semvec/pypi-packages huggingface dataset:
rocketloghandler 0.1.1
Send python logs to RocketChat server.
License: MIT

# rocketloghandler

Send your Python logs to RocketChat.

Implements a custom handler for the Python logging module.

For a full working example see `rocketlog_test.py`.

## Using Logging in multiple modules
Ensure there is a hierarchical structure in the logger name separated by dots. The name is set when calling 
`logging.getLogger`.

## Usage example
```python
import logging

from rocketloghandler.rocketloghandler import RocketLogHandler

rocketHandler = RocketLog(USER_ID, TOKEN, SERVER, CHANNEL_NAME, USER_ALIAS)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
log = logging.getLogger(__name__)
rocketHandler.setFormatter(formatter)
log.addHandler(rocketHandler)
log.warning("Never say no to the panda! Otherwise: https://www.youtube.com/watch?v=XYz3sl0LEA4")
```

## Contributing
Feel free to contribute to this project. A 
[contribution guide](https://mygit.th-deg.de/fwahl/rocketlog/blob/master/CONTRIBUTING.md) is available. Please add 
yourself to the [list of contributors](https://mygit.th-deg.de/fwahl/rocketlog/blob/master/CONTRIBUTORS.md) when you 
submit the pull request.
r-wrapper 0.0.2
Seamlessly integrate R packages in Python by allowing intuitive importing and providing opinionated data type conversion functions.
License: MIT
Requires Python: >=3.6,<4.0

# r_wrapper

[![pypi version](https://img.shields.io/pypi/v/r_wrapper.svg)](https://pypi.org/project/r_wrapper/)
[![license](https://img.shields.io/pypi/l/r_wrapper.svg)](https://pypi.org/project/r_wrapper/)

Warning: still highly experimental and likely to break.

Seamlessly integrate R packages in Python by allowing intuitive importing and providing opinionated data type conversion functions.


## Installation

```bash
pip install r_wrapper
```


## Usage

For example, accessing `biomaRt` can be as simple as follows:
```python
from r_wrapper import biomaRt

snp_list = ['rs7329174', 'rs4948523', 'rs479445']
ensembl = biomaRt.useMart('ENSEMBL_MART_SNP', dataset='hsapiens_snp')

df = biomaRt.getBM(
    attributes=['refsnp_id', 'chr_name', 'chrom_start', 'consequence_type_tv'],
    filters='snp_filter', values=snp_list, mart=ensembl)
```

Check the `r_wrapper/tests/` directory for more examples showing how to rewrite R scripts in Python.


## Tests

A comprehensive test suite aims at providing stability and avoiding regressions.
The examples in `r_wrapper/tests/` are validated using `pytest`.

Run tests as follows:
```bash
$ pytest r_wrapper/
```
brontes 0.0.2
Brontes is your helping cyclops for pytorch models training.
License: MIT

[![Build Status](https://travis-ci.org/PhosphorylatedRabbits/brontes.svg?branch=master)](https://travis-ci.org/PhosphorylatedRabbits/brontes)
[![codecov](https://codecov.io/gh/PhosphorylatedRabbits/brontes/branch/master/graph/badge.svg)](https://codecov.io/gh/PhosphorylatedRabbits/brontes)
[![Updates](https://pyup.io/repos/github/PhosphorylatedRabbits/brontes/shield.svg)](https://pyup.io/repos/github/PhosphorylatedRabbits/brontes)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/brontes.svg)](https://badge.fury.io/py/brontes)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/6dc4922e69954e96820dae93ed704736)](https://www.codacy.com/manual/drugilsberg/brontes?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=PhosphorylatedRabbits/brontes&amp;utm_campaign=Badge_Grade)

# Brontes

Brontes is your helping cyclops for pytorch models training.
It is based on [pytorch_lightning](https://github.com/williamFalcon/pytorch-lightning)
and comes with an example script in [examples/mnist/run.py](examples/mnist/run.py) that you can adapt for your needs.

Additionally, there is an example in [examples/mlflow](examples/mlflow) which describes how to use mlflow with Brontes.

## Just do this

Define your dataset_loaders as a dictionary: use `train`, `val` and optionally a `test` split:

```python
dataset_loaders = {
    'train':
        torch.utils.data.DataLoader(
            datasets.MNIST(
                root=DATA_PATH,
                train=True,
                download=True
            ),
            batch_size=BATCH_SIZE,
            shuffle=True,
            num_workers=NUMBER_OF_WORKERS
        ),
    'val':
        torch.utils.data.DataLoader(
            datasets.MNIST(
                root=DATA_PATH,
                train=False,
                download=True
            ),
            batch_size=BATCH_SIZE,
            shuffle=True,
            num_workers=NUMBER_OF_WORKERS
        )
}
```

define your acrhitecture as a `torch.nn.Module` (or pick an existing architecture):

```python
base_model = brontes.examples.Net()
```

and wrap it with Brontes:

```python
brontes_model = Brontes(
    model=base_model,
    loss=torch.nn.NLLLoss(),
    data_loaders=dataset_loaders,
    optimizers=optimizer
)
```

finally train the model using `pytorch_lighning`

```python
trainer = pl.Trainer(max_nb_epochs=EPOCHS)
trainer.fit(brontes_model)
```

## Development setup

Setup the conda environment

```sh
conda env create -f conda.yml
```

Activate it:

```sh
conda activate brontes
```