p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/start_runs.sh (shellscript)
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_16_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_1_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_2_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_4_nodes.sbatch
sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_8_nodes.sbatch

# sbatch scripts_horeka/batchsize_scaling/sqrt_lr/train_tokenizer_32_nodes.sbatch
[content] models/dynamics.py (python)
            batch_size = vid_embed.shape[0]
            rng1, rng2 = jax.random.split(batch["mask_rng"])
            mask_prob = jax.random.uniform(
                rng1, shape=(batch_size,), minval=self.mask_limit
            )
            mask_rngs = jax.random.split(rng2, batch_size)
            per_sample_shape = vid_embed.shape[1:-1]
            mask = jax.vmap(
                lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
                in_axes=(0, 0),
            )(mask_rngs, mask_prob)
            mask = mask.at[:, 0].set(False)
            vid_embed = jnp.where(jnp.expand_dims(mask, -1), self.mask_token, vid_embed)
[tab] slurm/jobs/franz/horeka/batchsize_scaling/dynamics_cotraining_new_arch/linear_lr/tester.sh (shellscript)
#!/usr/bin/env bash

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name="debug"
slurm_job_id="0000"

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

# tokenizer with the new structure supporting larger ffn_dim
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn/

env | grep SLURM

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --init_lr=0 \
    --max_lr=2e-5 \
    --num_latent_actions=20 \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
[terminal_command] TERMINAL ()
ython                   40    CANCELLED   19:29:13      3350302.0                         python                   40       FAILED   00:00:27                  3350302.1                         python                   40       FAILED   00:05:27                  3350302.2                         python                   40       FAILED   00:03:41                  3350302.3                         python                   40 CANCELLED b+   00:03:28                  3350302.4                         python                   40 CANCELLED b+   00:04:30                  3350302.5                         python                   40       FAILED   00:01:21                  3350302.6                         python                   40       FAILED   00:00:27                  3350302.7                         python                   40    CANCELLED   00:04:03                  3350418.0                         python    ython                   40    CANCELLED   19:29:13                  3350302.0                         python                   40       FAILED   00:00:27                  3350302.1                         python                   40       FAILED   00:05:27                  3350302.2                         python                   40       FAILED   00:03:41                  3350302.3                         python                   40 CANCELLED b+   00:03:28                  3350302.4                         python                   40 CANCELLED b+   00:04:30                  3350302.5                         python                   40       FAILED   00:01:21                  3350302.6                         python                   40       FAILED   00:00:27                  3350302.7                         python                   40    CANCELLED   00:04:03                  3350418.0                         python    ython                   40    CANCELLED   19:29:13                  3350302.0                         python                   40       FAILED   00:00:27                  3350302.1                         python                   40       FAILED   00:05:27                  3350302.2                         python                   40       FAILED   00:03:41                  3350302.3                         python                   40 CANCELLED b+   00:03:28                  3350302.4                         python                   40 CANCELLED b+   00:04:30                  3350302.5                         python                   40       FAILED   00:01:21                  3350302.6                         python                   40       FAILED   00:00:27                  3350302.7                         python                   40    CANCELLED   00:04:03                  3350418.0                         python
[tab] slurm/jobs/mihir/horeka/mask_prob_fix/train_dynamics_8_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log
#SBATCH --job-name=train_dynamics_maskprob_fix_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/checkpoints/maskgit-maskprob-fix/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

# tokenizer with the new structure supporting larger ffn_dim
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --init_lr=0 \
    --max_lr=8e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-batch-size-scaling-linear-lr-8-node-$slurm_job_id \
    --tags dynamics batch-size-scaling linear-lr 8-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir
[tab] slurm/dev/alfred/horeka/masked_lim_noise/masked_lim_yolo.sh (shellscript)
#!/bin/bash

mask_limits=(0.0 0.5)
job_name="dynamics-masked_lim_guassian_noise"

# job_name="dynamics-masked_lim_guassian_noise_dev"
# mask_limits=(0.0)

for mask_limit in ${mask_limits[@]}; do
    MASK_LIMIT=${mask_limit} \
    sbatch  \
    --job-name="${job_name}_${mask_limit}" \
    slurm/dev/alfred/horeka/masked_lim_noise/masked_lim_yolo.sbatch
    # slurm/dev/alfred/horeka/masked_lim_noise/masked_lim_dev.sbatch
done
[tab] slurm/dev/alfred/horeka/masked_lim_noise/masked_lim_yolo.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#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_alfred/dynamics-masked_lim/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/dynamics-masked_lim/%x_%j.log
#SBATCH --job-name=masked_lim_0.5-1
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi




# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv_jafar/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
tags="masked_lim dynamics MASK_LIMIT_${MASK_LIMIT} guassian_noise"

CHECKPOINT_DIR="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/checkpoints_alfred/${job_name}_${slurm_job_id}"
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4
env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --ckpt_dir $CHECKPOINT_DIR \
    --wandb_id $SLURM_JOB_ID \
    --batch_size=96 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name="${job_name}_${slurm_job_id}" \
    --tags $tags \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
    --mask_limit=$MASK_LIMIT &


child_pid=$!

wait $child_pid
[content] slurm/jobs/mihir/horeka/mask_prob_fix/train_dynamics_8_nodes.sbatch (shellscript)
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi
[tab] slurm/jobs/mihir/horeka/mask_prob_fix/train_dynamics_8_nodes copy.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log
#SBATCH --job-name=train_dynamics_maskprob_fix_8_node
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi



# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/maskgit-maskprob-fix/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

# tokenizer with the new structure supporting larger ffn_dim
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --init_lr=0 \
    --max_lr=8e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-maskprob-fix-8-node-$slurm_job_id \
    --tags dynamics maskprob-fix 8-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir &

child_pid=$!

wait $child_pid
[tab] slurm/jobs/mihir/horeka/mask_prob_fix/train_dynamics_8_nodes_dev.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log
#SBATCH --job-name=train_dynamics_maskprob_fix_8_node
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# --- signal trap to requeue job before timeout ---
requeue_job() {
    echo "[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID..."
    # optional: trigger checkpoint saving here
    # e.g., touch $checkpoint_dir/requeue_trigger
    scontrol requeue $SLURM_JOB_ID
    exit 0
}

trap requeue_job sigusr1

# set checkpoint flag based on restart count
restart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)

if [ $restart_count -eq 0 ]; then
    restore_ckpt_flag="--no-restore-ckpt"
else
    restore_ckpt_flag="--restore-ckpt"
fi



# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/maskgit-maskprob-fix/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

# tokenizer with the new structure supporting larger ffn_dim
tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    $restore_ckpt_flag \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --init_lr=0 \
    --max_lr=8e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-maskprob-fix-8-node-$slurm_job_id \
    --tags dynamics maskprob-fix 8-node \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir &

child_pid=$!

wait $child_pid
[tab] slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=24
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log
#SBATCH --job-name=train_dynamics_modelsize_scaling_500M_32_node
#SBATCH --mem=400G


# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.5e-5 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-modelsize-scaling-500M-$slurm_job_id \
    --tags dynamics modelsize-scaling 500M \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
    --dyna_dim=1536 \
    --dyna_num_blocks=24 \
    --dyna_num_heads=24