semvec/pypi-packages huggingface dataset:
octopulse-logger 0.2.1
A Python library to setup clean structured logs, for local development, and production with Google Cloud Logging.

# Octopulse Python Logger Package

This is a Python library to setup clean structured logs, for local development, and production with Google Cloud Logging.

This logger uses these external packages:
* `google-cloud-logging`
* `structlog`
* `colorama`
* `python-json-logger`

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the package.

```bash
pip install octopulse-logger
```

## Setup

To be able to sent logs to GCP, you need to provide authentication credentials to your application code by setting the environment variable `GOOGLE_APPLICATION_CREDENTIALS`.  
Replace `[PATH]` with the file path of the JSON file that contains your service account key.
```commandline
export GOOGLE_APPLICATION_CREDENTIALS="[PATH]"
```

## Usage

You have the possibility to add global arguments that will add contextual information to all logs.
```python
from octopulse_logger import logger_setup

# setup the logger
logger_setup.setup('logger_name', setup_google_logging=True, setup_stdout_logging=True, logger_level='DEBUG', my_global_var="foobar")
```
Setup `setup_google_logging` and/or `setup_stdout_logging` to send logs to Google Cloud Logging and/or std output.

Then you can start to log with [default logger](https://docs.python.org/3/howto/logging.html) or [structlog](https://www.structlog.org/en/stable/) :
```python
import logging
import structlog

structlog_logger = structlog.getLogger('logger_name')
structlog_logger.warning('Warning !', value_a=1)
# or
basic_logger = logging.getLogger('logger_name')
basic_logger.error('An error occurred :(')
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
AICovidVN-2021 0.0.1
A small example package
Requires Python: >=3.6

![visitors](https://visitor-badge.glitch.me/badge?page_id=Tson99.AICovidVN)
## 1. Working tree
```
└── AICovidVN
    ├── data
    │   ├── aicv115m_public_test
    │   └── aicv115m_public_train
    ├── models
    ├── results
    ├── venv
    │   ├── bin
    │   ├── include
    │   ├── lib
    │   └── lib64 -> lib
    ├── requirements.txt
    ├── resnet50.py
    ├── predict.py
    ├── README.md
    ├── models.py
    ├── Training_AICovidVN_notebook.ipynb
    ├── Training_AICovidVN_resnetish18_notebook.ipynb
    └── .gitignore
```
## 2. Install packages:
> pip install -r requirements
## 3. Dependencies
> Python 3.7
## 4. Datasets
* [Train set](https://drive.google.com/file/d/1MPhz3zYl2yefCq-J5XySbFJt99BfKIZD/view)
* [Test set](https://drive.google.com/file/d/1UrMudzopA3CyR1Ih2J63Kfi2mY_0uhRK/view)
## 5. Running
* Training colab: `Training_AICovidVN_notebook.ipynb`
* Print result: `python predict.py`
faust-bootstrap 1.2.0
Faust bootstrap app
Requires Python: >=3.6

# faust-bootstrap
This repository is a wrapper implementation of the [Faust](https://github.com/robinhood/faust) library.
It aims at easy development of Faust applications.
It uses the same interface as our common-kafka-streams project and can be deployed using the same Helm chart and configuration options.
Thereby, you can develop your Kafka applications in Java or Python as you wish without having to deal with different deployment configurations.

### Getting Started

You can install faust-bootstrap from pypi.

``pip install faust-bootstrap``

### Example

[Here](https://github.com/bakdata/common-kafka-streams-demo) can you get an use case example of both libraries.