p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/alfred/berlin/coinrun/coinrun_lam/lam_coinrun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/ali/lam/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/ali/lam/%x_%j.log
#SBATCH --job-name=train_lam_model_size_scaling_38M
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout



# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi



# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/coinrun/array_records

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

tags="lam coinrung 38M"

CHECKPOINT_PATH="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/lam/coinrun/${job_name}_${slurm_job_id}"
mkdir -p $CHECKPOINT_PATH

env | grep SLURM

srun python train_lam.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_PATH \
    --batch_size=48 \
    --image_height=64 \
    --image_width=64 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name="${job_name}_${slurm_job_id}" \
    --tags $tags \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/franz/berlin/coinrun/coinrun_dynamics/coinrun_dynamics_fp32_layernorm.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/coinrun/dynamics/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/coinrun/dynamics/%x_%j.log
#SBATCH --job-name=train_dynamics_fp32_layernorm_coinrun
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout


# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi



# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/coinrun/array_records

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

DYNATYPE="maskgit"
tags="dynamics coinrun 38M ${DYNATYPE}"

CHECKPOINT_DIR="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/dynamics/${job_name}/${slurm_job_id}"
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/tokenizer/train_tokenizer_1e-4_3414046"
lam_ckpt_dir="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/lam/train_lam_model_size_scaling_38M_18742"

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --image_height=64 \
    --image_width=64 \
    --dyna_type=${DYNATYPE} \
    --init_lr=0 \
    --max_lr=1e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name="${job_name}_${slurm_job_id}" \
    --tags ${tags} \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint="${tokenizer_ckpt_dir}" \
    --lam_checkpoint="${lam_ckpt_dir}" \
    --data_dir $array_records_dir &

child_pid=$!

wait $child_pid
[content] slurm/jobs/franz/berlin/coinrun/coinrun_lam/lam_coinrun_nan_investigation_100k_to_107k.sbatch (plaintext)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/ali/lam/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/ali/lam/%x_%j.log
#SBATCH --job-name=train_lam_model_size_scaling_38M
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout



# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi



# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/coinrun/array_records

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

tags="lam coinrung 38M"

CHECKPOINT_PATH="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/lam/coinrun/${job_name}_${slurm_job_id}"
mkdir -p $CHECKPOINT_PATH

env | grep SLURM

srun python train_lam.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_PATH \
    --batch_size=48 \
    --image_height=64 \
    --image_width=64 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name="${job_name}_${slurm_job_id}" \
    --tags $tags \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/franz/berlin/coinrun/coinrun_dynamics/coinrun_dynamics_fp32_adam_moments.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/coinrun/dynamics/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/coinrun/dynamics/%x_%j.log
#SBATCH --job-name=train_dynamics_fp32_adam_moments_coinrun
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout


# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi



# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/coinrun/array_records

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

DYNATYPE="maskgit"
tags="dynamics coinrun 38M ${DYNATYPE}"

CHECKPOINT_DIR="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/dynamics/${job_name}/${slurm_job_id}"
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/tokenizer/train_tokenizer_1e-4_3414046"
lam_ckpt_dir="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/lam/train_lam_model_size_scaling_38M_18742"

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --image_height=64 \
    --image_width=64 \
    --dyna_type=${DYNATYPE} \
    --init_lr=0 \
    --max_lr=1e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name="${job_name}_${slurm_job_id}" \
    --tags ${tags} \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint="${tokenizer_ckpt_dir}" \
    --lam_checkpoint="${lam_ckpt_dir}" \
    --data_dir $array_records_dir &

child_pid=$!

wait $child_pid