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Deploy hyper3labs/HyperView from Hyper3Labs/hyperview-spaces@ad6a212

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  1. Dockerfile +4 -4
  2. README.md +2 -2
Dockerfile CHANGED
@@ -33,12 +33,12 @@ COPY --chown=user demo.py ./demo.py
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  ENV HYPERVIEW_DATASETS_DIR=/home/user/app/demo_data/datasets \
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  HYPERVIEW_MEDIA_DIR=/home/user/app/demo_data/media
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- # Precompute at build time so the Space starts fast.
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- RUN python -c "from demo import build_dataset; build_dataset()"
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-
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  EXPOSE 7860
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- HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
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  CMD curl -f http://localhost:7860/__hyperview__/health || exit 1
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  CMD ["python", "demo.py"]
 
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  ENV HYPERVIEW_DATASETS_DIR=/home/user/app/demo_data/datasets \
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  HYPERVIEW_MEDIA_DIR=/home/user/app/demo_data/media
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  EXPOSE 7860
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+ HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=3 \
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  CMD curl -f http://localhost:7860/__hyperview__/health || exit 1
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+ # Dataset is built at first startup (model download + embedding computation).
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+ # Precomputing at Docker build time causes LanceDB to be stored in overlay
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+ # filesystem layers, which are extremely slow to reopen on free-tier CPUs.
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  CMD ["python", "demo.py"]
README.md CHANGED
@@ -19,8 +19,8 @@ This example runs HyperView with:
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  - CLIP embeddings (`openai/clip-vit-base-patch32`) for Euclidean layout
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  - HyCoCLIP embeddings (`hycoclip-vit-s`) for Poincaré layout
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- The Docker image installs released HyperView packages from PyPI and precomputes
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- the dataset, embeddings, and layouts during build for fast runtime startup.
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  ## Reuse This Template
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  - CLIP embeddings (`openai/clip-vit-base-patch32`) for Euclidean layout
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  - HyCoCLIP embeddings (`hycoclip-vit-s`) for Poincaré layout
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+ The Docker image installs released HyperView packages from PyPI. The dataset,
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+ embeddings, and layouts are computed at first startup.
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  ## Reuse This Template
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