p-doom/crowd-code-dataset-0.2 huggingface dataset:
[content] jasmine/models/dynamics.py (python)
# safe sqrt: clip argument to >= 0
one_minus_noise = jnp.clip(1.0 - noise_level_B.reshape(B, 1, 1, 1), a_min=0.0)
sqrt_one_minus = jnp.sqrt(one_minus_noise)
sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B.reshape(B, 1, 1, 1), a_min=0.0))

vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_breakout_longer
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

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

# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576
# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502

lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963
tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

export PYTHONUNBUFFERED=1

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=250 \
    --log_checkpoint_interval=250 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-noise-lvl-default-$slurm_job_id \
    --tags dyn breakout noise-lvl default \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 100 \
    --wsd_decay_steps 1000 \
    --num_steps 5000 \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --lam_checkpoint $lam_checkpoint \
    --val_interval 500 \
    --eval_full_frame \
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=06:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_breakout_longer
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

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

# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576
# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502

lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963
tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-50ksteps-3e-5-noise-lvl-$slurm_job_id \
    --tags dyn breakout 50ksteps 3e-5 noise-lvl \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 1000 \
    --wsd_decay_steps 10000 \
    --num_steps 50000 \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --lam_checkpoint $lam_checkpoint \
    --val_interval 1000 \
    --eval_full_frame \
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_50k.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=06:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_gt_actions_breakout_longer
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn-gt-actions/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=250 \
    --log_checkpoint_interval=250 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-50ksteps-noise-lvl-gt-actions-lr-3e-5-$slurm_job_id \
    --tags dyn breakout 50ksteps noise-lvl 3e-5 \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 100 \
    --wsd_decay_steps 1000 \
    --num_steps 50000 \
    --use_gt_actions \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --val_interval 750 \
    --eval_full_frame \
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_smaller_lr_50k.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=06:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_gt_actions_breakout_longer_smaller_lr
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn-gt-actions/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=1e-5 \
    --log_image_interval=250 \
    --log_checkpoint_interval=250 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-50ksteps-noise-lvl-gt-actions-lr-1e-5-$slurm_job_id \
    --tags dyn breakout 50ksteps noise-lvl 1e-5 \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 100 \
    --wsd_decay_steps 1000 \
    --num_steps 50000 \
    --use_gt_actions \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --val_interval 750 \
    --eval_full_frame \
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_breakout_longer
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn-gt-actions/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

export PYTHONUNBUFFERED=1

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-default-noise-lvl-gt-actions-$slurm_job_id \
    --tags dyn breakout default noise-lvl \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 100 \
    --wsd_decay_steps 1000 \
    --num_steps 5000 \
    --use_gt_actions \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --val_interval 1000 \
    --eval_full_frame \
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_smaller_lr_50k.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=06:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_breakout_longer_smaller_lr
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

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

# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576
# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502

lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963
tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=1e-5 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-50ksteps-noise-lvl-$slurm_job_id \
    --tags dyn breakout 50ksteps noise-lvl 1e-5 \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 1000 \
    --wsd_decay_steps 10000 \
    --num_steps 50000 \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --lam_checkpoint $lam_checkpoint \
    --val_interval 1000 \
    --eval_full_frame \
[content] jasmine/models/dynamics.py (python)
        # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of
        # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.
        noise_bucket_idx_B = jnp.floor(
            (noise_level_B * self.noise_buckets) / self.max_noise_level
        ).astype(jnp.int32)
        noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)
        noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)
        noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))
        noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)
[content] jasmine/models/dynamics.py (python)

        rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)
        noise_level_B = jax.random.uniform(
            _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level
        )
        noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))
        # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of
        # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.
        noise_bucket_idx_B = jnp.floor(
            (noise_level_B * self.noise_buckets) / self.max_noise_level
        ).astype(jnp.int32)
        noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)
        noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)
        noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))
        noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)

        # safe sqrt: clip argument to >= 0
        one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)
        sqrt_one_minus = jnp.sqrt(one_minus_noise)
        sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))

        vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM
[content] jasmine/models/dynamics.py (python)


    def _apply_noise_augmentation(self, vid_embed_BTNM, rng):
        B, T, N, M = vid_embed_BTNM.shape
        rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)
        noise_level_B = jax.random.uniform(
            _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level
        )
        noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))
        # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of
        # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.
        noise_bucket_idx_B = jnp.floor(
            (noise_level_B * self.noise_buckets) / self.max_noise_level
        ).astype(jnp.int32)
        noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)
        noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)
        noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))
        noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)

        # safe sqrt: clip argument to >= 0
        one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)
        sqrt_one_minus = jnp.sqrt(one_minus_noise)
        sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))

        noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM
        return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M
[content] jasmine/genie.py (python)

            rng, _rng_noise = jax.random.split(rng)
            noise_level_111 = noise_level.reshape(1, 1, 1)
            noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))
            noise_bucket_idx_B11 = jnp.floor(
                (noise_level_B11 * self.noise_buckets) / self.max_noise_level
            ).astype(jnp.int32)
            noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(
                noise_bucket_idx_B11
            )
            noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))

            vid_embed_BSNp2M = jnp.concatenate(
                [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2
            )
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu copy.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_breakout_longer
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

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

# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576
# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502

lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963
tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

export PYTHONUNBUFFERED=1

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=250 \
    --log_checkpoint_interval=250 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-noise-lvl-default-$slurm_job_id \
    --tags dyn breakout noise-lvl default \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 100 \
    --wsd_decay_steps 1000 \
    --num_steps 5000 \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --lam_checkpoint $lam_checkpoint \
    --val_interval 1000 \
    --eval_full_frame \
[tab] slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu-nan-debug.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log
#SBATCH --job-name=train_dyn_default_breakout_longer
#SBATCH --requeue
#SBATCH --signal=b:usr1@300 # 5 min before timeout

# 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_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train
array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

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

# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576
# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502

lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963
tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963

env | grep SLURM

export PYTHONUNBUFFERED=1

srun python jasmine/train_dynamics.py \
    --save_ckpt \
    --image_height=10 \
    --image_width=10 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=120 \
    --init_lr=0 \
    --max_lr=3e-5 \
    --log_image_interval=250 \
    --log_checkpoint_interval=250 \
    --dyna_type=maskgit \
    --log \
    --name=breakout-dyn-noise-lvl-default-$slurm_job_id \
    --tags dyn breakout noise-lvl default \
    --entity instant-uv \
    --project jafar \
    --patch_size 4 \
    --lam_patch_size 4 \
    --warmup_steps 100 \
    --wsd_decay_steps 1000 \
    --num_steps 5000 \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --lam_checkpoint $lam_checkpoint \
    --val_interval 1000 \
    --eval_full_frame \