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This PR contains the following updates:
0.3.0
->0.11.0
1.6.0
->1.8.0
0.3.0
->0.11.0
0.3.0
->0.11.0
Release Notes
deepjavalibrary/djl
v0.11.0
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
brew install djl-serving
.Enhancement
Documentation and examples
Breaking change
Bug Fixes
Known issues
Contributors
This release is thanks to the following contributors:
v0.10.0
DJL v0.10.0 brings the new engines PaddlePaddle 2.0 and TFLite 2.4.1, updates PyTorch to 1.7.1, and introduces several new features:
Key Features
Supports PaddlePaddle 2.0 engine inference: now you can run prediction using models trained in PaddlePaddle.
Introduces the PaddlePaddle Model Zoo with new models. Please see examples for how to run them.
Upgrades TFLite engine to v2.4.1. You can convert TensorFlow SavedModel to TFLite using this converter.
Introduces DJL Central to easily browse and view models available in DJL’s ModelZoo.
Introduces generic Bert Model in DJL (#105)
Upgrades PyTorch to 1.7.1
Enhancement
Documentation and examples
Breaking change
SoftmaxCrossEntropyLoss's
fromLogit
flag mean inputs are un-normalized (#639)Bug Fixes
Known issues
Contributors
This release is thanks to the following contributors:
v0.9.0
DJL 0.9.0 brings MXNet inference optimization, abundant PyTorch new feature support, TensorFlow windows GPU support and experimental DLR engine that support TVM models.
Key Features
MXNet
PyTorch
TensorFlow
Several Engines upgrade
Enhancement
Documentation and examples
Bug Fixes
Contributors
Thank you to the following community members for contributing to this release:
Frank Liu(@frankfliu)
Lanking(@lanking520)
Kimi MA(@kimim)
Lai Wei(@roywei)
Jake Lee(@stu1130)
Zach Kimberg(@zachgk)
0xflotus(@0xflotus)
Joshua(@euromutt)
mpskowron(@mpskowron)
Thomas(@thhart)
DocRozza(@docrozza)
Wai Wang(@waicool20)
Trijeet Modak(@uniquetrij)
v0.8.0
DJL 0.8.0 is a release closely following 0.7.0 to fix a few key bugs along with some new features.
Key Features
Documentation and examples
Bug Fixes
Known issues
Contributors
Thank you to the following community members for contributing to this release:
Dennis Kieselhorst, Frank Liu, Jake Cheng-Che Lee, Lai Wei, Qing Lan, Zach Kimberg, uniquetrij
v0.7.0
DJL 0.7.0 brings SetencePiece for tokenization, GravalVM support for PyTorch engine, a new set of Nerual Network operators, BOM module, Reinforcement Learning interface and experimental DJL Serving module.
Key Features
Documentation and examples
Enhancement
Breaking changes
FastTextWorkEmbedding
WarmUpTracker
MxPredictor
now doesn’t copy parameters by default, please make sure to useNaiveEngine
when you run inference in multi-threading environmentBug Fixes
Contributors
Thank you to the following community members for contributing to this release:
Christoph Henkelmann, Frank Liu, Jake Cheng-Che Lee, Jake Lee, Keerthan Vasist, Lai Wei, Qing Lan, Victor Zhu, Zach Kimberg, aksrajvanshi, gstu1130, 蔡舒起
v0.6.0
DJL 0.6.0 brings stable Android support, ONNX Runtime experimental inference support, experimental training support for PyTorch.
Key Features
Documentation and examples
Breaking changes
Known issues
Contributors
Thank you to the following community members for contributing to this release:
Christoph Henkelmann, Frank Liu, Jake Lee, JonTanS, Keerthan Vasist, Lai Wei, Qing, Qing Lan, Victor Zhu, Zach Kimberg, ai4java, aksrajvanshi
v0.5.0
DJL 0.5.0 release brings TensorFlow engine inference, initial NLP support and experimental Android inference with PyTorch engine.
Key Features
Documentation and examples
Breaking changes
ai.djl.repository
, useai.djl.api
instead.Know issues:
v0.4.1
DJL 0.4.1 release includes an important performance Improvement on MXNet engine:
Performance Improvement:
MxNDManager.newSubManager()
to repeatedly callinggetFeature()
which will make JNA calls to native code.Known Issues:
Same as v0.4.0 release:
v0.4.0
DJL 0.4.0 brings PyTorch and TensorFlow 2.0 inference support. Now you can use these engines directly from DJL with minimum code changes.
Note: TensorFlow 2.0 currently is in PoC stage, users will have to build from source to use it. We expect TF Engine finish in the future releases.
Key Features
Breaking Changes
There are a few changes in API and ModelZoo packages to adapt to multi-engine support. Please follow our latest examples to update your code base from 0.3.0 to 0.4.0.
Known Issues
Configuration
📅 Schedule: At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻️ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.
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