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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SAGE Control UI via ngrok\n",
"\n",
"This notebook clones the repo if needed, starts the real FastAPI server, protects the control UI with `SAGE_WEB_PASSWORD`, and exposes it through ngrok.\n",
"\n",
"The public URL serves:\n",
"\n",
"- `GET /` for the browser control panel\n",
"- `GET /health`\n",
"- `POST /generate` on the GPU server\n"
],
"id": "ff445bd1d37c1021"
},
{
"metadata": {},
"cell_type": "code",
"source": [
"\"\"\"\n",
"SAGE — 5 Billion Token Dataset Downloader\n",
"==========================================\n",
"Downloads ~5B tokens from free Hugging Face datasets and saves them\n",
"as JSONL files in your data/raw/ directory, ready for the SAGE pipeline.\n",
"\n",
"Token budget breakdown:\n",
" general_web.jsonl → 2.5B tokens (FineWeb)\n",
" code.jsonl → 1.0B tokens (The Stack v2 - Python, JS, Rust, Go, C++)\n",
" math_science.jsonl → 0.5B tokens (OpenWebMath)\n",
" multilingual.jsonl → 0.5B tokens (Wikipedia 20+ languages)\n",
" synthetic.jsonl → 0.5B tokens (OpenHermes instruction data)\n",
" ─────────────────────────────────────\n",
" TOTAL → ~5.0B tokens\n",
"\n",
"Usage:\n",
" pip install datasets huggingface_hub tqdm\n",
" python debug/download_5b_tokens.py --output-dir data/raw\n",
" python debug/download_5b_tokens.py --output-dir data/raw --resume\n",
"\"\"\"\n",
"\n",
"import argparse\n",
"import json\n",
"import sys\n",
"import time\n",
"from pathlib import Path\n",
"\n",
"missing = []\n",
"try:\n",
" from datasets import load_dataset\n",
"except ImportError:\n",
" missing.append(\"datasets\")\n",
"try:\n",
" from tqdm import tqdm\n",
"except ImportError:\n",
" missing.append(\"tqdm\")\n",
"\n",
"if missing:\n",
" print(f\"[ERROR] Missing packages: {', '.join(missing)}\")\n",
" print(f\" Run: pip install {' '.join(missing)}\")\n",
" sys.exit(1)\n",
"\n",
"\n",
"def estimate_tokens(text: str) -> int:\n",
" return max(1, len(text) // 4)\n",
"\n",
"def human_tokens(n: int) -> str:\n",
" if n >= 1_000_000_000:\n",
" return f\"{n/1_000_000_000:.2f}B\"\n",
" if n >= 1_000_000:\n",
" return f\"{n/1_000_000:.1f}M\"\n",
" return f\"{n:,}\"\n",
"\n",
"def human_bytes(n: int) -> str:\n",
" for unit in [\"B\", \"KB\", \"MB\", \"GB\"]:\n",
" if n < 1024:\n",
" return f\"{n:.1f} {unit}\"\n",
" n /= 1024\n",
" return f\"{n:.1f} TB\"\n",
"\n",
"\n",
"class JSONLWriter:\n",
" def __init__(self, path: Path, target_tokens: int, resume: bool = False):\n",
" self.path = path\n",
" self.target_tokens = target_tokens\n",
" self.tokens_written = 0\n",
" self.records_written = 0\n",
"\n",
" if resume and path.exists():\n",
" print(f\" [resume] Counting existing tokens in {path.name}...\")\n",
" with open(path, \"r\", encoding=\"utf-8\") as f:\n",
" for line in f:\n",
" try:\n",
" rec = json.loads(line)\n",
" self.tokens_written += estimate_tokens(rec.get(\"text\", \"\"))\n",
" self.records_written += 1\n",
" except json.JSONDecodeError:\n",
" pass\n",
" print(f\" [resume] Already have {human_tokens(self.tokens_written)} / {human_tokens(target_tokens)}\")\n",
" self._file = open(path, \"a\", encoding=\"utf-8\", buffering=1024 * 1024)\n",
" else:\n",
" path.parent.mkdir(parents=True, exist_ok=True)\n",
" self._file = open(path, \"w\", encoding=\"utf-8\", buffering=1024 * 1024)\n",
"\n",
" @property\n",
" def done(self) -> bool:\n",
" return self.tokens_written >= self.target_tokens\n",
"\n",
" def write(self, record: dict) -> int:\n",
" text = record.get(\"text\", \"\")\n",
" if not text or len(text.strip()) < 50:\n",
" return 0\n",
" toks = estimate_tokens(text)\n",
" self._file.write(json.dumps(record, ensure_ascii=False) + \"\\n\")\n",
" self.tokens_written += toks\n",
" self.records_written += 1\n",
" return toks\n",
"\n",
" def close(self):\n",
" self._file.flush()\n",
" self._file.close()\n",
"\n",
" def __enter__(self): return self\n",
" def __exit__(self, *_): self.close()\n",
"\n",
"\n",
"def download_general_web(writer):\n",
" print(\"\\n[1/5] general_web.jsonl — FineWeb\")\n",
" bar = tqdm(total=writer.target_tokens, initial=writer.tokens_written,\n",
" unit=\"tok\", unit_scale=True, desc=\" web tokens\")\n",
" ds = load_dataset(\"HuggingFaceFW/fineweb\", name=\"sample-10BT\",\n",
" split=\"train\", streaming=True)\n",
" for sample in ds:\n",
" if writer.done: break\n",
" bar.update(writer.write({\"text\": sample[\"text\"], \"source\": \"fineweb\",\n",
" \"url\": sample.get(\"url\", \"\"), \"language\": \"en\"}))\n",
" bar.close()\n",
" print(f\" ✓ {human_tokens(writer.tokens_written)} tokens | {writer.records_written:,} records\")\n",
"\n",
"\n",
"def download_code(writer):\n",
" print(\"\\n[2/5] code.jsonl — The Stack v2\")\n",
" LANGUAGES = [(\"python\",\"Python\"),(\"javascript\",\"JavaScript\"),(\"typescript\",\"TypeScript\"),\n",
" (\"rust\",\"Rust\"),(\"go\",\"Go\"),(\"cpp\",\"C++\"),(\"java\",\"Java\"),\n",
" (\"bash\",\"Bash\"),(\"sql\",\"SQL\"),(\"html\",\"HTML\")]\n",
" bar = tqdm(total=writer.target_tokens, initial=writer.tokens_written,\n",
" unit=\"tok\", unit_scale=True, desc=\" code tokens\")\n",
" tokens_per_lang = writer.target_tokens // len(LANGUAGES)\n",
" for lang_id, lang_name in LANGUAGES:\n",
" if writer.done: break\n",
" lang_tokens = 0\n",
" print(f\" → {lang_name}...\")\n",
" try:\n",
" ds = load_dataset(\"bigcode/the-stack-v2-train-smol-ids\",\n",
" data_dir=f\"data/{lang_id}\", split=\"train\",\n",
" streaming=True, trust_remote_code=True)\n",
" for sample in ds:\n",
" if writer.done or lang_tokens >= tokens_per_lang: break\n",
" content = sample.get(\"content\", \"\") or sample.get(\"text\", \"\")\n",
" if not content: continue\n",
" t = writer.write({\"text\": content, \"source\": \"the_stack_v2\",\n",
" \"language\": lang_id})\n",
" bar.update(t); lang_tokens += t\n",
" except Exception as e:\n",
" print(f\" [warn] {lang_name} failed ({e}), skipping.\")\n",
" bar.close()\n",
" print(f\" ✓ {human_tokens(writer.tokens_written)} tokens | {writer.records_written:,} records\")\n",
"\n",
"\n",
"def download_math(writer):\n",
" print(\"\\n[3/5] math_science.jsonl — OpenWebMath\")\n",
" bar = tqdm(total=writer.target_tokens, initial=writer.tokens_written,\n",
" unit=\"tok\", unit_scale=True, desc=\" math tokens\")\n",
" ds = load_dataset(\"open-web-math/open-web-math\", split=\"train\", streaming=True)\n",
" for sample in ds:\n",
" if writer.done: break\n",
" bar.update(writer.write({\"text\": sample[\"text\"], \"source\": \"open_web_math\",\n",
" \"url\": sample.get(\"url\", \"\")}))\n",
" bar.close()\n",
" print(f\" ✓ {human_tokens(writer.tokens_written)} tokens | {writer.records_written:,} records\")\n",
"\n",
"\n",
"def download_multilingual(writer):\n",
" print(\"\\n[4/5] multilingual.jsonl — Wikipedia (20 languages)\")\n",
" LANGUAGES = [(\"en\",\"English\"),(\"es\",\"Spanish\"),(\"fr\",\"French\"),(\"de\",\"German\"),\n",
" (\"zh\",\"Chinese\"),(\"ja\",\"Japanese\"),(\"pt\",\"Portuguese\"),(\"ar\",\"Arabic\"),\n",
" (\"ru\",\"Russian\"),(\"hi\",\"Hindi\"),(\"it\",\"Italian\"),(\"ko\",\"Korean\"),\n",
" (\"nl\",\"Dutch\"),(\"pl\",\"Polish\"),(\"sv\",\"Swedish\"),(\"tr\",\"Turkish\"),\n",
" (\"vi\",\"Vietnamese\"),(\"id\",\"Indonesian\"),(\"uk\",\"Ukrainian\"),(\"fa\",\"Persian\")]\n",
" bar = tqdm(total=writer.target_tokens, initial=writer.tokens_written,\n",
" unit=\"tok\", unit_scale=True, desc=\" multilingual tokens\")\n",
" tokens_per_lang = writer.target_tokens // len(LANGUAGES)\n",
" for lang_code, lang_name in LANGUAGES:\n",
" if writer.done: break\n",
" lang_tokens = 0\n",
" try:\n",
" ds = load_dataset(\"wikimedia/wikipedia\", f\"20231101.{lang_code}\",\n",
" split=\"train\", streaming=True, trust_remote_code=True)\n",
" for sample in ds:\n",
" if writer.done or lang_tokens >= tokens_per_lang: break\n",
" text = sample.get(\"text\", \"\")\n",
" if not text: continue\n",
" t = writer.write({\"text\": text, \"source\": \"wikipedia\",\n",
" \"language\": lang_code, \"title\": sample.get(\"title\",\"\")})\n",
" bar.update(t); lang_tokens += t\n",
" except Exception as e:\n",
" print(f\"\\n [warn] Wikipedia {lang_name} failed: {e}\")\n",
" bar.close()\n",
" print(f\" ✓ {human_tokens(writer.tokens_written)} tokens | {writer.records_written:,} records\")\n",
"\n",
"\n",
"def download_synthetic(writer):\n",
" print(\"\\n[5/5] synthetic.jsonl — OpenHermes 2.5\")\n",
" bar = tqdm(total=writer.target_tokens, initial=writer.tokens_written,\n",
" unit=\"tok\", unit_scale=True, desc=\" synthetic tokens\")\n",
" ds = load_dataset(\"teknium/OpenHermes-2.5\", split=\"train\", streaming=True)\n",
" rounds = 0\n",
" while not writer.done and rounds < 10:\n",
" for sample in ds:\n",
" if writer.done: break\n",
" convs = sample.get(\"conversations\", [])\n",
" parts = []\n",
" for turn in convs:\n",
" role, value = turn.get(\"from\",\"\"), turn.get(\"value\",\"\")\n",
" if role == \"human\": parts.append(f\"### Instruction\\n{value}\")\n",
" elif role == \"gpt\": parts.append(f\"### Response\\n{value}\")\n",
" text = \"\\n\\n\".join(parts) or sample.get(\"text\",\"\")\n",
" if not text: continue\n",
" bar.update(writer.write({\"text\": text, \"source\": \"openhermes_2.5\",\n",
" \"task\": \"instruction_following\"}))\n",
" rounds += 1\n",
" bar.close()\n",
" print(f\" ✓ {human_tokens(writer.tokens_written)} tokens | {writer.records_written:,} records\")\n",
"\n",
"\n",
"TARGETS = {\n",
" \"general_web.jsonl\": 2_500_000_000,\n",
" \"code.jsonl\": 1_000_000_000,\n",
" \"math_science.jsonl\": 500_000_000,\n",
" \"multilingual.jsonl\": 500_000_000,\n",
" \"synthetic.jsonl\": 500_000_000,\n",
"}\n",
"DOWNLOADERS = {\n",
" \"general_web.jsonl\": download_general_web,\n",
" \"code.jsonl\": download_code,\n",
" \"math_science.jsonl\": download_math,\n",
" \"multilingual.jsonl\": download_multilingual,\n",
" \"synthetic.jsonl\": download_synthetic,\n",
"}\n",
"\n",
"\n",
"def main():\n",
" parser = argparse.ArgumentParser(description=\"Download ~5B tokens for SAGE training.\")\n",
" parser.add_argument(\"--output-dir\", default=\"data/raw\")\n",
" parser.add_argument(\"--resume\", action=\"store_true\")\n",
" parser.add_argument(\"--only\", nargs=\"+\", choices=list(TARGETS.keys()))\n",
" parser.add_argument(\"--scale\", type=float, default=1.0)\n",
" args = parser.parse_args()\n",
"\n",
" out_dir = Path(args.output_dir)\n",
" out_dir.mkdir(parents=True, exist_ok=True)\n",
" files_to_run = args.only or list(TARGETS.keys())\n",
" total_target = sum(int(TARGETS[f] * args.scale) for f in files_to_run)\n",
"\n",
" print(\"=\" * 60)\n",
" print(\" SAGE — 5 Billion Token Downloader\")\n",
" print(\"=\" * 60)\n",
" print(f\" Output dir : {out_dir.resolve()}\")\n",
" print(f\" Resume : {args.resume}\")\n",
" print(f\" Scale : {args.scale}x\")\n",
" print(f\" Target : {human_tokens(total_target)} tokens\")\n",
" print(f\" Est. disk : ~{total_target // 40_000_000} GB\")\n",
" print(\"=\" * 60)\n",
"\n",
" grand_start = time.time()\n",
" grand_tokens = 0\n",
"\n",
" for filename in files_to_run:\n",
" target = int(TARGETS[filename] * args.scale)\n",
" with JSONLWriter(out_dir / filename, target, resume=args.resume) as writer:\n",
" if writer.done:\n",
" print(f\"\\n[skip] {filename} already complete ({human_tokens(writer.tokens_written)} tokens)\")\n",
" grand_tokens += writer.tokens_written\n",
" continue\n",
" t0 = time.time()\n",
" DOWNLOADERS[filename](writer)\n",
" elapsed = time.time() - t0\n",
" grand_tokens += writer.tokens_written\n",
" size = (out_dir / filename).stat().st_size\n",
" print(f\" Time: {elapsed/60:.1f} min | Size: {human_bytes(size)}\")\n",
"\n",
" elapsed_total = time.time() - grand_start\n",
" print(\"\\n\" + \"=\" * 60)\n",
" print(f\" DONE — {human_tokens(grand_tokens)} tokens downloaded\")\n",
" print(f\" Total time: {elapsed_total/3600:.2f} hours\")\n",
" print(f\" Files: {out_dir.resolve()}/\")\n",
" print(\"=\" * 60)\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
],
"id": "5751afbf64858f98",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": [
"# Colab one-cell launcher for the real SAGE server\n",
"# Before running:\n",
"# 1. In Colab, open the Secrets panel (Key icon on the left) and add your NGROK_AUTHTOKEN\n",
"# 2. If you want /generate, switch Colab to a T4 GPU runtime\n",
"\n",
"import os\n",
"import sys\n",
"import time\n",
"import atexit\n",
"import subprocess\n",
"import importlib\n",
"import secrets\n",
"from pathlib import Path\n",
"\n",
"REPO_URL = \"https://huggingface.co/sage002/sage\"\n",
"REPO_DIR = Path(\"/content/sage\")\n",
"PORT = 8000\n",
"RUN_GENERATE_SMOKE = False\n",
"\n",
"def run(cmd, cwd=None):\n",
" print(\"+\", \" \".join(cmd))\n",
" subprocess.run(cmd, cwd=cwd, check=True)\n",
"\n",
"# 1. Clone or update repo\n",
"if not REPO_DIR.exists():\n",
" run([\"git\", \"clone\", REPO_URL, str(REPO_DIR)])\n",
"else:\n",
" run([\"git\", \"-C\", str(REPO_DIR), \"pull\", \"--ff-only\"])\n",
"\n",
"# 2. Install dependencies\n",
"run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"-U\", \"pip\"])\n",
"run([\n",
" sys.executable, \"-m\", \"pip\", \"install\", \"-q\",\n",
" \"fastapi>=0.110.0\", \"uvicorn>=0.29.0\", \"python-multipart>=0.0.9\",\n",
" \"pydantic>=2.7.0\", \"pyyaml>=6.0.1\", \"psutil>=5.9.8\",\n",
" \"pyngrok>=7.2.0\", \"requests>=2.31.0\"\n",
"])\n",
"\n",
"try:\n",
" import torch\n",
"except ImportError:\n",
" run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"torch>=2.1.0\"])\n",
" import torch\n",
"\n",
"# Refresh path caches so the cell can instantly import newly installed modules\n",
"importlib.invalidate_caches()\n",
"import requests\n",
"from pyngrok import ngrok\n",
"\n",
"# 3. Retrieve Ngrok token securely via Colab Secrets (or fallback to environment variable)\n",
"try:\n",
" from google.colab import userdata\n",
" NGROK_AUTHTOKEN = userdata.get(\"NGROK_AUTHTOKEN\")\n",
"except Exception:\n",
" NGROK_AUTHTOKEN = os.environ.get(\"NGROK_AUTHTOKEN\")\n",
"\n",
"if not NGROK_AUTHTOKEN:\n",
" raise ValueError(\"Missing NGROK_AUTHTOKEN. Please add it to your Colab Secrets.\")\n",
"\n",
"# 4. Supply necessary SAGE environment variables for the server\n",
"env = os.environ.copy()\n",
"env[\"SAGE_WEB_PASSWORD\"] = env.get(\"SAGE_WEB_PASSWORD\") or secrets.token_urlsafe(12)\n",
"env[\"SAGE_MODEL_CONFIG\"] = env.get(\"SAGE_MODEL_CONFIG\", \"configs/model/1b.yaml\")\n",
"env[\"SAGE_CHECKPOINT_DIR\"] = env.get(\"SAGE_CHECKPOINT_DIR\", \"runs/sage-1b\")\n",
"env[\"SAGE_TOKENIZER_MODEL\"] = env.get(\"SAGE_TOKENIZER_MODEL\", \"tokenizer/tokenizer.model\")\n",
"\n",
"USE_GPU_SERVER = torch.cuda.is_available()\n",
"APP_TARGET = \"serve.server:app\" if USE_GPU_SERVER else \"serve.server_cpu:app\"\n",
"\n",
"print(f\"GPU available: {USE_GPU_SERVER}\")\n",
"print(f\"Starting app target: {APP_TARGET}\")\n",
"print(f\"SAGE_WEB_PASSWORD: {env['SAGE_WEB_PASSWORD']} <-- Use this to login to the IDE\")\n",
"\n",
"# 5. Start Uvicorn Server attached to the log file via Popen\n",
"log_path = REPO_DIR / \"uvicorn.log\"\n",
"log_file = open(log_path, \"w\", encoding=\"utf-8\")\n",
"\n",
"server_proc = subprocess.Popen(\n",
" [\n",
" sys.executable, \"-m\", \"uvicorn\",\n",
" APP_TARGET,\n",
" \"--host\", \"0.0.0.0\",\n",
" \"--port\", str(PORT),\n",
" ],\n",
" cwd=str(REPO_DIR),\n",
" env=env, # Required: Passes the SAGE environment variables to Uvicorn\n",
" stdout=log_file,\n",
" stderr=subprocess.STDOUT,\n",
")\n",
"\n",
"def cleanup():\n",
" global server_proc, log_file\n",
" print(\"Cleaning up...\")\n",
" try:\n",
" ngrok.disconnect(public_url)\n",
" ngrok.kill()\n",
" except Exception:\n",
" pass\n",
" if server_proc and server_proc.poll() is None:\n",
" server_proc.terminate()\n",
" try:\n",
" server_proc.wait(timeout=10)\n",
" except subprocess.TimeoutExpired:\n",
" server_proc.kill()\n",
" try:\n",
" log_file.close()\n",
" except Exception:\n",
" pass\n",
" print(\"Cleanup complete.\")\n",
"\n",
"atexit.register(cleanup)\n",
"\n",
"# 6. Wait for health check success\n",
"health_url = f\"http://127.0.0.1:{PORT}/health\"\n",
"for _ in range(60):\n",
" if server_proc.poll() is not None:\n",
" log_file.flush()\n",
" raise RuntimeError(\"Uvicorn exited early.\\n\\n\" + log_path.read_text(encoding=\"utf-8\", errors=\"ignore\"))\n",
" try:\n",
" r = requests.get(health_url, timeout=2)\n",
" if r.ok:\n",
" print(\"Local health OK:\", r.json())\n",
" break\n",
" except Exception:\n",
" pass\n",
" time.sleep(2)\n",
"else:\n",
" log_file.flush()\n",
" raise TimeoutError(\"Server did not become healthy.\\n\\n\" + log_path.read_text(encoding=\"utf-8\", errors=\"ignore\"))\n",
"\n",
"# 7. Start Ngrok HTTPs Tunnel\n",
"try:\n",
" ngrok.kill()\n",
" ngrok.set_auth_token(NGROK_AUTHTOKEN)\n",
" tunnel = ngrok.connect(addr=PORT, proto=\"http\", bind_tls=True) # Forces HTTPS UI which stops browser mixed-content blocks\n",
" public_url = tunnel.public_url\n",
"\n",
" print(\"\\n============================================\")\n",
" print(\" SAGE DASHBOARD \")\n",
" print(\"==============================================\")\n",
" print(f\"URL: {public_url}\")\n",
" print(f\"PWD: {env['SAGE_WEB_PASSWORD']}\")\n",
" print(\"==============================================\\n\")\n",
"\n",
" if USE_GPU_SERVER:\n",
" print(\"Generate :\", f\"{public_url}/generate\")\n",
" else:\n",
" print(\"Wait: Generate is not available on CPU server in this repo\")\n",
" print(\"Switch Colab to a GPU runtime if you want /generate.\")\n",
"except Exception as e:\n",
" print(\"Could not start Ngrok: \", e)\n",
"\n",
"\n",
"# Optional /generate smoke test\n",
"if USE_GPU_SERVER and RUN_GENERATE_SMOKE:\n",
" print(\"\\nRunning /generate smoke test...\")\n",
" try:\n",
" resp = requests.post(\n",
" f\"http://127.0.0.1:{PORT}/generate\",\n",
" json={\"input_ids\": [1, 42, 99], \"max_new_tokens\": 4},\n",
" timeout=300,\n",
" )\n",
" print(\"Generate response:\", resp.json())\n",
" except Exception as e:\n",
" print(\"Generate timeout or failure:\", e)\n",
"\n",
"\n",
"print(f\"\\nServer log path: {log_path}\")\n",
"print(\"The server will continuously run until you stop the Code Cell manually.\")\n"
],
"id": "98ae55680033f413",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "python debug/download_5b_tokens.py --output-dir data/raw --scale 0.01",
"id": "7cdcdbf0001d4933"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|