File size: 3,232 Bytes
78cb49e | 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 | #!/bin/bash
# Run once on a fresh training instance, after:
# git clone https://github.com/karvachiik-lgtm/threejstraining.git coder-training
# scp -r you@local:.../coder-training/{archive,datasets} coder-training/ # data, not in git — see README.md
set -euo pipefail
cd "$(dirname "$0")/.."
# Fresh minimal cloud/Docker images often lack python3-venv (→ `ensurepip is
# not available`) and build tooling bitsandbytes/flash-attn wheels sometimes
# need — best-effort install, skipped quietly if apt-get isn't usable (no
# root, non-Debian image, etc.) rather than failing the whole script over it.
if command -v apt-get >/dev/null 2>&1 && [ "$(id -u)" = "0" ]; then
echo "=== Ensuring python3-venv/build-essential (apt) ==="
apt-get update -qq && apt-get install -y -qq python3-venv python3-pip build-essential >/dev/null \
|| echo " (apt-get install failed or partially failed — continuing; venv creation below will surface any real problem)"
fi
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
echo
echo "=== Pre-flight checks (see README.md's checklist for what to do if these fail) ==="
python3 - <<'PY'
import importlib
import sys
checks = ["torch", "transformers", "trl", "peft", "bitsandbytes", "datasets", "wandb"]
for mod in checks:
try:
m = importlib.import_module(mod)
print(f" OK {mod:<14} {getattr(m, '__version__', '?')}")
except ImportError as e:
print(f" FAIL {mod:<14} {e}")
sys.exit(1)
import torch
print(f" CUDA available: {torch.cuda.is_available()} device_count: {torch.cuda.device_count()}")
if not torch.cuda.is_available():
print(" WARN no CUDA GPU visible — QLoRA/bitsandbytes below needs one; check `nvidia-smi` on this instance.")
try:
from transformers import AutoConfig
# Cheap check: does this transformers version know about the qwen3_5
# architecture Tooony133/Qwen-3.6-27B-* and Qwen/Qwen3.6-27B report?
# See requirements.txt's note — this is the biggest unverified risk here.
AutoConfig.for_model("qwen3_5")
print(" OK transformers recognizes model_type=qwen3_5")
except Exception as e:
print(f" WARN transformers may not support model_type=qwen3_5 yet: {e}")
print(" You likely need `pip install git+https://github.com/huggingface/transformers.git`")
PY
echo
if [ -f "datasets/preference.jsonl" ] && [ -f "datasets/sft.jsonl" ] && [ -f "datasets/kto.jsonl" ]; then
echo "=== datasets/ already present (scp'd) — data pipeline (Phase 0/1) already done, skip it ==="
echo "Next: source .venv/bin/activate && python train_sft.py --config configs/sft.yaml"
else
echo "=== datasets/ not found — either scp it over, or build it fresh: ==="
echo " 1) scp -r you@local:.../coder-training/{archive,datasets} . (fastest — data's already built)"
echo " or"
echo " 2) python data/scrape_duels.py --rounds-dir <path-to-404-active-competition/rounds> --out-dir ./archive"
echo " python data/build_datasets.py --manifest ./archive/duels_index.jsonl --out-dir ./datasets"
fi
echo "See README.md for the full pipeline; TRAINING_GUIDE.md for the algorithm/hyperparameter choices."
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