semvec/pypi-packages huggingface dataset:
pendragondi-api-pro 1.0.3
PendragoniDI API Pro: Context-rich duplicate detection (non-AI)
License: MIT
Requires Python: >=3.8

# PendragonDI API Pro

Context-rich, in-app duplicate detection for Python — real-time, local, and secure.

## 🚀 Quickstart (Simple Mode)

```python
from pendragondi_api_pro import duplicate_guard

@duplicate_guard
def call_api(...):
    ...
```

## ⚡️ Advanced Usage

```python
from pendragondi_api_pro import duplicate_guard_pro

@duplicate_guard_pro(window_ms=3000, capture_args=False, rate_limit_per_minute=60)
def call_api(...):
    ...
```

## 📂 Examples

- [examples/guard_simple.py](examples/guard_simple.py)
- [examples/guard_advanced.py](examples/guard_advanced.py)

## 📦 Export Reports

```python
from pendragondi_api_pro import get_event_log, export_html

events = get_event_log().snapshot()
export_html(events, "report.html")
```

## 🖥 Optional CLI (Windows Caveat)

You can also use the tool via command line:

```bash
python -m pendragondi_api_pro export --output myreport.html --format html
```

> ℹ️ Note: The `pendragondi-api-pro` CLI script may not resolve correctly on some Windows setups due to Python packaging limitations. We recommend using `python -m pendragondi_api_pro` for consistent cross-platform behavior.

## 🛠 Configuration

- `window_ms`: Deduplication window (ms, default: 2000)
- `capture_args`: Whether to log/redact args (default: True)
- `stack_depth`: How many frames to capture (default: 6)
- `rate_limit_per_minute`: Bucket per-op deduplication (optional)

## 🔒 Privacy

No telemetry. Redaction for PII by default. See `redact.py` for implementation.

## 💬 Support/Contact

- Open an issue or email [pendragondi@pendragondi.dev](mailto:pendragondi@pendragondi.dev)

---

## 📜 License

[MIT License](LICENSE)

---

## 🧭 Why PendragonDI?

PendragonDI API Pro was built to solve the *next layer* of API waste and developer friction — giving you real-time, context-rich duplicate detection with privacy and speed at the core.

No agents. No data sales. No telemetry. Just the signal you need to debug smarter.

---

## 🤝 Contributing

Contributions are welcome! If you have suggestions for improvements or find a bug, please open an issue or submit a pull request.

When contributing:
- Fork the repository and create your branch from `main`
- Ensure any new code has appropriate type hints and docstrings
- Run the test suite and add tests for new functionality
- Submit a descriptive pull request so changes can be reviewed efficiently

---

## 💖 Support the Project

PendragonDI API Pro is open-source and free to use.  
If you’ve found it useful and would like to support ongoing development, you can sponsor us on GitHub:  
[![Sponsor on GitHub](https://img.shields.io/badge/Sponsor-💖-pink?style=flat)](https://github.com/sponsors/jinpendragon)
zentorch 5.1.0
zentorch : A PyTorch* extension for AMD EPYC CPUs.
License: MIT
Keywords: pytorch tensor machine learning plugin ZenDNN AMD

__The latest ZenDNN Plugin for PyTorch* (zentorch) 5.1 is here!__

The ZenDNN PyTorch plugin is called zentorch. Combined with PyTorch's torch.compile, zentorch transforms deep learning pipelines into finely-tuned, AMD-specific engines, delivering unparalleled efficiency and speed for large-scale inference workloads.

This upgrade continues the focus on optimizing inference with Recommender Systems and Large Language Models on AMD EPYC™ CPUs.
ZenDNN includes AMD EPYC™ enhancements for bfloat16 performance, expanded support for cutting-edge models like Llama 3.1 and 3.2, Microsoft Phi, and more as well as support for INT4 quantized datatype.
This includes the advanced Activation-Aware Weight Quantization (AWQ) algorithm for LLMs and quantized support for the DLRM-v2 model with int8 weights.

Under the hood, ZenDNN’s enhanced AMD-specific optimizations operate at every level. In addition to highly optimized operator microkernels, these include comprehensive graph optimizations including pattern identification, graph reordering, and fusions.
They also incorporate optimized embedding bag kernels and enhanced zenMatMul matrix splitting strategies which leverage the AMD EPYC™ microarchitecture to deliver enhanced throughput and latency.

The vLLM-ZenTorch plugin extends these benefits to the vLLM inference engine, enabling plug-and-play acceleration of large language model inference on AMD EPYC™ CPUs. By integrating ZenTorch with vLLM, users can achieve significant throughput improvements for LLM workloads with zero code changes.

The zentorch 5.1 release plugs seamlessly with PyTorch versions from 2.7 and 2.6, offering a high-performance experience for deep learning on AMD EPYC™ platforms.

## Support

We welcome feedback, suggestions, and bug reports. Should you have any of the these, please kindly file an issue on the ZenDNN Plugin for PyTorch Github page [here](https://github.com/amd/ZenDNN-pytorch-plugin/issues)

## License

AMD copyrighted code in ZenDNN is subject to the [Apache-2.0, MIT, or BSD-3-Clause](https://github.com/amd/ZenDNN-pytorch-plugin/blob/main/LICENSE) licenses; consult the source code file headers for the applicable license.  Third party copyrighted code in ZenDNN is subject to the licenses set forth in the source code file headers of such code.