p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/dev/mihir/cremers/train_tokenizer_overfit_batch.sbatch (shellscript)
#!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=00:30:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1,VRAM:24G
#SBATCH --mem=50G
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --job-name=train_tokenizer_minecraft_overfit_batch

# Log the sbatch script
cat $0

tf_records_dir="/storage/user/mahajanm/Projects/world-modeling/knoms_tfrecords_500/"
ws_dir='/storage/user/mahajanm/Projects/world-modeling'
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

SLURM_STEP_NODELIST=$SLURM_NODELIST python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/mihir/horeka/preprocess_dataset.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=03:00:00
#SBATCH --partition=cpuonly
#SBATCH --cpus-per-task=8
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=mihir@pdoom.org
#SBATCH --job-name=preprocess_dataset
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

source .venv/bin/activate

input_path="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_npy_tmp/10fps_160x90/"
output_path="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_tmp_500_shards"

start_time=$(date +%s)  

python utils/preprocess_dataset.py --source_data_dir=$input_path --output_tfrecords_dir=$output_path --num_shards=500

end_time=$(date +%s)
echo "Time taken: $((end_time - start_time)) seconds"
[tab] slurm/dev/mihir/horeka/sync_runner.sh (shellscript)
#!/usr/bin/env bash

# Usage: ./sync_runner.sh <src_dir> <target_dir>
# Example: ./sync_runner.sh ./src /some/target/dir

set -e

SRC_DIR="$1"
TARGET_DIR="$2"

if [[ -z "$SRC_DIR" || -z "$TARGET_DIR" ]]; then
    echo "Usage: $0 <src_dir> <target_dir>"
    exit 1
fi

# Define paths to exclude (relative to SRC_DIR)
EXCLUDES=(
    ".git"
    "__pycache__"
    "*/__pycache__"
    "logs"
    "*.pyc"
    ".DS_Store"
    "data"
    "checkpoints"
    "venv"
    ".venv"
    "data_tfrecords"
    "data_tfrecord_duplicated"
    "wandb"
)

# Build rsync exclude arguments
EXCLUDE_ARGS=()
for path in "${EXCLUDES[@]}"; do
    EXCLUDE_ARGS+=(--exclude="$path")
done

# Perform the rsync
rsync -av "${EXCLUDE_ARGS[@]}" "$SRC_DIR/" "$TARGET_DIR/"
[tab] slurm/dev/mihir/horeka/train_lam.sh (shellscript)
#!/usr/bin/env bash

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="debug"
slurm_job_id="0000"

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

env | grep SLURM

python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/mihir/horeka/train_tokenizer_coinrun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=01:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_tokenizer_minecraft_overfit
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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


ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data'
tf_records_dir="/home/hk-project-p0023960/tum_cte0515/Projects/jafar/data_tfrecords"
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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=4 \
    --num_steps=75000 \
    --warmup_steps=2500 \
    --log_checkpoint_interval=18750 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/batch_size_scaling/train_tokenizer_1_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#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_1_node
#SBATCH --mem=50G

# 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 $CHECKPOINT_DIR \
    --batch_size=48 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=250 \
    --log \
    --name=const-lr-tokenizer-batch-size-scaling-1-node-$slurm_job_id \
    --tags const-lr tokenizer batch-size-scaling 1-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/const_lr/train_tokenizer_2_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#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_2_node
#SBATCH --mem=100G

# 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 $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=250 \
    --log \
    --name=const-lr-tokenizer-batch-size-scaling-2-node-$slurm_job_id \
    --tags const-lr tokenizer batch-size-scaling 2-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/batch_size_scaling/train_tokenizer_2_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#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_2_node
#SBATCH --mem=100G

# 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 $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=250 \
    --log \
    --name=const-lr-tokenizer-batch-size-scaling-2-node-$slurm_job_id \
    --tags const-lr tokenizer batch-size-scaling 2-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/train_lam/train_lam_full.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=16:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_ful_tfrecord
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

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

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards'

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --log_checkpoint_interval=500 \
    --log_image_interval=50 \
    --seed=0 \
    --min_lr=0.0000866 \
    --max_lr=0.0000866 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar
[content] slurm/dev/alfred/batch_size_scaling/train_tokenizer_1_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --account=hk-project-p0023960
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --error=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
#SBATCH --mem=100G

# Log the sbatch script
cat $0

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


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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

tags=("const-lr" "tokenizer" "batch-size-scaling" "2-node")

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

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=250 \
    --log \
    --name=$job_name-$slurm_job_id \
    --tags $tags \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/overfit_sample/train_lam_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_tokenizer_minecraft_overfit_sample
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# 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_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/overfit_sample/train_dynamics_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_yolo_lr
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# 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

echo Running dynamics model overfit run. Slurm id: $slurm_job_id

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/
lam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/

python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-36M-$slurm_job_id \
    --tags dynamics overfit yolo-lr \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/overfit_sample/train_tokenizer_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_tokenizer_minecraft_overfit_sample
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# 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=1 \
    --min_lr=4.3e-5 \
    --max_lr=4.3e-5 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/overfit_sample/train_tokenizer_overfit_sample copy.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_tokenizer_minecraft_overfit_sample
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# 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=1 \
    --min_lr=4.3e-5 \
    --max_lr=4.3e-5 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/overfit_sample/train_tokenizer_overfit_sample_0.5_mio.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_tokenizer_minecraft_overfit_sample
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# 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=1 \
    --min_lr=4.3e-5 \
    --max_lr=4.3e-5 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/dev/alfred/overfit_sample/train_tokenizer_overfit_sample_size_0_5.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_tokenizer_minecraft_overfit_sample
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# 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=1 \
    --min_lr=4.3e-5 \
    --max_lr=4.3e-5 \
    --log_image_interval=3 \
    --log \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[content] slurm/dev/alfred/overfit_sample/train_tokenizer_overfit_sample_size_0_5.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --account=hk-project-p0023960
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --error=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node