Releases: databricks/spark-deep-learning
Releases · databricks/spark-deep-learning
v1.6.0
v1.5.0
v1.4.0
Release 1.3.0
- Added HorovovodRunner API.
- Simplified test and doc build w/ Docker and conda.
- Updated public Python API docs.
- Removed persistence from DeepImageFeaturizer.
Release 1.2.0
- ignore nullable in DeepImageFeaturizer.validateSchema
- upgrade TensorFrames version to 0.5.0
- upgrade Tensorflow version to 1.10.0 and Keras version to 2.2.2
Release 1.1.0
- keras_image_file_estimator support both sparse and dense vectors
- upgrade TensorFrames version to 0.4.0
- add style checks to Travis CI
- doc fixes
Release 1.0.0
This is the 1.0.0 release. It brings compatibility with newer versions of Spark (2.3) and Tensorflow (1.6+). The custom image schema formerly defined in this package has been replaced with Spark's ImageSchema so there may be some breaking changes when updating to this version.
Notable changes:
- (breaking change) Using the definition of images from Spark 2.3.0. The new definition uses the BGR channel ordering for 3-channel images instead of the RGB ordering used in this project before the change.
- Persistence for DeepImageFeaturizer (both Python and Scala).
Release 0.3.0
This is the final release of dl-pipelines prior to migrating to new ImageSchema.
Notable changes:
- Added vgg16, vgg19 models to DeepImageFeaturizer/DeepImagePredictor (Python).
- Added a Scala API for DeepImageFeaturizer (for transfer learning for images).
- Added TFTransformer and KerasTransformer for applying TensorFlow graphs or TensorFlow-backed Keras models to a column of arrays in a Spark DataFrame.
Release 0.2.0
This is the final release for Deep Learning Pipelines 0.2.0
Notable additions since 0.1.0:
- KerasImageFileEstimator API (train a Keras model on image files)
- SQL UDF support for Keras models
- Added Xception, Resnet50 models to DeepImageFeaturizer/DeepImagePredictor.