build(backend): cache torch wheel + pip downloads across rebuilds
Splits torch+torchvision into its own RUN layer so edits to requirements.txt don't invalidate the ~200MB CPU-only torch download. Adds buildkit cache mounts on both pip-install layers so even when a layer is invalidated (or buildkit evicts it) the wheel is reused from the on-disk pip cache instead of refetching from download.pytorch.org. Triggered by two consecutive deploy failures where pytorch.org timed out mid-download (2026-05-13 ~21:41 and possibly ~22:47). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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@@ -1,3 +1,4 @@
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# syntax=docker/dockerfile:1.7
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FROM python:3.12-slim
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# Install system dependencies
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@@ -22,13 +23,23 @@ RUN apt-get update && apt-get install -y \
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WORKDIR /app
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# Copy requirements first for better caching
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# Install PyTorch CPU-only FIRST, in its own layer, so open-clip-torch
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# doesn't pull the full CUDA build (~7 GB). CPU inference is all we need
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# — the heavy lifting happens through ONNX Runtime.
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#
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# Two cache wins here:
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# 1. Its own RUN layer means edits to requirements.txt don't force a
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# re-pull of the ~200MB torch wheel.
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# 2. The buildkit cache mount keeps pip's download cache on disk
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# across builds even when the layer itself is invalidated, so a
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# torch-version bump or a builder cache eviction still reuses the
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# wheel from local cache instead of re-fetching from pytorch.org.
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
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COPY requirements.txt .
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# Install PyTorch CPU-only FIRST so open-clip-torch doesn't pull the full
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# CUDA build (~7 GB). CPU inference is all we need — the heavy lifting
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# happens through ONNX Runtime.
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RUN pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu \
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&& pip install --no-cache-dir -r requirements.txt
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip install -r requirements.txt
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# Copy application code
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COPY . .
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