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restore speedrun.sh

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Shizhe Diao 2025-10-22 22:36:12 -07:00
parent 29b94f35ec
commit 2a6276bfcb
1 changed files with 8 additions and 14 deletions
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speedrun.sh
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@ -9,10 +9,6 @@
# screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
# 3) Example launch with wandb logging, but see below for setting up wandb first:
# WANDB_RUN=speedrun screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
set -x
DATA_NAME=smollm
DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
# Default intermediate artifacts directory is in ~/.cache/nanochat
export OMP_NUM_THREADS=1
@ -38,18 +34,16 @@ source .venv/bin/activate
# `wandb login`
# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
# `WANDB_RUN=d26 bash speedrun.sh`
# if [ -z "$WANDB_RUN" ]; then
# # by default use "dummy" : it's handled as a special case, skips logging to wandb
# WANDB_RUN=dummy
# fi
export WANDB_API_KEY="ec7a9c0701d404122e4fc5c7c7518ed17f5b03ca"
export WANDB_RUN=fineweb_d20_test
if [ -z "$WANDB_RUN" ]; then
# by default use "dummy" : it's handled as a special case, skips logging to wandb
WANDB_RUN=dummy
fi
# -----------------------------------------------------------------------------
# During the course of the run, we will be writing markdown reports to the report/
# directory in the base dir. This command clears it out and writes a header section
# with a bunch of system info and a timestamp that marks the start of the run.
python -m nanochat.report reset --exp_name=$WANDB_RUN
python -m nanochat.report reset
# -----------------------------------------------------------------------------
# Tokenizer
@ -98,9 +92,9 @@ echo "Waiting for dataset download to complete..."
wait $DATASET_DOWNLOAD_PID
# pretrain the d20 model
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=20 --run=$WANDB_RUN --data_dir=$DATA_DIR
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=20 --run=$WANDB_RUN
# evaluate the model on a larger chunk of train/val data and draw some samples
torchrun --standalone --nproc_per_node=8 -m scripts.base_loss --data_dir=$DATA_DIR
torchrun --standalone --nproc_per_node=8 -m scripts.base_loss
# evaluate the model on CORE tasks
torchrun --standalone --nproc_per_node=8 -m scripts.base_eval
@ -140,4 +134,4 @@ torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
# -----------------------------------------------------------------------------
# Generate the full report by putting together all the sections
# report.md is the output and will be copied to current directory for convenience
python -m nanochat.report generate --exp_name=$WANDB_RUN
python -m nanochat.report generate