Fix C++ ABI compatibility: compile gsplat in Docker with exact PyTorch version

- Revert to source compilation in Docker to ensure PyTorch version compatibility
- Use single GPU architecture (8.0 for A100) to minimize memory usage
- Remove debug logging (tensor type issues are fixed)
- This fixes undefined symbol errors caused by PyTorch version mismatch
- Compile with exact same PyTorch version as runtime environment

Dockerfile

@@ -43,14 +43,15 @@ ENV HOME=/home/user \
     UV_CACHE_DIR=/home/user/.cache/uv
 
 # Copy dependency files
-COPY --chown=user pyproject.docker.toml ./pyproject.toml
+COPY --chown=user pyproject.toml ./pyproject.toml
+COPY --chown=user gsplat/ ./gsplat/
 
-# Download optimized gsplat wheel (built with HF Spaces GPU architectures)
-RUN mkdir -p wheels && \
-    wget -O wheels/gsplat-0.1.0-cp310-cp310-linux_x86_64.whl \
-    "https://huggingface.co/blanchon/image-gs-models-utils/resolve/main/gsplat-0.1.0-cp310-cp310-linux_x86_64.whl"
+# Set minimal CUDA compilation to avoid memory issues but ensure compatibility
+ENV TORCH_CUDA_ARCH_LIST="8.0" \
+    FORCE_CUDA=1 \
+    MAX_JOBS=1
 
-# Install dependencies with optimized wheel
+# Install dependencies (will compile gsplat with exact PyTorch version for compatibility)
 RUN --mount=type=cache,target=/tmp/uv-cache,sharing=locked,uid=1000,gid=1000 \
     UV_CACHE_DIR=/tmp/uv-cache uv sync --no-dev
 

gradio_models.py

@@ -400,13 +400,6 @@ class GradioGaussianSplatting2D(nn.Module):
         if not self.disable_tiles:
             enable_topk_norm = not self.disable_topk_norm
 
-            # Debug: Print tensor types to identify the Float tensor
-            self.worklog.info(
-                f"DEBUG - Tensor types: xy={xy.dtype}, radii={radii.dtype}, conics={conics.dtype}, num_tiles_hit={num_tiles_hit.dtype}, feat={feat.dtype}"
-            )
-            self.worklog.info(
-                f"DEBUG - Params: img_h={img_h} ({type(img_h)}), img_w={img_w} ({type(img_w)}), block_h={self.block_h} ({type(self.block_h)}), block_w={self.block_w} ({type(self.block_w)})"
-            )
 
             out_image = rasterize_gaussians_sum(
                 xy,

pyproject.docker.toml

@@ -24,7 +24,7 @@ dependencies = [
 # We use python 3.10 and cu124
 [tool.uv.sources]
 fused_ssim = { git = "https://github.com/rahul-goel/fused-ssim/" }
-gsplat = { path = "wheels/gsplat-0.1.0-cp310-cp310-linux_x86_64.whl" }
+gsplat = { path = "gsplat" }
 torch = [
     { index = "pytorch-cu124", marker = "sys_platform == 'linux'" },
 ]