***Note:*** BigDL v2.2.0 has been updated to include functional and security updates. Users should update to the latest version.
* Extend BigDL Nano inference to support iGPU and more data types (INT8/BF16/FP16 quantization)
* More performance features (e.g., InferenceOptimizer for Keras, Nano decorator for PyTorch training loop, Nano Context Manager for thread number control and autocast, etc.)
* Support installation with more PyTorch/TensorFlow versions and conditional dependencies on different platforms
* Upgrade BigDL PPML solution to support new LibOS (e.g., Gramine1.3.1, Occlum0.29.2) with better security, higher performance, more stability and easier deployment.
* Support more Big Data frameworks (Spark 3.1.3, Flink, Hive etc.), more Python and Data Science tools (Numpy, Pandas, sklearn, Torch Serv, Triton, Flask etc.), and distributed DL training using Orca
* Improve the Attestation (e.g., MREnclave Attestation), Key Management (e.g., multi-KMS) & Encryption (e.g., transparent encryption) features for better end-to-end secure pipeline.
* Initial support of BigDL PPML on SPR TDX (Virtual Machine and TDX Confidential Container)
* Extend BigDL Chronos to support Windows and Mac, and new Python versions (3.8/3.9)
* Provide a benchmark tool for Chronos users to evaluate Chronos performance on their platform
* More performance features (e.g., accuracy and performance improvement for TCNForecaster, lower memory usage, auto optimization search, faster and portable TSDataset, etc.)
* LightGBM training support
* Performance improvements for online serving pipeline
* Improve Orca Estimator APIs for better user experience
* Memory optimization for distributed training with Spark DataFrame,
* Better support for image inputs and visualization with Xshards
* Distributed MMCV applications using Orca
* Tutorials for running BigDL Orca on YARN/K8s/Databricks
* BigDL PPML solutions on Azure
* How-to guides and examples for Chronos forecasting and deployment process