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https://issues.apache.org/jira/browse/SYSTEMML-1819?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Mike Dusenberry resolved SYSTEMML-1819.
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Resolution: Fixed
Fix Version/s: SystemML 1.0
Merged in commit
[f6ea240|https://github.com/apache/systemml/commit/f6ea240ca7d0307ab8a5449345c455d1a0fe167b].
> Create Keras2DML: Keras frontend to SystemML
> --------------------------------------------
>
> Key: SYSTEMML-1819
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1819
> Project: SystemML
> Issue Type: New Feature
> Reporter: Mike Dusenberry
> Assignee: Anooj Patel
> Fix For: SystemML 1.0
>
> Time Spent: 1,200h
> Remaining Estimate: 0h
>
> This task covers the creation of a "Keras2DML" frontend for SystemML, built
> upon the [Caffe2DML |
> http://apache.github.io/systemml/beginners-guide-caffe2dml] infrastructure,
> that will allow users to define (and even train) models in Keras and then
> import them into SystemML for distributed training and prediction. As an
> initial set of thoughts, the input could be either (1) a Keras {{Model}}
> object, or (2) a saved Keras model hdf5 file, and the output of training
> could be either (1) a Keras {{Model}} object, (2) a saved Keras model hdf5
> file, or (3) a SystemML model.
> This would be a step towards a full-blown, official backend for Keras. The
> main goal here would be to allow users to be able to transparently make use
> of distributed training, without having to learn the details of SystemML.
> Initial steps:
> 1. Learn Keras
> 2. Learn Caffe2DML:
> [http://apache.github.io/systemml/beginners-guide-caffe2dml] Basically,
> Caffe2DML lets users import Caffe models (architecture and trained weights if
> available) into SystemML and train/predict on Spark with a scikit-learn
> compatible API without the user having to learn SystemML. The main benefit
> is distributed training without the user needing to think about it much. A
> bunch of the infrastructure is in place that I think Keras2DML would use.
> 3. Import a simple Keras model definition with a single Dense layer, and
> focus on hooking up the new Keras2DML class to the existing infrastructure.
> 4. Add reading of trained weights from Keras for the simple model, and hook
> up to existing infrastructure.
> 5. Expand out to increasingly complex models, aiming to be able to import all
> of the pretrained models from Keras, starting with VGG16 & ResNet50.
> [https://keras.io/applications/]
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