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docs/tutorial/user_guide_dlstreamer_docker_based_AI_app.md

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+# Example Workload: Docker‑Based Chat Q&A, Face Recognition & DL Streamer Face Detection
+
+*An example guide for running Docker-based AI workloads (Ollama chat, face recognition, DL Streamer face detection) on a Linux image created with OS Image Composer.*
+
+This tutorial assumes you have already built a base OS image using **OS Image Composer** and want to validate it or extend it with containerized edge‑AI workloads. For full details of the AI applications themselves (models, pipelines, etc.), refer to the corresponding guides in the `edge-ai-libraries` repository; this document focuses on how to deploy and run them on your composed image, including typical proxy and Docker configuration steps.
+---
+
+## 1. Prerequisites
+- Add the DL Streamer and Docker-based packages in the OS Image Composer build configuration using its [Multiple Package Repository Support](../architecture/os-image-composer-multi-repo-support.md) feature
+
+  After the image is built and the system is booted, verify the runtime environment.
+  First, confirm that Docker Engine is installed and running.
+  Next, confirm that Intel DL Streamer is installed by checking that the directory '/opt/intel/dlstreamer' exists.
+
+---
+
+## 2. Proxy Configuration (Generic Templates)
+
+> Replace placeholders with your organization values:
+>
+> - `<HTTP_PROXY_URL>` — e.g., `http://proxy.example.com:8080`
+> - `<HTTPS_PROXY_URL>` — e.g., `http://proxy.example.com:8443`
+> - `<NO_PROXY_LIST>` — e.g., `localhost,127.0.0.1,::1,*.example.com,10.0.0.0/8,172.16.0.0/12,192.168.0.0/16,/var/run/docker.sock`
+
+### 2.1 Configure Docker Engine (systemd)
+
+```bash
+sudo mkdir -p /etc/systemd/system/docker.service.d
+
+sudo tee /etc/systemd/system/docker.service.d/http-proxy.conf <<'EOF'
+[Service]
+Environment="HTTP_PROXY=<HTTP_PROXY_URL>"
+Environment="HTTPS_PROXY=<HTTPS_PROXY_URL>"
+Environment="NO_PROXY=<NO_PROXY_LIST>"
+EOF
+
+sudo systemctl daemon-reload
+sudo systemctl restart docker
+```
+
+### 2.2 Configure Docker CLI (`~/.docker/config.json`)
+
+```bash
+mkdir -p ~/.docker
+
+tee ~/.docker/config.json <<'EOF'
+{
+  "proxies": {
+    "default": {
+      "httpProxy":  "<HTTP_PROXY_URL>",
+      "httpsProxy": "<HTTPS_PROXY_URL>",
+      "noProxy":    "<NO_PROXY_LIST>"
+    }
+  }
+}
+EOF
+```
+
+---
+
+## 3. Chat Q&A with Ollama (Docker)
+
+### 3.1 Start the container
+
+```bash
+sudo docker run -d --name ollama \
+  --mount source=ollama-data,target=/root/.ollama \
+  --memory="4g" --cpus="1" \
+  -e HTTP_PROXY="<HTTP_PROXY_URL>" \
+  -e HTTPS_PROXY="<HTTPS_PROXY_URL>" \
+  -e NO_PROXY="<NO_PROXY_LIST>" \
+  ollama/ollama
+```
+
+### 3.2 Pull a lightweight model
+
+```bash
+sudo docker exec -it ollama ollama pull llama3.2:1b
+```
+
+### 3.3 Start interactive chat
+
+```bash
+sudo docker exec -it ollama ollama run llama3.2:1b
+```
+
+> Tip: For one-shot queries, you can pass a prompt: `ollama run llama3.2:1b -p "Hello"`
+
+---
+
+## 4. Basic Face Recognition (Docker)
+
+### 4.1 Run the container and enter shell
+
+```bash
+sudo docker run -it aaftio/face_recognition /bin/bash
+```
+
+### 4.2 Prepare folders & sample images (inside container)
+
+```bash
+mkdir -p /images/known /images/unknown
+
+# Known faces
+wget -P /images/known https://raw.githubusercontent.com/ageitgey/face_recognition/master/examples/biden.jpg
+wget -P /images/known https://raw.githubusercontent.com/ageitgey/face_recognition/master/examples/obama.jpg
+
+# Unknown images
+wget -P /images/unknown https://raw.githubusercontent.com/ageitgey/face_recognition/master/examples/two_people.jpg
+wget -P /images/unknown https://raw.githubusercontent.com/ageitgey/face_recognition/master/examples/alex-lacamoire.png
+
+# Note: For production or security-sensitive environments, verify downloaded files.
+# Example (replace <EXPECTED_SHA256_* > with known-good hashes):
+# echo "<EXPECTED_SHA256_BIDEN>  /images/known/biden.jpg" | sha256sum -c -
+# echo "<EXPECTED_SHA256_OBAMA>  /images/known/obama.jpg" | sha256sum -c -
+# echo "<EXPECTED_SHA256_TWO_PEOPLE>  /images/unknown/two_people.jpg" | sha256sum -c -
+# echo "<EXPECTED_SHA256_ALEX_LACAMOIRE>  /images/unknown/alex-lacamoire.png" | sha256sum -c -
+```
+
+### 4.3 Match faces (inside container)
+
+```bash
+face_recognition /images/known /images/unknown/alex-lacamoire.png
+face_recognition /images/known /images/unknown/two_people.jpg
+```
+
+---
+
+## 5. DL Streamer – Face Detection Pipeline 
+
+Run face detection on a video file using **Open Model Zoo**’s *Face Detection ADAS‑0001* model.
+
+### 5.1 Environment (DL Streamer)
+
+```bash
+export GST_PLUGIN_PATH=/opt/intel/dlstreamer/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:/opt/intel/dlstreamer/streamer/lib/
+
+export LD_LIBRARY_PATH=/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/lib:/opt/intel/dlstreamer/lib/gstreamer-1.0:/usr/lib:/usr/local/lib/gstreamer-1.0:/usr/local/lib
+
+export PATH=/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/bin:$PATH
+
+export MODELS_PATH=/home/${USER}/intel/models
+```
+
+Verify plugins:
+```bash
+gst-inspect-1.0 | grep -E "gvadetect|gvawatermark|gvatrack|gvaclassify"
+```
+
+### 5.2 Download the model (OMZ tools)
+
+> If you don’t have OMZ tools: `python3 -m pip install openvino-dev` (use a venv if your distro enforces PEP 668).
+
+```bash
+omz_downloader --name face-detection-adas-0001
+```
+
+The IR will be available at (example):
+```
+~/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.xml
+```
+
+### 5.3 Run the pipeline (save to WebM)
+
+```bash
+gst-launch-1.0 filesrc location=/path/to/face-demographics-walking.mp4 ! \
+  decodebin ! videoconvert ! \
+  gvadetect model=/home/<YOUR_USERNAME>/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.xml device=CPU ! \
+  gvawatermark ! videoconvert ! \
+  vp8enc ! webmmux ! \
+  filesink location=face_detected_output.webm
+```
+
+### 5.4 Alternative: Display on screen
+
+```bash
+gst-launch-1.0 filesrc location=/path/to/face-demographics-walking.mp4 ! \
+  decodebin ! videoconvert ! \
+  gvadetect model=/path/to/models/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.xml device=CPU ! \
+  gvawatermark ! videoconvert ! \
+  autovideosink
+```
+
+---
+
+## 6. Notes & Troubleshooting
+
+- **Proxy cert errors (Docker pulls)**: import your corporate root CA into the OS trust store (for example, using `sudo apt-get install -y ca-certificates` and `sudo update-ca-certificates`) and into `/etc/docker/certs.d/<registry>/ca.crt`, then run `systemctl restart docker`.
+- **No GVA plugins?** Ensure DL Streamer is installed and `GST_PLUGIN_PATH` is exported.
+- **Headless systems**: prefer the file-output pipeline (WebM/MP4) instead of `autovideosink`.
+- **Model path errors**: ensure `.xml` and `.bin` are co-located in the same `FP32`/`FP16` folder.
+##  Additional DL Streamer Applications & Examples
+
+For more DL Streamer (DLS) pipelines, advanced video analytics, multi-model graphs, and edge AI applications, refer to the official Open Edge Platform AI Libraries:
+
+ **https://github.com/open-edge-platform/edge-ai-libraries**
+
+This repository contains:
+- Ready‑to‑run DL Streamer pipelines  
+- Comprehensive model‑proc files  
+- Multi-stage pipelines (detect → track → classify → action recognition)  
+- Optimized GStreamer graphs for edge deployments  
+- Reusable components for real‑time video analytics  
+- Integrations with OpenVINO, VA-API, and hardware accelerators  
+
+Use these examples to extend your application beyond basic face detection into:
+- Person/vehicle tracking  
+- Object classification  
+- Action recognition  
+- Multi-camera pipelines  
+- Custom edge AI applications  
+
+
+---
+
+## 7. License
+
+This guide contains example commands and scripts provided for convenience. Review third‑party container/images licenses before redistribution.
+
+
+
+
+
+