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tune miniseries just a bit, fairly cosmetic, keep to even depths where the math works out nicely in model sizing

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Andrej Karpathy 2026-02-08 17:54:12 +00:00
parent aeff095e97
commit ff46300720
1 changed files with 9 additions and 3 deletions
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12
runs/miniseries.sh
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@ -28,7 +28,7 @@ fi
# Series name: from arg, env var, or default to today's date (e.g., jan11)
SERIES_NAME="${1:-${SERIES_NAME:-$(date +%b%d | tr '[:upper:]' '[:lower:]')}}"
# Depths to train (the "miniseries")
DEPTHS=(10 11 12 13 14 15 16 17 18 19 20)
DEPTHS=(12 14 16 18 20 22 24 26)
# Hardware
NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
# Logging
@ -57,8 +57,13 @@ for d in "${DEPTHS[@]}"; do
TAG="${SERIES_NAME}_miniseries_d${d}"
START_TIME=$(date +%s)
# Train the model with natural horizon (target_param_data_ratio default)
# No --target-flops, let it use the default ratio from base_train
# For depths >= 22, use smaller device batch size to avoid OOM
if [ $d -ge 22 ]; then
DEVICE_BATCH_SIZE_ARG="--device-batch-size=16"
else
DEVICE_BATCH_SIZE_ARG="--device-batch-size=32"
fi
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- \
--depth=$d \
--run="${WANDB_RUN}_d${d}" \
@ -67,6 +72,7 @@ for d in "${DEPTHS[@]}"; do
--core-metric-max-per-task=-1 \
--sample-every=-1 \
--save-every=-1 \
$DEVICE_BATCH_SIZE_ARG \
2>&1 | tee "$RESULTS_DIR/${TAG}_train.log"
END_TIME=$(date +%s)