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# Framework and Library Supports | ||
With the built-in Python API, NNI naturally supports the hyper parameter tuning and neural network search for all the AI frameworks and libraries who support Python models(`version >= 3.5`). NNI had also provided a set of examples and tutorials for some of the popular scenarios to make jump start easier. | ||
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## Supported AI Frameworks | ||
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* <b>[PyTorch]</b> https://github.com/pytorch/pytorch | ||
<ul> | ||
<li><a href="../../examples/trials/mnist-distributed-pytorch">MNIST-pytorch</a><br/></li> | ||
<li><a href="TrialExample/Cifar10Examples.md">CIFAR-10</a><br/></li> | ||
<li><a href="../../examples/trials/kaggle-tgs-salt/README.md">TGS salt identification chanllenge</a><br/></li> | ||
<li><a href="../../examples/trials/network_morphism/README.md">Network_morphism</a><br/></li> | ||
</ul> | ||
* <b>[TensorFlow]</b> https://github.com/tensorflow/tensorflow | ||
<ul> | ||
<li><a href="../../examples/trials/mnist-distributed">MNIST-tensorflow</a><br/></li> | ||
<li><a href="../../examples/trials/ga_squad/README.md">Squad</a><br/></li> | ||
</ul> | ||
* <b>[Keras]</b> https://github.com/keras-team/keras | ||
<ul> | ||
<li><a href="../../examples/trials/mnist-keras">MNIST-keras</a><br/></li> | ||
<li><a href="../../examples/trials/network_morphism/README.md">Network_morphism</a><br/></li> | ||
</ul> | ||
* <b>[MXNet]</b> https://github.com/apache/incubator-mxnet | ||
* <b>[Caffe2]</b> https://github.com/BVLC/caffe | ||
* <b>[CNTK (Python language)]</b> https://github.com/microsoft/CNTK | ||
* <b>[Spark MLlib]</b> http://spark.apache.org/mllib/ | ||
* <b>[Chainer]</b> https://chainer.org/ | ||
* <b>[Theano]</b> https://pypi.org/project/Theano/ <br/> | ||
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You are encouraged to [contribute more examples](Tutorial/Contributing.md) for other NNI users. | ||
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## Supported Library | ||
NNI also supports all libraries written in python.Here are some common libraries, including some algorithms based on GBDT: XGBoost, CatBoost and lightGBM. | ||
* <b>[Scikit-learn]</b> https://scikit-learn.org/stable/ | ||
<ul> | ||
<li><a href="TrialExample/SklearnExamples.md">Scikit-learn</a><br/></li> | ||
</ul> | ||
* <b>[XGBoost]</b> https://xgboost.readthedocs.io/en/latest/ | ||
* <b>[CatBoost]</b> https://catboost.ai/ | ||
* <b>[LightGBM]</b> https://lightgbm.readthedocs.io/en/latest/ | ||
<ul> | ||
<li><a href="TrialExample/GbdtExample.md">Auto-gbdt</a><br/></li> | ||
</ul> | ||
Here is just a small list of libraries that supported by NNI. If you are interested in NNI, you can refer to the [tutorial](TrialExample/Trials.md) to complete your own hacks. | ||
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In addition to the above examples, we also welcome more and more users to apply NNI to your own work, if you have any doubts, please refer [Write a Trial Run on NNI](TrialExample/Trials.md). In particular, if you want to be a contributor of NNI, whether it is the sharing of examples , writing of Tuner or otherwise, we are all looking forward to your participation.More information please refer to [here](Tutorial/Contributing.md). |