Datasets:
File size: 29,813 Bytes
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This command intentionally stops after building the local release and training
inputs. It never uploads to Hugging Face and never launches a GPU training
run. The checked-in server runbook continues from its output.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import shutil
import subprocess
import sys
import tarfile
import urllib.error
import urllib.request
import zipfile
from collections.abc import Mapping, Sequence
from dataclasses import dataclass
from pathlib import Path, PurePosixPath
from typing import IO, Any
import yaml
from ..atomic_io import atomic_write_json
from ..paths import repo_root
_CHUNK_BYTES = 8 * 1024 * 1024
_PROGRESS_BYTES = 256 * 1024 * 1024
class ServerPreparationError(RuntimeError):
"""The server cannot safely complete the registered preparation plan."""
@dataclass(frozen=True)
class ServerLayout:
root: Path
downloads: Path
raw: Path
tools: Path
plotqa_release: Path
release: Path
training_inputs: Path
gqa_image_cache: Path
clevr_dataset_gen: Path
blender_root: Path
def _mapping(value: Any, label: str) -> Mapping[str, Any]:
if not isinstance(value, Mapping):
raise ServerPreparationError(f"{label} must be a mapping")
return value
def _load_json(path: Path) -> dict[str, Any]:
try:
value = json.loads(path.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError) as exc:
raise ServerPreparationError(f"cannot read {path}: {exc}") from exc
return dict(_mapping(value, str(path)))
def _load_yaml(path: Path) -> dict[str, Any]:
try:
value = yaml.safe_load(path.read_text(encoding="utf-8"))
except (OSError, yaml.YAMLError) as exc:
raise ServerPreparationError(f"cannot read {path}: {exc}") from exc
return dict(_mapping(value, str(path)))
def _git_head(root: Path) -> str:
completed = _run(
["git", "rev-parse", "HEAD"],
cwd=root,
capture=True,
)
revision = completed.stdout.strip()
if len(revision) != 40 or any(char not in "0123456789abcdef" for char in revision):
raise ServerPreparationError(f"repository HEAD is not a full Git revision: {revision!r}")
return revision
def _verify_clean_master(root: Path, expected_commit: str) -> None:
if _git_head(root) != expected_commit:
raise ServerPreparationError("--code-commit differs from the current repository HEAD")
branch = _run(
["git", "branch", "--show-current"],
cwd=root,
capture=True,
).stdout.strip()
if branch != "master":
raise ServerPreparationError(f"server preparation requires master, found {branch!r}")
dirty = _run(
["git", "status", "--porcelain"],
cwd=root,
capture=True,
).stdout.strip()
if dirty:
raise ServerPreparationError("server preparation requires a clean Git checkout")
def _layout(workspace: Path, published: Mapping[str, Any], code_commit: str) -> ServerLayout:
extracted = _mapping(published["extracted_artifacts"], "extracted_artifacts")
release_relative = _safe_relative_path(
str(extracted["release_directory"]),
label="PlotQA seed release_directory",
)
return ServerLayout(
root=workspace,
downloads=workspace / "downloads",
raw=workspace / "raw",
tools=workspace / "tools",
plotqa_release=workspace / "plotqa-seed" / release_relative,
release=workspace / "releases" / f"evi-core-v2-{code_commit[:12]}",
training_inputs=workspace / "training-inputs" / f"evi-core-v2-{code_commit[:12]}",
gqa_image_cache=workspace / "cache" / "gqa-selected-images",
clevr_dataset_gen=workspace / "tools" / "clevr-dataset-gen",
blender_root=workspace / "tools" / "blender-2.79b",
)
def _safe_relative_path(value: str, *, label: str) -> Path:
path = PurePosixPath(value)
if path.is_absolute() or not path.parts or any(part in {"", ".", ".."} for part in path.parts):
raise ServerPreparationError(f"{label} is not a safe relative path: {value!r}")
return Path(*path.parts)
def _assert_workspace_outside_repo(workspace: Path, root: Path) -> None:
resolved = workspace.resolve()
repository = root.resolve()
try:
resolved.relative_to(repository)
except ValueError:
return
raise ServerPreparationError(
f"data workspace must be outside the Git checkout so data cannot be committed: {resolved}"
)
def _run(
command: Sequence[str],
*,
cwd: Path | None = None,
env: Mapping[str, str] | None = None,
capture: bool = False,
) -> subprocess.CompletedProcess[str]:
try:
completed = subprocess.run(
list(command),
cwd=cwd,
env=dict(env) if env is not None else None,
check=False,
text=True,
capture_output=capture,
)
except OSError as exc:
raise ServerPreparationError(f"cannot run {command[0]}: {exc}") from exc
if completed.returncode != 0:
details = (completed.stderr or completed.stdout or "").strip()
if len(details) > 2000:
details = details[-2000:]
raise ServerPreparationError(
f"command failed ({completed.returncode}): {' '.join(command)}"
+ (f"\n{details}" if details else "")
)
return completed
def _sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
while chunk := handle.read(_CHUNK_BYTES):
digest.update(chunk)
return digest.hexdigest()
def _verify_download(
path: Path,
*,
expected_bytes: int,
expected_sha256: str,
label: str,
) -> None:
if not path.is_file():
raise ServerPreparationError(f"{label} is missing: {path}")
actual_bytes = path.stat().st_size
if actual_bytes != expected_bytes:
raise ServerPreparationError(
f"{label} byte count differs: {actual_bytes} != {expected_bytes}"
)
actual_sha256 = _sha256(path)
if actual_sha256 != expected_sha256:
raise ServerPreparationError(
f"{label} download checksum differs: {actual_sha256} != {expected_sha256}"
)
def _download(
*,
url: str,
destination: Path,
expected_bytes: int,
expected_sha256: str,
label: str,
) -> Path:
if destination.is_file():
_verify_download(
destination,
expected_bytes=expected_bytes,
expected_sha256=expected_sha256,
label=label,
)
print(f"[prepare] reuse {label}: {destination}", file=sys.stderr, flush=True)
return destination
destination.parent.mkdir(parents=True, exist_ok=True)
partial = destination.with_name(destination.name + ".part")
request = urllib.request.Request(
url,
headers={"User-Agent": "explicit-learning-server-prepare/1"},
)
written = 0
next_progress = _PROGRESS_BYTES
try:
with urllib.request.urlopen(request, timeout=120) as response, partial.open("wb") as output:
while chunk := response.read(_CHUNK_BYTES):
output.write(chunk)
written += len(chunk)
if written >= next_progress:
print(
f"[prepare] {label}: {written / (1024**3):.2f} GiB",
file=sys.stderr,
flush=True,
)
next_progress += _PROGRESS_BYTES
os.replace(partial, destination)
except (OSError, urllib.error.URLError) as exc:
partial.unlink(missing_ok=True)
raise ServerPreparationError(f"cannot download {label}: {exc}") from exc
_verify_download(
destination,
expected_bytes=expected_bytes,
expected_sha256=expected_sha256,
label=label,
)
return destination
def _download_plotqa_seed(
*,
published: Mapping[str, Any],
destination_root: Path,
) -> Path:
repository = _mapping(published["dataset_repository"], "dataset_repository")
bundle = _mapping(published["bundle"], "bundle")
relative = _safe_relative_path(str(bundle["path_in_repo"]), label="HF bundle path")
destination_root.mkdir(parents=True, exist_ok=True)
try:
from huggingface_hub import hf_hub_download
except ImportError as exc:
raise ServerPreparationError(
"huggingface-hub is required; install requirements/data.in"
) from exc
try:
downloaded = Path(
hf_hub_download(
repo_id=str(repository["repo_id"]),
repo_type="dataset",
filename=relative.as_posix(),
revision=str(repository["revision"]),
token=os.environ.get("HF_TOKEN"),
local_dir=destination_root,
)
)
except Exception as exc:
raise ServerPreparationError(f"cannot download private PlotQA seed: {exc}") from exc
_verify_download(
downloaded,
expected_bytes=int(bundle["bytes"]),
expected_sha256=str(bundle["sha256"]),
label="private PlotQA seed bundle",
)
return downloaded
def _write_stream_atomic(source: IO[bytes], destination: Path) -> None:
destination.parent.mkdir(parents=True, exist_ok=True)
partial = destination.with_name(destination.name + ".part")
try:
with partial.open("wb") as output:
shutil.copyfileobj(source, output, length=_CHUNK_BYTES)
os.replace(partial, destination)
except OSError:
partial.unlink(missing_ok=True)
raise
def _extract_zip_members(
archive: Path,
destination: Path,
members: Mapping[str, str],
) -> None:
destination.mkdir(parents=True, exist_ok=True)
try:
with zipfile.ZipFile(archive) as source:
available = set(source.namelist())
missing = sorted(set(members) - available)
if missing:
raise ServerPreparationError(
f"{archive.name} is missing required members: {missing}"
)
for member_name, output_name in members.items():
target = destination / _safe_relative_path(output_name, label="ZIP output path")
info = source.getinfo(member_name)
if target.is_file() and target.stat().st_size == info.file_size:
continue
with source.open(info) as handle:
_write_stream_atomic(handle, target)
except (OSError, zipfile.BadZipFile) as exc:
raise ServerPreparationError(f"cannot extract {archive}: {exc}") from exc
def _extract_tar_prefix(archive: Path, destination: Path, prefix: str) -> None:
safe_prefix = PurePosixPath(prefix)
if safe_prefix.is_absolute() or ".." in safe_prefix.parts:
raise ServerPreparationError(f"unsafe TAR prefix: {prefix!r}")
found = False
try:
with tarfile.open(archive, mode="r:*") as source:
for member in source:
member_path = PurePosixPath(member.name)
if member_path == safe_prefix:
relative = PurePosixPath(".")
else:
try:
relative = member_path.relative_to(safe_prefix)
except ValueError:
continue
found = True
target = destination / Path(*relative.parts)
if member.isdir():
target.mkdir(parents=True, exist_ok=True)
continue
if not member.isfile():
raise ServerPreparationError(
f"unsupported link/device in PlotQA seed archive: {member.name}"
)
handle = source.extractfile(member)
if handle is None:
raise ServerPreparationError(f"cannot read TAR member: {member.name}")
with handle:
_write_stream_atomic(handle, target)
except (OSError, tarfile.TarError) as exc:
raise ServerPreparationError(f"cannot extract {archive}: {exc}") from exc
if not found:
raise ServerPreparationError(f"PlotQA seed archive has no {prefix!r} subtree")
def _extract_tar_all(archive: Path, destination: Path) -> None:
try:
with tarfile.open(archive, mode="r:*") as source:
for member in source:
relative = _safe_relative_path(member.name, label="TAR member")
target = destination / relative
if member.isdir():
target.mkdir(parents=True, exist_ok=True)
continue
if not member.isfile():
raise ServerPreparationError(
f"unsupported link/device in tool archive: {member.name}"
)
if target.is_file() and target.stat().st_size == member.size:
continue
handle = source.extractfile(member)
if handle is None:
raise ServerPreparationError(f"cannot read TAR member: {member.name}")
with handle:
_write_stream_atomic(handle, target)
except (OSError, tarfile.TarError) as exc:
raise ServerPreparationError(f"cannot extract {archive}: {exc}") from exc
def _prepare_repository(
*,
destination: Path,
url: str,
revision: str,
) -> None:
if (destination / ".git").is_dir():
head = _git_head(destination)
if head == revision:
return
_run(["git", "fetch", "--depth", "1", "origin", revision], cwd=destination)
_run(["git", "checkout", "--detach", revision], cwd=destination)
elif destination.exists():
raise ServerPreparationError(
f"official CLEVR tool path exists but is not a Git checkout: {destination}"
)
else:
destination.parent.mkdir(parents=True, exist_ok=True)
_run(["git", "clone", "--no-checkout", url, str(destination)])
_run(["git", "checkout", "--detach", revision], cwd=destination)
if _git_head(destination) != revision:
raise ServerPreparationError("official CLEVR generator did not resolve to pinned revision")
def _blender_environment(runtime_library_dir: Path | None) -> dict[str, str]:
environment = dict(os.environ)
if runtime_library_dir is not None:
current = environment.get("LD_LIBRARY_PATH")
environment["LD_LIBRARY_PATH"] = (
f"{runtime_library_dir}{os.pathsep}{current}" if current else str(runtime_library_dir)
)
return environment
def _check_blender(executable: Path, runtime_library_dir: Path | None) -> None:
if not executable.is_file():
raise ServerPreparationError(f"Blender executable is missing: {executable}")
try:
completed = subprocess.run(
[str(executable), "--version"],
env=_blender_environment(runtime_library_dir),
check=False,
text=True,
capture_output=True,
timeout=30,
)
except (OSError, subprocess.TimeoutExpired) as exc:
raise ServerPreparationError(f"cannot execute Blender 2.79b: {exc}") from exc
if completed.returncode != 0:
details = (completed.stderr or completed.stdout or "").strip()[-2000:]
raise ServerPreparationError(
"Blender 2.79b runtime check failed. Install libglu1-mesa or pass "
f"--blender-runtime-library-dir. Details: {details}"
)
def _validate_existing_training_inputs(path: Path) -> bool:
manifest_path = path / "manifest.json"
if not manifest_path.is_file():
return False
manifest = _load_json(manifest_path)
expected = {
"kind": "certified_model_independent_training_inputs",
"trained": False,
"group_count": 12000,
"view_count": 46000,
"comparison_slot_count": 46000,
"full_only_raw_rows": 12000,
"full_plus_intervention_raw_rows": 46000,
"admission_mode": "structural_no_content_hash_replay",
"reasoning_vlm_calls": 0,
"per_example_human_decisions": 0,
}
if any(manifest.get(key) != value for key, value in expected.items()):
return False
return all(
(path / name).is_file()
for name in (
"comparison-slots.jsonl",
"full-only.raw.jsonl",
"full-plus-intervention.raw.jsonl",
)
)
def _plan(
*,
layout: ServerLayout,
published: Mapping[str, Any],
resources: Mapping[str, Any],
code_commit: str,
) -> dict[str, Any]:
repository = _mapping(published["dataset_repository"], "dataset_repository")
gqa = _mapping(_mapping(resources["structured_sources"], "structured_sources")["gqa"], "gqa")
clevr = _mapping(
_mapping(resources["structured_sources"], "structured_sources")["clevr"],
"clevr",
)
return {
"contract": "server_prepare_evi_core_v2",
"code_commit": code_commit,
"workspace": str(layout.root),
"inputs": {
"plotqa_seed": {
"repo_id": repository["repo_id"],
"revision": repository["revision"],
"groups": 4000,
},
"gqa": {
"revision": gqa["revision"],
"groups": 6000,
},
"clevr": {
"revision": clevr["revision"],
"groups": 5000,
"renderer": "official Blender 2.79b Cycles",
},
},
"outputs": {
"release": str(layout.release),
"training_inputs": str(layout.training_inputs),
"train_groups": 12000,
"validation_groups": 2000,
"certified_eval_groups": 1000,
"views": 58000,
"training_slots": 46000,
},
"reasoning_vlm_calls": 0,
"per_example_human_decisions": 0,
"content_hash_replay_required": False,
"hf_upload_performed": False,
}
def prepare_server_data(args: argparse.Namespace) -> dict[str, Any]:
root = repo_root()
workspace = args.workspace.resolve()
_assert_workspace_outside_repo(workspace, root)
resources = _load_yaml(root / "configs" / "resources.yaml")
published = _load_json(root / "configs" / "published_release.json")
code_commit = args.code_commit or _git_head(root)
layout = _layout(workspace, published, code_commit)
plan = _plan(
layout=layout,
published=published,
resources=resources,
code_commit=code_commit,
)
if args.dry_run:
return plan
_verify_clean_master(root, code_commit)
layout.root.mkdir(parents=True, exist_ok=True)
structured = _mapping(resources["structured_sources"], "structured_sources")
gqa = _mapping(structured["gqa"], "structured_sources.gqa")
gqa_archives = _mapping(gqa["source_archives"], "gqa.source_archives")
clevr = _mapping(structured["clevr"], "structured_sources.clevr")
clevr_archive = _mapping(clevr["no_images_archive"], "clevr.no_images_archive")
renderer = _mapping(clevr["official_renderer"], "clevr.official_renderer")
repositories = _mapping(resources["repositories"], "repositories")
generator = _mapping(
repositories[str(renderer["repository"])],
"repositories.clevr_dataset_gen",
)
print("[prepare] download/extract pinned PlotQA seed", file=sys.stderr, flush=True)
plotqa_archive = _download_plotqa_seed(
published=published,
destination_root=layout.downloads / "plotqa-seed",
)
if not (layout.plotqa_release / "manifest.json").is_file():
release_prefix = str(
_mapping(published["extracted_artifacts"], "extracted_artifacts")["release_directory"]
)
_extract_tar_prefix(
plotqa_archive,
layout.plotqa_release,
release_prefix,
)
print("[prepare] download/extract official GQA annotations", file=sys.stderr, flush=True)
gqa_question_spec = _mapping(gqa_archives["questions"], "GQA questions archive")
gqa_scene_spec = _mapping(gqa_archives["scene_graphs"], "GQA scene archive")
gqa_questions = _download(
url=str(gqa_question_spec["url"]),
destination=layout.downloads / "gqa" / str(gqa_question_spec["filename"]),
expected_bytes=int(gqa_question_spec["bytes"]),
expected_sha256=str(gqa_question_spec["sha256"]),
label="GQA 1.2 questions",
)
gqa_scenes = _download(
url=str(gqa_scene_spec["url"]),
destination=layout.downloads / "gqa" / str(gqa_scene_spec["filename"]),
expected_bytes=int(gqa_scene_spec["bytes"]),
expected_sha256=str(gqa_scene_spec["sha256"]),
label="GQA 1.2 scene graphs",
)
gqa_root = layout.raw / "gqa"
_extract_zip_members(
gqa_questions,
gqa_root,
{
"train_balanced_questions.json": "train_balanced_questions.json",
"val_balanced_questions.json": "val_balanced_questions.json",
"readme.txt": "questions-readme.txt",
},
)
_extract_zip_members(
gqa_scenes,
gqa_root,
{
"train_sceneGraphs.json": "train_sceneGraphs.json",
"val_sceneGraphs.json": "val_sceneGraphs.json",
"readme.txt": "scene-graphs-readme.txt",
},
)
print("[prepare] download/extract official CLEVR annotations", file=sys.stderr, flush=True)
clevr_zip = _download(
url=str(clevr_archive["url"]),
destination=layout.downloads / "clevr" / str(clevr_archive["filename"]),
expected_bytes=int(clevr_archive["bytes"]),
expected_sha256=str(clevr_archive["sha256"]),
label="CLEVR v1 no-images annotations",
)
clevr_root = layout.raw / "clevr" / "CLEVR_v1.0"
_extract_zip_members(
clevr_zip,
clevr_root,
{
"CLEVR_v1.0/questions/CLEVR_train_questions.json": (
"questions/CLEVR_train_questions.json"
),
"CLEVR_v1.0/questions/CLEVR_val_questions.json": ("questions/CLEVR_val_questions.json"),
"CLEVR_v1.0/scenes/CLEVR_train_scenes.json": ("scenes/CLEVR_train_scenes.json"),
"CLEVR_v1.0/scenes/CLEVR_val_scenes.json": "scenes/CLEVR_val_scenes.json",
"CLEVR_v1.0/LICENSE.txt": "LICENSE.txt",
},
)
print("[prepare] install pinned official CLEVR renderer", file=sys.stderr, flush=True)
_prepare_repository(
destination=layout.clevr_dataset_gen,
url=str(generator["url"]),
revision=str(generator["revision"]),
)
blender_archive = _download(
url=str(renderer["blender_url"]),
destination=layout.downloads / "clevr" / str(renderer["blender_archive"]),
expected_bytes=int(renderer["blender_archive_bytes"]),
expected_sha256=str(renderer["blender_archive_sha256"]),
label="Blender 2.79b Linux archive",
)
blender_executable = layout.blender_root / "blender"
if not blender_executable.is_file():
_extract_tar_all(blender_archive, layout.tools)
runtime_library_dir = (
args.blender_runtime_library_dir.resolve()
if args.blender_runtime_library_dir is not None
else layout.blender_root / "lib"
)
_check_blender(blender_executable, runtime_library_dir)
if layout.release.exists():
_run(
[
sys.executable,
"-m",
"explicit_learning.cli.data",
"validate-evi-core-release",
"--release",
str(layout.release),
],
cwd=root,
)
print(f"[prepare] reuse validated release: {layout.release}", file=sys.stderr)
else:
print("[prepare] build PlotQA+GQA+CLEVR EVI core release", file=sys.stderr, flush=True)
_run(
[
sys.executable,
"-m",
"explicit_learning.cli.data",
"build-evi-core-release",
"--plotqa-release",
str(layout.plotqa_release),
"--gqa-train-questions",
str(gqa_root / "train_balanced_questions.json"),
"--gqa-train-scene-graphs",
str(gqa_root / "train_sceneGraphs.json"),
"--gqa-val-questions",
str(gqa_root / "val_balanced_questions.json"),
"--gqa-val-scene-graphs",
str(gqa_root / "val_sceneGraphs.json"),
"--gqa-image-cache",
str(layout.gqa_image_cache),
"--clevr-train-questions",
str(clevr_root / "questions" / "CLEVR_train_questions.json"),
"--clevr-train-scenes",
str(clevr_root / "scenes" / "CLEVR_train_scenes.json"),
"--clevr-val-questions",
str(clevr_root / "questions" / "CLEVR_val_questions.json"),
"--clevr-val-scenes",
str(clevr_root / "scenes" / "CLEVR_val_scenes.json"),
"--clevr-blender-executable",
str(blender_executable),
"--clevr-dataset-gen-root",
str(layout.clevr_dataset_gen),
"--clevr-blender-runtime-library-dir",
str(runtime_library_dir),
"--output",
str(layout.release),
"--release-config",
str(root / "configs" / "release.evi-core-v2.json"),
"--code-commit",
code_commit,
"--workers",
str(args.workers),
"--download-workers",
str(args.download_workers),
],
cwd=root,
)
_run(
[
sys.executable,
"-m",
"explicit_learning.cli.data",
"validate-evi-core-release",
"--release",
str(layout.release),
],
cwd=root,
)
if layout.training_inputs.exists():
if not _validate_existing_training_inputs(layout.training_inputs):
raise ServerPreparationError(
f"existing training-input directory is incomplete: {layout.training_inputs}"
)
print(
f"[prepare] reuse validated training inputs: {layout.training_inputs}",
file=sys.stderr,
)
else:
print("[prepare] build 46K registered training slots", file=sys.stderr, flush=True)
_run(
[
sys.executable,
"-m",
"explicit_learning.cli.train",
"build-training-inputs",
"--dataset",
str(layout.release / "train" / "groups.jsonl"),
"--asset-root",
str(layout.release),
"--release-manifest",
str(layout.release / "manifest.json"),
"--output-dir",
str(layout.training_inputs),
"--code-commit",
code_commit,
"--max-slots",
"46000",
"--workers",
str(args.workers),
],
cwd=root,
)
if not _validate_existing_training_inputs(layout.training_inputs):
raise ServerPreparationError("final training-input validation failed")
completed = {
**plan,
"status": "ready_for_model_dependent_server_steps",
"release_manifest": str(layout.release / "manifest.json"),
"training_inputs_manifest": str(layout.training_inputs / "manifest.json"),
}
atomic_write_json(layout.root / "server-preparation-summary.json", completed)
return completed
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
prog="explicit-prepare",
description=(
"Download the PlotQA seed and official GQA/CLEVR sources, build the local "
"EVI core v2 release with official Blender, and create training inputs. "
"No Hugging Face upload is performed."
),
)
parser.add_argument("--workspace", type=Path, required=True)
parser.add_argument("--code-commit")
parser.add_argument("--workers", type=int, default=8)
parser.add_argument("--download-workers", type=int, default=24)
parser.add_argument("--blender-runtime-library-dir", type=Path)
parser.add_argument(
"--dry-run",
action="store_true",
help="Print the immutable plan without network, files, rendering, or training.",
)
return parser
def main(argv: list[str] | None = None) -> int:
args = build_parser().parse_args(argv)
if args.workers < 1 or args.download_workers < 1:
print("explicit-prepare: worker counts must be positive", file=sys.stderr)
return 2
if args.code_commit is not None and (
len(args.code_commit) != 40
or any(char not in "0123456789abcdef" for char in args.code_commit)
):
print("explicit-prepare: --code-commit must be a full lowercase 40-hex", file=sys.stderr)
return 2
try:
result = prepare_server_data(args)
except ServerPreparationError as exc:
print(f"explicit-prepare: {exc}", file=sys.stderr)
return 1
print(json.dumps(result, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
|