Add accelerated tensor-native media runtimes

.github/workflows/docker.jetson.7.2.0.yml

@@ -0,0 +1,74 @@
+name: Build and Push Jetson 7.2.0 Media Container
+
+permissions:
+  contents: read
+
+on:
+  release:
+    types: [created]
+  push:
+    branches: [main]
+    paths:
+      - ".github/workflows/docker.jetson.7.2.0.yml"
+      - "docker/dockerfiles/Dockerfile.media.jetson.7.2.0"
+      - "docker/native/jetson_tensor_bridge/**"
+      - "docker/scripts/**"
+      - "LICENSE"
+      - "LICENSE.core"
+  workflow_dispatch:
+    inputs:
+      force_push:
+        type: boolean
+        description: "Do you want to push the image after building?"
+        default: false
+      custom_tag:
+        type: string
+        description: "Custom tag to use for the image"
+        default: ""
+
+env:
+  VERSION: "0.0.0"
+  BASE_IMAGE: "roboflow/roboflow-inference-media-jetson-7.2.0"
+
+jobs:
+  docker:
+    runs-on:
+      labels: depot-ubuntu-24.04-4
+      group: public-depot
+    timeout-minutes: 180
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - name: Login to Docker Hub
+        if: ${{ github.event_name == 'release' || inputs.force_push }}
+        uses: docker/login-action@v4
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Checkout
+        uses: actions/checkout@v6
+      - name: Read version from file
+        run: echo "VERSION=$(DISABLE_VERSION_CHECK=true python ./inference/core/version.py)" >> "$GITHUB_ENV"
+      - name: Determine image tags
+        id: tags
+        uses: ./.github/actions/determine-tags
+        with:
+          custom_tag: ${{ inputs.custom_tag }}
+          version: ${{ env.VERSION }}
+          base_image: ${{ env.BASE_IMAGE }}
+          force_push: ${{ inputs.force_push }}
+          token: ${{ secrets.GITHUB_TOKEN }}
+      - name: Set up Depot CLI
+        uses: depot/setup-action@v1
+      - name: Build image
+        uses: depot/build-push-action@v1
+        with:
+          project: v1xzfwkc4b
+          context: .
+          file: ./docker/dockerfiles/Dockerfile.media.jetson.7.2.0
+          platforms: linux/arm64
+          push: ${{ github.event_name == 'release' || inputs.force_push }}
+          save: ${{ github.event_name != 'release' && !inputs.force_push }}
+          save-tag: ${{ github.event_name != 'release' && !inputs.force_push && format('media-jp72-{0}', github.sha) || '' }}
+          tags: ${{ steps.tags.outputs.image_tags }}

.github/workflows/test.nvidia_t4.yml

@@ -56,7 +56,32 @@ jobs:
       - name: �🔨 Build and Push Test Docker - GPU
         run: |
           docker build -t roboflow/roboflow-inference-server-gpu:test -f docker/dockerfiles/Dockerfile.onnx.gpu .
-
+
+      - name: Verify CUDA OpenCV - GPU
+        run: |
+          docker run --rm --gpus all --entrypoint sh \
+            -e OPENCV_REQUIRE_CUDA_BUILD=true \
+            -e OPENCV_REQUIRE_CUDA_RUNTIME=true \
+            -v "$PWD/docker/scripts/verify_opencv.sh:/tmp/verify_opencv:ro" \
+            roboflow/roboflow-inference-server-gpu:test \
+            /tmp/verify_opencv
+
+      - name: Verify NVIDIA GStreamer codecs - GPU
+        run: |
+          docker run --rm --gpus all --entrypoint sh \
+            -e GSTREAMER_REQUIRE_NVCODEC=true \
+            -e GSTREAMER_REQUIRE_NVCODEC_RUNTIME=true \
+            -v "$PWD/docker/scripts/verify_gstreamer.sh:/tmp/verify_gstreamer:ro" \
+            roboflow/roboflow-inference-server-gpu:test \
+            /tmp/verify_gstreamer
+
+      - name: Verify CUDA GStreamer tensor bridge - GPU
+        run: |
+          docker run --rm --gpus all --entrypoint python3 \
+            -v "$PWD/docker/scripts/verify_gstreamer_cuda_tensor_runtime.py:/tmp/verify_gstreamer_cuda_tensor_runtime.py:ro" \
+            roboflow/roboflow-inference-server-gpu:test \
+            /tmp/verify_gstreamer_cuda_tensor_runtime.py
+
       - name: 🔋 Start Test Docker without Torch Preprocessing - GPU
         if: ${{ github.event.inputs.test_name == '' || github.event.inputs.test_name == 'regression_without_torch' }}
         run: |

development/stream_interface/run_workflow_on_video.py

@@ -34,19 +34,9 @@
         --model-id yolov8n-640
 """
 
-# jetson_utils must be imported before anything that initialises CUDA or GStreamer
-# state (cv2/torch, pulled in below via `inference`), so it comes first. It only
-# exists on the Jetson images — on dGPU builds the import fails and the hardware
-# decoder probes in VideoSource handle the absence at runtime.
-try:
-    import jetson_utils  # noqa: F401
-except Exception:  # noqa: BLE001 - any failure means "not a Jetson build"
-    jetson_utils = None
-
 import argparse
 import json
 import os
-import sys
 import time
 from collections import deque
 from typing import Any, Dict, List, Optional