v0.6.0

We are excited to announce the release of TorchEasyRec 0.6.0, the first public release for TorchEasyRec.

Major Features and Improvements

• High-performance training, evaluation, and prediction with GPUs.
• Supported a variety of input data types, including MaxCompute Table, OSS files, CSV files, Parquet files doc here.
• Supported a variety of feature types, including IdFeature, RawFeature, ComboFeature, LookupFeature, MatchFeature, ExprFeature, OverlapFeature, TokenizeFeature, SequenceIdFeature, SequenceRawFeature, and SequenceFeature. The feature generation operations is also efficient and robust doc here.
• Supported a variety of models, including DSSM, TDM, DeepFM, MultiTower, DIN, MMoE, DBMTL, PLE. It is also easy to implement customized models.
• Supported a variety of loss, including binary_cross_entropy, softmax_cross_entropy, l2_loss, jrc_loss doc here.
• Supported VariationalDropout feature selection.
• Easy to deploy a TorchEasyRec model as a high-performance inference service using the TorchEasyRec Processor.

Bug Fixes and Other Changes

• [bugfix] fix train_eval may hang when use OdpsDataset and set is_orderby_partition=true by @tiankongdeguiji in
• [bugfix] fix offline predict input tile model with sequence by @tiankongdeguiji in #14

Note

For TorchEasyRec 0.6.x, you should use Docker image version 0.6.

• For the GPU version (CUDA 12.1):
  • mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/tzrec-devel:0.6-cu121
• For the CPU version:
  • mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/tzrec-devel:0.6-cpu

New Contributors

• @tiankongdeguiji made their first contribution in #1
• @jjbbong made their first contribution in #3
• @chengaofei made their first contribution in #4

Full Changelog: https://github.com/alibaba/TorchEasyRec/commits/v0.6.0