p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] sbatch_scripts/sample_jafar/sample_knoms.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=00:15:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_sample/%x_%j.log
#SBATCH --error=logs/logs_sample/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=sample_knoms
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

# data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards'
# checkpoint_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3282489/genie_1750610596_12500'
# seq_len=16
# output_dir='sample_results/knoms/'

# python sample.py --checkpoint $checkpoint_dir --data_dir $data_dir --batch_size 12 --seq_len $seq_len --output_dir $output_dir
# # CUDA_VISIBLE_DEVICES=0 python sample.py --checkpoint $checkpoint_dir --data_dir $data_dir --batch_size 12


# Overfit tfrecords 10
data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards_overfit_10'
checkpoint_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_13000'
seq_len=16
output_dir='sample_results/knoms_overfit_tfrecords_10/'

srun python sample_resolution_batches.py --checkpoint $checkpoint_dir --data_dir $data_dir --batch_size 12 --seq_len $seq_len --output_dir $output_dir
[content] logs/logs_sample/sample_knoms_3292243.log (log)
2025-06-25 19:03:23.072378: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.
2025-06-25 19:03:42.500815: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_6.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=00:10:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_6
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 6
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_tokenizer_coinrun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=00:10:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --error=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_tokenizer_coinrun
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_tokenizer.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_6 copy.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_6
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 6
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_12.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_6
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 6
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_12 copy.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_6
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 12
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_24.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_6
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 12
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_24 copy.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_24
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 24
[tab] sbatch_scripts/coinrun/latent_action_ablation/train_lam_48.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --account=hk-project-p0023960
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training_lam/%x_%j.log
#SBATCH --error=logs/logs_training_lam/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_lam_24
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

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

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'

CHECKPOINT_DIR=$ws_dir/data/checkpoints/$job_name_$slurm_job_id
LOG_DIR=$ws_dir/logs/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR
mkdir -p $LOG_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun/coinrun_tfrecords'

srun python train_lam.py \
    --batch_size=192 \
    --experiment_name $job_name \
    --ckpt_dir $CHECKPOINT_DIR \
    --log_checkpoint_interval=1000 \
    --log_image_interval=10 \
    --image_height 64 \
    --image_width 64 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar \
    --num_latents 24