Alison ShaoandAlison Shao
f21d23a211
Co-authored-by: Alison Shao <alison.shao@MacBook-Pro-D2W773R9CD.local>
39 lines
1.8 KiB
Bash
Executable File
39 lines
1.8 KiB
Bash
Executable File
#!/bin/bash
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# Cache and pre-install nvidia wheels that torch pins.
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#
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# pypi.nvidia.com returns Cache-Control: no-store, so pip re-downloads
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# ~2 GB of NVIDIA wheels on every CI run. This script:
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# 1. Caches cudnn + nvshmem wheels locally and pre-installs them
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# 2. Points pip at a local wheel directory via PIP_FIND_LINKS so that
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# all NVIDIA torch dependencies (cublas, cufft, nvrtc, etc.) are
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# installed from local files instead of re-downloading.
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#
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# Pre-cache the wheels on the host at /opt/ci-cache/nvidia-pip-wheels/
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# (mounted as /root/.cache/nvidia-pip-wheels inside containers).
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# See the 5090 ops guide post-reboot checklist for how to populate this.
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#
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# Integrity: uses `unzip -t` to detect partial/corrupt downloads.
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#
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# Usage: source scripts/ci/cuda/cache_nvidia_wheels.sh
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NVIDIA_WHEEL_CACHE="/root/.cache/nvidia-wheels"
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NVIDIA_PIP_WHEELS="/root/.cache/nvidia-pip-wheels"
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mkdir -p "$NVIDIA_WHEEL_CACHE"
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for url in \
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"https://pypi.nvidia.com/nvidia-cudnn-cu12/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl" \
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"https://pypi.nvidia.com/nvidia-nvshmem-cu12/nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl"; do
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whl="$NVIDIA_WHEEL_CACHE/$(basename "$url")"
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[ -f "$whl" ] && unzip -tq "$whl" &>/dev/null || curl -fL -o "$whl" "$url"
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done
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pip install --no-deps "$NVIDIA_WHEEL_CACHE"/nvidia_cudnn_cu12-*.whl \
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"$NVIDIA_WHEEL_CACHE"/nvidia_nvshmem_cu12-*.whl 2>/dev/null || true
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# If pre-cached NVIDIA pip wheels exist, tell pip to check there first.
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# This avoids re-downloading ~2 GB of cublas/cufft/nvrtc/etc. every run
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# (pypi.nvidia.com sends Cache-Control: no-store).
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if [ -d "$NVIDIA_PIP_WHEELS" ] && ls "$NVIDIA_PIP_WHEELS"/*.whl &>/dev/null; then
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export PIP_FIND_LINKS="${PIP_FIND_LINKS:+$PIP_FIND_LINKS }$NVIDIA_PIP_WHEELS"
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fi
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