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546e98c e6f0ced 546e98c e6f0ced 546e98c e6f0ced 546e98c e6f0ced 546e98c e6f0ced 546e98c e6f0ced 546e98c e6f0ced 546e98c e6f0ced 546e98c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | import marimo
__generated_with = "0.24.0"
app = marimo.App(width="medium", auto_download=["html"])
@app.cell
def _():
# ===================================================================== #
# DragonCode LLM Family — production notebook (thin wrapper)
# (c) 2026 Dragon Limited. All rights reserved.
#
# This notebook is a THIN LAUNCHER over scripts/run_dragoncode.py.
# The single source of truth for training logic lives in
# ~/DragonCode/scripts/*.py (NOT duplicated in cells).
#
# Industrial-standard guarantees enforced by the scripts:
# * 100% code-domain data — codeparrot/codeparrot-clean +
# open-r1/codeforces-cots (permissive licenses only). NO generic web.
# * 4 training bugs fixed: grad-accum dead-loop (local_steps counter),
# lr=0 (token-progress schedule), bytes JSON serialization (base64),
# allow_duplicate_filename (removed for hf_hub 1.24.0).
# * resume-aware + idempotent stage markers (never restart from zero).
# ===================================================================== #
import os, sys, subprocess, json, time
SCRIPT_DIR = os.path.expanduser("~/DragonCode/scripts")
CONFIG_DIR = os.path.expanduser("~/DragonCode/configs")
LOG_DIR = os.path.expanduser("~/DragonCode/logs")
# Auth: the scripts force the "dragonlimited" account token internally, so
# we only need to make sure the box has *some* HF_TOKEN exported.
os.environ.setdefault("HF_TOKEN", os.environ.get("HF_TOKEN", ""))
os.environ["HF_HOME"] = os.path.expanduser("~/.cache/huggingface")
# 5-model tier order (Chinchilla-optimal, 20 tokens/param).
TIER_ORDER = ["150M", "387M", "787M", "1.2B", "2.4B"]
CHINCHILLA = {
"150M": 3_000_000_000,
"387M": 7_740_000_000,
"787M": 15_740_000_000,
"1.2B": 24_000_000_000,
"2.4B": 48_000_000_000,
}
return (SCRIPT_DIR, CONFIG_DIR, LOG_DIR, TIER_ORDER, CHINCHILLA, os, subprocess, time)
@app.cell
def _(SCRIPT_DIR, CONFIG_DIR, LOG_DIR, os, subprocess, time):
# ===================================================================== #
# Stage runner — delegates every stage to scripts/run_dragoncode.py
# (the single source of truth). Stages per tier (domain-only 12-step):
# pretrain → sft → dpo → golf → merge → verify → gguf
# 150M/387M: pretrain only. 2.4B: no DPO this cycle.
# ===================================================================== #
TIER_STAGES = {
"150M": ["pretrain"],
"387M": ["pretrain"],
"787M": ["pretrain", "sft", "dpo", "golf", "merge", "verify", "gguf"],
"1.2B": ["pretrain", "sft", "dpo", "golf", "merge", "verify", "gguf"],
"2.4B": ["pretrain", "sft", "golf", "merge", "verify", "gguf"],
}
def _run(args, logfile):
"""Run a CLI stage, streaming stdout to its own log file (tail-friendly)."""
os.makedirs(LOG_DIR, exist_ok=True)
with open(logfile, "a") as lf:
lf.write(f"\n=== {time.strftime('%Y-%m-%dT%H:%M:%SZ')} {' '.join(args)} ===\n")
lf.flush()
proc = subprocess.Popen(
args, cwd=SCRIPT_DIR,
stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
text=True, bufsize=1,
)
assert proc.stdout is not None
for line in proc.stdout:
lf.write(line); lf.flush()
print(line, end="", flush=True)
rc = proc.wait()
return rc
def run_stage(tier, stage):
script = f"dragoncode_{stage}.py"
config = os.path.join(CONFIG_DIR, f"DragonCode-{tier}.yaml")
cmd = [sys.executable, script, "--tier", tier, "--config", config]
logfile = os.path.join(LOG_DIR, f"DragonCode-{tier}-{stage}.log")
print(f"\n[DRIVE] {tier}/{stage} -> {' '.join(cmd)}", flush=True)
rc = _run(cmd, logfile)
if rc != 0:
print(f"[DRIVE] {tier}/{stage} FAILED rc={rc} (see {logfile})", flush=True)
return False
print(f"[DRIVE] {tier}/{stage} OK", flush=True)
return True
return (TIER_STAGES, run_stage)
@app.cell
def _(TIER_ORDER, TIER_STAGES, run_stage, time):
# ===================================================================== #
# Full pipeline — sequential, resume-safe, single epoch per tier.
# Unique stop condition: user interrupt. No early-exit, no auto-exit.
# ===================================================================== #
def run_all():
for tier in TIER_ORDER:
for stage in TIER_STAGES[tier]:
ok = run_stage(tier, stage)
attempt = 0
while not ok and attempt < 3:
attempt += 1
time.sleep(8)
print(f"[DRIVE] {tier}/{stage} retry {attempt}/3", flush=True)
ok = run_stage(tier, stage)
if not ok:
print(f"[DRIVE] {tier}/{stage} failed after retries — stopping pipeline", flush=True)
return
print("[DRIVE] All 5 models complete.", flush=True)
return (run_all,)
@app.cell
def _(run_all):
# ===================================================================== #
# LAUNCH — press Run (▶) on this cell to train all 5 models end to end.
# ===================================================================== #
run_all()
return
if __name__ == "__main__":
app.run()
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