Bigdl

Highlights
***Note:*** BigDL v2.4.0 has been updated to include functional and security updates. Users should update to the latest version.

Highlights
***Note:*** BigDL v2.3.0 has been updated to include functional and security updates. Users should update to the latest version.

Nano
- Enhanced `trace` and `quantization` process (for PyTorch and TensorFlow model optimizations)
- New inference optimization methods (including Intel ARC series GPU support, CPU fp16, JIT int8, etc.)
- New inference/training features (including TorchCCL support, async inference pipeline, compressed model saving, automatic channels_last_3d, multi-instance training for customized TF train loop, etc.)
- Performance enhancement and overhead reduction for inference optimized model
- More user-friendly document and API design

Orca:
- Step-by-step distributed TensorFlow and PyTorch tutorials for different data inputs.
- Improvement and examples for distributed MMCV pipelines.
- Further enhancement for Orca Estimator (more flexible PyTorch train loops via Hook, improved multi-output prediction, memory optimization for OpenVINO, etc.)

Chronos
- 70% latency reduction for Forecasters
- New `bigdl.chronos.aiops` module for AIOps use case on top of Chronos algorithms.
- Enhanced TF-based TCNForecaster to better accuracy

Friesian:
- Automatic deployment of RecSys serving pipeline on Kubernetes with Helm Chart

PPML
- TDX (both VM and CoCo) support for Big Data, DL Training & Serving (including TDX-VM orchestration & k8s deployment, TDXCC installation & deployment, attestation and key management support, etc.)
- New Trusted Machine Learning toolkit (with secure and distributed SparkML & LightGBM support)
- Trusted Big Data toolkit upgrade (>2x EPC usage reduction, Apache Flink support, Azure MAA support, multi-KMS support, etc.)
- Trusted Deep Learning toolkit upgrade (with improved performance using BigDL Nano, tcmalloc, etc.)
- Trusted DL Serving toolkit upgrade (with Torch Serve, TF-Serving, and improved throughput and latency)

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

Highlights
***Note:*** BigDL v2.1.0 has been updated to include functional and security updates. Users should update to the latest version.

- Orca
- Improve user experience and API consistency for Orca Estimators.
- Support directly save and load TensorFlow model format in Orca TensorFlow2 Estimator.
- Provide more examples (e.g. PyTorch brain image segmentation, XShards tutorials for distributed Python data processing), etc.
- Support customized metrics in Orca PyTorch Estimator.
- Nano
- New inference optimization pipelines, with more optimization methods and a new InferenceOptimizer
- More training optimization methods (bf16, channel last)
- Add TorchNano support for PyTorch model customized training loop
- Auto-scale learning rate for multi-instance training
- Built-in AutoML support through hyperparameter optimization
- Support a wide range versions of pytorch (1.9-1.12) and tensorflow (2.7-2.9)
- DLlib
- Add LightGBM support
- Improve Keras-style model summary API
- Add Python support for loading HDFS files
- Chronos
- Add new Autoformer (https://arxiv.org/abs/2106.13008) Forecaster and pipeline that are optimized on CPU
- Tensorflow 2 support for LSTM, Seq2Seq, TCN and MTNet Forecasters
- Add light-weight (does not rely on Spark/Ray Tune) auto tunning
- Better support on distributed workflow (spark df and distributed pandas processing)
- Add more installation options is now supported to make the installation lighter
- Friesian:
- Integration of DeepRec (https://github.com/alibaba/DeepRec) with Friesian.
- Add more reference examples, e.g. multi-task recommendation, TFRS (https://www.tensorflow.org/recommenders) list-wise ranking, LightGBM training, etc.
- Add a reference example for offline distributed similarity search (using FAISS)
- More operations in FeatureTable (e.g. string embeddings with BERT, etc.).
- PPML
- Upgrade BigDL PPML on Gramine.
- Improve the attestation and key managing process
- More Big Data frameworks on BigDL PPML (including spark, flink, hive, hdfs, etc.)
- Add PPMLContext API for encryption IO and KMS, supports different file formats, encryption algorithms and KMS services
- Support PSI, Pytorch NN, Keras NN, FGBoost (federated XGBoost) in VFL scenario, linear regression & logistic regression for VFL

Highlights
***Note:*** BigDL v2.0.0 has been updated to include functional and security updates. Users should update to the latest version.