p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/mihir/horeka/yolo-runs/sampling.sh (shellscript)

# 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=$ws_dir/checkpoints/$job_name/$slurm_job_id
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
dynamics_ckpt_dir=$1
echo $dynamics_ckpt_dir

env | grep SLURM

srun python sample.py \
    --checkpoint $dynamics_ckpt_dir \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4 \
    --seq_len=2 \
    --start_frame=0 \
    --data_dir $array_records_dir

# srun python sample.py \
    # --checkpoint $dynamics_ckpt_dir \
    # --start_frame=0 \
    # --batch_size=12 \
    # --seq_len=2 \
    # --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_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 \
    --restore_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/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_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/mihir/horeka/lr_tuning/dynamics/train_dyn_default_cos.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/yoloruns/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/yoloruns/%x_%j.log
#SBATCH --job-name=train_dynamics_lr_schedule_cos

# 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/$job_name/$slurm_job_id
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 \
    --num_steps=40000 \
    --warmup_steps=2500 \
    --lr_schedule=cos \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --init_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --log_checkpoint_keep_period=10000 \
    --name=dynamics-lr-schedule-cos-$slurm_job_id \
    --tags dynamics lr-schedule cos \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
[tab] slurm/jobs/mihir/horeka/lr_tuning/dynamics/train_dyn_default_const.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/yoloruns/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/yoloruns/%x_%j.log
#SBATCH --job-name=train_dynamics_lr_schedule_const

# 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/$job_name/$slurm_job_id
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 \
    --num_steps=40000 \
    --warmup_steps=0 \
    --lr_schedule=wsd \
    --wsd_decay_steps=0 \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --init_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --log_checkpoint_keep_period=10000 \
    --name=dynamics-lr-schedule-const-$slurm_job_id \
    --tags dynamics lr-schedule const \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_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_chunked

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

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-debug-$slurm_job_id \
    --tags dynamics debug \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/overfit_sample/causal/dynamics_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/yoloruns/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/yoloruns/%x_%j.log
#SBATCH --job-name=train_dynamics_overfit_sample_causal

# 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=$ws_dir/checkpoints/causal/overfit-seed69-1/$job_name/$slurm_job_id
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 \
    --seed=69 \
    --num_steps=2000 \
    --warmup_steps=0 \
    --wsd_decay_steps=0 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --init_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-causal-overfit-$slurm_job_id \
    --tags dynamics causal overfit \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4