Use NVIDIA NGC PyTorch container for GB10 support

NGC containers are optimized for latest NVIDIA hardware including GB10.
PyTorch comes pre-installed with proper CUDA/GPU support.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

docker/Dockerfile.spark

@@ -1,30 +1,22 @@
-# GPU-enabled Dockerfile for DGX Spark deployment (ARM64/aarch64 + CUDA 13.0)
-FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04
+# GPU-enabled Dockerfile for DGX Spark deployment (ARM64/aarch64 + GB10)
+# Using NVIDIA NGC PyTorch container - optimized for latest NVIDIA hardware
+# https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch
+FROM nvcr.io/nvidia/pytorch:24.11-py3
 
-# Install Python and system dependencies
+# Install additional system dependencies
 RUN apt-get update && apt-get install -y \
-    python3 \
-    python3-pip \
-    python3-dev \
     curl \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/* \
-    && ln -s /usr/bin/python3 /usr/bin/python
+    && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
 
-# Install PyTorch nightly with CUDA support (for newer GPU architectures like GB10/sm_121)
-# Using nightly builds which may have support for newer compute capabilities
-RUN pip3 install --no-cache-dir --pre \
-    torch \
-    --index-url https://download.pytorch.org/whl/nightly/cu124
-
-# Copy requirements and install (excluding packages with ARM64 issues)
+# PyTorch is pre-installed in NGC container with proper GPU support
+# Just install remaining requirements (excluding torch, zarr, numcodecs)
 COPY requirements.txt .
 RUN grep -v "^torch==" requirements.txt | \
     grep -v "^zarr==" | \
     grep -v "^numcodecs==" > requirements-spark.txt && \
-    pip3 install --no-cache-dir -r requirements-spark.txt
+    pip install --no-cache-dir -r requirements-spark.txt
 
 # Copy backend code
 COPY backend/ ./backend/