p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/train_tokenizer_1_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=36:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-1-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 1-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/train_tokenizer_2_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=36:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=2e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-2-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 2-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/train_tokenizer_4_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=4
#SBATCH --ntasks-per-node=4
#SBATCH --time=36:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=192 \
    --min_lr=0 \
    --max_lr=4e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-4-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 4-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/train_tokenizer_8_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=36:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --min_lr=0 \
    --max_lr=8e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-8-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 8-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/train_tokenizer_16_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=16
#SBATCH --ntasks-per-node=4
#SBATCH --time=36:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node

# 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/'
tf_records_dir=$ws_dir/data/open_ai_minecraft_tfrecord

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 $CHECKPOINT_DIR \
    --batch_size=768 \
    --min_lr=0 \
    --max_lr=16e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-16-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 16-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/tester.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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord
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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=10 \
    --log \
    --name=tokenizer-batch-size-scaling-1-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 1-node debug \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/linear_lr/run_sbatch.sh (shellscript)
sbatch /home/hk-project-p0023960/tum_cte0515/Projects/jafar/scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_1_nodes.sbatch
sbatch /home/hk-project-p0023960/tum_cte0515/Projects/jafar/scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_2_nodes.sbatch
sbatch /home/hk-project-p0023960/tum_cte0515/Projects/jafar/scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_4_nodes.sbatch