Major Features and Improvements
- Publically released TFX docker image in tensorflow/tfx will use GPU
compatible based TensorFlow images from Deep Learning Containers. This allow
these images to be used with GPU out of box. - Added an example pipeline for a ranking model (using
tensorflow_ranking)
attfx/examples/ranking
. More documentation will be available in future
releases. - Added a spans_resolver
that can resolve spans based on range_config.
Breaking Changes
For Pipeline Authors
- Custom arg key in
google_cloud_ai_platform.tuner.executor
is renamed to
ai_platform_tuning_args
fromai_platform_training_args
, to better
distinguish usage with Trainer.
For component authors
- N/A
Deprecations
- Deprecated input/output compatibility aliases for Transform and SchemaGen.
Bug Fixes and Other Changes
- Change Bigquery ML Pusher to publish the model to the user specified project
instead of the default project from run time context. - Depends on
apache-beam[gcp]>=2.28,<3
. - Depends on
ml-metadata>=0.28.0,<0.29.0
. - Depends on
kfp-pipeline-spec>=0.1.6,<0.2
. - Depends on
struct2tensor>=0.28.0,<0.29.0
. - Depends on
tensorflow-data-validation>=0.28.0,<0.29.0
. - Depends on
tensorflow-model-analysis>=0.28.0,<0.29.0
. - Depends on
tensorflow-transform>=0.28.0,<0.29.0
. - Depends on
tfx-bsl>=0.28.1,<0.29.0
.
Documentation Updates
- Published a migration instruction
for legacy custom launcher developers.