p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_16_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=16
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
#SBATCH --mem=200G

# Log the sbatch script
cat $0

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

tf_records_dir=$ws_dir/knoms_tfrecords_500_shards
ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=768 \
    --min_lr=1.20e-3 \
    --max_lr=1.20e-3 \
    --log_image_interval=250 \
    --log \
    --name=tokenizer-batch-size-scaling-16-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 16-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir