p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] experiments/train_dynamics_lr_1e-4_mixed_precision.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#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_franz/big-runs/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/%x_%j.log
#SBATCH --job-name=train_dynamics_modelsize_scaling_36M_2_node_mixed_precision_flash_attention

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --init_lr=0 \
    --max_lr=1.5e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-mixed-precision-flash-attention-$slurm_job_id \
    --tags dynamics mixed-precision flash-attention \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
[tab] experiments/train_dynamics_lr_1e-4_mixed_precision.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#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_franz/big-runs/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/%x_%j.log
#SBATCH --job-name=train_dynamics_modelsize_scaling_36M_2_node_mixed_precision_flash_attention

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --init_lr=0 \
    --max_lr=1.5e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-mixed-precision-flash-attention-$slurm_job_id \
    --tags dynamics mixed-precision flash-attention \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
[tab] slurm/templates/horeka_cpu.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/%x_%j.log
#SBATCH --error=logs/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=default_cpu
#SBATCH --mail-type=ALL
[tab] slurm/templates/horeka_gpu.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=1:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:1
#SBATCH --output=logs/%x_%j.log
#SBATCH --error=logs/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=default_gpu
#SBATCH --mail-type=ALL
[tab] slurm/utils/create_dev_dir.sh (shellscript)
#!/bin/bash

src_dir=/home/hk-project-pai00039/tum_ind3695/projects/jafar/*
dst_dir=/home/hk-project-pai00039/tum_ind3695/projects/jafar_dyn/
exclude_file=./shell_scripts/exclude.txt

rsync -av --progress --exclude-from=$exclude_file  $src_dir $dst_dir
[tab] slurm/utils/init_slurm_repo.sh (shellscript)
#!/bin/bash

# Name of the repo (change if needed)
REPO_NAME="slurm"

# Create the main structure
mkdir -p $REPO_NAME/{jobs,common,templates,utils,logs}
mkdir -p $REPO_NAME/dev/{franz,mihir,alfred}

# Create README stubs
cat > $REPO_NAME/README.md << 'EOF'
# slurm-launchpad

This repository contains Slurm job scripts and tools for ML workflows on the HPC cluster.

## Structure

- `jobs/` — Stable, production-ready sbatch scripts
- `dev/` — Individual folders for development or experimental jobs
- `common/` — Shared setup code (env, sbatch headers)
- `templates/` — Optional Jinja2 or shell templates for job generation
- `utils/` — Helper scripts (e.g., run wrapper)
- `logs/` — Optional log output directory

Team: Franz, Mihir, Alfred
EOF

cat > $REPO_NAME/common/env_setup.sh << 'EOF'
#!/bin/bash
# Common environment setup for all jobs

module load python/3.10
source ~/envs/ml/bin/activate
EOF
chmod +x $REPO_NAME/common/env_setup.sh

cat > $REPO_NAME/common/defaults.sbatch.sh << 'EOF'
#!/bin/bash
# Common SBATCH directives (source this from your sbatch scripts)

#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16G
#SBATCH --time=01:00:00
#SBATCH --output=logs/%x_%j.out
EOF

cat > $REPO_NAME/utils/run_job.sh << 'EOF'
#!/bin/bash
# Run a job with a given sbatch file

if [ -z "$1" ]; then
  echo "Usage: ./run_job.sh <sbatch_file>"
  exit 1
fi

echo "Launching job: $1"
sbatch "$1"
EOF
chmod +x $REPO_NAME/utils/run_job.sh

echo "Slurm repo '$REPO_NAME' created!"
[tab] slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=01:00:00
#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_dyn_yolorun

# 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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked
# 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

lam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-tf-records-$slurm_job_id \
    --tags dynamics yolo-run tf_records \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
    # --lam_checkpoint=$lam_ckpt_dir
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/start_runs.sh (shellscript)
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_16_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_1_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_2_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_4_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_8_nodes.sbatch

# sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_32_nodes.sbatch
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'

job_name="debug"
slurm_job_id="0000"

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


lam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=3e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-debug-$slurm_job_id \
    --tags dynamics debug \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
    --lam_checkpoint=$lam_ckpt_dir
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_1_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#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/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --min_lr=0 \
    --max_lr=1e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-batch-size-scaling-1-node-$slurm_job_id \
    --tags dynamics batch-size-scaling 1-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_2_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#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/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-$slurm_job_id \
    --tags dynamics batch-size-scaling 2-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_2_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_4_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=4
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#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/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=192 \
    --min_lr=0 \
    --max_lr=2.1e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-batch-size-scaling-4-node-$slurm_job_id \
    --tags dynamics batch-size-scaling 4-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_8_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#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/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --min_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-batch-size-scaling-8-node-$slurm_job_id \
    --tags dynamics batch-size-scaling 8-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_16_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=16
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#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/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=768 \
    --min_lr=0 \
    --max_lr=4.2e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-batch-size-scaling-16-node-$slurm_job_id \
    --tags dynamics batch-size-scaling 16-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_4_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_8_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_16_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_16_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_8_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_4_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
[content] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining/linear_lr/train_dynamics_2_nodes.sbatch (shellscript)
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/big-runs/dynamics-cotraining-batchsize-scaling-linear-lr/%x_%j.log