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BigDL release 2.1.0
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@glorysdj glorysdj released this 28 Sep 03:06
· 5 commits to branch-2.1 since this release
v2.1.0
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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
Assets 2