semvec/pypi-packages huggingface dataset:
ga4gh.vrsatile.pydantic 0.2.0
"Translation of the GA4GH VRS and VRSATILE Schemas to a Pydantic data model"
Requires Python: >=3.8
Keywords: bioinformatics,ga4gh,genomics,hgvs,spdi,variation

# vrsatile-pydantic
Translation of the GA4GH [VRS](https://vrs.ga4gh.org/en/stable/) and [VRSATILE](https://vrsatile.readthedocs.io/en/latest/) schemas to a Pydantic data model

The ga4gh/vrsatile/pydantic repo depends on VRS and VRSATILE models, and therefore each ga4gh.vrsatile.pydantic package on PyPI uses a particular version of VRS and VRSATILE. The correspondences between the packages may be summarized as:

| ga4gh.vrsatile.pydantic branch | ga4gh.vrsatile.pydantic version | VRS version | VRSATILE version |
| ---- | --- | --- | --- |
| main | 0.2.X | [1.X.X](https://github.com/ga4gh/vrs) | [main](https://github.com/ga4gh/vrsatile/tree/main) |
| metaschema-update | 0.1.X | [metaschema-update](https://github.com/ga4gh/vrs/tree/metaschema-update) | [metaschema-update](https://github.com/ga4gh/vrsatile/tree/metaschema-update) |


## Installation

vrsatile-pydantic is available via PyPI:
```commandline
pip install ga4gh.vrsatile.pydantic
```

## Developer Instructions

Following are sections include instructions specifically for developers.

### Installation

For a development install, we recommend using Pipenv. See the
[pipenv docs](https://pipenv-fork.readthedocs.io/en/latest/#install-pipenv-today)
for direction on installing pipenv in your compute environment.

Once installed, clone the repo and initialize the environment:

```commandline
git clone https://github.com/ga4gh/vrsatile-pydantic
cd vrsatile-pydantic
pipenv shell
pipenv update
pipenv install --dev
```

### Init coding style tests

Code style is managed by [flake8](https://github.com/PyCQA/flake8) and checked prior to commit.

We use [pre-commit](https://pre-commit.com/#usage) to run conformance tests.

This ensures:

* Check code style
* Check for added large files
* Detect AWS Credentials
* Detect Private Key

Before first commit run:

```commandline
pre-commit install
```

### Running unit tests

Running unit tests is as easy as pytest.

```commandline
pipenv run pytest tests
```
bioimageit-formats 0.2.0
Manage data formats for BioImageIT project
Requires Python: >=3.7


# BioImageIT formats

Manage data formats for the BioImageIT project

# Documentation

The documentation is available [here](https://bioimageit.github.io/bioimageit_formats/).

# Development

## Run tests

Test are written with unittest python package. All tests are located in the subpackage bioimagepy/tests.
Run tests with the command:

```bash
cd bioimageit_core
pipenv run python -m unittest discover -v
```

## Build the documentation

The documentation is written with Sphinx. To build is run the commands:

```bash
cd docs
pipenv run sphinx-build -b html ./source ./build
```

## Generate the requirements.txt

The `requirements.txt` file is generated from Pipenv with:

```bash
pipenv lock --requirements > requirements.txt
```
openrai 0.0.6
Responsible AI framework.

.. image:: https://raw.githubusercontent.com/cisco-open/ResponsibleAI/main/docs/images/rai_logo_blue3.png

OpenRAI is a python library that is designed to help AI developers in
various aspects of responsible AI development. It consists of a core API
and a corresponding web-based dashboard application. OpenRAI can easily be
integrated into AI development projects and measures various metrics for
an AI project during each phase of AI development, from data quality
assessment to model selection based on performance, fairness and
robustness criteria. In addition, it provides interactive tools and
visualizations to understand and explain AI models and provides a
generic framework to perform various types of analysis including
adversarial robustness.

Note: - The dashboard GUI is currently designed around 1920x1080
displays

Documentation
=============

Documentation is available online:
https://responsibleai.readthedocs.io/en/latest/

To Install
==========

1) The project uses sqlite as storage and requires no additional
   installation for this.

    ``pip install openrai``

2) If you need to use the dashboard as well you must  install the additional requirements

    ``pip install openrai[dashboard]``

3) To start using the library you must configure the folder where the results will be stored and be used by the dashboard

    ``export DATABASE_FOLDER=YOUR_LOCATION``

4) To start the dashboard server use the following command:

    ``python3 -m RAIDashboard``


Demos:
======

::

   We have added a few demo projects to showcase some of the capabilities of OpenRAI.
   to run any of the demos please use 'python demo_filename'. For instance : 
   python ./demos/adult_demo_grid_search.py

   below is a short description of the provided demos:

   File: adult_demo_grid_search.py 
   Description: This demo uses the Adults dataset (https://archive.ics.uci.edu/ml/datasets/adult) to show how OpenRAI can be used in model selection

   File: image_class_analysis.py 
   Description: this demo uses Cifar10 dataset and shows how openRAI can be used to evaluate image classification tasks

   File: image_class_training.py 
   Description: this demo uses Cifar10 dataset and shows how openRAI can be used monitor image processing tasks during training


   File: tabular_class_console.py 
   Description: this demo shows how openRAI can be used without the dashboard to calculate and report on the metrics for a machine learning task

   File: text.py 
   File: text_output.py 
   Description: these demos show how openRAI and its dashboard can be used for evaluating the natural language modeling tasks

    

Cisco Research, Emerging Tech and Incubations,

Cisco Systems Inc.