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OpenVINO™ Model Server 2021.4.1
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@dkalinowski dkalinowski released this 13 Sep 11:26
· 1614 commits to main since this release
v2021.4.1
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The 2021.4.1 version of Model Server is primarily a hotfix release. It also includes a preview version of the simplified python client library and a sample client written in C++. We added also a version of the model server docker image based on Ubuntu 20.04. The public docker image in DockerHub is using now the Ubuntu 20.04 base OS. The model server image based on CentOS 7 will be discontinued starting from next release.

Bug Fixes:

  • Removed limitation in the DAG configuration which required the pipeline input to be connected to at least one neural network model while using the binary input format. Now the input can be connected also exclusively to a custom node. An example of such use case is documented in ovms_onnx_example.
  • Removed an invalid error message in the server logs while loading models from the google cloud storage.
  • Fixed a very rare race condition preventing detection of updates in the configuration file.
  • Improvements in the error messages reporting an invalid DAG pipeline configuration with unmatched data shape between nodes.
  • Corrected reported model state in the model states queries in the loading error condition. When the model cannot be loaded, it will now report status Loading>Error instead of End>OK.
  • The model server was ignoring incorrect parameters in the configuration file. It was typically a situation with a spelling mistake for a valid parameter. Now an error will be raised when an invalid parameter is defined.
  • Corrected issue related to a scenario with demultiplexed output connected both to a custom node and a neural network model (DL node).

Python client library - the lightweight client library provides simplified mechanism to communicate with OVMS and TensorFlow Serving. Contrary to tensorflow-server-api, it does not include Tensorflow as a dependency, which reduces its size dramatically. It also has simplified API which allows sending the prediction requests with just few commands. Currently gRPC protocol is included. REST API is to be added. Learn more in client lib documentation.

C++ client example - client code example compatible with OVMS and TensorFlow Serving. It can run the predict requests both in a format of jpeg/png images or as arrays converted to tensor_content format. It includes a receipt for building it using bazel and a dockerfile. Learn more in example documentation.

You can use an OpenVINO Model Server public Docker image based on Ubuntu via the following command:
docker pull openvino/model_server:2021.4.1 or
docker pull openvino/model_server:2021.4.1-gpu

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