semvec/pypi-packages huggingface dataset:
image-ascii-arusteph 0.0.1
For generating A E S T H E T I C ascii art
Requires Python: >=3.7

# Aesthetic ASCII

Generate **A E S T H E T I C** ASCII art.

## Instructions

1. Install:

```
pip install aesthetic-ascii
```

2. Generate an aesthetic ASCII visual:

```python
from aesthetic_ascii import synthesize

# initialize drive object (to generate visuals)
drive = synthesize.Drive()
# generate a ASCII visual (dark_mode optional)
drive.generate(dark_mode=True)
# save to png
drive.to_png('aesthetic.png')
```

3. Ride the space skyway home to 80s Miami
proto-pixplus-pb2 1.0.20
A package for Pix+ services, which contain proto types
Requires Python: >=3.8

#### File with proto types for Pix+ services.

In order to push changes to the `proto_pixplus_pb2.py`, be sure that:

- The `proto_pixplus.proto` file contains the changes, which were compiled **successfully**.
- New .py file is compiled and overwritten `proto_pixplus_pb2.py` module with current using this command **`protoc -I=. --python_out=. ./proto_pixplus.proto`**

Please, import needed types with an `ptype` alias
```shell
import proto_pixplus_pb2 as ptype
```

#### Deploy new version

To deploy new version, be sure you have the latest version of PyPA’s build installed:
```shell
python3 -m pip install --upgrade build
```


Go to the `/proto_pypi` folder and run next command:
```shell
python3 -m build
```

This command should output a lot of text and once completed should generate two folder in the directory:
**dist** and **proto_pixplus_pb2.egg-info**

To upload new version of the package, also be sure to update the **version** of the package. See the semantics of the versioning at *https://semver.org/*

Install **twine**, to upload all the archives under dist:
```shell
python3 -m pip install --upgrade twine
```

After, you can push new version of the package with `python3 -m twine upload --skip-existing dist/* --verbose`. You will be prompted for *username* and *password*. 

Once uploaded, you can check your new version at *https://pypi.org/manage/project/proto-pixplus-pb2/releases/*

If any issues are met, look at the complete [tutorial](https://packaging.python.org/en/latest/tutorials/packaging-projects/).
yaket 1.3.10
YAML Keras Trainer for quick AI development
Requires Python: >=3.7

# YAKET: Yaml Keras Trainer (or Yet Another Keras Trainer)

[![pipeline status](https://gitlab.com/andreafavia/yaket/badges/main/pipeline.svg)](https://gitlab.com/andreafavia/yaket/-/commits/main) 
[![coverage report](https://gitlab.com/andreafavia/yaket/badges/main/coverage.svg)](https://gitlab.com/andreafavia/yaket/-/commits/main) 
[![Latest Release](https://gitlab.com/andreafavia/yaket/-/badges/release.svg)](https://gitlab.com/andreafavia/yaket/-/releases)

## Installation 💻
    pip install yaket

## Description 🔥
Yaket is a lightweight and simple module to train Keras modules by defining parameters directly using YAML file. 

YAML parameters are validated using Pydantic, hence typos or not allowed parameters will throw errors at the beginning of the execution.
This allows developer to focus uniquely on what matters: data and model development.

Data Scientists and ML Engineer won't need to add manually all training parameters, such as optimizer, callbacks, schedulers, thus reducing the
likelihood of human-induced code bugs.

## Features 🎊

1. Train models with tensorflow default optimizers, metrics, callbacks, and losses.
2. Convert the saved model to **ONNX** or **Tensorflow-Lite** for on edge-deploymnet or faster inference.
3. Quickly use distributed multi-gpu and TPU training with `tf.distributed.strategy` *(Experimental)*
4. Train models with custom modules defined in python script.
5. Log training parameters, models, and results using `mlflow.tensorflow.autolog()` module. The run will be saved in `mlruns` folder. 
6. Save the model in a particular folder and particular format (i.e., SavedModel,H5, or .pb)
7. Train with `sample_weight_mode = 'temporal'` when training sequence models.

8. More to come!

## Visuals 📖

The YAML file contains most of the parameters used in Keras model.fit, such as epochs, verbose, callbacks. Below an example:

```yaml
    autolog: False
    optimizer: 
    - Adam:
       learning_rate: 0.001
    batch_size: 64 
    loss: 
    SparseCategoricalCrossentropy: 
        from_logits: True
    callbacks:
        - EarlyStopping:
            monitor: val_accuracy
            patience: 2
            restore_best_weights: True  
    verbose: 1 
    epochs: 100
    shuffle: True
    accelerator: mgpu 
```

The usage is very simple using python:

```python
    
    model = ... # define your tf.keras.Model

    # Define path to yaml file
    path = "/yaket/examples/files/trainer.yaml"

    # Initialize trainer
    trainer = Trainer(
        config_path=path,
        train_dataset=(x_train, y_train),
        val_dataset=(x_test, y_test),
        model=model, 
    )
    trainer.train() # train based on the parameters defined in the yaml file
    trainer.clear_ram() # clear RAM after training

    trainer.convert_model(format_model = 'onnx') # Convert to ONNX

    
```

Other scenarios are visible in **examples** folder.

## License
MIT License
python-faytheclient 0.0.15
Faythe API Client Library
Requires Python: >=3.6

# Faythe Python Client

[![license](https://img.shields.io/badge/license-Apache%20v2.0-blue.svg)](LICENSE) [![PyPI version](https://badge.fury.io/py/faytheclient.svg)](https://badge.fury.io/py/faytheclient)

This is a client library for [Faythe](https://github.com/vCloud-DFTBA/faythe) built on the its API. It provides a Python API (the faytheclient module).

- **Python 3 only.**
- **This client is compatible with Faythe version >= v0.4.4.**

## Installation

Install the client:

```bash
python setup.py install
```

This package can be found on [PyPI](https://pypi.python.org/pypi/python-faytheclient).

```bash
pip install python-faytheclient
```

## Examples

The example can be found [here](./examples/).

- Create a copy of the environment variables example. Then adjust it:

```bash
cp .env.example .env
```

- Source the .env file:

```bash
source .env
```

- Run the example:

```bash
python example.py
```