Merge branch 'main' into gst_to_1_26_10

Author: tbujewsk

.github/workflows/dls-pr-workflow.yaml

@@ -120,14 +120,13 @@ jobs:
       DLS_COVERITY_EMAIL: ${{ secrets.DLS_COVERITY_EMAIL }}
       DLS_COVERITY_PROJECT: ${{ secrets.DLS_COVERITY_PROJECT }}
 
-  dls-filter-docker-or-optimizer-related-changes:
+  dls-filter-docker-related-changes:
     permissions:
       contents: read
-    name: "DLS SCAN: detect changes in docker and optimizer dir"
+    name: "DLS SCAN: detect changes in docker dir"
     runs-on: dlstreamer
     outputs:
       docker_changed: ${{ steps.check.outputs.docker_changed }}
-      optimizer_changed: ${{ steps.check.outputs.optimizer_changed }}
     steps:
       - name: Check out dlstreamer repository
         uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd #6.0.2
@@ -141,33 +140,22 @@ jobs:
           cd dlstreamer-repo
           git fetch origin main
 
-      - name: Detect changes in docker or optimizer directory
+      - name: Detect changes in docker  directory
         id: check
         run: |
-          echo "🔍 Checking for changes in 'docker/' and 'scripts/optimizer'..."
+          echo "🔍 Checking for changes in 'docker/' dir"
           cd dlstreamer-repo
           CHANGED_FILES_DOCKER=$(git diff --name-only origin/main...HEAD -- 'docker/')
-          CHANGED_FILES_OPTIMIZER=$(git diff --name-only origin/main...HEAD -- 'scripts/optimizer')
 
           echo "docker_changed=false" >> "$GITHUB_OUTPUT"
-          echo "optimizer_changed=false" >> "$GITHUB_OUTPUT"
 
           if [ -n "${CHANGED_FILES_DOCKER}" ]; then
             echo "📄 Changed Docker-related files:"
             echo "${CHANGED_FILES_DOCKER}"
             echo "docker_changed=true" >> "$GITHUB_OUTPUT"
             echo "🟡 Docker-related changes detected."
-          fi
-
-          if [ -n "${CHANGED_FILES_OPTIMIZER}" ]; then
-            echo "📄 Changed Optimizer-related files:"
-            echo "${CHANGED_FILES_OPTIMIZER}"
-            echo "optimizer_changed=true" >> "$GITHUB_OUTPUT"
-            echo "🟡 Optimizer-related changes detected."
-          fi
-
-          if [ -z "${CHANGED_FILES_DOCKER}" ] && [ -z "${CHANGED_FILES_OPTIMIZER}" ]; then
-            echo "✅ No docker or optimizer related changes."
+          else
+            echo "✅ No docker related changes."
           fi
 
       - name: Clean up
@@ -177,8 +165,8 @@ jobs:
   dls-trivy-config-scan:
     permissions:
       contents: read
-    needs: [dls-filter-docker-or-optimizer-related-changes]
-    if: needs.dls-filter-docker-or-optimizer-related-changes.outputs.docker_changed == 'true'
+    needs: [dls-filter-docker-related-changes]
+    if: needs.dls-filter-docker-related-changes.outputs.docker_changed == 'true'
     name: "DLS SCAN: Trivy ${{ matrix.name }}"
     strategy:
       fail-fast: false
@@ -208,8 +196,8 @@ jobs:
     permissions:
       contents: read
       pull-requests: write
-    needs: [dls-filter-docker-or-optimizer-related-changes]
-    if: needs.dls-filter-docker-or-optimizer-related-changes.outputs.docker_changed == 'true'
+    needs: [dls-filter-docker-related-changes]
+    if: needs.dls-filter-docker-related-changes.outputs.docker_changed == 'true'
     name: "DLS SCAN: Hadolint"
     runs-on: dlstreamer
     strategy:
@@ -405,8 +393,6 @@ jobs:
     permissions:
       contents: read
       packages: read
-    needs: [dls-filter-docker-or-optimizer-related-changes]
-    if: needs.dls-filter-docker-or-optimizer-related-changes.outputs.optimizer_changed == 'true'
     name: "DLS TEST: Optimizer"
     uses: ./.github/workflows/dls-test-optimizer.yaml
     with:

.github/workflows/dls-test-optimizer.yaml

@@ -144,14 +144,15 @@ jobs:
             -w /workspace \
             ${deb_final_img} \
             bash /workspace/optimizer_tests/scripts/run_optimizer_tests.sh --config-file "/workspace/optimizer_tests/test_config.json"
+
       - name: Upload Docker test results
         if: always()
         uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f #7.0.0
         with:
           name: DLS_optimizer_Docker_${{ matrix.runner_print_label }}_${{ matrix.ubuntu_version }}_results
           path: ${{ env.OPTIMIZER_TESTS_PATH }}/optimizer_results/docker/FINAL_TEST_REPORT.txt
 
-      # ======================================================== OH HOST TESTING PART ========================================================
+      # ======================================================== ON HOST TESTING PART ========================================================
       - name: Link DL Streamer to home directory
         if: always()
         run: |
@@ -160,7 +161,9 @@ jobs:
         if: always()
         run: |
           $DLS_REL_PATH/tests/scripts/installation-on-host-entrypoint.sh $DLS_REL_PATH/deb_packages
+
       - name: Run Optimizer on host
+        id: run_optimizer_on_host
         if: always()
         run: |
           mkdir $OPTIMIZER_TESTS_PATH/optimizer_results/host
@@ -172,6 +175,34 @@ jobs:
         with:
           name: DLS_optimzier_host_${{ matrix.runner_print_label }}_${{ matrix.ubuntu_version }}_results
           path: ${{ env.OPTIMIZER_TESTS_PATH }}/optimizer_results/host/FINAL_TEST_REPORT.txt
+
+      # ======================================================== TEST RESULTS SUMMARY ========================================================
+      - name: Add optimizer test results to summary
+        env:
+          DOCKER_OUTCOME: ${{ steps.run_optimizer_in_docker.outcome }}
+          HOST_OUTCOME: ${{ steps.run_optimizer_on_host.outcome }}
+        if: always()
+        run: |
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "## 🧪 Optimizer Test Results" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "| Test Environment | Status |" >> $GITHUB_STEP_SUMMARY
+          echo "|------------------|--------|" >> $GITHUB_STEP_SUMMARY
+          
+          # Docker test status
+          if [ "$DOCKER_OUTCOME" == "success" ]; then
+            echo "| Optimizer in Docker | ✅ **PASSED** |" >> $GITHUB_STEP_SUMMARY
+          else
+            echo "| Optimizer in Docker | ❌ **FAILED** |" >> $GITHUB_STEP_SUMMARY
+          fi
+          
+          # Host test status
+          if [ "$HOST_OUTCOME" == "success" ]; then
+            echo "| Optimizer on Host | ✅ **PASSED** |" >> $GITHUB_STEP_SUMMARY
+          else
+            echo "| Optimizer on Host | ❌ **FAILED** |" >> $GITHUB_STEP_SUMMARY
+          fi
+
       - name: Uninstall dlstreamer
         if: always ()
         run: |

SPECS/download_sources.sh

@@ -73,16 +73,14 @@ main() {
     done
 
     # Download the DL Streamer src code
-    cd ../../..
-    git submodule update --init libraries/dl-streamer/thirdparty/spdlog
-    cd libraries
+    REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+    cd "$REPO_ROOT"
+    git submodule update --init thirdparty/spdlog
     rm -rf ~/intel-dlstreamer-${DLSTREAMER_VERSION}*
-    cp -r dl-streamer ~
-    mv ~/dl-streamer ~/intel-dlstreamer-${DLSTREAMER_VERSION}
+    cp -r "$REPO_ROOT" ~/intel-dlstreamer-${DLSTREAMER_VERSION}
     cd ~
     tar czf intel-dlstreamer-${DLSTREAMER_VERSION}.tar.gz intel-dlstreamer-${DLSTREAMER_VERSION}
-    cd -
-    mv ~/intel-dlstreamer-${DLSTREAMER_VERSION}.tar.gz dl-streamer/SPECS/
+    mv ~/intel-dlstreamer-${DLSTREAMER_VERSION}.tar.gz "$SCRIPT_DIR/"
     log_info ""
 }
 

SPECS/intel-dlstreamer/intel-dlstreamer.spec

@@ -1,6 +1,6 @@
 %define debug_package %{nil}
 Name:           intel-dlstreamer
-Version:        2025.2.0
+Version:        2026.0.0
 Release:        1%{?dist}
 Summary:        Intel Deep Learning Streamer framework
 
@@ -20,14 +20,14 @@ BuildRequires:  libva-devel libva-intel-media-driver
 BuildRequires:  python3-devel python3-pip
 BuildRequires:  pkgconfig patchelf
 BuildRequires:  opencv-devel >= 4.12.0
-BuildRequires:  gstreamer-devel >= 1.26.1
+BuildRequires:  gstreamer-devel >= 1.26.6
 BuildRequires:  paho-mqtt-c-devel >= 1.3.4
 BuildRequires:  librdkafka-devel
 
 # Runtime dependencies
 Requires:       paho-mqtt-c-devel >= 1.3.4
 Requires:       ffmpeg >= 6.1.1
-Requires:       gstreamer >= 1.26.1
+Requires:       gstreamer >= 1.26.6
 Requires:       opencv >= 4.12.0
 Requires:       libva2 libva-intel-media-driver
 Requires:       python3 python3-pip python3-gobject
@@ -139,6 +139,8 @@ rm -rf %{buildroot}
 /opt/intel/dlstreamer/lib/pkgconfig/*
 
 %changelog
+* Wed Mar 25 2026 DL Streamer Team <dlstreamer@intel.com> - 2026.0.0-1
+- Update DL Streamer version
 * Wed Dec 02 2025 DL Streamer Team <dlstreamer@intel.com> - 2025.2.0-1
 - Update DL Streamer version
 * Thu Aug 07 2025 DL Streamer Team <dlstreamer@intel.com> - 2025.1.2-1

docs/user-guide/dev_guide/advanced_install/advanced_install_guide_compilation.md

@@ -150,7 +150,7 @@ packages:
 
 ### (Optional) Step 6: Install OpenVINO™ GenAI (only for Ubuntu)
 
-To use [gvagenai element](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/elements/gvagenai.html)
+To use [gvagenai element](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/elements/gvagenai.html)
 you need to install the [OpenVINO GenAI archive](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-genai.html) package.
 
 <!--hide_directive::::{tab-set}

docs/user-guide/dev_guide/lvms.md

@@ -2,7 +2,7 @@
 
 This article explains how to prepare models based on the [Hugging Face](https://huggingface.co/welcome) [`transformers`](https://github.com/huggingface/transformers) library for integration with the Deep Learning Streamer pipeline.
 
-Many transformer-based models can be converted to OpenVINO™ IR format using [optimum-cli](https://huggingface.co/docs/optimum-intel/en/openvino/export). DL Streamer supports selected Hugging Face architectures for tasks such as image classification, object detection, audio transcription, and more. See the [Supported Models](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/supported_models.html) table for details.
+Many transformer-based models can be converted to OpenVINO™ IR format using [optimum-cli](https://huggingface.co/docs/optimum-intel/en/openvino/export). DL Streamer supports selected Hugging Face architectures for tasks such as image classification, object detection, audio transcription, and more. See the [Supported Models](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/supported_models.html) table for details.
 
 > **NOTE:** The instructions below are comprehensive, but for convenience, we recommend using the
 > [download_hf_models.py](https://github.com/open-edge-platform/dlstreamer/blob/main/scripts/download_models/download_hf_models.py)
@@ -12,7 +12,7 @@ Many transformer-based models can be converted to OpenVINO™ IR format using [o
 
 ## Optimum-Intel Supported Models
 
-The list available [here](https://huggingface.co/docs/optimum-intel/en/openvino/models) includes models that can be converted to IR format with a single `optimum-cli` command. If a model architecture is [supported by DL Streamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/supported_models.html#supported-architectures), it can typically be prepared as follows:
+The list available [here](https://huggingface.co/docs/optimum-intel/en/openvino/models) includes models that can be converted to IR format with a single `optimum-cli` command. If a model architecture is [supported by DL Streamer](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/supported_models.html#supported-architectures), it can typically be prepared as follows:
 
 ```bash
 optimum-cli export openvino --model provider_id/model_id --weight-format=int8 output_path

docs/user-guide/dev_guide/yolo_models.md

@@ -5,7 +5,7 @@ integration with the Deep Learning Streamer pipeline.
 
 ## Ultralytics Model Preparation
 
-All models supported by the [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) library can be converted to OpenVINO™ IR format by using the [Ultralytics exporter](https://docs.ultralytics.com/integrations/openvino/). DL Streamer supports many Ultralytics YOLO architectures for tasks such as zero-shot object detection, oriented object detection, segmentation, pose estimation, and more. See the [Supported Models](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/supported_models.html) table for details.
+All models supported by the [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) library can be converted to OpenVINO™ IR format by using the [Ultralytics exporter](https://docs.ultralytics.com/integrations/openvino/). DL Streamer supports many Ultralytics YOLO architectures for tasks such as zero-shot object detection, oriented object detection, segmentation, pose estimation, and more. See the [Supported Models](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/supported_models.html) table for details.
 
 > **NOTE:** The instructions below are comprehensive, but for convenience, we recommend using the
 > [download_ultralytics_models.py](https://github.com/open-edge-platform/dlstreamer/blob/main/scripts/download_models/download_ultralytics_models.py)

docs/user-guide/index.md

@@ -21,7 +21,7 @@ or at the Edge. DL Streamer consists of:
   for designing, creating, building, and running media analytics
   pipelines. It includes C++ and Python APIs.
 - [Deep Learning Streamer Pipeline
-  Server](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/microservices/dlstreamer-pipeline-server)
+  Server](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/dlstreamer-pipeline-server)
   for deploying and scaling media analytics pipelines as
   micro-services on one or many compute nodes. It includes REST APIs
   for pipelines management.
@@ -92,7 +92,7 @@ FasterRCNN, and other models.
 reference apps for the most common media analytics use cases. They are
 included in
 [Deep Learning Streamer Pipeline Framework](https://github.com/open-edge-platform/dlstreamer/tree/main),
-[Deep Learning Streamer Pipeline Server](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/microservices/dlstreamer-pipeline-server),
+[Deep Learning Streamer Pipeline Server](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/dlstreamer-pipeline-server),
 [Open Visual Cloud](https://github.com/OpenVisualCloud), and
 [Intel® Edge Software Hub](https://www.intel.com/content/www/us/en/edge-computing/edge-software-hub.html)
 The samples demonstrate C++ and/or Python based: Action Recognition, Face Detection and