Changelogs » Tfx

Tfx

0.13.0

Major Features and Improvements

*   Adds support for Python 3.5
*   Initial version of following orchestration platform supported:
*   Kubeflow
*   Added TensorFlow Model Analysis Colab example
*   Supported split ratio for ExampleGen components
*   Supported running a single executor independently

Bug fixes and other changes

*   Fixes issue 43 that prevent new execution in some scenarios
*   Fixes issue 47 that causes ImportError on chicago_taxi execution on dataflow
*   Depends on `apache-beam[gcp]>=2.12,<3`
*   Depends on `tensorflow-data-validation>=0.13.1,<0.14`
*   Depends on `tensorflow-model-analysis>=0.13.2,<0.14`
*   Depends on `tensorflow-transform>=0.13,<0.14`
*   Deprecations:
*    PipelineDecorator is deprecated. Please construct a pipeline directly from a list of components instead.
*   Increased verbosity of logging to container stdout when running under
Kubeflow Pipelines.
*   Updated developer tutorial to support Python 3.5+

Breaking changes
*   Examples code are moved from 'examples' to 'tfx/examples': this ensures that PyPi package contains only one top level python module 'tfx'.

Things to notice for upgrading
*   Multiprocessing on Mac OS >= 10.13 might crash for Airflow. See
[AIRFLOW-3326](https://issues.apache.org/jira/browse/AIRFLOW-3326)
for details and solution.

0.12.0

Major Features and Improvements

*   Adding TFMA Architecture doc
*   TFX User Guide
*   Initial version of the following TFX components:
*   CSVExampleGen - CSV data ingestion
*   BigQueryExampleGen - BigQuery data ingestion
*   StatisticsGen - calculates statistics for the dataset
*   SchemaGen - examines the dataset and creates a data schema
*   ExampleValidator - looks for anomalies and missing values in the dataset
*   Transform - performs feature engineering on the dataset
*   Trainer - trains the model
*   Evaluator - performs analysis of the model performance
*   ModelValidator - helps validate exported models ensuring that they are
"good enough" to be pushed to production
*   Pusher - deploys the model to a serving infrastructure, for example the
TensorFlow Serving Model Server
*   Initial version of following orchestration platform supported:
*   Apache Airflow
*   Polished examples based on the Chicago Taxi dataset.

Bug fixes and other changes

*   Cleanup Colabs to remove TF warnings
*   Performance improvement during shuffling of post-transform data.
*   Changing example to move everything to one file in plugins
*   Adding instructions to refer to README when running Chicago Taxi notebooks

Breaking changes