CI: deploy Docker/PDM Space

Dockerfile

@@ -28,14 +28,14 @@ ENV PIP_NO_CACHE_DIR=1 \
 RUN mkdir -p /tmp/.streamlit /tmp/.cache /tmp/matplotlib /tmp/Ultralytics \
     && chmod -R 777 /tmp/.streamlit /tmp/.cache /tmp/matplotlib /tmp/Ultralytics
 
-# Python deps (CPU-only PyTorch wheels)
+# Python deps (CUDA-enabled PyTorch wheels)
 RUN python -m pip install --upgrade pip setuptools wheel && \
     pip install --no-cache-dir \
     streamlit==1.49.1 \
     moviepy==2.2.1 \
     pillow>=10.0.0 \
     ultralytics==8.0.196 \
-    torch==2.5.1 torchvision==0.20.1 --extra-index-url https://download.pytorch.org/whl/cpu
+    torch==2.5.1 torchvision==0.20.1 --extra-index-url https://download.pytorch.org/whl/cu121
 
 
 # Copy entire repository and download script

README.md

@@ -5,6 +5,7 @@ colorFrom: red
 colorTo: red
 sdk: docker
 pinned: false
+hardware: gpu-t4-small
 ---
 
 # Introduction
@@ -33,7 +34,7 @@ Computer Vision object detection for National Search and Rescue (NATSAR)
 5. Intstall packages and dependencies
 
 hello
-   `pdm install`
+`pdm install`
 
 ## Running the project locally
 

services/app_service/deim_model.py

@@ -64,7 +64,7 @@ class DeimHgnetV2MDrone(BaseModel):
                 + (", ".join(sorted(set(available))) or "<none>")
             )
 
-        self.device = device
+        self.device = "cuda" if device == "gpu" and torch.cuda.is_available() else "cpu"
         cfg_path = weights_path.with_suffix(".json")
         if not cfg_path.exists():
             raise FileNotFoundError(
@@ -88,7 +88,7 @@ class DeimHgnetV2MDrone(BaseModel):
             )
 
         self.cfg = json.load(open(cfg_path, "r"))
-        self.model = torch.jit.load(weights_path, map_location=device).eval()
+        self.model = torch.jit.load(weights_path, map_location=self.device).eval()
 
     def _preprocess_image(self, image: Image):
         transforms = T.Compose(

services/app_service/pages/bushland_beacon.py

@@ -4,9 +4,8 @@ import time
 from pathlib import Path
 
 import streamlit as st
-from PIL import Image
-
 from deim_model import DeimHgnetV2MDrone
+from PIL import Image
 
 # =============== Page setup ===============
 st.set_page_config(
@@ -56,8 +55,9 @@ with st.sidebar:
     st.markdown("<h4>Parameters</h4>", unsafe_allow_html=True)
     conf_thr = st.slider("Minimum confidence threshold", 0.05, 0.95, 0.50, 0.01)
 
+
 @st.cache_resource
-def load_model(device: str = "cpu"):
+def load_model(device: str = "cuda"):
     return DeimHgnetV2MDrone(device=device)
 
 

services/app_service/pages/lost_at_sea.py

@@ -59,7 +59,7 @@ with st.sidebar:
 
 
 @st.cache_resource
-def load_model(device: str = "cpu"):
+def load_model(device: str = "cuda"):
     return DeimHgnetV2MDrone(device=device)
 
 

services/app_service/pages/signal_watch.py

@@ -56,7 +56,7 @@ MODEL_ENTRIES = _discover_model_entries()
 
 
 @st.cache_resource
-def load_model(model_key: str, device: str = "cpu"):
+def load_model(model_key: str, device: str = "cuda"):
     if model_key == "deim":
         return DeimHgnetV2MDrone(device=device)
     if not model_key.startswith("yolo:"):