semvec/pypi-packages huggingface dataset:
pai-easydist 0.1.0
Efficient Automatic Training System for Super-Large Models

# EasyDist

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems, offering the following key features:

- **Usability**. With EasyDist, parallelizing your training or inference code to a larger scale becomes effortless with just a single line of change.

- **Ecological Compatibility**. EasyDist serves as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. Currently, EasyDist currently supports PyTorch, Jax natively, and the TVM Tensor Expression operator for SPMD rules.

- **Infrastructure**. EasyDist decouples auto-parallel algorithms from specific machine learning frameworks and IRs. This design choice allows for the development and benchmarking of different auto-parallel algorithms in a more flexible manner, leveraging the capabilities and abstractions provided by EasyDist.

## One Line of Code for Parallelism

To parallelize your training loop using EasyDist, you can use the `easydist_compile` decorator. Here's an example of how it can be used with PyTorch:

```python
@easydist_compile()
def train_step(net, optimizer, inputs, labels):

    outputs = net(inputs)
    loss = nn.CrossEntropyLoss()(outputs, labels)
    loss.backward()

    optimizer.step()
    optimizer.zero_grad()

    return loss
```

This one-line decorator parallelizes the training step. You can find more examples in the [`./examples/`](./examples/) directory.

## Overview

EasyDist introduces the concept of MetaOp and MetaIR to decouple automatic parallelization methods from specific intermediate representations (IR) and frameworks. Additionally, it presents the ShardCombine Algorithm, which defines operator Single-Program, Multiple-Data (SPMD) sharding rules without requiring manual annotations. The architecture of EasyDist is as follows:

<br><div id="top" align="center">
<img src="./assets/arch.svg" width="650">
</div><br>

## Installation

To install EasyDist, you can use pip and install from PyPI:

```shell
# For PyTorch users
pip install pai-easydist[torch]

# For Jax users
pip install pai-easydist[jax] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```

If you prefer to install EasyDist from source, you can clone the GitHub repository and then install it with the appropriate extras:

```shell
git clone https://github.com/alibaba/easydist.git && cd easydist

# EasyDist with PyTorch installation
pip install -e '.[torch]'

# EasyDist with Jax installation
pip install -e '.[jax]' -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```

## Contributing

See CONTRIBUTING.md for details.

## Contributors

EasyDist is developed by Alibaba Group and NUS HPC-AI Lab. This work is supported by [Alibaba Innovative Research(AIR)](https://damo.alibaba.com/air/).

## License

EasyDist is licensed under the Apache License (Version 2.0). See LICENSE file.
This product contains some third-party testcases under other open source licenses. 
See the NOTICE file for more information.
GPAD 0.0.1
GPAD for CARLA PythonAPI
Requires Python: >=3.7

# GPAD
__GPAD for [CARLA](https://github.com/carla-simulator/carla) PythonAPI__

## Forward
I finished my Ph.D. and I don't expect to spend much time on this project anymore.
But I shared it on GitHub just in case.

The code is not super explicit... 
So if you have questions or issues,
don't hesitate to open an issue on the [GitHub page](https://github.com/renaudponcelet/GPAD),
I would answer you when I can :blush:.

If you want to use this code or the methods detailed in it, please cite me.
I first mentioned this code at ICARCV2020 in the following paper __Safe Geometric Speed Planning Approach
for Autonomous Driving through Occluded Intersections.
R. Poncelet, A. Verroust-Blondet and F. Nashashibi. [[PDF]](https://hal.science/hal-02967740v2/document)__
```
@inproceedings{poncelet:hal-02967740,
 TITLE = {{Safe Geometric Speed Planning Approach for Autonomous Driving through Occluded Intersections}},
 AUTHOR = {Poncelet, Renaud and Verroust-Blondet, Anne and Nashashibi, Fawzi},
 URL = {https://hal.science/hal-02967740},
 BOOKTITLE = {{ICARCV 2020 - 16th International Conference on Control, Automation, Robotics and Vision}},
 ADDRESS = {Shenzhen, China},
 SERIES = {16th International Conference on Control, Automation, Robotics and Vision},
 YEAR = {2020},
 MONTH = Dec
}
```

## Requirements

This package is made to be cloned into the "PythonAPI" folder in [CARLA](https://github.com/carla-simulator/carla).
I also packaged it to allow "import GPAD".

It should run on Carla 0.9.14.

You can check the requirements.txt file.

## Structure

### Example
- main.py is an example to run the GPAD Planner detailed (in French) in my [Ph.D. thesis](https://renaudponcelet.zetmus.fr/these.pdf).

### Packages
- The Common package contains GPAD Planner, World Manager, and Utils.
- The Approaches package contains:
- [MMRIS](#MMRIS)
- [SGSPA](#SGSPA)
- Common

## Methods

### MMRIS
The MMRIS method is detailed (in French) in my [Ph.D. thesis](https://renaudponcelet.zetmus.fr/these.pdf).
This method is based on this paper (in English) [RIS](https://inria.hal.science/hal-01903318).
- RIS are calculated for different speed profiles. I compute three speed profiles: 
- A brake speed profile
- A constant speed profile
- And an acceleration speed profile
- Paths are computed for each speed profile
- The "fastest" speed profile is selected

### SGSPA
The SGSPA is detailed in the following paper:
[Safe Geometric Speed Planning Approach for Autonomous Driving through Occluded Intersections](https://hal.science/hal-02967740v2)
Cvwriter 0.0.3
The Video Writer Package is a Python library designed to simplify the process of capturing frames and saving them as video files. It provides an easy-to-use interface for creating video files from sequences of frames using the OpenCV library.
Requires Python: >=3.8

# Video Writer Package

The Video Writer Package is a simple Python library that provides a wrapper around OpenCV's VideoWriter functionality, allowing you to easily capture frames and save them as video files.

## Installation

To install the Video Writer Package, you can use `pip`:

```bash
pip install Cvwriter
```

## Parameters

* filename: The name of the output video file.
* fps: Frames per second for the output video.
* frame_size: Tuple representing the dimensions (width, height) of the frames.

## Supported Codecs (FourCC)

* 'XVID': Xvid codec
* 'H264' or 'H265': H.264 (AVC) or H.265 (HEVC) codecs for MP4 videos
* 'MP4': Motion JPEG codec
  
## Uses
```bash

from Cvwriter.video_writer import  VideoWriterWrapper


video_writer = VideoWriterWrapper('output.avi/output.mp4..', fps, size)

video_writer.write_frame(frame)
video_writer.save_image(frame, name)
```
nonebot-plugin-quickban 0.0.3
快捷禁言

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<a href="https://pypi.python.org/pypi/nonebot-plugin-sleep">
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