Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://github.com/awslabs/autogluon
https://autogluon.mxnet.io/
AutoGluon automatically inferences the problem type. However, you are still able to specify the probelm type if AutoGluon fails to infer the right problem type.
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AutoGluon auto saves all models into the specified output directory during training. And the save models can be load back using the
loadmethod of the corresponding predictor. -
GPU support in AutoGluon is for image/text but not Tabular data currently. For more details, please refer to issue 262.
Questions
Can I choose a model to save and choose a model to load?
Customization
Hyperparameter Tuning
Use AutoGluon for Hyperparameter Optimization for MNIST Training in PyTorch
Neural Architecture Search
https://autogluon.mxnet.io/tutorials/nas/enas_mnist.html
References
https://futurumresearch.com/aws-releases-autogluon-an-innovative-open-source-tooling-for-automated-machine-learning/
https://www.amazon.science/amazons-autogluon-helps-developers-get-up-and-running-with-state-of-the-art-deep-learning-models-with-just-a-few-lines-of-code
https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec
https://venturebeat.com/2020/01/09/amazons-autogluon-produces-ai-models-with-as-little-as-three-lines-of-code/
https://autogluon.mxnet.io/tutorials/course/distributed.html