DJL v0.11.0 release note
DJL v0.11.0 brings the new engines XGBoost 1.3.1, updates PyTorch to 1.8.1, TensorFlow to 2.4.1, Apache MXNet 1.8.0, PaddlePaddle to 2.0.2 and introduces several new features:
Key Features
- Supports XGBoost 1.3.1 engine inference: now you can run prediction using models trained in XGBoost.
- Upgrades PyTorch to 1.8.1 with CUDA 11.1 support.
- Upgrades TensorFlow to 2.4.1 with CUDA 11.0 support.
- Upgrades Apache MXNet to 1.8.0 with CUDA 11.0 support.
- Upgrades PaddlePaddle to 2.0.2.
- Upgrades SentencePiece to 0.1.95.
- Introduces the djl-serving brew package: now you can install djl-serving with
brew install djl-serving
. - Introduces the djl-serving plugins.
- Introduces Amazon Elastic Inference support.
Enhancement
- Improves TensorFlow performance by reducing GC and fixed memory leaking issue (#892)
- djl-serving now can run all the engines out-of-box (#886)
- Improves DJL training by using multi-threading on each GPU (#743)
- Implements several operators:
- Adds setGraphExecutorOptimize option for PyTorch engine. (#904)
- Introduces String tensor support for ONNXRuntime (#724)
- Introduces several API improvements
- Introduces model searching feature in djl central (#799)
Documentation and examples
- Introduces DJL tutorials - How to load model on DJL Youtube Channel
- Adds the PaddlePaddle load model documentation (#811)
- Adds the documentations for profiler (#722)
- Adds face detection and face recognition examples (#814)
- Adds model training visualization demo using Vue
Breaking change
- Renames CheckpointsTrainingListener to SaveModelTrainingListener (#686)
- Removes erroneous random forest application (#726)
- Deletes DataManager class (#691)
- Classes under ai.djl.basicdataset packages has been moved into each sub-packages.
Bug Fixes
- Fixes BufferOverflowException when handling handling subimage (#866)
- Fixes ONNXRuntime 2nd engine dependency from IrisTranslator (#853)
- Fixes sequenceMask error when n dimension is 2 (#828)
- Fixes TCP port range buf in djl-serving (#773)
- Fixes one array case for concat operator (#739)
- Fixes non-zero operator for PyTorch (#704)
Known issues
- TensorFlow engine has known memory leak issue due to JavaCPP dependency. The memory leak issue has been fixed in javacpp 1.5.6-SNAPSHOT. User has to manually include javacpp 1.5.6-SNAPSHOT to avoid memory leak. See: https://github.com/deepjavalibrary/djl/tree/master/tensorflow/tensorflow-engine#installation for more detail.
Contributors
This release is thanks to the following contributors:
- Akshay Rajvanshi(@aksrajvanshi (https://github.com/ghost))
- Anthony Feenster(@anfee1 (https://github.com/anfee1))
- Calvin(@mymagicpower (https://github.com/mymagicpower))
- enpasos(@enpasos (https://github.com/enpasos))
- Erik Bamberg(@ebamberg (https://github.com/ebamberg))
- Frank Liu(@frankfliu (https://github.com/frankfliu))
- G Goswami(@goswamig (https://github.com/goswamig))
- Hugo Miguel Ferreira(@hmf (https://github.com/hmf))
- Hodovo(@hodovo (https://github.com/Hodovo))
- Jake Lee(@stu1130 (https://github.com/stu1130))
- Lai Wei(@roywei (https://github.com/roywei))
- Qing Lan(@lanking520 (https://github.com/lanking520))
- Marcos(@markbookk (https://github.com/markbookk))
- Stan Kirdey(@skirdey (https://github.com/skirdey))
- Zach Kimberg(@zachgk (https://github.com/zachgk))
- 付颖志 (@fuyz (https://github.com/fuyz))
- 石晓伟(@Shixiaowei02 (https://github.com/Shixiaowei02))