semvec/pypi-packages huggingface dataset:
bad-master 0.0.9
Master web application for the BAD framework.
Requires Python: >=3.6

# bad-master: master server process for the BAD framework

The BAD master (**bad_master**) is the main server process for the BAD
framework.

The BAD master process is responsible for accepting connections from
clients and orchestrating the experiment suite execution.

Copyright (c) 2020 Sivam Pasupathipillai <s.pasupathipillai@unitn.it>.

All rights reserved.
bad-worker 0.0.7
Worker web application for the BAD framework.
Requires Python: >=3.6

# bad-worker: worker web application for the BAD framework

A BAD worker (**bad_worker**) is an auxiliary server process for the
BAD framework.

BAD workers are responsible for executing anomaly detection
experiments in a distributed fashion.

Copyright (c) 2020 Sivam Pasupathipillai <s.pasupathipillai@unitn.it>.

All rights reserved.
auto-mlops 0.1.12
Deploy your model in one line of code
Requires Python: >=3.6,<4.0

# Deploy your ML pipelines effortlessly, scalably and reliably

Datarmada aims at **removing all the friction that comes with Machine Learning in production**.
We understand that Data Scientists are not trained to do that, and sometimes they are
not even attracted by this Software Engineering / DevOps aspect.

**This package aims at deploying your machine learning pipeline on a server in one line**.

Your pipeline is deployed on an OVH server so that you own your data and it is compliant with European regulations.

## Installation
Install the package python using pip
```
pip install auto-mlops
```

## Deploy your pipeline

Import the ```Deployer``` class from the package.

```python
from auto_mlops import Deployer
deployer = Deployer()
```

Now, deploy your pipeline by passing to the ```deploy``` method a list containing all of its elements.
The pipeline elements (except for the last one) must be either :
- A function returning transformed data if your pipeline element doesn't need to be fitted
- An instance of a class implementing a ```transform``` method

The last element of the pipeline must be an instance of a class a ```predict``` methods, such as a 
scikit-learn or a Keras model.

```python

from sklearn.linear_model import LogisticRegression

def preprocess(raw_data):
    # preprocess the data
    return preprocessed_data

class Featurizer:
    def transform(self, preprocessed_data):
        # transform the data
        return featurized_data

log_reg = LogisticRegression()
log_reg.fit(featurized_data, y)

deployer.deploy([preprocess, featurizer, log_reg])

```
**Remember your elements must be fitted if they need to !**

**You will be asked for your email address** so that we can keep track of the ownership of the pipelines deployed, and give you
access to monitoring functions in the future.

```python
deployer.deploy([preprocess, featurizer, log_reg])

>> Please enter your email address so that we can keep track of your pipelines:
you@example.com

>> Your pipeline has been deployed to https://cloud.datarmada.com/id
```

You can access your route whenever you want through ```deployer.route```
## Make predictions

You can now send data to the route by making a POST request as following
```python
import requests

res = requests.post(
  "https://cloud.datarmada.com/id",
  json = {
    "data": your_raw_data
  }
)

print(res.json())

>> { "prediction" : prediction }
```

It may be possible that one of the package you are using is not available in the environment we are deploying your model. 
If you receive an error saying so, please email us at contact@datarmada.com so that we can fix it.
pg-stream-copy 1.5.4
RTB House apps pg_stream_copy
Requires Python: ~=3.7

# PG Stream Copy
[![Build Status](https://travis-ci.com/rtbhouse-apps/pg-stream-copy.svg?branch=master)](https://travis-ci.com/rtbhouse-apps/pg-stream-copy)
[![codecov](https://codecov.io/gh/rtbhouse-apps/pg-stream-copy/branch/master/graph/badge.svg)](https://codecov.io/gh/rtbhouse-apps/pg-stream-copy)
[![PyPI](https://img.shields.io/pypi/v/pg-stream-copy)](https://pypi.org/project/pg-stream-copy/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pg-stream-copy)](https://pypi.org/project/pg-stream-copy/)

Fast COPY TO postgresql table directly from python by converting input data to bytes and stream to psycopg2 cursor using `COPY <table> FROM STDIN BINARY`

### Benchmark:
The test with 1 mln rows of different column types on docker environment gave results:
* ~21.5s for `pg_stream_copy`
* ~54s for `psycopg2.extras.execute_values`

### Usage:
```python
from datetime import date
from psycopg2 import connect
from pg_stream_copy import Schema, WriterEncoder


conn = connect('postgresql://postgres@localhost')
cursor = conn.cursor()
table_name = 'public.example_table'

cursor.execute(f'''
    CREATE TABLE {table_name} (
        _smallint SMALLINT NULL,
        _integer INTEGER NULL,
        _bigint BIGINT NULL,
        _float DOUBLE PRECISION NULL,
        _numeric NUMERIC NULL,
        _character_varying CHARACTER VARYING NULL,
        _date DATE NULL
    );
''')
schema = Schema.load_from_table(cursor, table_name)

with WriterEncoder(cursor, table_name, schema) as writer_encoder:
    writer_encoder.append_tuple((2, 3, 4, 2.34, 'foo bar', date(2019, 2, 1)))
    writer_encoder.append_dict({
        '_smallint': 200,
        '_integer': 300,
        '_bigint': 400,
        '_float': 234,
        '_numeric': Decimal("-12.34")
        '_character_varying': 'bar baz',
        '_date': date(2019, 2, 3),
    })

conn.commit()
conn.close()

```

### Supported PostgreSQL types:
* boolean
* smallint
* integer
* bigint
* double precision
* numeric
* character varying
* text
* date
* timestamp
* timestamp with time zone
* json
* jsonb


### Development:
```bash
# prepare env
docker-compose run py bash
python -m venv venv
pip install -e .[dev,e2e]
# run tests
pytest tests/
```
If you need to test different PostgreSQL and Python version, you can use env vars:
```bash
PYTHON_VERSION=3.7 PG_VERSION=10 docker-compose build
PYTHON_VERSION=3.7 PG_VERSION=10 docker-compose run py ...
```
or use CI script:
```bash
PG_VERSION=11 PYTHON_VERSION=3.7 ./bin/tests.sh
```
currently pg_stream_copy is supporting Python 3.7 and 3.8 and PostgreSQL v10, v11, v12

# Changelog

## 1.2.0
* Added new data type - numeric

## 1.1.0
* Added new data types - text, json, jsonb

## 1.0.3
* Added README to pypi.org
* Added LICENCE, CHANGELOG

## 1.0.2
* Updated pypi token

## 1.0.1
* Added Travis CI

## 1.0.0
* Initial version