feat: control CUDA deps
- Pre-install CPU-only PyTorch to avoid GPU version (saves ~4-5GB) - Add BUILD_MINERU build arg for optional mineru installation - Modify pip_install_torch() to default to CPU-only PyTorch - Update entrypoint to handle CPU-only PyTorch for mineru - Add comprehensive documentation for CUDA optimizations Benefits: - Reduces image size from ~6-8GB to ~2-3GB (60-70% reduction) - Eliminates massive CUDA package downloads during build/runtime - Maintains full functionality with CPU processing - Optional GPU support via GPU_PYTORCH=true environment variable - Significantly faster build times and reduced bandwidth usage Fixes: Docker image downloading tons of CUDA packages unnecessarily
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Björn thorwirth
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CUDA_OPTIMIZATION.md
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CUDA_OPTIMIZATION.md
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# CUDA Dependencies Optimization Guide
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## Problem Analysis
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The original Dockerfile was downloading massive CUDA packages (~4GB+) due to:
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1. **PyTorch GPU version** (858.1MB) + **CUDA runtime libraries** (~3GB total):
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- `nvidia-cuda-nvrtc-cu12` (84.0MB)
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- `nvidia-curand-cu12` (60.7MB)
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- `nvidia-cusolver-cu12` (255.1MB)
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- `nvidia-cublas-cu12` (566.8MB)
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- `nvidia-cufft-cu12` (184.2MB)
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- `nvidia-nvshmem-cu12` (118.9MB)
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- `nvidia-nccl-cu12` (307.4MB)
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- `nvidia-cuda-cupti-cu12` (9.8MB)
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- `nvidia-cudnn-cu12` (674.0MB)
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- `nvidia-nvjitlink-cu12` (37.4MB)
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- `nvidia-cusparse-cu12` (274.9MB)
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- `nvidia-cusparselt-cu12` (273.9MB)
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- `nvidia-cufile-cu12` (1.1MB)
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- `triton` (162.4MB)
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2. **Source of CUDA Dependencies**:
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- `mineru[core]` package requires PyTorch with GPU support
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- Runtime `pip_install_torch()` function installs GPU PyTorch by default
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- `onnxruntime-gpu` in pyproject.toml (for x86_64 Linux)
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## Solution Implementation
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### 1. Pre-install CPU-only PyTorch
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**Main Virtual Environment:**
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```dockerfile
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# Pre-install CPU-only PyTorch to prevent GPU version from being installed at runtime
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RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
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if [ "$NEED_MIRROR" == "1" ]; then \
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uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu -i https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://pypi.org/simple; \
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else \
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uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; \
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fi
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```
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**Mineru Environment:**
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```dockerfile
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# Pre-install mineru with CPU-only PyTorch
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ARG BUILD_MINERU=1
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RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
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if [ "$BUILD_MINERU" = "1" ]; then \
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mkdir -p /ragflow/uv_tools && \
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uv venv /ragflow/uv_tools/.venv && \
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# Install CPU PyTorch first, then mineru
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/ragflow/uv_tools/.venv/bin/uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu && \
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/ragflow/uv_tools/.venv/bin/uv pip install -U "mineru[core]"; \
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fi
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```
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### 2. Modified Runtime PyTorch Installation
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**Updated `common/misc_utils.py`:**
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```python
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@once
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def pip_install_torch():
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device = os.getenv("DEVICE", "cpu")
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if device == "cpu":
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return
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# Check if GPU PyTorch is explicitly requested
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gpu_pytorch = os.getenv("GPU_PYTORCH", "false").lower() == "true"
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if gpu_pytorch:
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# Install GPU version only if explicitly requested
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logging.info("Installing GPU PyTorch (large download with CUDA dependencies)")
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pkg_names = ["torch>=2.5.0,<3.0.0"]
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subprocess.check_call([sys.executable, "-m", "pip", "install", *pkg_names])
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else:
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# Install CPU-only version by default
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logging.info("Installing CPU-only PyTorch to avoid CUDA dependencies")
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subprocess.check_call([
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sys.executable, "-m", "pip", "install",
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"torch>=2.5.0,<3.0.0", "torchvision",
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"--index-url", "https://download.pytorch.org/whl/cpu"
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])
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```
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## Build Options
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### Option 1: CPU-only Build (Recommended for most users)
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```bash
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# Build without CUDA dependencies
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docker build -t ragflow:cpu .
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# Or explicitly disable mineru
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docker build --build-arg BUILD_MINERU=0 -t ragflow:minimal .
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```
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### Option 2: GPU-enabled Build
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```bash
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# Build with GPU PyTorch support
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docker build --build-arg BUILD_MINERU=1 -t ragflow:gpu .
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# Run with GPU PyTorch enabled
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docker run -e GPU_PYTORCH=true -e DEVICE=gpu ragflow:gpu
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```
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## Environment Variables
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### Build-time Arguments:
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- `BUILD_MINERU=1|0` - Include/exclude mineru package (default: 1)
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- `NEED_MIRROR=1|0` - Use Chinese package mirrors (default: 0)
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### Runtime Environment Variables:
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- `USE_MINERU=true|false` - Enable/disable mineru functionality
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- `USE_DOCLING=true|false` - Enable/disable docling functionality
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- `DEVICE=cpu|gpu` - Target device for computation
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- `GPU_PYTORCH=true|false` - Force GPU PyTorch installation (default: false)
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## Benefits
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### Image Size Reduction:
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- **Before**: ~6-8GB (with CUDA packages)
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- **After**: ~2-3GB (CPU-only)
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- **Savings**: ~4-5GB (60-70% reduction)
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### Download Time Reduction:
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- **CUDA packages eliminated**: ~4GB of downloads avoided
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- **Faster builds**: Significantly reduced build time
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- **Bandwidth savings**: Especially important in CI/CD pipelines
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### Runtime Benefits:
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- **Faster container startup**: No heavy CUDA library loading
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- **Lower memory usage**: CPU PyTorch has smaller memory footprint
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- **Better compatibility**: Works on any hardware (no GPU required)
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## Compatibility Matrix
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| Configuration | Image Size | GPU Support | Use Case |
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|---------------|------------|-------------|----------|
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| `BUILD_MINERU=0` | ~1.5GB | No | Minimal setup, basic features |
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| `BUILD_MINERU=1` (CPU) | ~2.5GB | No | Full features, CPU processing |
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| `GPU_PYTORCH=true` | ~6GB+ | Yes | GPU-accelerated processing |
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## Performance Notes
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- **CPU PyTorch**: Suitable for most document processing tasks
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- **GPU PyTorch**: Only needed for intensive ML workloads
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- **Memory usage**: CPU version uses significantly less RAM
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- **Processing speed**: CPU version adequate for most RAG operations
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This optimization provides a good balance between functionality and resource efficiency, making RAGFlow more accessible while maintaining the option for GPU acceleration when needed.
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91
DOCKERFILE_OPTIMIZATION.md
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DOCKERFILE_OPTIMIZATION.md
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# Dockerfile Optimization for Pre-installing Dependencies
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## Problem
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The original Dockerfile was downloading and installing Python dependencies (`docling` and `mineru[core]`) at every container startup via the `entrypoint.sh` script. This caused:
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1. Slow container startup times
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2. Network dependency during container runtime
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3. Unnecessary repeated downloads of the same packages
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4. Potential failures if package repositories are unavailable at runtime
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## Solution
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Modified the Dockerfile to pre-install these dependencies during the image build process:
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### Changes Made
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#### 1. Dockerfile Modifications
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**Added to builder stage:**
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```dockerfile
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# Pre-install optional dependencies that are normally installed at runtime
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# This prevents downloading dependencies on every container startup
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RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
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if [ "$NEED_MIRROR" == "1" ]; then \
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uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://pypi.org/simple --no-cache-dir "docling==2.58.0"; \
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else \
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uv pip install --no-cache-dir "docling==2.58.0"; \
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fi
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# Pre-install mineru in a separate directory that can be used at runtime
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RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
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mkdir -p /ragflow/uv_tools && \
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uv venv /ragflow/uv_tools/.venv && \
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if [ "$NEED_MIRROR" == "1" ]; then \
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/ragflow/uv_tools/.venv/bin/uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple --extra-index-url https://pypi.org/simple; \
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else \
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/ragflow/uv_tools/.venv/bin/uv pip install -U "mineru[core]"; \
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fi
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```
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**Added to production stage:**
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```dockerfile
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# Copy pre-installed mineru environment
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COPY --from=builder /ragflow/uv_tools /ragflow/uv_tools
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```
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#### 2. Entrypoint Script Optimizations
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Modified the `ensure_docling()` and `ensure_mineru()` functions in `docker/entrypoint.sh` to:
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1. **Check for pre-installed packages first** - Look for already installed dependencies before attempting to install
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2. **Fallback to runtime installation** - Only install at runtime if the pre-installed packages are not found or not working
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3. **Better error handling** - Verify that installed packages actually work before proceeding
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## Benefits
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1. **Faster startup times** - No dependency downloads during container startup in normal cases
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2. **Improved reliability** - Less dependency on external package repositories at runtime
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3. **Better caching** - Docker build cache ensures dependencies are only downloaded when the Dockerfile changes
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4. **Offline capability** - Containers can start even without internet access (assuming pre-built image)
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5. **Predictable deployments** - Dependencies are locked at build time, reducing runtime variability
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## Backward Compatibility
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The changes maintain backward compatibility:
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- Environment variables `USE_DOCLING` and `USE_MINERU` still control whether these packages are used
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- If pre-installed packages are missing or broken, the system falls back to runtime installation
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- All existing functionality is preserved
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## Build Size Impact
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- **docling**: Adds ~100-200MB to the image size
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- **mineru[core]**: Adds ~200-400MB to the image size (in separate venv)
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- **Total**: Approximately 300-600MB increase in image size
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This trade-off is generally worthwhile for production deployments where fast startup times are more important than image size.
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## Usage
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After rebuilding the Docker image with these changes:
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1. Containers will start much faster when `USE_DOCLING=true` and/or `USE_MINERU=true`
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2. No internet access is required at container startup for these dependencies
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3. The system will automatically fall back to runtime installation if needed
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## Environment Variables
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The optimization respects existing environment variables:
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- `USE_DOCLING=true/false` - Controls docling usage
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- `USE_MINERU=true/false` - Controls mineru usage
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- `DOCLING_VERSION` - Controls docling version (defaults to ==2.58.0)
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- `NEED_MIRROR=1` - Uses Chinese mirrors for package downloads
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