semvec/pypi-packages huggingface dataset:
automl_gs 0.2.1
Provide an input CSV and a target field to predict, generate a model + code to run it.
License: MIT
Keywords: deep learning


Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see *exactly* how the data is processed, how the model is constructed, and you can make tweaks as necessary.

automl-gs is an AutoML tool which, unlike Microsoft's [NNI](https://github.com/Microsoft/nni), Uber's [Ludwig](https://github.com/uber/ludwig), and [TPOT](https://github.com/EpistasisLab/tpot), offers a *zero code/model definition interface* to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow.

Nowadays, the cost of computing many different models and hyperparameters is much lower than the oppertunity cost of an data scientist's time. automl-gs is a Python 3 module designed to abstract away the common approaches to transforming tabular data, architecting machine learning/deep learning models, and performing random hyperparameter searches to identify the best-performing model. This allows data scientists and researchers to better utilize their time on model performance optimization.

* Generates native Python code; no platform lock-in, and no need to use automl-gs after the model script is created.
* Train model configurations super-fast *for free* using a **TPU** in Google Colaboratory.
* Handles messy datasets that normally require manual intervention, such as datetime/categorical encoding and spaced/parathesized column names.
* Each part of the generated model pipeline is its own function w/ docstrings, making it much easier to integrate into production workflows.
* Extremely detailed metrics reporting for every trial stored in a tidy CSV, allowing you to identify and visualize model strengths and weaknesses.
* Correct serialization of data pipeline encoders on disk (i.e. no pickled Python objects!)
* Retrain the generated model on new data without making any code/pipeline changes.
* Quit the hyperparameter search at any time, as the results are saved after each trial.

The models generated by automl-gs are intended to give a very strong *baseline* for solving a given problem; they're not the end-all-be-all that often accompanies the AutoML hype, but the resulting code is easily tweakable to improve from the baseline.
colosseum-exporter-launcher 0.2.5
launches exporters for colosseum via docker container
License: MIT
Requires Python: >=3.5
Keywords: colosseum,prometheus,exporter

# colosseum exporter launcher

### 필요사항
- Docker 18.06.0-ce or higher
- Python 3.6 or higher
- hawkeye에서 부여한 모니터링 대상 머신의 ID(hawkeye 웹페이지에서 머신 등록시 발급해줌)
- kafka를 모니터링할 경우 kafka는 `9092`포트로 실행해야 하며, 수집한 메트릭을 송신하기 위해 `9308` 포트가 개방돼 있어야 한다.
- MySQL을 모니터링할 경우 MySQL은 `3306`포트로 실행해야 하며, 수집한 메트릭을 송신하기 위해 `9104` 포트가 개방돼 있어야 한다.
- elasticsearch를 모니터링할 경우 elasticsearch는 `9300`포트로 실행해야 하며, 수집한 메트릭을 송신하기 위해 `9108` 포트가 개방돼 있어야 한다.
- redis를 모니터링할 경우 redis는 `6379`포트로 실행해야 하며, 수집한 메트릭을 송신하기 위해 `9121` 포트가 개방돼 있어야 한다.

### 실행방법
```$python launcher.py {모니터링 대상 머신의 ID}```

### MySQL 모니터링 하기
- 모니터링 대상에 MySQL이 있다면 `PROCESS`와 `REPLICATION CLIENT`의 권한을 가진 계정이 필요하다.
- NOTE: It is recommended to set a max connection limit for the user to avoid overloading the server with monitoring scrapes under heavy load.
- 모니터링 대상에 MySQL이 있을 경우 프로그램 실행 과정에서 MySQL 계정의 ID와 Password를 입력하는 프롬프트가 나타난다. `PROCESS`와 `REPLICATION CLIENT`의 권한을 가진 계정의 ID와 Password를 입력하면 계속 진행된다.
- 다음은 해당 권한을 가진 계정을 새로 생성하는 구문이다.
```
CREATE USER 'exporter'@'localhost' IDENTIFIED BY 'XXXXXXXX' WITH MAX_USER_CONNECTIONS 3;
GRANT PROCESS, REPLICATION CLIENT, SELECT ON *.* TO 'exporter'@'localhost';
```

(끝)