p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] scripts_horeka/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] scripts_horeka/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] scripts_horeka/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] scripts_horeka/modelsize_scaling/dynamics/A_train_dyn_1.5M.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=10:00:00
#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_1.5M
#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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/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 (tiny model A, ~1.5M params). 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/

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-1.5M-$slurm_job_id \
    --tags dynamics model-size-scaling 1.5M tiny A \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4
[tab] scripts_horeka/modelsize_scaling/dynamics/B_train_dyn_3.5.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=10:00:00
#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_3.5M
#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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/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/

# Option B: dyna_dim=256, dyna_num_blocks=2, dyna_num_heads=4 (~3.5M params)
srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-3.5M-$slurm_job_id \
    --tags dynamics model-size-scaling 3.5M \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --dyna_dim=256 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4
[tab] scripts_horeka/modelsize_scaling/dynamics/C_train_dyn_6M.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=10:00:00
#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_6M
#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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/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 (tiny model C, ~6M params). 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/

# Option C: dyna_dim=256, dyna_num_blocks=4, dyna_num_heads=4 (~6M params)
srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-6M-$slurm_job_id \
    --tags dynamics model-size-scaling 6M tiny C \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --dyna_dim=256 \
    --dyna_num_blocks=4 \
    --dyna_num_heads=4
[tab] scripts_horeka/modelsize_scaling/dynamics/D_train_dyn_12M.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=10:00:00
#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_12M
#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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/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 (tiny model D, ~12M params). 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/

# Option D: dyna_dim=384, dyna_num_blocks=4, dyna_num_heads=6 (~12M params)
srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-12M-$slurm_job_id \
    --tags dynamics model-size-scaling 12M tiny D \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --dyna_dim=384 \
    --dyna_num_blocks=4 \
    --dyna_num_heads=6
[tab] scripts_horeka/modelsize_scaling/dynamics/E_train_dyn_18M.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=10:00:00
#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_18M
#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=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/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 (tiny model E, ~18M params). 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/

# Option E: dyna_dim=512, dyna_num_blocks=4, dyna_num_heads=8 (~18M params)
srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-18M-$slurm_job_id \
    --tags dynamics model-size-scaling 18M tiny E \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --dyna_dim=512 \
    --dyna_num_blocks=4 \
    --dyna_num_heads=8
[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=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] scripts_horeka/overfit_sample_tiny/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] scripts_horeka/train_tokenizer.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_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=4.24e-4 \
    --max_lr=4.24e-4 \
    --log_image_interval=3 \
    --log \
    --name=test-wandb-tags-$slurm_job_id \
    --tags test tokenizer debug \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[content] 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

python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=4.24e-4 \
    --max_lr=4.24e-4 \
    --log_image_interval=3 \
    --log \
    --name=test-wandb-tags-$slurm_job_id \
    --tags test tokenizer debug \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[content] scripts_horeka/overfit_sample_tiny/tester.sh (shellscript)
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=4.3e-5 \
    --max_lr=4.3e-5 \
    --log_image_interval=100 \
    --log \
    --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/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=10:00:00
#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_180M
#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/

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=100 \
    --log \
    --name=dynamics-model-size-scaling-180M-$slurm_job_id \
    --tags dynamics model-size-scaling 180M \
    --log_checkpoint_interval=500 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $tf_records_dir \
    --dyna_dim=1024 \
    --dyna_num_blocks=16 \
    --dyna_num_heads=16
[content] scripts_horeka/overfit_sample_tiny/tester.sh (shellscript)
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=4.3e-5 \
    --max_lr=4.3e-5 \
    --log_image_interval=100 \
    --log \
    --name=tokenizer-tiny-overfit-$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_sample_tiny/tester.sh (shellscript)
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 \
    --model_dim=384 \
    --latent_dim=32 \
    --num_latents=6 \
    --patch_size=16 \
    --num_blocks=8 \
    --num_heads=8 \
    --codebook_dropout=0.0
[content] scripts_horeka/overfit_sample_tiny/tester.sh (shellscript)
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --min_lr=5e-7 \
    --max_lr=5e-6 \
    --warmup_steps=125 \
    --log_image_interval=3 \
    --log \
    --name=lam-tiny-overfit-$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