runpod-parameter-golf

name	runpod-parameter-golf
description	Run Parameter Golf competition submissions on RunPod GPU Pods. Covers required operator inputs, RunPod pod specs, FineWeb SP1024 data caching, record-folder hygiene, torchrun launch commands, monitoring, artifact-size checks, and result collection.
tags	["runpod","parameter-golf","gpu-training","fineweb","competition","torchrun","h100"]

RunPod Parameter Golf

Use this skill when launching or preparing a Parameter Golf submission on RunPod, especially when the operator expects the agent to provision a Pod or run a competition-style training job.

Non-Negotiables

• A valid competition-style run happens in a Parameter Golf checkout from inside one record folder.
• The submission must be self-contained: no local package imports, unrelated training code, local-only checkpoints, or hidden dependencies.
• Use stock FineWeb SP1024 cached shards unless the submission intentionally changes tokenizer/data and proves BPB accounting.
• The 16MB cap is decimal: 16,000,000 bytes.
• Do not claim completion before the log contains final_int8_zlib_roundtrip_exact and the artifact-size line.

What To Ask The Operator For

Before you can run it yourself, request:

RunPod API key or SSH command:
Budget ceiling:
Run mode: smoke / final / smoke-then-final
GPU: 1xH100 smoke? 8xH100 final?
Cloud/storage: Secure Cloud? volume GB or networkVolumeId?
File transfer: Git clone / rsync/SCP / runpodctl?
Source folder or repo URL/branch:
Cleanup policy: stop or terminate after artifacts are safe?

If API access is provided, use least privilege that can create/list/start/stop/delete Pods and optionally create/list network volumes. Treat keys as secrets; never write them into logs, commits, or durable memory.

Recommended RunPod Pod Specs

Final 8xH100 run

{
  "cloudType": "SECURE",
  "computeType": "GPU",
  "gpuTypeIds": ["NVIDIA H100 80GB HBM3"],
  "gpuCount": 8,
  "imageName": "runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04",
  "containerDiskInGb": 50,
  "volumeInGb": 200,
  "volumeMountPath": "/workspace",
  "ports": ["22/tcp", "8888/http"],
  "supportPublicIp": true,
  "interruptible": false
}

Smoke run

Use the same image and ports with gpuCount=1. If the FineWeb cache must be downloaded on the smoke pod, keep volumeInGb=200; otherwise 100GB is acceptable.

Setup Commands On The Pod

cd /workspace
if [ ! -d parameter-golf ]; then
  git clone https://github.com/openai/parameter-golf.git
fi
cd /workspace/parameter-golf
python3 -m pip install --upgrade pip
pip install numpy sentencepiece huggingface-hub datasets tqdm

Download data:

# Smoke cache
python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 1

# Final cache
python3 data/cached_challenge_fineweb.py --variant sp1024

Expected paths:

/workspace/parameter-golf/data/datasets/fineweb10B_sp1024/
/workspace/parameter-golf/data/tokenizers/fineweb_1024_bpe.model

Submission Placement

The run folder must look like this:

/workspace/parameter-golf/records/<track>/<date_or_name>/
  README.md
  submission.json
  train_gpt.py

For a prepared non-record ConvGPT-style folder:

mkdir -p /workspace/parameter-golf/records/track_non_record_16mb
# Copy or clone the folder here, then:
cd /workspace/parameter-golf/records/track_non_record_16mb/<submission-folder>

Launch Commands

Final 8xH100

RUN_ID=convgpt_hybridconv_sp1024_8h100 \
DATA_PATH=/workspace/parameter-golf/data/datasets/fineweb10B_sp1024 \
TOKENIZER_PATH=/workspace/parameter-golf/data/tokenizers/fineweb_1024_bpe.model \
VOCAB_SIZE=1024 \
MODEL_DIM=256 \
NUM_LAYERS=8 \
MLP_MULT=2 \
GRID_SIZE=32 \
MAX_WALLCLOCK_SECONDS=600 \
TRAIN_LOG_EVERY=50 \
VAL_LOSS_EVERY=1000 \
torchrun --standalone --nproc_per_node=8 train_gpt.py 2>&1 | tee runpod_console.log

1xH100 smoke

RUN_ID=convgpt_hybridconv_sp1024_smoke \
DATA_PATH=/workspace/parameter-golf/data/datasets/fineweb10B_sp1024 \
TOKENIZER_PATH=/workspace/parameter-golf/data/tokenizers/fineweb_1024_bpe.model \
VOCAB_SIZE=1024 \
MODEL_DIM=256 \
NUM_LAYERS=8 \
MLP_MULT=2 \
GRID_SIZE=32 \
ITERATIONS=5 \
MAX_WALLCLOCK_SECONDS=0 \
TRAIN_BATCH_TOKENS=65536 \
VAL_BATCH_SIZE=65536 \
TRAIN_LOG_EVERY=1 \
VAL_LOSS_EVERY=0 \
torchrun --standalone --nproc_per_node=1 train_gpt.py 2>&1 | tee smoke_console.log

Monitoring Checklist

Watch for:

• dependency import failures (sentencepiece, torch, numpy)
• missing data paths
• CUDA out of memory
• final_model.int8.ptz creation
• Total submission size int8+zlib: ...
• final_int8_zlib_roundtrip_exact val_loss:... val_bpb:...

After success:

cp logs/${RUN_ID}.txt train.log
python3 - <<'PY'
from pathlib import Path
log = Path('train.log').read_text(errors='ignore')
for key in ['Total submission size int8+zlib', 'final_int8_zlib_roundtrip_exact']:
    print('\n'.join(line for line in log.splitlines() if key in line)[-2000:])
PY

Update submission.json with exact val_bpb, val_loss, artifact size, command, GPU count, and run status.

File Transfer Notes

RunPod docs distinguish:

• basic proxied SSH: quick shell access, no SCP/SFTP
• full SSH with public IP and exposed 22/tcp: supports SCP/SFTP/rsync
• runpodctl send/receive: easy for small-to-medium transfers
• rsync: best for large or repeated transfers

For agents, prefer full SSH or Git clone so the workflow is reproducible and resumable.

Cleanup

Before stopping/terminating:

1. Confirm train.log, submission.json, README.md, and train_gpt.py are present.
2. Copy out train.log and final_model.int8.ptz if the Pod volume is not persistent.
3. Stop the Pod to release GPU if preserving /workspace data.
4. Terminate only after artifacts are safely copied or stored on a network volume.