This is the first release of ElasticDL. It supports TensorFlow 2.0.
Includes a master-worker architecture, where the master controls task
generation and entire job progress. Workers communicate with the master to get
the tasks to execute and report execution results.
Supports different job types: training-only, training-with-evaluation,
evaluation-only and prediction-only.
Provides high-level APIs and CLI for training, evaluation and prediction.
Supports running in environments, including MiniKube, GCP, and on-prem
clusters.
Adds experimental integration with SQLFlow for ODPS data source.