Initial commit

docker-compose.yml

@@ -0,0 +1,59 @@
+# version: "3.9"
+
+services:
+  dl:
+    build:
+      context: .
+      dockerfile: Dockerfile
+      args:
+        BASE_IMAGE: "pytorch/pytorch:2.9.0-cuda13.0-cudnn9-devel"
+        USER: "dev"
+        UID: "1000"
+        GID: "1000"
+    container_name: dl
+    # GPUs + large DataLoader shared memory
+    gpus: all
+    shm_size: "12g"
+    ipc: host
+
+    environment:
+      # Always use GPUs (you can limit to some: e.g., "0,1")
+      - NVIDIA_VISIBLE_DEVICES=0
+      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
+      # Prefer NCCL for multi-GPU
+      - TORCH_DISTRIBUTED_DEBUG=INFO
+      - NCCL_P2P_DISABLE=0
+      - NCCL_ASYNC_ERROR_HANDLING=1
+      # Persisted virtualenv on PATH (lives in a named volume)
+      - VIRTUAL_ENV=./venv
+      - PATH=./venv/bin:/usr/local/bin:/usr/bin:/bin
+      - PYTHONUNBUFFERED=1
+      - TZ=America/Los_Angeles
+
+    volumes:
+      # your code/data
+      - .:/workspace
+      - /home/image1325/ssd1/dataset/coco:/data
+      # persisted venv: your pip installs live here and survive image/container removal
+      - venv:./venv
+      # (optional) speed up installs
+      - pip-cache:/home/dev/.cache/pip
+
+    working_dir: /workspace
+    ulimits:
+      memlock: -1
+      stack: 67108864
+
+    # On first run, create the venv if it doesn't exist; then drop to a shell.
+    command: >
+      bash -lc "
+        if [ ! -d /opt/venv/bin ]; then
+          python -m venv /opt/venv;
+          /opt/venv/bin/python -m pip install --upgrade pip;
+        fi;
+        exec bash
+      "
+
+volumes:
+  venv:
+  pip-cache: