semvec/pypi-packages huggingface dataset:
OrzMC 1.0.118
A project for deployment of minecraft game.
Requires Python: <4,>=3
Keywords: minecraft python

# OrzPythonMC

OrzMC Writed by Python Language

## 命令行工具

使用 Python3 编写，可以运行在`Ubuntu/MacOS`系统上（系统需要配置有`JAVA`和`Python3`运行环境），功能包括:

1. 部署`Minecraft`私人服务器(Vanilla/Paper/spigot/forge)
2. 启动`Minecraft`客户端功能（Vanilla)
3. 支持的`1.13`以上正式版

工具已上传到`Python`包管理网站 [PyPi][orzmc-pypi]，可以使用`pip`进行安装

```python
$ pip install orzmc
$ orzmc -h # 查看使用帮助
```

如果你有兴趣和我一起开发这个Python项目，拉项目到本地, 并配置开发环境，运行下面命令即可配置好开发环境：🤒

```bash
$ git clone --recurse-submodules \
      https://github.com/OrzGeeker/OrzMC.git && \
      cd OrzMC && ./config_orzmc_dev && pipenv shell
```

## 项目待办

- [ ] 自动安装JRE运行环境
- [ ] 并发下载提高文件下载速度

---

[orzmc-pypi]: <https://pypi.org/project/OrzMC/>
2048-cli 1.0.3
A small clone of 2048 for terminal, and also the implementation of an A.I. for it
Requires Python: >=3.10

# 2048 cli
A small clone of [2048](https://play2048.co/). In it, the player moves numbered blocks in a 4x4 grid, combining them to create a block with the number 2048. 
The challenge lies in the strategy to avoid running out of moves possible. 
The game ends when the grid is full and there are no more combinations available


In this project, I also try to develop an A.I. using the Monte Carlo method to try to achieve the best possible score.

## Installation
Install with pip or your favorite PyPI package manager
```bash
$ pipx install 2048-cli
```
Then run the following command in the terminal
```bash
$ 2048-cli
```

## Modes
### Single Player
In single player mode, you command the moves with the aim of achieving the highest possible score.

<div align="center">

![menu](./assets/menu.png)

</div>

### AI Playing
In AI game mode, moves will be generated using a Monte Carlo search algorithm.

<div align="center">

![montecarlo](./assets/montecarlo.png)

</div>
2048_cli 1.0.2
A small clone of 2048 for terminal, and also the implementation of an A.I. for it
Requires Python: <4.0,>=3.10

# 2048 cli
A small clone of [2048](https://play2048.co/). In it, the player moves numbered blocks in a 4x4 grid, combining them to create a block with the number 2048. 
The challenge lies in the strategy to avoid running out of moves possible. 
The game ends when the grid is full and there are no more combinations available


In this project, I also try to develop an A.I. using the Monte Carlo method to try to achieve the best possible score.

## Installation
Install with pip or your favorite PyPI package manager
```bash
$ pipx install 2048-cli
```
Then run the following command in the terminal
```bash
$ 2048-cli
```

## Modes
### Single Player
In single player mode, you command the moves with the aim of achieving the highest possible score.

<div align="center">

![menu](./assets/menu.png)

</div>

### AI Playing
In AI game mode, moves will be generated using a Monte Carlo search algorithm.

<div align="center">

![montecarlo](./assets/montecarlo.png)

</div>
fast-gem 0.0.5
Efficient and general implementation of Generalized Mean Pooling (GeM)

# fast-GeM

Efficient and general implementation of Generalized Mean Pooling (GeM).

![benchmark_result](./assets/benchmark.png)
<!-- ![benchmark_result](https://raw.githubusercontent.com/Kitsunetic/fast-GeM/master/assets/benchmark.png?token=GHSAT0AAAAAACQVW5PRDT3UYGFC6QHKAWNQZT426SA) -->

The [original implementation](https://amaarora.github.io/posts/2020-08-30-gempool.html#pytorch-implementation) is quite slower due to the `F.avg_pool` function and multiple kernel executions.

We provide a new PyTorch implementation that is 4\~20 times faster than the original.
This implementation is suitable for environments without OpenAI Triton or when the input is a CPU tensor.

Additionally, we offer a Triton-based implementation.
We achieved 3\~4 times faster than our new PyTorch implementation and 6\~85 times faster than the original by utilizing kernel-fusion.

Our implementation is easy to use, maintaining a similar interface to the original while supporting flexible input data shapes.



## Installation

```sh
pip install fast-gem
```



## Usage

For 2D image tensor (batch, channel, width, height):
```py
import torch
from fast_gem import GeM

# for 4D tensor (batch, channel, height, width) case
gem = GeM().cuda()
x = torch.rand(2, 3, 224, 224, device="cuda")
y = gem(x)
y.shape  # shape: (2, 3, 1, 1)

# for 3D tensor (batch, channel, length) case
gem = GeM(dim=-1).cuda()
x = torch.rand(2, 32, 1024, device="cuda")
y = gem(x)
y.shape  # shape: (2, 32, 1)

# for 4D tensor (batch, channel, depth, height, width) case
gem = GeM(dim=-1).cuda()
x = torch.rand(2, 32, 64, 64, 64, device="cuda")
y = gem(x)
y.shape  # shape: (2, 32, 1, 1, 1)

# or you can make not keep 1s and different initial `p` value instead of 3.0
gem = GeM(p=2.0, dim=-1, keepdim=False).cuda()
x = torch.rand(2, 32, 64, 64, 64, device="cuda")
y = gem(x)
y.shape  # shape: (2, 32)
```