Deep Learning on Flink aims to integrate Flink and deep learning frameworks (e.g. TensorFlow, PyTorch, etc) to enable distributed deep learning training and inference on a Flink cluster.

同类别比较
名称	排名	指标
HasnainRaz/Fast-SRGAN	14,879	语言: Python 星标数: 695 分叉数: 120 简介: A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
aleju/mario-ai	14,883	语言: Lua 星标数: 695 分叉数: 142 简介: Playing Mario with Deep Reinforcement Learning
mv-lab/swin2sr	14,889	语言: Python 星标数: 695 分叉数: 83 简介: [ECCV] Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration. Advances in Image Manipulation (AIM) workshop ECCV 2022. Try it out! over 3.3M runs https://replicate.com/mv-lab/swin2sr
soobinseo/Transformer-TTS	14,890	语言: Python 星标数: 695 分叉数: 139 简介: A Pytorch Implementation of "Neural Speech Synthesis with Transformer Network"
khalil-research/PyEPO	14,902	语言: Jupyter Notebook 星标数: 694 分叉数: 90 简介: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
KarhouTam/FL-bench	14,908	语言: Python 星标数: 693 分叉数: 116 简介: Benchmark of federated learning. Dedicated to the community. 🤗
ankane/eps	14,913	语言: Ruby 星标数: 693 分叉数: 15 简介: Machine learning for Ruby
elastic/eland	14,915	语言: Python 星标数: 693 分叉数: 112 简介: Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

名称

排名

指标

HasnainRaz/Fast-SRGAN

14,879

语言:: Python
星标数:: 695
分叉数:: 120
简介:: A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps

aleju/mario-ai

14,883

语言:: Lua
星标数:: 695
分叉数:: 142
简介:: Playing Mario with Deep Reinforcement Learning

mv-lab/swin2sr

14,889

语言:: Python
星标数:: 695
分叉数:: 83
简介:: [ECCV] Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration. Advances in Image Manipulation (AIM) workshop ECCV 2022. Try it out! over 3.3M runs https://replicate.com/mv-lab/swin2sr

soobinseo/Transformer-TTS

14,890

语言:: Python
星标数:: 695
分叉数:: 139
简介:: A Pytorch Implementation of "Neural Speech Synthesis with Transformer Network"

khalil-research/PyEPO

14,902

语言:: Jupyter Notebook
星标数:: 694
分叉数:: 90
简介:: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

KarhouTam/FL-bench

14,908

语言:: Python
星标数:: 693
分叉数:: 116
简介:: Benchmark of federated learning. Dedicated to the community. 🤗

ankane/eps

14,913

语言:: Ruby
星标数:: 693
分叉数:: 15
简介:: Machine learning for Ruby

elastic/eland

14,915

语言:: Python
星标数:: 693
分叉数:: 112
简介:: Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

关键参数与规格
参数	值
语言	Java
类别	Model Development
子类别	Deep Learning Frameworks
关键词	Distributed training
星标数	694
分叉数	196
贡献者	17
创建日期	2019-06-17
更新日期	2026-09-26
1 天星标数	0
7 天星标数	0
排名	14,901
上次排名	14,736
排名变化	-165
7 天星标排名	14,709
上次 7 天星标排名	14,718
7 天星标排名变化	9
总星标排名	14,978
上次总星标排名	14,955
总星标排名变化	-23

参数

值

语言

Java

类别

Model Development

子类别

Deep Learning Frameworks

关键词

Distributed training

星标数

694

分叉数

196

贡献者

创建日期

2019-06-17

更新日期

2026-09-26

1 天星标数

7 天星标数

排名

14,901

上次排名

14,736

排名变化

-165

7 天星标排名

14,709

上次 7 天星标排名

14,718

7 天星标排名变化

总星标排名

14,978

上次总星标排名

14,955

总星标排名变化

-23

Deep Learning on Flink

Deep Learning on Flink aims to integrate Flink and deep learning frameworks (e.g. TensorFlow, PyTorch, etc.) to enable distributed deep learning training and inference on a Flink cluster.

It runs the deep learning tasks inside a Flink operator so that Flink can help establish a distributed environment, manage the resource, read/write the data with the rich connectors in Flink and handle the failures.

Currently, Deep Learning on Flink supports TensorFlow.

Supported Operating System

Deep Learning on Flink is tested and supported on the following 64-bit systems:

Ubuntu 18.04
macOS 10.15

Support Framework Version

TensorFlow: 1.15.x & 2.4.x
PyTorch: 1.11.x
Flink: 1.14.x

Getting Started

Deep learning on Flink currently works with Tensorflow and PyTorch. You can see the following pages for the usage and examples.

Build From Source

Requirements

python: 3.7
cmake >= 3.6
java 1.8
maven >=3.3.0

Deep Learning on Flink requires Java and Python works together. Thus, we need to build for both Java and Python.

Initializing Submodules before Building Deep Learning on Flink from Source

Please use the following command to initialize submodules before building from source.

git submodule update --init --recursive

Build Java

mvn -DskipTests clean install

After finish, you can find the target distribution in the dl-on-flink-dist/target folder.

Build Python

Install from Source

You can run the following commands to install the Python packages from source

# Install dl-on-flink-framework first
pip install dl-on-flink-framework/python

# Note that you should only install one of the following as they require
# different versions of Tensorflow 
# For tensorflow 1.15.x
pip install dl-on-flink-tensorflow/python
# For tensorflow 2.4.x
pip install dl-on-flink-tensorflow-2.x/python

Build wheels

We provide a script to build wheels for Python packages, you can run the following command.

bash tools/build_wheel.sh

After finish, you can find the wheels at tools/dist. Then you can install the python package with the wheels.

pip install tools/dist/

For More Information

Design document

License

Apache License 2.0

dl-on-flink

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