p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] scripts_horeka/overfit_sample_tiny/sample.sh (shellscript)
#!/usr/bin/env bash

# Unload modules that may interfere
module unload mpi/openmpi/5.0
module unload devel/cuda/12.4

# Activate virtual environment
source .venv/bin/activate

# Set workspace and checkpoint directory (update slurm_job_id as needed)
ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'
# Replace the following with the actual job id/checkpoint you want to sample from
slurm_job_id=3301029

# job_name=train_dynamics_minecraft_overfit_sample_tiny
CHECKPOINT_DIR=$ws_dir/checkpoints/${slurm_job_id}

# Example: If you want to use a specific checkpoint, set it here
# CHECKPOINT_PATH=$ws_dir/checkpoints/3299272/dynamics-tiny-overfit-big-lr-3299272_50000/
# Or use the latest in the directory
# CHECKPOINT_PATH=$(ls -d $CHECKPOINT_DIR/*/ | sort | tail -n 1)
CHECKPOINT_PATH=$CHECKPOINT_DIR/genie_1751067601_200000/
# CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/0000/genie_1751301068_2000/
# CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/../checkpoints/3307618/genie_1751322003_15500/
CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3307619/genie_1751322003_200000/

echo "Sampling from checkpoint: $CHECKPOINT_PATH"

python sample.py \
    --checkpoint "$CHECKPOINT_PATH" \
    --tokenizer_dim=384 \
    --latent_patch_dim=32 \
    --num_patch_latents=1024 \
    --patch_size=4 \
    --tokenizer_num_blocks=8 \
    --tokenizer_num_heads=8 \
    --lam_dim=384 \
    --latent_action_dim=32 \
    --lam_patch_size=16 \
    --lam_num_blocks=8 \
    --lam_num_heads=8 \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4 \
    --maskgit_steps=1000 \
    --num_latent_actions=6 \
    --seq_len=16 \
    --start_frame=0
[tab] scripts_horeka/overfit_batch_tiny/train_tokenizer_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=15:00:00
#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 $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=500 \
    --name=tokenizer-tiny-overfit-$slurm_job_id \
    --tags tokenizer overfit tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim 384 \
    --latent_dim 32 \
    --num_latents 1024 \
    --patch_size 4 \
    --num_blocks 8 \
    --num_heads 8 \
    --codebook_dropout 0.0
[tab] scripts_horeka/overfit_batch_tiny/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=$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

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3299272/tokenizer_1751037678_153500/
lam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3299259/lam_1751036759_200000/


python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log \
    --log_checkpoint_interval=500 \
    --name=dynamics-tiny-overfit-$slurm_job_id \
    --tags dynamics overfit tiny \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --tokenizer_dim=384 \
    --latent_patch_dim=32 \
    --num_patch_latents=1024 \
    --patch_size=4 \
    --tokenizer_num_blocks=8 \
    --tokenizer_num_heads=8 \
    --lam_dim=384 \
    --latent_action_dim=32 \
    --num_latent_actions=6 \
    --lam_patch_size=16 \
    --lam_num_blocks=8 \
    --lam_num_heads=8 \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4
[content] scripts_horeka/overfit_batch_tiny/tester.sh (shellscript)
python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=500 \
    --name=tokenizer-tiny-overfit-$slurm_job_id \
    --tags tokenizer overfit tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim 384 \
    --latent_dim 32 \
    --num_latents 1024 \
    --patch_size 4 \
    --num_blocks 8 \
    --num_heads 8 \
    --codebook_dropout 0.0
[tab] scripts_horeka/overfit_batch_tiny/train_tokenizer_overfit_batch.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=15:00:00
#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 $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=500 \
    --name=tokenizer-tiny-overfit-batch-$slurm_job_id \
    --tags tokenizer overfit-batch tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim 384 \
    --latent_dim 32 \
    --num_latents 1024 \
    --patch_size 4 \
    --num_blocks 8 \
    --num_heads 8 \
    --codebook_dropout 0.0
[tab] scripts_horeka/overfit_batch_tiny/train_lam_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=15:00:00
#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_lam_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 $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --warmup_steps=125 \
    --log_image_interval=3 \
    --log \
    --log_checkpoint_interval=500 \
    --name=lam-tiny-overfit-$slurm_job_id \
    --tags lam overfit tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=384 \
    --latent_dim=32 \
    --num_latents=6 \
    --patch_size=16 \
    --num_blocks=8 \
    --num_heads=8 \
    --codebook_dropout=0.0
[tab] scripts_horeka/overfit_batch_tiny/train_lam_overfit_batch.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=15:00:00
#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_lam_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 $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --warmup_steps=125 \
    --log_image_interval=3 \
    --log \
    --log_checkpoint_interval=500 \
    --name=lam-tiny-overfit-$slurm_job_id \
    --tags lam overfit tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=384 \
    --latent_dim=32 \
    --num_latents=6 \
    --patch_size=16 \
    --num_blocks=8 \
    --num_heads=8 \
    --codebook_dropout=0.0
[tab] scripts_horeka/overfit_sample_tiny/train_tokenizer_overfit_sample.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=15:00:00
#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 $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=500 \
    --name=tokenizer-tiny-overfit-$slurm_job_id \
    --tags tokenizer overfit tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim 384 \
    --latent_dim 32 \
    --num_latents 1024 \
    --patch_size 4 \
    --num_blocks 8 \
    --num_heads 8 \
    --codebook_dropout 0.0
[content] scripts_horeka/overfit_batch_tiny/tester.sh (shellscript)
python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=500 \
    --name=lam-tiny-overfit-batch-$slurm_job_id \
    --tags lam overfit-batch tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=384 \
    --latent_dim=32 \
    --num_latents=6 \
    --patch_size=16 \
    --num_blocks=8 \
    --num_heads=8 \
    --codebook_dropout=0.0 \
    --num_latents=1
[tab] scripts_cremers/train_lam_overfit_sample.sh (shellscript)
#!/usr/bin/env bash

# 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_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] scripts_horeka/overfit_sample_tiny/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=$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

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3299272/tokenizer_1751037678_153500/
lam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3299259/lam_1751036759_200000/


python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log \
    --log_checkpoint_interval=500 \
    --name=dynamics-tiny-overfit-$slurm_job_id \
    --tags dynamics overfit tiny \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --tokenizer_dim=384 \
    --latent_patch_dim=32 \
    --num_patch_latents=1024 \
    --patch_size=4 \
    --tokenizer_num_blocks=8 \
    --tokenizer_num_heads=8 \
    --lam_dim=384 \
    --latent_action_dim=32 \
    --num_latent_actions=6 \
    --lam_patch_size=16 \
    --lam_num_blocks=8 \
    --lam_num_heads=8 \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4
[tab] scripts_horeka/overfit_sample_tiny/train_dynamics_overfit_sample_big_lr.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_tiny
#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

# Use checkpoints from tokenizer/lam overfit sample runs
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3299272/tokenizer_1751037678_153500/
lam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3299259/lam_1751036759_200000/

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log \
    --log_checkpoint_interval=500 \
    --name=masklim-0-dynamics-tiny-overfit-big-lr-$slurm_job_id \
    --tags dynamics overfit tiny masklimit-0 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --tokenizer_dim=384 \
    --latent_patch_dim=32 \
    --num_patch_latents=1024 \
    --patch_size=4 \
    --tokenizer_num_blocks=8 \
    --tokenizer_num_heads=8 \
    --lam_dim=384 \
    --latent_action_dim=32 \
    --num_latent_actions=6 \
    --lam_patch_size=16 \
    --lam_num_blocks=8 \
    --lam_num_heads=8 \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4 \
    --mask_limit=0.0
[content] scripts_horeka/overfit_sample_tiny/tester.sh (shellscript)
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log \
    --log_checkpoint_interval=500 \
    --name=masklim-0-dynamics-tiny-overfit-big-lr-$slurm_job_id \
    --tags dynamics overfit tiny masklimit-0 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --tokenizer_dim=384 \
    --latent_patch_dim=32 \
    --num_patch_latents=1024 \
    --patch_size=4 \
    --tokenizer_num_blocks=8 \
    --tokenizer_num_heads=8 \
    --lam_dim=384 \
    --latent_action_dim=32 \
    --num_latent_actions=6 \
    --lam_patch_size=16 \
    --lam_num_blocks=8 \
    --lam_num_heads=8 \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4 \
    --mask_limit=0.0