semvec/pypi-packages huggingface dataset:
fairops 0.2.7
A Python library for FAIR AI/ML ops pipelines
License: MIT
Requires Python: >=3.10

# FAIRops
[![PyPi Build Status](https://github.com/acomphealth/fairops/actions/workflows/ci.yml/badge.svg)](https://pypi.org/project/fairops/)

[![CodeCov Status](https://codecov.io/gh/acomphealth/fairops/graph/badge.svg)](https://codecov.io/gh/acomphealth/fairops)

[![ReadTheDocs](https://readthedocs.org/projects/fairops/badge/?version=latest)](https://fairops.readthedocs.io/en/latest/?badge=latest)

# Installation
To install the fairops library:
```
pip install fairops
```

For developers of the fairops library:
```
conda env create -f environment_dev.yml
conda activate fairopsdev
```

# Programmatic Usage
Documentation for programmatic usage of the fairops library can be found at: https://fairops.readthedocs.io/en/latest

# CLI Usage
## Configure Environment Variables
The python-dotenv library is used for configuration management via environment variables (only needed if using methods that depend on the variable, such as Zenodo/Figshare). The environment variables can be set manually or within a configuration file. The order or priority is:

1. .env file within the current working directory
2. .env file located in /USERHOME/.config/fairops
3. Existing environment variables

If a .env configuration file is present, use of the fairops CLI will overwrite the existing environment variables. The .env files can be generated manually or via the CLI. For details on which modules need to be/can be configured, run:
```
fairops configure
```

The .env file can also be generated manually with the following variables:
```
FIGSHARE_API_TOKEN=yourtoken
ZENODO_API_TOKEN=yourtoken
```

To determine which variables are currently being used by the CLI, run:
```
fairops configure which
```

## Docker Image Preservation
To generate an archive/artifact for a Docker image and publish it to a repository, run the following command (requires Docker to be running locally):
```
fairops docker publish IMAGEREPO IMAGETAG
```

# Demo Applications
Two demo applications have been developed to showcase implementation and integration of the fairops library:

1. DataOps Demo: https://github.com/acomphealth/dataops
2. MLOps Demo: https://github.com/acomphealth/mlops
metaso-sdk 0.1.6
The official Python SDK for https://metaso.cn
License: MIT
Requires Python: >=3.8

# metaso-sdk

[![ci](https://github.com/meta-sota/metaso-sdk/workflows/ci/badge.svg)](https://github.com/meta-sota/metaso-sdk/actions?query=workflow%3Aci)
[![documentation](https://img.shields.io/badge/docs-mkdocs-708FCC.svg?style=flat)](https://meta-sota.github.io/metaso-sdk/)
[![pypi version](https://img.shields.io/pypi/v/metaso-sdk.svg)](https://pypi.org/project/metaso-sdk/)

[秘塔AI搜索](https://metaso.cn) Python SDK。在使用这个 SDK 之前，请先通过[API文档专题](https://metaso.cn/s/hXHmJkx)了解 HTTP 接口的更多细节。

## 安装

```bash
pip install metaso-sdk
```

## 配置 METASO_API_KEY

metaso-sdk 从环境变量 `METASO_API_KEY` 读取用于认证的 API 密钥，可以在 shell 里进行设置：

```bash
export METASO_API_KEY="mk-EE2..."
```

或者在 Python 代码里进行设置：

```python
import os
os.environ["METASO_API_KEY"] = "mk-EE2..."
```

## 搜索

### 搜索问题
```python
from metaso_sdk import search, Query
search(Query(question="abc"))
```

### 追问

```python
search(Query(question="广播公司", sessionId="8550018047390023680"))
```

### 流式返回

```python
for chunk in search(Query(question="abc"), stream=True):
    print(chunk)

...
{'type': 'heartbeat'}
{'text': '因此，“abc”可以指代字母表的前三个字母、美籍华裔、美国广播公司、一种音乐记谱法以及一种编程语言。具体含义需要根据上下文来确定。', 'type': 'append-text'}
```

## 专题

### 递归上传文件夹

```
from metaso_sdk import create_topic, upload_directory, Topic

topic = create_topic(Topic(name="functional programing"))
files = upload_directory(topic, "dir")
```

### 搜索特定专题

```python
from metaso_sdk import search, Query

query = Query(question="functional")
search(query, topic=topic)
```
mlgear 0.5
Utility scripts for machine learning
License: MIT
Requires Python: <4.0,>=3.7

## MLGear

Some utility functions to make ML with Python / Pandas / sklearn even easier

### Example Usage

```Python
from mlgear.cv import run_cv_model
from mlgear.models import runLGB
from mlgear.metrics import rmse

lgb_params = {'application': 'regression',
              'boosting': 'gbdt',
              'metric': 'rmse',
              'num_leaves': 15,
              'learning_rate': 0.01,
              'bagging_fraction': 0.9,
              'feature_fraction': 0.9,
              'verbosity': -1,
              'seed': 1,
              'lambda_l1': 1,
              'lambda_l2': 1,
              'early_stop': 20,
              'verbose_eval': 10,
              'num_rounds': 500,
              'num_threads': 3}

results = run_cv_model(train, test, target, runLGB, lgb_params, rmse)
```

### Installation

```
pip install mlgear
```

For development:

```
# Install poetry if you don't have it
pip install poetry

# Install dependencies
poetry install

# Build the package
poetry build

# Publish to PyPI
poetry publish
```