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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 updated SYSTEMML-1819:
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Description:
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/]
was:
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.
> 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
>
> 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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