semvec/pypi-packages huggingface dataset:
gangplank 0.5.0
Prometheus metrics collectors for Keras
Requires Python: >=3.10

# Gangplank
## Exposing Keras Metrics to Prometheus
[Keras](https://keras.io/) is a library for creating artificial neural networks. [Prometheus](https://prometheus.io/) is a monitoring system that pulls metrics from applications and infrastructure.
Gangplank is a bridge from Keras to Prometheus that exports Keras training, evaluation and inference metrics to Prometheus.

Keras metrics are exposed in two ways:
 * Training and testing metrics use Keras [callbacks](https://keras.io/api/callbacks/) to push metrics to a Prometheus [Pushgateway](https://prometheus.io/docs/instrumenting/pushing/).
 * Inference metrics are exposed by instrumenting a proxy of a Keras model.

The [examples](https://github.com/hammingweight/gangplank/tree/main/examples) demonstrate both techniques to export metrics to Prometheus.

## What Metrics are exported?
### Training Metrics
During training, the following metrics are exported:
 * The number of completed training epochs
 * The time spent training
 * The number of model weights (both trainable and non-trainable)
 * The model's loss
 * All metrics configured for the model (e.g. accuracy for a classification model or mean absolute error for a regression model)
 * (Optionally) A histogram of the model's trainable weights at the end of the training run

### Testing (Evaluation) Metrics
For testing (i.e. evaluation), the following metrics are exported:
 * The time spent testing
 * The model's loss
 * All metrics configured for the model (accuracy, mean absolute error, etc.)
 * (Optionally) A histogram of the model's trainable weights

### Prediction (Inference) Metrics
A deployed model can expose the following metrics:
 * The total number of model predictions
 * The time spent doing inference
 * (Optionally) Drift metrics; e.g. a *p*-value

## Installing Gangplank
Gangplank can be installed from PyPI

```
pip install gangplank
```

The installation will also install Keras. Keras needs a tensor arithmetic backend like TensorFlow, JAX or PyTorch. You can install a
backend at the same time as installing Gangplank by running one of the following

```
pip install gangplank[tensorflow]
pip install gangplank[jax]
pip install gangplank[torch]
```

Note: Running, e.g., `pip install gangplank[jax]` will install a CPU-only version of JAX. If you want, say, CUDA support you should install JAX separately

```
pip install gangplank
pip install jax[cuda12]
```

Similar comments apply to TensorFlow and PyTorch.

## Examples
Examples of using Gangplank can be found [here](https://github.com/hammingweight/gangplank/tree/main/examples).

## Acknowledgement
The example code uses a model from ["Deep Learning with Python, Second Edition"](https://www.manning.com/books/deep-learning-with-python-second-edition) by François Chollet.
Gangplank was inspired by the same book's coverage of callbacks and TensorBoard.
zensols.clinicamr 0.0.1
Clincial Domain Abstract Meaning Representation Graphs
Keywords: tooling

# Clincial Domain Abstract Meaning Representation Graphs

[![PyPI][pypi-badge]][pypi-link]
[![Python 3.9][python39-badge]][python39-link]

This package parses clinical notes in to Abstract Meaning Representation Graphs
(AMR).  It uses the following packages to create the graphs and features:

* THYME [AMR SPRING parser] to create the graphs
* [MedCAT] for entity linking `CUI` attribution nodes
* [MedSecId] for the clinical note sectioning
* [Zensols Natural Language Parsing] for language parsing and feature creation


## Documentation

See the [full documentation](https://plandes.github.io/clinicamr/index.html).
The [API reference](https://plandes.github.io/clinicamr/api.html) is also
available.


## Obtaining

The library can be installed with pip from the [pypi] repository:
```bash
pip3 install zensols.clinicamr
```


## Changelog

An extensive changelog is available [here](CHANGELOG.md).


## Citation

This package and the [docker](./docker) image uses the original [AMR SPRING
parser] source code base from the paper *"One SPRING to Rule Them Both:
Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline"*:

```bibtex
@inproceedings{bevilacquaOneSPRINGRule2021,
  title = {One {{SPRING}} to {{Rule Them Both}}: {{Symmetric AMR Semantic Parsing}} and {{Generation}} without a {{Complex Pipeline}}},
  shorttitle = {One {{SPRING}} to {{Rule Them Both}}},
  booktitle = {Proceedings of the {{AAAI Conference}} on {{Artificial Intelligence}}},
  author = {Bevilacqua, Michele and Blloshmi, Rexhina and Navigli, Roberto},
  date = {2021-05-18},
  volume = {35},
  number = {14},
  pages = {12564--12573},
  location = {Virtual},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/17489},
  urldate = {2022-07-28}
}
```


## License

[MIT License](LICENSE.md)

Copyright (c) 2024 - 2025 Paul Landes


<!-- links -->
[pypi]: https://pypi.org/project/zensols.clinicamr/
[pypi-link]: https://pypi.python.org/pypi/zensols.clinicamr
[pypi-badge]: https://img.shields.io/pypi/v/zensols.clinicamr.svg
[python39-badge]: https://img.shields.io/badge/python-3.9-blue.svg
[python39-link]: https://www.python.org/downloads/release/python-390

[AMR SPRING parser]: https://github.com/SapienzaNLP/spring
[MedCAT]: https://github.com/CogStack/MedCAT
[MedSecId]: https://github.com/plandes/mimicsid
[Zensols Natural Language Parsing]: https://github.com/plandes/nlparse
hermes-framing-nodes 1.0.2
Hermes framing nodes translating between streams and frames
Requires Python: >=3.10

# Hermes framing nodes
[![PyPI version](https://badge.fury.io/py/hermes-framing-nodes.svg)](https://pypi.org/project/hermes-framing-nodes)

A collection of nodes to provide translation between stream interfaces.

## Installation

### From PyPI

Install the package directly from PyPI using pip:

```bash
pip install hermes-framing-nodes
```

### From Source

Clone the repository and install dependencies:

```bash
git clone https://github.com/node-hermes/hermes-framing-nodes
pip install -e hermes-framing-nodes
```

## Development

This project depends on UV for managing dependencies.
Make sure you have UV installed and set up in your environment.

You can find more information about UV [here](https://docs.astral.sh/uv/getting-started/installation/).

```bash
uv venv
```

```bash
uv sync --all-extras --dev
```