Adlik Bear Release (V0.2.0)

Owned by Liya Yuan
Last updated: Nov 12, 2020

Release date

November 20th, 2020

Major features and improvements

New Compiler

  • 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

Inference Engine

  • 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)

Benchmark Test

  • Support the following models:


    		ResNet-50Inception V3Yolo V3Bert
    Tf GPU√√√
    Tf CPU√√√
    TensorRT√√√
    OpenVINO√√

    TFLite√√
    		√

BugFixes

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.

5) 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 https://github.com/Adlik/Adlik/issues?q=is%3Aissue+is%3Aclosed for more information.