时，被监控程序的输出会被捕获但不显示

## 许可证

MIT License

## 贡献

欢迎提交 Issue 和 Pull Request！
djprod 0.2.2
Yet another production-ready Django project.
Requires Python: >=3.11

# djprod

### Yet another production-ready Django project.

`djprod` provides a CLI for creating Django projects with a clean structure, ready for real-world deployments. It includes preconfigured settings, static directories, and environment support.

## Features

- CLI to generate Django apps and projects
- Pre-separated settings: `base`, `local`, `production`
- `.env` support via `django-environ`
- PostgreSQL, Sentry, S3 configuration ready
- Includes basic templates and static structure

## Installation

```bash
pip install djprod
````

## Usage

```bash
djprod new myproject
cd myproject
````

Rename `env.example` to `.env`:

```bash
mv env.example .env
```

Run the project using [`uv`](https://github.com/astral-sh/uv):

```bash
uv run manage.py runserver
```