Objective: "Communication-efficient training and evaluation at scale"
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Data-Parallel Training and Evaluation
- Bucketized Gradients Aggregation using AllReduce
- Global Metric Operations
- Out-Of-Range Coordination
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Hybrid-Parallel Embedding Learning
- Bucketized Embedding Exchanging using AllToAllv
- Fusion and Quantization of AllToAllv
- Fusion of Partitioning and Stitching
Objective: "Easy to use with existing AI workflows"
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Usability
- Support of MonitoredSession and Estimator
- Declarative API for Model Definition
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Compatibility
- Support of NVIDIA TensorFlow and DeepRec
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Interoperability
- Inference Pipeline Needs No Change
- Support of SavedModel
- Support of Variable, XDL HashTable and PAI Embedding Variable
Objective: "Memory-efficient loading of categorical data"
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Parquet Dataset
- Reading batch of tensors from numeric fields in zero-copy way
- Reading batch of sparse tensors from numeric list fields in zero-copy way
- Support of string fields
- Support of local filesystem, HDFS, S3 and OSS
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Data Pipeline Functions
- Resizing batch of tensors and ragged tensors
- Converting ragged tensors to sparse tensors
Objective: "Easy to use with existing AI workflows"
- Compatibility
- Support of TensorFlow 1.15 and Tensorflow 1.14
- GitHub actions for uploading wheels to PyPI