sync to huggingface

.github/workflows/deploy_huggingface.yaml

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://CuriousDolphin:[email protected]/spaces/CuriousDolphin/DetrDetectionSegmentation main

Dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.11 as base-img
+ARG DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && apt-get install -y python3 ffmpeg  libxext6  libsm6 libgtk2.0-dev pkg-config libgtk2.0-dev  libavcodec-dev python3-opencv libopencv-dev 
+RUN apt-get install -y python3-pip
+
+RUN pip3 install torch torchvision 
+
+FROM base-img as app
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user requirements.txt requirements.txt 
+COPY --chown=user data data
+COPY --chown=user detr detr
+RUN pip3 install -r requirements.txt
+ENTRYPOINT [ "python","detr/main_gradio.py" ]

Dockerfile.nvidia

@@ -0,0 +1,13 @@
+FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04 as base-img
+ARG DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && apt-get install -y python3 ffmpeg  libxext6  libsm6 libgtk2.0-dev pkg-config libgtk2.0-dev  libavcodec-dev python3-opencv libopencv-dev 
+RUN apt-get install -y python3-pip
+RUN pip3 install torch torchvision  --index-url https://download.pytorch.org/whl/cu118
+
+FROM base-img as app
+WORKDIR /app
+COPY requirements.txt requirements.txt
+COPY data data
+COPY detr detr
+RUN pip3 install -r requirements.txt
+ENTRYPOINT [ "python3","detr/main_gradio.py" ]

README.md

@@ -1 +1,9 @@
-# detr_gradio
+---
+title: DETR (object detections & panoptic segmentation)
+emoji: 🌭
+sdk: docker
+app_port: 7000
+pinned: true
+---
+
+# Simple DETR gradio implementation (object detections & panoptic segmentation)

detr/main_gradio.py

@@ -42,11 +42,9 @@ with gr.Blocks() as inference_gradio:
 
             with gr.Row():
                 start_btn = gr.Button("Start", variant="primary")
-
         with gr.Column():
             annotated_img = gr.Image(label="Annotated Image")
             speed = gr.JSON(label="speed")
-            json_out = gr.JSON(label="output")
     examples = gr.Examples(
         examples=[
             [path]
@@ -59,7 +57,7 @@ with gr.Blocks() as inference_gradio:
     start_btn.click(
         fn=run_inference,
         inputs=[img_file, conf, model_name],
-        outputs=[annotated_img, speed, json_out],
+        outputs=[annotated_img, speed],
     )
 
 if __name__ == "__main__":

docker-compose.yaml

@@ -0,0 +1,25 @@
+services:
+  detr_cpu:
+    build:
+       context: .
+    ports:
+       - 7000:7000
+    volumes:
+       - ./data:/app/data
+  detr_gpu:
+    build:
+      dockerfile: Dockerfile.nvidia
+      target: app
+    profiles: [ "gpu" ]
+    shm_size: '4gb' # pytorch dataloader crash
+    ports:
+       - 7000:7000
+    volumes:
+       - ./data:/app/data
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [ gpu ]