Highlights

Note: BigDL v2.2.0 has been updated to include functional and security updates. Users should update to the latest version.

• Nano
  • 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
• PPML
  • 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)
• Chronos
  • 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.)
• Friesian
  • LightGBM training support
  • Performance improvements for online serving pipeline
• Orca
  • 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
• Documentation
  • 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