Highlights

• Add MKL-DNN Int8 support, especially for VNNI acceleration support. Low precision inference accelerates both latency and throughput significantly
• Add support for runnning MKL-BLAS models under MKL-DNN. We leverage MKL-DNN to speed up both training and inference for MKL-BLAS models
• Add Spark 2.4 support. Our examples and APIs are fully compatible with Spark 2.4, we released the binary for Spark 2.4 together with other Spark versions

Details

• [New Feature] Add MKL-DNN Int8 support, especially for VNNI support
• [New Feature] Add support for runnning MKL-BLAS models under MKL-DNN
• [New Feature] Add Spark 2.4 support
• [New Feature] Add auto fusion to speed up model inference
• [New Feature] Memoery reorder support for low precision inference
• [New Feature] Add bytes support for DNN Tensor
• [New Feature] Add SAME padding in MKL-DNN layers
• [New Feature] Add combined (add/or) triggers for training completion
• [Enhancement] Inception-V1 python training support enhancement
• [Enhancement] Distributed Optimizer enhancement to support customized optimizer
• [Enhancement] Add compute output shape for DNN supported layers
• [Enhancement] New MKL-DNN computing thread pool
• [Enhancement] Add MKL-DNN support for Predictor
• [Enhancement] Documentation enhancement for Sparse Tensor, MKL-DNN support, etc
• [Enhancement] Add ceilm mode for AvgPooling and MaxPooling layers
• [Enhacement] Add binary classification support for DLClassifierModel
• [Enhacement] Improvement to support conversion between NHWC and NCHW for memory reoder
• [Bug Fix] Fix SoftMax layer with narrowed input
• [Bug Fix] TensorFlow loader to support checking all data types
• [Bug Fix] Fix Add operation bug to support double type when loading TensorFlow graph
• [Bug Fix] Fix one-step weight update missing issue in validation during training
• [Bug Fix] Fix scala compiler security issue in 2.10 & 2.11
• [Bug Fix] Fix model broadcast cache UUID issue
• [Bug Fix] Fix predictor issue for batch size == 1