Merge branch 'docs/rkatakol/npu_docs_update' of https://github.com/open-edge-platform/edge-ai-suites into docs/rkatakol/npu_docs_update

Author: sajeevrajput

metro-ai-suite/metro-vision-ai-app-recipe/loitering-detection/docs/user-guide/how-to-guides.md

@@ -13,6 +13,7 @@ This section collects guides for the Loitering Detection sample application.
 
 ./how-to-guides/customize-application
 ./how-to-guides/use-gpu-for-inference
+./how-to-guides/use-npu-for-inference
 ./how-to-guides/view-telemetry-data
 ./how-to-guides/benchmark
 

metro-ai-suite/metro-vision-ai-app-recipe/loitering-detection/docs/user-guide/how-to-guides/use-npu-for-inference.md

@@ -0,0 +1,49 @@
+# Use NPU for Inference
+
+## Pre-requisites
+
+In order to benefit from hardware acceleration, pipelines can be constructed in a manner that
+different stages such as decoding, inference etc., can make use of these devices.
+For containerized applications built using the DL Streamer Pipeline Server, first we need to
+provide NPU device(s) access to the container user.
+
+### Provide NPU access to the container
+This can be done by making the following changes to the docker compose file.
+
+```yaml
+services:
+  dlstreamer-pipeline-server:
+    group_add:
+      # render group ID for ubuntu 22.04 host OS
+      - "110"
+      # render group ID for ubuntu 24.04 host OS
+      - "992"
+    devices:
+      # you can add specific devices in case you don't want to provide access to all like below.
+      - "/dev:/dev"
+```
+The changes above adds the container user to the `render` group and provides access to the NPU
+devices.
+
+### Hardware specific encoder/decoders
+Unlike the changes done for the container above, the following requires a modification to the
+media pipeline itself.
+
+Gstreamer has a variety of hardware specific encoders and decoders elements such as Intel
+specific VA-API elements that you can benefit from by adding them into your media pipeline.
+Examples of such elements are `vah264dec`, `vah264enc`, `vajpegdec`, `vajpegdec`, etc.
+
+## Tutorial on how to use NPU specific pipelines
+
+> **Note:** This sample application already provides a default `compose-without-scenescape.yml`
+> file that includes the necessary NPU access to the containers.
+
+The pipeline `object_tracking_npu` in [pipeline-server-config](../../../src/dlstreamer-pipeline-server/config.json)
+contains NPU specific elements and uses NPU backend for inferencing. We can start the pipeline
+as follows:
+
+```sh
+./sample_start.sh npu
+```
+
+Go to Grafana as explained in [Get Started](../get-started.md) to view the dashboard.

metro-ai-suite/metro-vision-ai-app-recipe/smart-parking/docs/user-guide/how-to-guides.md

@@ -15,6 +15,7 @@ This section collects guides for the Smart Parking sample application.
 ./how-to-guides/customize-application
 ./how-to-guides/generate-offline-package
 ./how-to-guides/use-gpu-for-inference
+./how-to-guides/use-npu-for-inference
 ./how-to-guides/view-telemetry-data
 ./how-to-guides/benchmark
 

metro-ai-suite/metro-vision-ai-app-recipe/smart-parking/docs/user-guide/how-to-guides/use-npu-for-inference.md

@@ -0,0 +1,49 @@
+# Use NPU for Inference
+
+## Pre-requisites
+In order to benefit from hardware acceleration, pipelines can be constructed in a manner that
+different stages such as decoding, inference etc., can make use of these devices.
+For containerized applications built using the DL Streamer Pipeline Server, first we need to
+provide NPU device(s) access to the container user.
+
+### Provide NPU access to the container
+This can be done by making the following changes to the docker compose file.
+
+```yaml
+services:
+  dlstreamer-pipeline-server:
+    group_add:
+      # render group ID for ubuntu 22.04 host OS
+      - "110"
+      # render group ID for ubuntu 24.04 host OS
+      - "992"
+    devices:
+      # you can add specific devices in case you don't want to provide access to all like below.
+      - "/dev:/dev"
+```
+
+The changes above adds the container user to the `render` group and provides access to the
+NPU devices.
+
+### Hardware specific encoder/decoders
+Unlike the changes done for the container above, the following requires a modification to the
+media pipeline itself.
+
+Gstreamer has a variety of hardware specific encoders and decoders elements such as Intel
+specific VA-API elements that you can benefit from by adding them into your media pipeline.
+Examples of such elements are `vah264dec`, `vah264enc`, `vajpegdec`, `vajpegdec`, etc.
+
+## Tutorial on how to use NPU specific pipelines
+
+> **Note:** This sample application already provides a default `compose-without-scenescape.yml`
+> file that includes the necessary NPU access to the containers.
+
+The pipeline `yolov11s_npu` in [pipeline-server-config](../../../src/dlstreamer-pipeline-server/config.json)
+contains NPU specific elements and uses NPU backend for inferencing. We can start the pipeline
+as follows:
+
+```sh
+./sample_start.sh npu
+```
+
+Go to Grafana as explained in [Get Started](../get-started.md) to view the dashboard.