semvec/pypi-packages huggingface dataset:
fastbench 0.1.5
A pure-python based benchmarking package for Python 🤪
License: MIT
Requires Python: <4.0,>=3.8
Keywords: bench, fastbench, benchmark, pure, python, simple, performance, package, code, execution, time, cpu, memory, usage, lightweight, api, measure, function, track, monitor, test, efficiency, speed, algorithm, script, development, debugging, optimized, library, analyze, profile, scalable, reliable, tool, optimizations, profiling, debug, evaluate, performance testing, profiler, codebase, analytical, scalability, efficiency, analyzing, optimization, code optimization, proficiency, analytic, performance analysis

<h1 align="center">
  <br>
  <img src="https://raw.githubusercontent.com/itsmeadarsh2008/fastbench/main/fastbench.svg" width="200" height="200">
  <br>
  FastBench
  <br>
  <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/fastbench">
  <img alt="GitHub repo size" src="https://img.shields.io/github/repo-size/itsmeadarsh2008/fastbench">
  <br>
</h1>

FastBench is a high-performance Python package for benchmarking code execution time, CPU usage, and memory usage. It's implemented in Python for simplicity and provides a simple API for measuring the performance of your Python code.

## ✨ Features

- ⏱️ Measure the execution time of a function or code block
- 📊 Track CPU usage during code execution
- 🖥️ Monitor memory usage during code execution
- ⚡ Lightweight and fast
- 🔄 Simple and easy-to-use API

##  Installation

You can install FastBench via pip:

```bash
pip install fastbench
```

##  Usage

Here's an example of how to use FastBench to benchmark Python code:

```python
from fastbench import mt, mc, mm

# Define a sample function for testing
def sample_function(n):
  return sum(range(n))

# Test the mt function (measure execution time)
time_taken = mt(sample_function, n=1000000)
print("Time taken:", time_taken)

# Test the mc function (measure CPU usage)
cpu_usage = mc(sample_function, n=1000000)
print("CPU usage:", cpu_usage)

# Test the mm function (measure memory usage)
memory_usage = mm(sample_function, n=1000000)
print("Memory usage:", memory_usage)
```

##  Contributing

Contributions are welcome! Check out the [Contribution Guidelines](https://github.com/itsmeadarsh2008/fastbench/blob/main/CONTRIBUTING.md).

##  License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/itsmeadarsh2008/fastbench?tab=MIT-1-ov-file) file for details.
package-fileDO 0.1.2
A python SDK for ioc-ic
License: MIT

# python SDK

## 接口设计

### getFileLocalPath() 方法

- **功能描述**：获取文件地址
- **输入参数**：DO标识，如："ids:dataset/999a2b6ca6ce8a0d0c88ca99b602613e83d926daf9de1c72bca5db15a399c986"
- **输出**：文件地址，如："C:/ada/icc"
- **调用Java端接口**：`toLocalPath`

### downloadFileDO() 方法

- **功能描述**：输入DO标识作为参数，下载文件
- **输入参数**：DO标识，如："ids:dataset/999a2b6ca6ce8a0d0c88ca99b602613e83d926daf9de1c72bca5db15a399c986"
- **输出**：无
- **调用Java端接口**：`downloadFileDO`

### batchDownloadFileDO() 方法

- **功能描述**：批量下载
- **输入参数**：DO标识组
- **输出**：无
- **调用Java端接口**：`downloadFileDO`

### getDownloadProgress() 方法

- **功能描述**：获取下载进度
- **输入参数**：DO标识，如："ids:dataset/999a2b6ca6ce8a0d0c88ca99b602613e83d926daf9de1c72bca5db15a399c986"
- **输出**：实时下载进度结果
- **调用Java端接口**：`getDownloadProgress`

## Python SDK设计步骤

http://127.0.0.1:21030/SCIDE/SCManager?action=executeContract&contractID=IODIC&operation=downloadFileDO&arg={"dpml":"<dpml><data>ids:dataset/999a2b6ca6ce8a0d0c88ca99b602613e83d926daf9de1c72bca5db15a399c986</data></dpml>"}

mime

pypi
numimagejkk 0.0.2
验证打包功能的测试包, 请勿下载
License: MIT
Requires Python: >=3.6
Keywords: jacky package demo

# 韩灵稚打包测试
Hanlingzhi's Package PyPi Demo
##工程结构说明
<pre>
.
├── LICENSE.txt    	// 证书文件
├── README.md		// MD文件
├── hanlingzhi
│   ├── __init__.py
│   └── math_h.py
├── setup.py			// 打包配置
└── string_h
    ├── __init__.py
    └── reverse.py
</pre>

##测试服务器打包流程

* 升级setuptools和wheel包
<pre>python3 -m pip install --user --upgrade setuptools wheel
</pre>

* 打包到本地文件
<pre>python3 setup.py sdist bdist_wheel
会生成build、dist、hanlingzhi_test.egg-info文件，其中dist文件就是制品
├── dist
│   ├── hanlingzhi-test-0.0.1.tar.gz
│   └── hanlingzhi_test-0.0.1-py3-none-any.whl
</pre>

* 分发到PyPi私服(pypi提供的测试版 test.pypi.org)，首先安装twine
<pre>
pip3 install twine
python3 -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*
Uploading distributions to https://test.pypi.org/legacy/
Enter your username: hanlingzhi  // 私服注册的用户名
Enter your password:                 // 私服注册的密码
Uploading hanlingzhi_test-0.0.1-py3-none-any.whl
100%|████| 7.96k/7.96k [00:16<00:00, 486B/s]
Uploading hanlingzhi-test-0.0.1.tar.gz
100%|████| 7.28k/7.28k [00:01<00:00, 4.34kB/s]
</pre>

* 下载依赖
<pre>
最好在虚拟环境下测试  python3 -m venv venv
python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps hanlingzhi_test
Looking in indexes: https://test.pypi.org/simple/
Collecting hanlingzhi_test
  Downloading https://test-files.pythonhosted.org/packages/a7/ca/3e5ecda3c564a721642a756e31ec2f6f340067cfa4dddb119e2eb2c98dd0/hanlingzhi_test-0.0.1-py3-none-any.whl
Installing collected packages: hanlingzhi-test
Successfully installed hanlingzhi-test-0.0.1
</pre>

* 测试
<pre>
python3
>>> from hanlingzhi import math_h
>>> math_h.addition(3,4)
7			// 成功
</pre>

* 上传正式服务
<pre>
python3 -m twine upload --repository-url https://upload.pypi.org/legacy/  dist/*
</pre>