HybridBackend v0.6.0

Objectives:

1. Communication-efficient training and evaluation at scale
2. Easy to use with existing AI workflows

Features:

1. Data-Parallel Training and Evaluation
   - Bucketized Gradients Aggregation using AllReduce
   - Global Metric Operations
   - Out-Of-Range Coordination

2. Hybrid-Parallel Embedding Learning
   - Bucketized Embedding Exchanging using AllToAllv
   - Fusion and Quantization of AllToAllv
   - Fusion of Partitioning and Stitching

3. Usability
   - Support of MonitoredSession and Estimator
   - Declarative API for Model Definition

4. Compatibility
   - Support of NVIDIA TensorFlow and DeepRec

5. Interoperability
   - Inference Pipeline Needs No Change
   - Support of SavedModel
   - Support of Variable, XDL HashTable and PAI Embedding Variable

Bug Fixes:

[#46] Fixes rebatching in ParquetDataset.