semvec/pypi-packages huggingface dataset:
faber-ml 0.0.6
Quick start ml experimentation pipelines
Requires Python: >=3.6

[![Pylint](https://github.com/pinata-brad/faber/actions/workflows/pylint.yml/badge.svg?branch=main&event=push)](https://github.com/pinata-brad/faber/actions/workflows/pylint.yml)

# faber

### what is faber? 
faber is a lightweight pipelining tool for machine learning and data projects

### why though?
so our pipelines look clean - like the below
```python
from faber import node
from data_science.nodes.quick_ml import *

ml_pipe = [
    node(
        prepare_ml,
        ['df_processed', 'ml_params'],
        ['df_master_train', 'df_master_test'],
    ),
    node(
        train_models,
        ['df_master_train', 'ml_params'],
        ['ml'],
    ),
    node(
        get_best_params,
        ['ml'],
        ['best_params']
    ),
]
```
and our data is connected externally and defined in a yaml file; meaning if a data location changes only one place is changed
. wildcards are also supported with {{}}
```yaml
df_raw:
    filepath: data/00_raw/df_raw.csv
    read_func: read_csv_pandas
    
df_processed:
    filepath: data/01_master/df_processed_{{run_number}}.csv
    read_func: read_csv_pandas
    write_func: write_csv_pandas
    
output_profile:
    filepath: data/02_insight/output_profile.csv
    read_func: read_csv_pandas
    write_func: write_csv_pandas
    
ml:
    filepath: data/models/ml_object.pickle
    read_func: read_pickle
    write_func: write_pickle
    
```

### install using
 
```shell script
git clone https://github.com/pinata-brad/faber.git
cd faber
python3 setup.py sdist bdist_wheel && pip install dist/faber-0.0.3.tar.gz 
```
pywevolor 0.0.2
Python wrapper for interfaceing with the Wevolor API
Requires Python: >=3.6

# PyWevolor

This package is a lightweight wrapper around the local API for the [Wevolor](https://wevolor.com/) device.

Wevolor is a WiFi to bluetooth bridge device for controlling Levolor motorized blinds via the Levolor 6-channel bluetooth remote.

## Requirements

Wevolor device with firmware 5.4 or higher, accessible on the local network.

## Usage

Instantiate a `Wevolor` object with the host IP address.

Commands are triggered with methods on the `Wevolor` object, e.g.:

```python
from pywevolor import Wevolor

wevolor = Wevolor(host='192.168.1.1')

# Send open command to channel 3 on the remote.
wevolor.open_blind(3)

# Send open command to channels 1 and 4 on the remote.
wevolor.open_blinds([1, 4])

# Send close command to channels 3 and 4 on the remote.
wevolor.close_blinds([3, 4])

# Get status info from Wevolor API
wevolor.get_status()
```
pckt 0.1.1
O pacote pckt retornará os resultados da tabuada de um número
Requires Python: >=3.5

# pckt

DescriÃ§Ã£o:
O projeto pckt tem uma estrutura:
    Processo:
        - Entrada de um nÃºmero
        - SaÃ­da da operaÃ§Ã£o

## InstalaÃ§Ã£o

Use o gerenciador de pacotes [pip](https://pip.pypa.io/en/stable/) para instalar o pacote pckt

'''bash
pip install pckt
'''

## Author
Daniel
nvflops 0.0.1.dev2
The New Version of the Free-style machine-Learning Open Platform System
Requires Python: >=3.7

# nvflops
The **N**ew **V**ersion of the **F**ree-style machine-**L**earning **O**pen **P**latform **S**ystem

# Key concepts

- All results from participants are recorded with mandatory metadata, such as parents, creator, creator's role.
- Blobs of results (the large binary information) are stored in S3 or other dedicate blob storage space.  Their addresses (bucket and object in S3 use case) are stored in the mandatory metadata.
- Parents and children are many-to-many relationship.  That is, one result can have multiple parents (aggregation operation).  One parents can have multiple results (client's local training).
- Results can include additional metadata (custom_field) as flat dictionary.  Search can perform against those custom_field.

Aggregation style operation as shown in this graph.
![alt text](https://github.com/IsaacYangSLA/nvflops/blob/main/docs/resources/key_concepts.png?raw=true)

# Package requirements
In current implementation, vflops requires a blob storage space and only S3 is supported.  Full S3 authentication with AWS IAM credential will be implemented in the future.  For local development and test, you can install [minio][https://min.io/download#/linux).  Minio Python package is required, but it should be easy to replace it with boto3.

The backend database can be any SQL database supported by SQLAlchemy.  However, you will need to setup your own database management system.

# Installation
We plan to have nvflops wheel package available on [PyPi](https://pypi.org/project/nvflops/).  You can choose to install via source codes.

  ```
  pip install -e .
  ```
  in nvflops folder (same level as setup.py)