File size: 8,289 Bytes
7039798 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | """Pull the proof run's pile (plan/PROOF_DATA.md) into /workspace/shadow/data/<lane>/<modality>/<source>.
Each job: repo, file regex, size budget (GB, None = all), destination, licence tag. When the
matching files exceed the budget, files are taken evenly spaced across the sorted list so the
share spans the whole source. One manifest per job in manifests/: repo, commit, files, bytes,
licence. Resumable: files already on disk with the right size are skipped.
python3 pull.py # everything
python3 pull.py knowledge # jobs whose destination starts with this
"""
import json, os, re, sys, time, pathlib, concurrent.futures as cf
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import HfApi, hf_hub_download
ROOT = pathlib.Path("/workspace/shadow"); DATA = ROOT / "data"; MAN = ROOT / "manifests"
api = HfApi()
FV = "HuggingFaceM4/FineVision"
FV_SUBSETS = {"densefusion_1m": 40, "image_textualization(filtered)": 20, "sharegpt4o": 20, "LLaVA_Instruct_150K": 25,
"lvis_instruct4v": 20, "docvqa": 12, "chartqa": None, "CoSyn_400k_chart": 10, "dvqa": None, "plotqa": None,
"infographic_vqa": None, "textocr(gpt4v)": 10, "textvqa": None, "ai2d_merged": None, "scienceqa": None,
"scienceqa(nona_context)": None, "tqa": None, "screen2words": None, "screenqa": 10, "geo3k": None,
"geometry3k(mathv360k)": None, "vsr": None, "tallyqa": None, "ocrvqa": 6}
JOBS = [
# ---- knowledge
("HuggingFaceTB/cosmopedia-v2", r"^cosmopedia-v2/.*\.parquet$", 6, "knowledge/cosmopedia-v2", "odc-by"),
("openbmb/Ultra-FineWeb-L3", r"^data/ultrafineweb_en_l3/qa/", 1.5, "knowledge/ultra-fineweb-l3", "apache-2.0"),
("openbmb/Ultra-FineWeb-L3", r"^data/ultrafineweb_en_l3/multi_style/", 1.5, "knowledge/ultra-fineweb-l3", "apache-2.0"),
("openbmb/Ultra-FineWeb", r"^data/ultrafineweb_en/", 2, "knowledge/ultra-fineweb", "apache-2.0"),
("wikimedia/wikipedia", r"^20231101\.en/", 1.5, "knowledge/wikipedia", "cc-by-sa-3.0"),
("nvidia/OpenMathInstruct-2", r"^data/.*\.parquet$", None, "knowledge/openmathinstruct-2", "cc-by-4.0"),
("HuggingFaceTB/finemath", r"^finemath-4plus/", 1.5, "knowledge/finemath", "odc-by"),
("nvidia/OpenCodeInstruct", r"^data/.*\.parquet$", None, "knowledge/opencodeinstruct", "cc-by-4.0"),
("openbmb/UltraData-Code", r"^data/UltraData-Code-L3/py/", 2, "knowledge/ultradata-code", "apache-2.0"),
# ---- sft (text)
("nvidia/Nemotron-Post-Training-Dataset-v1", r"^data/(chat|stem)-", 15, "sft/talking/nemotron-post-training-v1", "cc-by-4.0"),
("openbmb/UltraData-SFT-2605", r"^data/no_think/Knowledge/", None, "sft/talking/ultradata-sft-2605", "apache-2.0"),
("openbmb/UltraData-SFT-2605", r"^data/no_think/IF/", None, "sft/talking/ultradata-sft-2605", "apache-2.0"),
("openbmb/UltraData-SFT-2605", r"^data/no_think/Code/", 3, "sft/talking/ultradata-sft-2605", "apache-2.0"),
("openbmb/UltraData-SFT-2605", r"^data/no_think/Math/", 6, "sft/talking/ultradata-sft-2605", "apache-2.0"),
("HuggingFaceTB/smoltalk2", r"(?i)^SFT/.*(smoltalk|everyday|if|magpie|systemchats).*\.parquet$", 3, "sft/talking/smoltalk2", "see-card"),
("Agent-Ark/Toucan-1.5M", r".*\.parquet$", None, "sft/tools/toucan-1.5m", "apache-2.0"),
("Team-ACE/ToolACE", r".*\.(json|jsonl|parquet)$", None, "sft/tools/toolace", "apache-2.0"),
("Salesforce/xlam-function-calling-60k", r".*\.(json|jsonl|parquet)$", None, "sft/tools/xlam", "cc-by-4.0"),
("allenai/pixmo-ask-model-anything", r".*\.parquet$", None, "sft/vision/pixmo-ask", "odc-by"),
("nvidia/AF-Chat", r".*", None, "sft/audio/af-chat", "nvidia-other"),
("nvidia/AF-Think", r".*", None, "sft/audio/af-think", "nvidia-other"),
# ---- understanding
("HuggingFaceM4/OBELICS", r"\.parquet$", 22, "understanding/image/obelics", "cc-by-4.0"),
("allenai/pixmo-cap", r".*\.parquet$", None, "understanding/image/pixmo-cap", "odc-by"),
("friedrichor/MSR-VTT", r".*", None, "understanding/video/msrvtt", "see-card"),
("lmms-lab/LLaVA-Video-178K", r"\.json$", None, "understanding/video/llava-video-178k", "see-card"),
("lmms-lab/LLaVA-Video-178K", r"^0_30_s_academic_v0_1/.*\.tar", None, "understanding/video/llava-video-178k", "see-card"),
("lmms-lab/LLaVA-Video-178K", r"^0_30_s_perceptiontest/.*\.tar", None, "understanding/video/llava-video-178k", "see-card"),
("Video-R1/Video-R1-data", r"^[^/]+\.(json|jsonl)$", None, "sft/video/video-r1", "apache-2.0"),
("Video-R1/Video-R1-data", r"^(General|Spatial|CLEVRER|Math)/", None, "sft/video/video-r1", "apache-2.0"),
("nvidia/AudioSkills", r".*", None, "understanding/audio/audioskills-xl", "nvidia-other"),
("agkphysics/AudioSet", r"^data/bal_train/", None, "understanding/audio/audioset", "cc-by-4.0"),
("agkphysics/AudioSet", r"^data/unbal_train/", 95, "understanding/audio/audioset", "cc-by-4.0"),
("CLAPv2/Clotho", r".*\.parquet$", None, "understanding/audio/clotho", "see-card"),
("cvssp/WavCaps", r"^json_files/", None, "understanding/audio/wavcaps", "cc-by-4.0"),
("cvssp/WavCaps", r"^Zip_files/(AudioSet_SL|SoundBible)/", None, "understanding/audio/wavcaps", "cc-by-4.0"),
# ---- generation
("LucasFang/FLUX-Reason-6M", r"\.parquet$", 24, "generation/image/flux-reason-6m", "apache-2.0"),
("nkp37/OpenVid-1M", r"^data/train/", None, "generation/video/openvid-1m", "cc-by-4.0"),
("nkp37/OpenVid-1M", r"^OpenVid_part\d+\.zip$", 50, "generation/video/openvid-1m", "cc-by-4.0"),
("OpenSound/AudioCaps", r"\.parquet$", None, "generation/audio/audiocaps", "cc-by-nc-4.0"),
# ---- sft (vision), big last
("lmms-lab/LLaVA-OneVision-Data", r"^(sharegpt4o|allava_instruct_laion4v|aokvqa\(cauldron,llava_format\))/", 15, "sft/vision/llava-onevision", "apache-2.0"),
("gpt-omni/VoiceAssistant-400K", r"^data/.*\.parquet$", 27, "sft/audio/voiceassistant-400k", "apache-2.0"),
] + [(FV, "^" + re.escape(s) + "/", cap, "understanding/image/finevision", "cc-by-4.0") for s, cap in FV_SUBSETS.items()]
def pick(files, budget_gb):
tot = sum(s for _, s in files)
if budget_gb is None or tot <= budget_gb * 1e9: return files
n = len(files); out = []; acc = 0
avg = tot / n; k = max(1, int(budget_gb * 1e9 / avg))
for j in range(k):
f = files[int(j * n / k)]
if acc + f[1] > budget_gb * 1e9 * 1.05 and out: break
out.append(f); acc += f[1]
return out
def log(*a): print(time.strftime("%H:%M:%S"), *a, flush=True)
def run(job, sel=None):
repo, rx, budget, dest, lic = job
if sel and not dest.startswith(sel): return
try:
info = api.dataset_info(repo, files_metadata=True)
except Exception as e:
log(f"SKIP {repo}: {str(e)[:120]}"); return
files = sorted([(f.rfilename, f.size or 0) for f in info.siblings if re.search(rx, f.rfilename)])
take = pick(files, budget); out = DATA / dest; out.mkdir(parents=True, exist_ok=True)
log(f"START {repo} [{rx}] -> {dest}: {len(take)}/{len(files)} files, {sum(s for _, s in take)/1e9:.1f} GB")
def one(f):
p = out / f[0]
if p.exists() and p.stat().st_size == f[1]: return f[1]
for attempt in range(4):
try:
hf_hub_download(repo, f[0], repo_type="dataset", revision=info.sha, local_dir=str(out)); return f[1]
except Exception as e:
if attempt == 3: log(f"FAIL {repo}/{f[0]}: {str(e)[:120]}"); return 0
time.sleep(10 * (attempt + 1))
with cf.ThreadPoolExecutor(8) as ex: got = sum(ex.map(one, take))
tag = re.sub(r"[^A-Za-z0-9]+", "_", repo + "_" + rx)[:120]
(MAN / f"{tag}.json").write_text(json.dumps({"repo": repo, "pattern": rx, "commit": info.sha, "dest": dest, "licence": lic,
"files": [{"path": n, "bytes": s} for n, s in take], "bytes": got, "pulled": time.strftime("%Y-%m-%d %H:%M")}, indent=1))
log(f"DONE {repo} [{rx}] {got/1e9:.1f} GB")
if __name__ == "__main__":
sel = sys.argv[1] if len(sys.argv) > 1 else None
MAN.mkdir(parents=True, exist_ok=True)
with cf.ThreadPoolExecutor(4) as ex: list(ex.map(lambda j: run(j, sel), JOBS))
tot = sum(sum(f.stat().st_size for f in p.rglob("*") if f.is_file()) for p in DATA.iterdir())
log(f"ALL DONE, {tot/1e9:.0f} GB on disk")
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