HF Spaces Docker: Solving Git LFS Pointer Errors
RuntimeError: [enforce fail at inline_container.cc:222] . file not found typically points to a corrupted or missing binary, but in my recent deployment of an Attention U-Net (approx 300MB), the file existed—it was just a 1KB Git LFS pointer instead of the actual PyTorch weights.The environment is a Flask app running in a Docker container on Hugging Face Spaces. Local tests passed perfectly, but the container crashed immediately upon trying to load archive/data.pkl.
The Diagnosis
When using
COPY . . in a Dockerfile on HF Spaces, the build context sometimes captures the LFS pointer file rather than the resolved binary. This happens because the underlying git clone used by the build agent hasn't performed a full git lfs pull before the Docker daemon starts bundling the context.To resolve this and ensure a stable AI workflow, I've identified a few critical points regarding the deployment pipeline:
1. LFS Resolution: HF Spaces generally handles LFS resolution for the repository, but when you wrap everything in a custom Dockerfile, the COPY command is literal. If the build agent is looking at the pointer, that's what goes into the image.
2. The Fix: The most reliable way to ensure the binary is present is to verify the .gitattributes file is correctly tracking the .pkl or .bin extensions before the push.
3. Build Context: If you are using a custom Dockerfile, avoid relying on the local build context for large models. Instead, use a RUN command to pull the model from the HF Hub using the huggingface_hub library during the build process.
# Example of pulling the model explicitly during build to avoid pointer issues
RUN pip install huggingface_hub
RUN python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='username/model', filename='data.pkl', local_dir='archive/')"For anyone running into this, check your file sizes inside the container using ls -lh. If your 300MB model is showing as 130 bytes, you're dealing with a pointer file. Moving the model download into the Docker image build layers via the API is far more robust than relying on COPY.