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paddlepaddle 3.3.0

Parallel Distributed Deep Learning

12-18-2021 213 words One minute 0 views

Contents

Parallel Distributed Deep Learning

Stars: 23631, Watchers: 23631, Forks: 5946, Open Issues: 1576

The PaddlePaddle/Paddle repo was created 9 years ago and the last code push was 34 minutes ago.
The project is extremely popular with a mindblowing 23631 github stars!

How to Install paddlepaddle

You can install paddlepaddle using pip

pip install paddlepaddle

or add it to a project with poetry

poetry add paddlepaddle

Package Details

Author: None
License: Apache Software License
Homepage: https://www.paddlepaddle.org.cn/
PyPi:: https://pypi.org/project/paddlepaddle/
GitHub Repo:: https://github.com/PaddlePaddle/Paddle

Classifiers

No paddlepaddle pypi packages just yet.

Errors

A list of common paddlepaddle errors.

Code Examples

Here are some paddlepaddle code examples and snippets.

GitHub Issues

The paddlepaddle package has 1576 open issues on GitHub

Fix moe_permute compile and accuacry error
Remove FIXME comments regarding complex tensor padding
关于STFT算子无法被导出onnx但是onnx却支持这个算子的问题
[Cpp API Compatibility] add TensorAccessor related API
[API Compatibility] Replace floor_divide_decorator with param_two_alias
test fleet not merge
【Hackathon 10th Spring No.1】Add dilation option for MaxPool1D/2D/3D
[0-size XPU Tensor] Fix 0-size Tensor handling in blha_get_max_len_kernel and weight_only_linear_kernel with added unit tests
[CodeStyle][Xdoctest] change python to pycon
[Communication Library] upgrade nccl version for ncclCommInitRankConfig, remove useless BCCL config
paddle-xpu基础检查不通过
[API Compatibility] Fix _device_to_paddle
Revert "Moe Permute performence optimization"
Cherry-pick for RL2.4
[release/3.3] fix 0size for setitem

See more issues on GitHub

Related Packages & Articles

onnx 1.20.1

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).