feat: .gitignore & deploy file

Dockerfile

@@ -1,8 +1,7 @@
-# YOLOv8 training environment (NVIDIA GPU)
+# YOLOv8 training image (NVIDIA GPU)
 #
-# The image contains Python, PyTorch and Ultralytics only. The project
-# directory (including data.yaml, train/valid/test and *.pt files) is mounted
-# at /workspace when the container is started; this keeps the image small.
+# Image contains Python / PyTorch / Ultralytics + train script.
+# Dataset (*.pt weights, /data/facepp, runs/) are mounted at runtime.
 
 FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04
 
@@ -22,11 +21,10 @@ RUN apt-get update \
         libxext6 \
         libxrender1 \
         python3 \
-        python3-dev \
         python3-pip \
     && rm -rf /var/lib/apt/lists/*
 
-# PyTorch 2.5.1 is paired with torchvision 0.20.1 and CUDA 12.4.
+# PyTorch 2.5.1 + torchvision 0.20.1 (CUDA 12.4)
 RUN python3 -m pip install --upgrade pip \
     && python3 -m pip install \
         torch==2.5.1 \
@@ -36,8 +34,7 @@ RUN python3 -m pip install --upgrade pip \
 
 WORKDIR /workspace
 
-# These files make the image usable for a quick smoke test. The normal run
-# command mounts the whole project over /workspace.
 COPY train_yolov8.py data.yaml ./
 
 ENTRYPOINT ["python3", "train_yolov8.py"]
+CMD ["--model", "yolov8n.pt", "--device", "0"]