DJL v0.14.0 release

DJL v0.14.0 updates the engines PyTorch to 1.9.1 and introduces several new features:

Key Features

• Upgrades PyTorch engine to 1.9.1
• Adds support for Neuron SDK 1.16.1
• Adds autoscale in djl-serving for AWS Inferentia model
• Introduces OpenCV extension to provide high performance image processing
• Adds support for older version of PyTorch engine, user now can use PyTorch 1.8.1 with latest DJL
• Adds support for precxx11 PyTorch native library in auto detection mode
• Adds AWS Inferentia support in djl-bench
• Adds support for TorchServe .mar format, user can deploy TorchServe model archive in djl-serving

Enhancement

• Introduces several new features in djl-serving:
  • Adds autoscale feature for AWS Inferentia (#31)
  • Creates SageMaker hosting compatible docker image for AWS Inferentia (#36)
  • Adds auto detect number of neuron cores feature for AWS Inferentia (#34)
  • Adds autoscale support for SageMaker style .tar.gz model (#35)
  • Adds support to load torchserve model (#32)
  • Adds support to pip installed dependency per model (#37)
  • Adds custom environment variable support for python engine (#29)
  • Adds nested folder support in model archive file (#38)
  • Improves model status with model version support (#25)
  • Adds model warn up feature for python engine. (#23)
  • Adds WorkLoadManager.unregisterModel (#33)
  • Adds testing tool to test python model locally (#22)
  • Adds set python executable path for python engine (#21)
  • Creates Workflow for ModelServing (#26)
• Adds OpenCV extension (#1331)
• Introduces several new features in djl-bench:
  • Adds support for AWS Inferentia (#1329)
• Introduces several new features in Apache MXNet engine:
  • Implements LayerNorm for Apache MXNet (#1342)
• Introduces several new features in PyTorch engine:
  • Upgrades PyTorch to 1.9.1 (#1297)
  • Implements padding to bert tokenizer (#1328)
  • Makes pytorch-native-auto package optional (#1326)
  • Adds support to use different version of PyTorch native library (#1323)
  • Adds map_location support for load model from InputStream (#1314)
  • Makes map_location optional (#1312)
• Introduces several new features in TensorFlow Lite engine:
  • Makes tensor-native-auto package optional (#1301)
• Introduces several API improvements:
  • Adds support for nested folder in model archive (#1349)
  • Improves translator output error message (#1348)
  • Improves Predictor API to support predict with device (#1346)
  • Improves BufferedImageFactory.fromNDArray performance (#1339)
  • Adds support for downloading .mar file (#1338)
  • Adds debugging toString to Input and Output (#1327)
  • Refactors BERT Translator and Tokenizer (#1318)
  • Makes question answering model serving ready (#1311)
  • Refactors minMaxWorkers from ModelInfo to WorkerPool (#30)

Documentation and examples

• Adds huggingface Inferentia serving example (#184)
• Adds AWS SageMaker hosting document
• Adds python hybrid engine demo (#182)
• Improves DJL examples project gradle build script (#1344)

Breaking change

• PyTorch 1.9.1 no longer supports Amazon Linux 2, AL2 user has to use pytorch-native-cpu-precxx11
• Image.Type is removed and Image.duplicate() function no longer take Image.Type as input
• Image.getSubimage() is renamed to Image.getSubImage()
• PaddlePaddle model loading may break due to prefix changes.

Bug Fixes

• Fixes 2nd inference throw exception bug (#1351)
• Fixes calculation for SigmoidBinaryCrossEntropyLoss from sigmoid (#1345)
• Fixes jar model url download bug (#1336)
• Fixes memory in Trainer.checkGradients (#1319)
• Fixes NDManager is closed bug (#1308)
• Fixes PyTorch GPU model loading issue (#1302)
• Fixes MXNet EngineException message (#1300)
• Fixes python resnet18 demo model GPU bug (#24)
• Fixes python engine get_as_bytes() bug (#20)

Contributors

This release is thanks to the following contributors:

• Frank Liu (@frankfliu)
• Jake Lee (@stu1130)
• enpasos (@enpasos)
• Qing Lan (@lanking520)
• Zach Kimberg (@zachgk)