semvec/pypi-packages huggingface dataset:
stackit-resourcemanager 0.7.0
Resource Manager API
Requires Python: <4.0,>=3.9

# stackit.resourcemanager
API v2 to manage resource containers - organizations, folders, projects incl. labels

### Resource Management
STACKIT resource management handles the terms _Organization_, _Folder_, _Project_, _Label_, and the hierarchical structure between them. Technically, organizations, 
folders, and projects are _Resource Containers_ to which a _Label_ can be attached to. The STACKIT _Resource Manager_ provides CRUD endpoints to query and to modify the state.

### Organizations
STACKIT organizations are the base element to create and to use cloud-resources. An organization is bound to one customer account. Organizations have a lifecycle.
- Organizations are always the root node in resource hierarchy and do not have a parent

### Projects
STACKIT projects are needed to use cloud-resources. Projects serve as wrapper for underlying technical structures and processes. Projects have a lifecycle. Projects compared to folders may have different policies.
- Projects are optional, but mandatory for cloud-resource usage
- A project can be created having either an organization, or a folder as parent
- A project must not have a project as parent
- Project names under the same parent must not be unique
- Root organization cannot be changed

### Label
STACKIT labels are key-value pairs including a resource container reference. Labels can be defined and attached freely to resource containers by which resources can be organized and queried.
- Policy-based, immutable labels may exists

For more information, please visit [https://support.stackit.cloud/servicedesk](https://support.stackit.cloud/servicedesk)

This package is part of the STACKIT Python SDK. For additional information, please visit the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.


## Installation & Usage
### pip install

```sh
pip install stackit-resourcemanager
```

Then import the package:
```python
import stackit.resourcemanager
```

## Getting Started

[Examples](https://github.com/stackitcloud/stackit-sdk-python/tree/main/examples) for the usage of the package can be found in the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.
b10-kernel 0.1.6
Baseten Kernel Library
Requires Python: >=3.12
Keywords: machine learning, gpu, cuda, kernels, pytorch

# b10-kernel

Baseten Kernel Library - High-performance GPU kernels for AI inference workloads.

## Installation

### From PyPI
```bash
pip install b10-kernel
```

**Requirements:**
- Python >= 3.12
- CUDA-compatible GPU and drivers
- PyTorch >= 2.8.0 with CUDA support

### From Source
```bash
git clone <repository>
cd mp/kernels/b10-kernel
pip install -e .
```

### For Development
```bash
# Install with test dependencies
pip install -e .[test]

# Install with all development dependencies  
pip install -e .[dev]
```

## Development guide
- Build the library from source
```bash
make build
make rebuild
```
- Run unit tests
```bash
make test
```
- Format code
```bash
make format
```

## Kernel Development Workflow
Steps to add a new kernel:
- Implement the kernel in `csrc`
- Expose the interface in `include/b10_kernel_ops.h`
- Create torch extension in `csrc/common_extension.cc`
- Update `CMakeLists.txt` to include new CUDA source
- Expose Python interface in `python/b10_kernel/xxx.py` and `python/b10_kernel/__init__.py`
- Add unit test for the kernel in `test/test_xxx.py`
- Add benchmark script for the kernel in `benchmark/bench_xxx.py`
- Format code with `make format`

## PyPI Release
```bash
# update to expected version
make update_version VERSION=0.1.x
make build
make rename_wheel
make upload_wheel
```
iflow-mcp_ffmpeg-mcp 0.1.4
使用ffmpeg,对视频进行裁剪，拼接，滤镜等操作
Requires Python: >=3.10

# FFmpeg-MCP
Using ffmpeg command line to achieve an mcp server, can be very convenient, through the dialogue to achieve the local video search, tailoring, stitching, playback and other functions

<a href="https://glama.ai/mcp/servers/@video-creator/ffmpeg-mcp">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@video-creator/ffmpeg-mcp/badge" alt="FFmpeg-Server MCP server" />
</a>

## Support Tools
The server implements the following tools: <br/>
- `find_video_path`
  The parameters are directory and file name, file name can be complete, or is not suffixed, recursive search in the directory, return the full path
- `get_video_info`
  The parameters are video path, return the video info, linkes duration/fps/codec/width/height.
- `clip_video`
  The parameter is the file path, start time, end time or duration, and returns the trimmed file path
- `concat_videos`
  The parameters are the list of files, the output path, and if the video elements in the list of files, such as width, height, frame rate, etc., are consistent, quick mode synthesis is automatically used
- `play_video`
  Play video/audio with ffplay, support many format, like mov/mp4/avi/mkv/3gp, video_path: video path speed: play rate loop: play count
- `overlay_video`
  Two video overlay. <br/>
  background_video: backgroud video path <br/>
  overlay_video: front video path <br/>
  output_path: output video path<br/>
  position: relative location<br/>
  dx: x offset<br/>
  dy: y offset<br/>
- `scale_video`
  Video scale. <br/>
  video_path: in video path <br/>
  width: out video width, -2 keep aspect <br/>
  height: out video height, -2 keep aspect <br/>
  output_path: output video path <br/>
- `extract_frames_from_video`
  Extract images from a video.<br/>
  Parameters: <br/>
  video_path (str): The path to the video.<br/>
  fps (int): Extract one frame every specified number of seconds. If set to 0, extract all frames; if set to 1, extract one frame per second.<br/>
  output_folder (str): The directory where the images will be saved.<br/>
  format (int): The format of the extracted images; 0: PNG, 1: JPG, 2: WEBP.<br/>
  total_frames (int): The maximum number of frames to extract. If set to 0, there is no limit<br/>
<br/>
More features are coming

## Installation procedure
1. Download project
```
git clone  https://github.com/video-creator/ffmpeg-mcp.git
cd ffmpeg-mcp
uv sync
```

2. Configuration in Cline
```
{
  "mcpServers": {
    "ffmpeg-mcp": {
      "autoApprove": [],
      "disabled": false,
      "timeout": 60,
      "command": "uv",
      "args": [
        "--directory",
        "/Users/xxx/Downloads/ffmpeg-mcp",
        "run",
        "ffmpeg-mcp"
      ],
      "transportType": "stdio"
    }
  }
}
```
Note: the value:`/Users/XXX/Downloads/ffmpeg` in args  need to replace the actual download ffmpeg-mcp directory

## Supported platforms
Currently, only macos platforms are supported, including ARM64 or x86_64
smart-errorx 0.1.6
Smart-errorx is the error handler library and help to detect the error and after detect the error give the solution of error.
Requires Python: >=3.11

🧠 SmartErrorX — AI-Powered Error Detection & Fix Suggestions.



SmartErrorX is an intelligent **Python error-handling library** That automatically detects in user code, analyzes them using **OpenAI**, and displays **the cause and fix suggestions** in a clean, formatted terminal output using the **Rich** Library.


⚙️ How It Works:-

When you decorate any Python function with `@catch`, SmartErrorX automatically:

1. Executes your function.
2. If an error occurs:
   - Captures the **error message**, **function source code**, and **arguments**.
   - Sends that data to the **OpenAI model** using your API key from `.env`.
   - Receives a structured response containing:
     - 🧩 **Cause** – why the error occurred.
     - 🔧 **Fix** – how to correct it.
   - Displays both with colorful Rich panels.