p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_6.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_lam_action_space_scaling_6
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=6 \
    --name=lam-action-space-scaling-6-$slurm_job_id \
    --tags lam action-space-scaling 6 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_8.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_lam_action_space_scaling_8
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=8 \
    --name=lam-action-space-scaling-8-$slurm_job_id \
    --tags lam action-space-scaling 8 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_12.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_lam_action_space_scaling_12
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=12 \
    --name=lam-action-space-scaling-12-$slurm_job_id \
    --tags lam action-space-scaling 12 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_8_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
#SBATCH --mem=100G

# 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 $CHECKPOINT_DIR \
    --batch_size=384 \
    --min_lr=8.49e-4 \
    --max_lr=8.49e-4 \
    --log_image_interval=250 \
    --log \
    --name=tokenizer-batch-size-scaling-8-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 8-node time-step \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/batchsize_scaling/adjusted_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
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=192 \
    --min_lr=0 \
    --max_lr=2.00e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-4-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 4-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/batchsize_scaling/adjusted_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
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=48 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-1-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 1-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/batchsize_scaling/adjusted_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
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-2-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 2-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_20.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_lam_action_space_scaling_20
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=20 \
    --name=lam-action-space-scaling-20-$slurm_job_id \
    --tags lam action-space-scaling 20 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/modelsize_scaling/tokenizer/train_tokenizer_37M.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_model_size_scaling_37M
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-model-size-scaling-38M-$slurm_job_id \
    --tags tokenizer model-size-scaling 38M \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/modelsize_scaling/tokenizer/train_tokenizer_74M.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_model_size_scaling_74M
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=7e-5 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-modelsize-74M-$slurm_job_id \
    --tags tokenizer model-size-scaling 74M \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=640 \
    --num_blocks=10 \
    --num_heads=10 \
    --latent_dim=48 \
    --num_latents=1536
[tab] scripts_horeka/modelsize_scaling/tokenizer/train_tokenizer_127M.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_model_size_scaling_127M
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=5e-5 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-modelsize-127M-$slurm_job_id \
    --tags tokenizer model-size-scaling L1 127M \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=768 \
    --num_blocks=12 \
    --num_heads=12 \
    --latent_dim=64 \
    --num_latents=2048
[tab] scripts_horeka/modelsize_scaling/tokenizer/train_tokenizer_227M.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_model_size_scaling_227M
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=500 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-modelsize-227M-$slurm_job_id \
    --tags tokenizer model-size-scaling L2 227M \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=1024 \
    --num_blocks=12 \
    --num_heads=16 \
    --latent_dim=128 \
    --num_latents=2048
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_12 copy.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_lam_action_space_scaling_12
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=12 \
    --name=lam-action-space-scaling-12-$slurm_job_id \
    --tags lam action-space-scaling 12 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_10.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_lam_action_space_scaling_12
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=12 \
    --name=lam-action-space-scaling-12-$slurm_job_id \
    --tags lam action-space-scaling 12 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/action_space_scaling/train_lam_actionspace_50.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_lam_action_space_scaling_12
#SBATCH --constraint=BEEOND

# 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

ODFSDIR=/mnt/odfs/$SLURM_JOB_ID/stripe_default/checkpoints/
mkdir -p $ODFSDIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --tmp_ckpt_dir $ODFSDIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --num_latents=12 \
    --name=lam-action-space-scaling-12-$slurm_job_id \
    --tags lam action-space-scaling 12 \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[content] genie.py (python)
Autoregressively samples up to `seq_len` future frames, following the procedure in Figure 8 of the paper.

- Input frames are tokenized once.
- Future frames are generated autoregressively in token space.
- All frames (input and generated) are detokenized in a single pass for efficiency.

Note:
- For interactive or stepwise sampling, detokenization should occur after each action.
- To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.
- Temporal causal structure is preserved by reapplying the attention mask before each decoding step.

Dimension keys:
    B: batch size  
    T: number of input (conditioning) frames  
    N: patches per frame  
    S: sequence length  
    A: action space  
    D: modality/token dimension
[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/
CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3309699/genie_1751384516_74500/


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=1 \
    --num_latent_actions=6 \
    --seq_len=16 \
    --start_frame=0

# python sample.py \
    # --checkpoint "$CHECKPOINT_PATH" \
    # --data_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun_episodes