semvec/pypi-packages huggingface dataset:
modo 0.0.15
Bootstrap Framework for immutable machines
License: MIT
Keywords: bootstrap

# Modo

modo is a framework for building bootstrap images
for bootstrapping immutable machines by running docker containers

# Table of Contents
1. [Usage](##Usage)
2. [Installation](###Installation)
3. [Example](###Example)
4. [Workflow](docs/workflow.md)



## Usage

### Installation

```bash
pip install modo
```

for developing:

```bash
git clone git@bitbucket.org:mindcurv/modo.git
cd modo; virtualenv venv --python=python3
. venv/bin/activate
pip install -e .[dev]
python setup.py develop
```

### Example

```python

from modo import RoseCLI, SowRose, GrowRose

class SowExample(SowRose):

    def run(self):
       print(self.jinja_context)

class GrowExample(GrowRose):

    def run(self):
       print('@}->--')

cli = RoseCLI(SowExample, GrowExample)

if __name__ == '__main__':
    cli()
```    

for detailed workflow [click here](docs/workflow.md)
telegraf-kubernetes 0.1.6
Plugin for Telegraf for gathering statistics from Kubernetes
License: MIT

# telegraf-kubernetes
[![Build Status](https://travis-ci.com/pmastalerz/telegraf-kubernetes.svg?branch=master)](https://travis-ci.com/pmastalerz/telegraf-kubernetes/branches) [![License](https://img.shields.io/badge/license-MIT-brightgreen.svg)](https://github.com/pmastalerz/telegraf-kubernetes/blob/master/LICENSE)

Plugin for Telegraf for gathering statistics from Kubernetes

This script is in beta version, use with caution

## Installation
```bash
$ pip install telegraf-kubernetes
```

## Usage
```bash
$ telegraf-kubernetes --stats-url http://10.0.0.222:10255/stats/summary
```

### Optional parameters
* `--timeout` (int)

### Example telegraf configuration
```
[[inputs.exec]]
  commands = ["telegraf-kubernetes --stats-url http://10.0.7.222:10255/stats/summary"]
  data_format = "influx"
```

## License
See [LICENSE](https://github.com/pmastalerz/telegraf-kubernetes/blob/master/LICENSE) file.
ifeature 0.0.6
description
License: MIT
Requires Python: >=3, <4
Keywords: DeepLearning,313303303@qq.com

## 特征工程

### Install
```bash
pip install git+https://github.com/Jie-Yuan/iFeature.git
```

```
_clf = LGBMClassifier(n_estimators=1)
X = iris.data[:100, :]
y = iris.target[:100]
_clf.fit(X, y)
show_info = ['split_gain', 'internal_value', 'internal_count', 'leaf_count']
lgb.plot_tree(_clf.booster_, figsize=(60, 80), show_info=show_info)

model = _clf.booster_.dump_model()
tree_infos = model['tree_info'] # xgb_._Booster.get_dump()
```