p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/mihir/horeka/yolo-runs/tester.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
ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'

job_name="debug"
slurm_job_id="debug-mihir"

CHECKPOINT_DIR=$ws_dir/checkpoints/$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 $CHECKPOINT_DIR \
    --batch_size=82 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-tf-records-$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/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=10:00:00
#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 $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-tf-records-$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
[content] slurm/jobs/mihir/horeka/yolo-runs/tester.sh (shellscript)
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-tf-records-$slurm_job_id \
    --tags dynamics yolo-run tf_records \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[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=10:00:00
#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 $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-tf-records-$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/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=6 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-6-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 6 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=8 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-8-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 8 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=12 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-12-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 12 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-20-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 20 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=50 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-50-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 50 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics/sqrt_lr/train_dynamics_32_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=32
#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_32_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/10fps_160x90

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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1536 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=250 \
    --log \
    --name=tokenizer-batch-size-scaling-32-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 32-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --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=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

# 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/10fps_160x90

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

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/
lam_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 $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=0 \
    --max_lr=3e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-batch-size-scaling-1-node-$slurm_job_id \
    --tags dynamics debug \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --lam_checkpoint=$lam_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.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_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/10fps_160x90

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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --min_lr=0 \
    --max_lr=3e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[content] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch (shellscript)
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=82 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-tf-records-$slurm_job_id \
    --tags dynamics yolo-run tf_records \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_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

# 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/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 $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-$slurm_job_id \
    --tags dynamics yolo-run \
    --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/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_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

# 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/10fps_160x90

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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=192 \
    --min_lr=0 \
    --max_lr=2.00e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 4-node sqrt-lr \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=16
#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_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/10fps_160x90

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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=768 \
    --min_lr=0 \
    --max_lr=4e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[content] slurm/utils/alfred/sqrt_lr_scaling.py (python)
import math as mt

batch_size_og = 128
lr_old = 3e-4

batch_sizes = [2,4,8, 12, 24, 48, 96, 192, 384, 768]

for batch_size_new in batch_sizes:
    lr_new = mt.sqrt(batch_size_new/batch_size_og) * lr_old
    print(f"Batch size: {batch_size_new}, Learning rate: {lr_new}")
[content] slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4 (plaintext)
#!/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

# 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/10fps_160x90
array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset

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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.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

# 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/10fps_160x90
array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset

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

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4 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_tokenizer_lr_sweep_1e-4

# 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/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \
    --tags tokenizer lr-sweep 1e-4 \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.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_lr_sweep_1e-4

# 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/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \
    --tags tokenizer lr-sweep 1e-4 \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.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_lr_sweep_1e-4

# 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/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \
    --tags tokenizer lr-sweep 1e-4 \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.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_lr_sweep_5e-5

# 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/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=5e-5 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-lr-sweep-5e-5-$slurm_job_id \
    --tags tokenizer lr-sweep 5e-5 \
    --entity instant-uv \
    --project jafar \
    --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=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

# 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/data/checkpoints/$job_name/$slurm_job_id
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 \
    --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 debug \
    --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/train_dynamics_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

# 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/data/checkpoints/$job_name/$slurm_job_id
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 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-yolorun-$slurm_job_id \
    --tags dynamics yolo-run \
    --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/train_dynamics_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

# 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/data/checkpoints/$job_name/$slurm_job_id
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 \
    --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 debug \
    --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/train_dynamics_8_nodes.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_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/data/checkpoints/$job_name/$slurm_job_id
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 \
    --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 debug \
    --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/train_dynamics_16_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=16
#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_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/data/checkpoints/$job_name/$slurm_job_id
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 \
    --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 debug \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=6 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-6-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 6 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=8 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-8-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 8 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=12 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-12-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 12 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=20 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-20-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 20 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.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=dynamics_cotraining_action_space_scaling

# 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/data/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/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --num_latent_actions=50 \
    --log_checkpoint_interval=1000 \
    --name=dynamics-cotraining-action-space-scaling-50-$slurm_job_id \
    --tags dynamics cotraining action-space-scaling 50 \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir