docker compose up working

label_studio_ml/examples/yolo_sam2_tracker/Dockerfile

@@ -29,9 +29,6 @@ ENV PYTHONUNBUFFERED=1 \
     SEGMENT_ANYTHING_2_REPO_PATH=/segment-anything-2 \
     PYTHONPATH=/app
 
-# Install CUDA toolkit via Conda
-RUN conda install -c "nvidia/label/cuda-12.1.1" cuda -y
-
 # Install Python dependencies
 COPY requirements-base.txt .
 RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
@@ -48,26 +45,38 @@ RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
     pip install -e .
 RUN cd checkpoints && ./download_ckpts.sh
 
+# Clone and install label-studio-ml-backend
+WORKDIR /app
+RUN git clone https://github.com/HumanSignal/label-studio-ml-backend.git \
+    && cd label-studio-ml-backend/ \
+    && pip install -e .
+
 # Return to app working directory
 WORKDIR /app
 
 # Install test dependencies (optional)
 COPY requirements-test.txt .
-RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
-    if [ "$TEST_ENV" = "true" ]; then \
+RUN if [ "$TEST_ENV" = "true" ]; then \
       pip install -r requirements-test.txt; \
+    else \
+      echo "Skipping test dependencies installation"; \
     fi
 
-# Download YOLO models
-RUN /bin/sh -c 'if [ ! -f /app/models/yolov8m.pt ]; then \
+# Download YOLO models (only if the image exists)
+RUN /bin/sh -c 'if [ -f /app/tests/car.jpg ]; then \
     yolo predict model=/app/models/yolov8m.pt source=/app/tests/car.jpg \
     && yolo predict model=/app/models/yolov8n.pt source=/app/tests/car.jpg \
     && yolo predict model=/app/models/yolov8n-cls.pt source=/app/tests/car.jpg \
     && yolo predict model=/app/models/yolov8n-seg.pt source=/app/tests/car.jpg; \
+    else \
+      echo "Image not found, skipping YOLO model tests"; \
     fi'
 
 # Copy app files
 COPY . ./
 
+# Ensure the script has executable permissions
+RUN chmod +x /app/start.sh
+
 # Default command
 CMD ["/app/start.sh"]

label_studio_ml/examples/yolo_sam2_tracker/docker-compose.yml

@@ -1,9 +1,9 @@
 version: "3.8"
 
 services:
-  MOT_yolo_sam:
-    container_name: MOT_yolo_sam
-    image: humansignal/MOT_yolo_sam:v0
+  MOT_yolo_sam_tracker:
+    container_name: MOT_yolo_sam_tracker
+    image: humansignal/mot_yolo_sam_tracker:v0
     build:
       context: .
       args:
@@ -20,7 +20,7 @@ services:
       - BASIC_AUTH_USER=
       - BASIC_AUTH_PASS=
       # set the log level for the model server
-      - LOG_LEVEL=DEBUG
+      - LOG_LEVEL=INFO
       # any other parameters that you want to pass to the model server
       - ANY=PARAMETER
       # specify the number of workers and threads for the model server
@@ -32,6 +32,7 @@ services:
       - DEVICE=cuda  # or 'cpu' (coming soon)
       # SAM2 model config
       - MODEL_CONFIG=./configs/sam2.1/sam2.1_hiera_l.yaml
+      - SEGMENT_ANYTHING_2_REPO_PATH=./sam2
       # SAM2 checkpoint
       - MODEL_CHECKPOINT=sam2.1_hiera_large.pt
       - MAX_FRAMES_TO_TRACK=2000

label_studio_ml/examples/yolo_sam2_tracker/requirements.txt

@@ -1,5 +1,8 @@
-ultralytics~=8.2.76
-tqdm
+ultralytics~=8.2.103
+tqdm~=4.67.1
 torchmetrics~=1.4.2
 opencv-python
-cuda-python
+cuda-python
+av~=14.0.1
+numpy~=1.24.4
+pycocotools~=2.0.8

label_studio_ml/examples/yolo_sam2_tracker/start.sh

@@ -1,9 +1,5 @@
 #!/bin/bash
 
-# Export environment variables
-export LABEL_STUDIO_URL=
-export LABEL_STUDIO_API_KEY=
-export LOG_LEVEL="DEBUG"
 
 # Start the label-studio-ml backend
-label-studio-ml start ./../yolo_sam2_tracker
+exec gunicorn --bind :${PORT:-9090} --workers ${WORKERS:-1} --threads ${THREADS:-4} --timeout 0 _wsgi:app