Fix broken links in docs and demos#4399
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Pull request overview
This PR targets documentation/demo cleanup by removing references to docs and demo paths that no longer exist, reducing broken-link surface area across custom node READMEs and user guides.
Changes:
- Removed links to retired demos/config examples from several
src/custom_nodes/*/README.mdfiles. - Pruned references to deprecated/removed dynamic-shape/dynamic-batch docs and pipeline examples in
docs/. - Removed multiple retired demo entries from
demos/README.mdand related demo index pages.
Reviewed changes
Copilot reviewed 11 out of 11 changed files in this pull request and generated 5 comments.
Show a summary per file
| File | Description |
|---|---|
| src/custom_nodes/model_zoo_intel_object_detection/README.md | Removes a NOTE pointing to demo configs that no longer exist. |
| src/custom_nodes/horizontal_ocr/README.md | Removes demo references that appear to be removed from the repo. |
| src/custom_nodes/face_blur/README.md | Removes demo/config links, but also inadvertently removes build commands (needs fix). |
| src/custom_nodes/east_ocr/README.md | Removes demo/config NOTE that appears to be obsolete. |
| docs/shape_batch_size_and_layout.md | Removes reference to a removed doc; introduces trailing whitespace (minor). |
| docs/dynamic_input.md | Removes deprecated bullets, but leaves a toctree entry for a non-existent page (needs fix). |
| docs/demultiplexing.md | Removes a link to a removed “dynamic batch size guide” doc. |
| docs/dag_scheduler.md | Removes pipeline example links; leaves an empty “Pipelines Examples” section (needs fix). |
| demos/real_time_stream_analysis/python/README.md | Removes a reference to a removed horizontal text detection doc. |
| demos/README.md | Removes multiple retired demo entries; section cleanup still needed (inline fix suggested). |
| demos/image_classification/README.md | Removes Go demo table row, but leaves broken/empty Go navigation (needs fix). |
Comments suppressed due to low confidence (2)
src/custom_nodes/face_blur/README.md:33
- The BASE_OS example code fence is empty after the change; it should include the
make BASE_OS=redhat NODES=face_blurinvocation (consistent with the other custom node READMEs).
You can also select base OS between RH 8.10 (redhat) and Ubuntu 22.04 (ubuntu) by setting `BASE_OS` environment variable.
```bash
**demos/image_classification/README.md:21**
* The Go demo entry was removed from the table, but the page still includes a toctree entry (`ovms_demo_image_classification_go`) and a Go section header with an empty table. If the Go demo directory was removed, the toctree entry and Go section should be removed as well to avoid broken doc navigation.
ovms_demo_image_classification
ovms_demo_image_classification_go
## Python
| Demo | Description |
|---|---|
|[Image Classification](python/README.md)|Run prediction on a JPEG image using image classification model via gRPC API.|
## Go
| Demo | Description |
|---|---|
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| ## With DAG Pipelines | ||
| | Demo | Description | | ||
| |---|---| | ||
| |[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md) | Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). | | ||
| |[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md) | 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](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). | | ||
| |[Single Face Analysis Pipeline](single_face_analysis_pipeline/python/README.md)|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](../docs/dag_scheduler.md) | | ||
| |[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|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](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md) | | ||
| |[Model Ensemble Pipeline](model_ensemble/python/README.md)|Combine multiple image classification models into one [pipeline](../docs/dag_scheduler.md) and aggregate results to improve classification accuracy. | | ||
| |[Face Blur Pipeline](face_blur/python/README.md)|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](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/face_blur). | | ||
| |[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|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](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection). | | ||
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| ## Pipelines Examples | ||
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| [Single face analysis with combined models](../demos/single_face_analysis_pipeline/python/README.md) | ||
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| [Multiple vehicles analysis using demultiplexer with model_zoo_object_detection example custom node](../demos/vehicle_analysis_pipeline/python/README.md) | ||
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| [Optical Character Recognition pipeline with east_ocr example custom node](../demos/optical_character_recognition/python/README.md) | ||
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| *Note:* In case of frequent batch size changes in predict requests, consider using [demultiplexing feature](./demultiplexing.md) from [Directed Acyclic Graph Scheduler](./dag_scheduler.md) which is more | ||
| performant in such situations because it is not adding an extra overhead with model reloading between requests like --batch_size auto setting. Exemplary usage of this feature can be found in [dynamic_batch_size](./dynamic_bs_demultiplexer.md) document. | ||
| performant in such situations because it is not adding an extra overhead with model reloading between requests like --batch_size auto setting. |
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| You can build the shared library of the custom node simply by running command in the context of custom node examples directory: | ||
| ```bash | ||
| git clone https://github.com/openvinotoolkit/model_server && cd model_server/src/custom_nodes | ||
| make NODES=face_blur | ||
| ``` | ||
| It will compile the library inside a docker container and save the results in `lib/<OS>/` folder. |
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| - [dynamic input shape with dynamic IR/ONNX model](./dynamic_shape_dynamic_model.md) - leverage OpenVINO native dynamic shape feature to send data with arbitrary shape. Consider using this option if model accepts dynamic dimensions. | ||
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| - [dynamic input shape with binary input format](./dynamic_shape_binary_inputs.md) - send data in binary format (JPEG or PNG encoded), so the Model Server will adjust the input during data decoding. Consider this option in case of slower networks to minimize amount of data transferred over the network and fit image size to the size accepted by endpoint. | ||
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| - [**DEPRECATED**] [dynamic batch size with automatic model reloading](./dynamic_bs_auto_reload.md) - configure the Model Server to reload the model each time it receives a request with a batch size other than what is currently set. Consider using this option when request batch size may change, but usually stays the same. Each request with varying batch size will impact the performance due to model reloading. | ||
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| - [**DEPRECATED**] [dynamic shape with automatic model reloading](./dynamic_shape_auto_reload.md) - configure the Model Server to reload the model each time the model receives a request with data in shape other than what is currently set. Consider using this option when request shape may change, but usually stays the same. Each request with varying shape will impact the performance due to model reloading. | ||
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| **DAG Pipelines**: | ||
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| - [dynamic batch size with a demultiplexer](./dynamic_bs_demultiplexer.md) - create a simple pipeline that splits data of any batch size and performs inference on each element in the batch separately. Consider using this option if incoming requests will be containing various batch size. This option does not need to reload underlying model, therefore there is no model reloading impact on the performance. | ||
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| - [dynamic input shape with a custom node](./dynamic_shape_custom_node.md) - create a simple pipeline by pairing your model with a custom node that performs data preprocessing and provides the model with data in an acceptable shape. Consider this option if you want to fit the image into model shape by performing image resize operation before inference. This may affect accuracy. |
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