Inference model server implementation with gRPC interface, compatible with TensorFlow serving API and OpenVINO™ as the execution backend.
“OpenVINO™ model server” is a flexible, high-performance inference serving component for artificial intelligence models.
The software makes it easy to deploy new algorithms and AI experiments,
while keeping the same server architecture and APIs like in TensorFlow Serving.
It provides out-of-the-box integration with models supported by OpenVINO™ and allows frameworks such as AWS Sagemaker to serve AI models with OpenVINO™.
OpenVINO Model Server supports for the models storage, beside local filesystem, also GCS, S3 and Minio.
It is implemented as a python service using gRPC library interface; data serialization and deserialization using TensorFlow; and OpenVINO™ for inference execution. It acts as an integrator and a bridge exposing CPU optimized inference engine over gRPC network interface.
Review the Architecture concept document for more details.
OpenVINO Model Server, beside CPU, can employ Intel® Movidius™ Neural Compute Sticks AI accelerator. It can be enabled both on Bare Metal Hosts or in Docker containers.
Landing on bare metal or virtual machine
Using FPGA (TBD)
OpenVINO™ model server API is documented in proto buffer files in tensorflow_serving_api. Note: The implementations for Predict and GetModelMetadata function calls are currently available. These are the most generic function calls and should address most of the usage scenarios.
predict Function Spec has two message definitions: PredictRequest and PredictResponse.
- PredictRequest specifies information about the model spec, a map of input data serialized via TensorProto to a string format and an optional output filter.
- PredictResponse includes a map of outputs serialized by TensorProto and information about the used model spec.
get_model_metadata Function spec has three message definitions: SignatureDefMap, GetModelMetadataRequest, GetModelMetadataResponse. A function call GetModelMetadata accepts model spec information as input and returns Signature Definition content in the format similar to TensorFlow Serving.
Refer to the example client code to learn how to use this API and submit the requests using gRPC interface.
OpenVINO™ model server accepts 3 logging levels:
- ERROR: Logs information about inference processing errors and server initialization issues.
- INFO: Presents information about server startup procedure.
- DEBUG: Stores information about client requests.
The default setting is INFO, which can be altered by setting environment variable LOG_LEVEL.
The captured logs will be displayed on the model server console. While using docker containers or kubernetes the logs
can be examined using docker logs or kubectl logs commands respectively.
It is also possible to save the logs to a local file system by configuring an environment variable LOG_PATH with the absolute path pointing to a log file. Please see example below for usage details.
docker run --name ie-serving --rm -d -v /models/:/opt/ml:ro -p 9001:9001 --env LOG_LEVEL=DEBUG --env LOG_PATH=/var/log/ie_serving.log \
ie-serving-py:latest /ie-serving-py/start_server.sh ie_serving config --config_path /opt/ml/config.json --port 9001
docker logs ie-serving
OpenVINO™ model server will fail to start when any of the defined model cannot be loaded successfully. The root cause of the failure can be determined based on the collected logs on the console or in the log file.
The following problem might occur during model server initialization and model loading:
- Missing model files in the location specified in the configuration file.
- Missing version sub-folders in the model folder.
- Model files require custom CPU extension.
When the model server starts successfully and all the models are imported, there could be a couple of reasons for errors in the request handling. The information about the failure reason is passed to the gRPC client in the response. It is also logged on the model server in the DEBUG mode.
The possible issues could be:
- Incorrect shape of the input data.
- Incorrect input key name which does not match the tensor name or set input key name in
mapping_config.json. - Incorrectly serialized data on the client side.
RAM consumption might depend on the size and volume of the models configured for serving. It should be measured experimentally, however it can be estimated that each model will consume RAM size equal to the size of the model weights file (.bin file). Every version of the model creates a separate inference engine object, so it is recommended to mount only the desired model versions.
OpenVINO™ model server consumes all available CPU resources unless they are restricted by operating system, docker or kubernetes capabilities.
Usually, there is no need to tune any environment variables according to the allocated resources.
It is possible to track the usage of the models including processing time while DEBUG mode is enabled. With this setting model server logs will store information about all the incoming requests. You can parse the logs to analyze: volume of requests, processing statistics and most used models.
- Currently, only Predict and GetModelMetadata calls are implemented using Tensorflow Serving API. Classify, Regress and MultiInference are planned to be added.
- Currently, model versions are detected at server start time. Adding new versions requires service restart or starting new docker container. There are plans to add online detection of new model versions and config file changes.
- Output_filter is not effective in the Predict call. All outputs defined in the model are returned to the clients.
All contributed code must be compatible with the Apache 2 license.
All changes needs to have passed style, unit and functional tests.
All new features need to be covered by tests.
Docker image with OpenVINO Model Server can be built with several options:
make docker_build_bin- using Intel Distribution of OpenVINO binary package (ubuntu base image)make docker_build_src_ubuntu- using OpenVINO source code with ubuntu base imagemake docker_build_src_intelpython- using OpenVINO source code with 'intelpython/intelpython3_core' base image (Intel optimized python distribution with conda and debian)make docker_build_clearlinux- using ClearLinux base image and computer vision bundle
make style to run linter tests
make unit to execute unit tests (it requires OpenVINO installation followed by make install)
make test to execute functional tests (it requires building the docker image in advance). Running
the tests require also preconfigured env variables GOOGLE_APPLICATION_CREDENTIALS, AWS_ACCESS_KEY_ID,
AWS_SECRET_ACCESS_KEY and AWS_REGION with permissions to access models used in tests.
To run tests limited to models to locally downloaded models use command:
make test_local_only
Submit Github issue to ask question, submit a request or report a bug.