Demos

OpenVINO Model Server demos have been created to showcase the usage of the model server as well as demonstrate it’s capabilities.

Check Out New Generative AI Demos

• Text Generation

• Stable Diffusion

See also new multi-modal demo:

• CLIP image classification

Check out the list below to see complete step-by-step examples of using OpenVINO Model Server with real world use cases:

With Python Client

Demo	Description
Text Generation	Generate text using one of popular LLMs sending prompts via gRPC API unary or interactive streaming endpoint.
Stable Diffusion	Generate image using Stable Diffusion model sending prompts via gRPC API unary or interactive streaming endpoint.
CLIP image classification	Classify image according to provided labels using CLIP model embedded in a multi-node MediaPipe graph.
Seq2seq translation	Translate text using seq2seq model via gRPC API.
Age gender recognition	Run prediction on a JPEG image using age gender recognition model via gRPC API.
Horizontal Text Detection in Real-Time	Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses pipeline with horizontal_ocr custom node and demultiplexer.
Optical Character Recognition Pipeline	Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses pipeline with east_ocr custom node and demultiplexer.
Face Detection	Run prediction on a JPEG image using face detection model via gRPC API.
Single Face Analysis Pipeline	Run prediction on a JPEG image using a simple pipeline of age-gender recognition and emotion recognition models via gRPC API to analyze image with a single face. This demo uses pipeline
Multi Faces Analysis Pipeline	Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses pipeline with model_zoo_intel_object_detection custom node and demultiplexer
Model Ensemble Pipeline	Combine multiple image classification models into one pipeline and aggregate results to improve classification accuracy.
Image Classification	Run prediction on a JPEG image using image classification model via gRPC API.
Using ONNX Model	Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses pipeline with image_transformation custom node.
Using TensorFlow Model	Run image classification using directly imported TensorFlow model.
Person, Vehicle, Bike Detection	Run prediction on a video file or camera stream using person, vehicle, bike detection model via gRPC API.
Vehicle Analysis Pipeline	Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses pipeline with model_zoo_intel_object_detection custom node.
Real Time Stream Analysis	Analyze RTSP video stream in real time with generic application template for custom pre and post processing routines as well as simple results visualizer for displaying predictions in the browser.
Segmentation with PaddlePaddle	Perform segmentation on an image with a PaddlePaddle model.
Natural Language Processing with BERT	Provide a knowledge source and a query and use BERT model for question answering use case via gRPC API. This demo uses dynamic shape feature.
GPT-J Causal Language Modeling	Write start of the sentence and let GPT-J continue via gRPC API. This demo uses dynamic shape feature.
Using inputs data in string format with universal-sentence-encoder model	Handling AI model with text as the model input.
Benchmark App	Generate traffic and measure performance of the model served in OpenVINO Model Server.
Face Blur Pipeline	Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses pipeline with face_blur custom node.

With C++ Client

Demo	Description
C API applications	How to use C API from the OpenVINO Model Server to create C and C++ application.
Image Classification	Run prediction on a JPEG image using image classification model via gRPC API.
Benchmark App	Generate traffic and measure performance of the model served in OpenVINO Model Server.

With Go Client

Demo	Description
Image Classification	Run prediction on a JPEG image using image classification model via gRPC API.