November 20th, 2020
Major features and improvements
Support DAG generation for end-to-end compilation of models with different representation
Source representation: H5, Ckpt, Pb, Pth, Onnx and SavedModel
Target representation: SavedModel, OpenVINO IR, TensorRT Plan and Tflite
Support model quantization for TfLite and TensorRT
Int8 quantization for TfLite
Int8 and fp16 quantization for TensorRT
Support hybrid scheduling of ML and DL inference jobs
Support image based deployment of Adlik compiler and inference engine in cloud native environment, deployment and function test has been done in:
Cloud environment based on docker (V19.03.12)
Cloud environment based on Kubernetes (V1.13)
Support the newest version of OpenVINO (2021.1.110) and TensorFlow (2.3.1)
Support the following models:
ResNet-50 Inception V3 Yolo V3 Bert Tf GPU √ √ √ Tf CPU √ √ √ TensorRT √ √ √ OpenVINO √ √ TFLite √ √ √
The following bugs are fixed:
1) Can Not Convert Yolo.h5 To Openvino Runtime.
2) gRPC:Received message larger than max.
3) Return Message Is Wrong When cudaMalloc() Failed In initializeOutputBindings() Method.
4) Can Not Do Predict With Following Transferred YoloV3 Model.
adlik_serving --help should exit successfully.
6) benchmark cant auto infer by tensorflow gpu image.
7) Prediction will fail if information in model.pbtxt and model representation not consistent in tensorflowLite runtime.
Please see for more information.