semvec/pypi-packages huggingface dataset:
mkwikidata 0.14
Utility functions for working with Wikidata
Keywords: sparql wikidata

############
mkwikidata
############

Run SPARQL queries on Wikidata or other services

.. code:: python

        query = """
            SELECT ?unicode_charater
            WHERE 
            {
                wd:$id wdt:P487 ?unicode_charater.
            }
        """
        result = mkwikidata.run_query(query, params={ "id": "Q44" }) 
        result # 🍺
jphmm-tools 0.1.4
Tools for extracting information from jpHMM (http://jphmm.gobics.de) output.
Keywords: jpHMM,HIV,subtyping,recombinants

# jpHMM tools

__jphmm_tools__ provides methods for extracting information 
from [jpHMM](http://jphmm.gobics.de) [[Schulz *et al.* NAR 2012]](https://academic.oup.com/nar/article/40/W1/W193/1073895) output.

It implements tool for
* converting [Los Alamos](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html) alignment file
into a reference file suitable for jpHMM:
```bash
jphmm_ref -h
```
* aligning sequences based on the jpHMM output:
```bash
jphmm_align -h
```
* extracting sequence subtype information from the jpHMM output:
```bash
jphmm_subtype -h
```
servz 0.0.0.40
DO NOT USE - This is a sample program
Requires Python: >=3.6

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# Servz
##  Machine Learning Model Serving

This library is a pre-alpha right now

The intent of this package is to provide a machine learning deplpoyment layer for model predictions.

## Features
* pipeline driven  
*  deployment via task runner
*  deployment via flask or other endpoint
*  MLFlow based deployment
*  Seldon deployment (in progress)

## Pipeline formats

### Pipeline Composer
Appends all the pipelines from the yaml file(s)

```buildoutcfg
"pipelines": self._pipelines

return [self.__build_flow(pipeline) for pipeline in flows['pipelines']]

self._pipelines.append(pipeline)
```

### Artifact Builder

```buildoutcfg
"artifact": self._workflows

_res = self._build(pipelines=kwargs.get('packager'))

results = [self._compile_workflow(pipe) for pipe in pipelines]
        self._workflows = pipelines

for task in pipe['workflow']:
    do stuff
success = self.__build_prefect_flow()
return {
            'success': success,
        }
```

### Endpoint Appender

### Packager

### Package Publisher

## structure
````
/core - core files for servz
/orchestration_artifact_builder - artifact packager
/orchestration_artifact_deployer - deployment runner
/packager - manifest packager
/pipeline - loading and validation of serving pipeline
/server_templates - artifacts for artifact builder to construct endpoints
/tests - unit tests and e2e tests

```