DataClaw / script /docker_save_image.sh
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Release v1.0: add dataset card and unify MIT license for Hugging Face
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#!/usr/bin/env bash
# Build the benchmark image and write a tarball under Images/.
# Usage (from repo root): ./script/docker_save_image.sh
# Optional: IMAGE_VERSION=0.2.0 ./script/docker_save_image.sh
set -euo pipefail
ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
cd "${ROOT}"
if [[ -n "${IMAGE_VERSION:-}" ]]; then
VERSION="${IMAGE_VERSION}"
else
VERSION="$(
grep -E '^version[[:space:]]*=[[:space:]]*"' pyproject.toml \
| head -1 \
| sed -E 's/^version[[:space:]]*=[[:space:]]*"([^"]+)".*/\1/'
)"
fi
if [[ -z "${VERSION}" ]]; then
echo "Could not read version from pyproject.toml" >&2
exit 1
fi
TAG="${DOCKER_IMAGE_NAME:-dataclaw}:${VERSION}"
OUT_DIR="${OUT_DIR:-${ROOT}/Images}"
TAR_BASENAME="${TAR_BASENAME:-dataclaw_ubuntu_v${VERSION}.tar}"
COMPRESS="${COMPRESS:-0}"
mkdir -p "${OUT_DIR}"
TAR_PATH="${OUT_DIR}/${TAR_BASENAME}"
echo "Building image ${TAG}..."
docker build -t "${TAG}" "${ROOT}"
echo "Saving to ${TAR_PATH}..."
docker save -o "${TAR_PATH}" "${TAG}"
if [[ "${COMPRESS}" == "1" ]]; then
echo "Compressing..."
gzip -f "${TAR_PATH}"
TAR_PATH="${TAR_PATH}.gz"
fi
echo ""
echo "Done: ${TAR_PATH}"
echo "Publish: upload this file (e.g. Hugging Face dataset repo)."
echo "Consumer: docker load -i $(basename "${TAR_PATH}")"
echo "Run: python dataclaw/eval/run_batch.py --model <model_id>"