[tab] source(1).csv (plaintext)
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1,5,"sbatch_scripts/preprocess/mp4_to_npy_6xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_first_try_6xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try/6xx/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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3,2010,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:17:13 AM [info] Activating crowd-code
10:17:13 AM [info] Welcome back tum_ind3695. Your user-id is '507ab0ec0dfe0c18ad7778dd15e072f92367194c94623114de802c8ed9c52e20'. Happy coding!
10:17:13 AM [info] Recording started
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91,183747,"TERMINAL",0,0,"          JobID                        JobName        Partition All        State    Elapsed  Timelimit 
--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- 
        3273476 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:06   00:10:00 
        3273480 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:32   00:10:00 
        3273494 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:40   00:10:00 
        3273503 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273681 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273687 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273795 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273820 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:31   00:15:00 
        3273828 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273829 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273831 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:29   12:00:00 
        3273841 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:13   12:00:00 
        3273846 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:28   12:00:00 
        3274766         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:02   00:10:00 
        3276030 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:01:35   00:10:00 
        3276039 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:00:56   00:10:00 
        3276048 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:10:03   00:10:00 
        3276051 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:30:01   00:30:00 
        3276052 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276053 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276058 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:03   01:00:00 
        3276715         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:13   00:10:00 
        3277318         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   16:00:27   16:00:00 
        3282472      train_dyn_knoms_full-test      accelerated  32      TIMEOUT   00:10:24   00:10:00 
        3282485           train_dyn_knoms_full      accelerated  32       FAILED   00:00:27 1-00:00:00 
        3282489           train_dyn_knoms_full      accelerated  32      TIMEOUT 1-00:00:27 1-00:00:00 
        3285784 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:28   00:10:00 
        3285785  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:32   00:10:00 
        3285797  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:28   00:10:00 
        3285798 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:27   00:10:00 
        3285811 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT   00:10:07   00:10:00 
        3285820  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:06   00:10:00 
        3286113  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286114 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286195   train_dyn_knoms_full_nodes_2      accelerated  64      TIMEOUT   00:10:26   00:10:00 
        3287192                   download_7xx      accelerated  32       FAILED   00:00:27   00:10:00 
        3287194                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287195                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287198                   download_6xx      accelerated  32      TIMEOUT   00:10:18   00:10:00 
        3287213                   download_6xx          cpuonly 152    COMPLETED   01:44:02   06:00:00 
        3287214                   download_7xx          cpuonly 152    COMPLETED   04:27:48   06:00:00 
        3287215                   download_8xx          cpuonly 152    COMPLETED   01:07:53   06:00:00 
        3287216                   download_9xx          cpuonly 152    COMPLETED   02:11:44   06:00:00 
        3287217                  download_10xx          cpuonly 152    COMPLETED   01:10:01   06:00:00 
        3289199                   download_6xx          cpuonly 152    COMPLETED   03:05:47   06:00:00 
        3289204                   download_7xx          cpuonly 152    COMPLETED   05:07:25   06:00:00 
        3289205                   download_8xx          cpuonly 152    COMPLETED   01:27:47   06:00:00 
        3289206                   download_9xx          cpuonly 152    COMPLETED   02:27:37   06:00:00 
        3289207                  download_10xx          cpuonly 152    COMPLETED   01:25:46   06:00:00 
        3289242 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:32   00:10:00 
        3289247 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289322 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:20   00:10:00 
        3289376 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:17   00:10:00 
        3289389 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289394 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:03:42   00:10:00 
        3289433 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:16   00:10:00 
        3289439 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:49   00:10:00 
        3289469 mp4_to_npy_openai_first_try_6+          cpuonly 152    COMPLETED   00:18:29   01:00:00 
        3289483 mp4_to_npy_openai_first_try_7+          cpuonly 152    COMPLETED   00:48:15   01:00:00 
        3289484 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:43   01:00:00 
        3289485 mp4_to_npy_openai_first_try_9+          cpuonly 152    COMPLETED   00:32:34   01:00:00 
        3289486 mp4_to_npy_openai_first_try_1+          cpuonly 152    COMPLETED   00:08:10   01:00:00 
        3289548  train_dyn_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3289577  train_dyn_overfit_tfrecord_10      accelerated  32      RUNNING   14:43:41 1-00:00:00 
",,terminal_output
92,200969,"sbatch_scripts/preprocess/mp4_to_npy_7xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_first_try_7xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try/7xx/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/7xx""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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99,204071,"sbatch_scripts/preprocess/mp4_to_npy_8xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_first_try_8xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try/8xx/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/8xx""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_first_try_9xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try/9xx/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/9xx""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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115,211156,"sbatch_scripts/preprocess/mp4_to_npy_10xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_first_try_10xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try/10xx/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/10xx""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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>",,terminal_output
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>",,terminal_output
325,365654,"utils/preprocess.py",0,0,"import dm_pix as pix
import einops
import jax
import jax.numpy as jnp


def patchify(videos: jax.Array, size: int) -> jax.Array:
    B, T, H, W, C = videos.shape
    x = jnp.pad(videos, ((0, 0), (0, 0), (0, -H % size), (0, -W % size), (0, 0)))
    return einops.rearrange(
        x, ""b t (hn hp) (wn wp) c -> b t (hn wn) (hp wp c)"", hp=size, wp=size
    )


def unpatchify(patches: jax.Array, size: int, h_out: int, w_out: int) -> jax.Array:
    h_pad = -h_out % size
    hn = (h_out + h_pad) // size
    x = einops.rearrange(
        patches,
        ""b t (hn wn) (hp wp c) -> b t (hn hp) (wn wp) c"",
        hp=size,
        wp=size,
        hn=hn,
    )
    return x[:, :, :h_out, :w_out]
",python,tab
326,368493,"utils/preprocess_dataset.py",0,0,"from dataclasses import dataclass

import tensorflow as tf
import numpy as np
import logging
import tyro
from pathlib import Path

logging.basicConfig(level=logging.INFO)


@dataclass
class Args:
    source_data_dir: str = ""data/coinrun_episodes""
    output_tfrecords_dir: str = ""data_tfrecords""
    num_shards: int = 50


args = tyro.cli(Args)


def _bytes_feature(value):
    if isinstance(value, type(tf.constant(0))):
        value = value.numpy()
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def create_tfrecord_example(episode_numpy_array):
    feature = {
        ""height"": _int64_feature(episode_numpy_array.shape[1]),
        ""width"": _int64_feature(episode_numpy_array.shape[2]),
        ""channels"": _int64_feature(episode_numpy_array.shape[3]),
        ""sequence_length"": _int64_feature(episode_numpy_array.shape[0]),
        ""raw_video"": _bytes_feature(episode_numpy_array.tobytes()),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))


def main_preprocess(data_dir_str, output_dir_str, num_shards):
    data_dir = Path(data_dir_str)
    output_dir = Path(output_dir_str)
    output_dir.mkdir(parents=True, exist_ok=True)

    metadata = np.load(data_dir / ""metadata.npy"", allow_pickle=True)
    episode_source_paths = [Path(item[""path""]) for item in metadata]
    num_total_episodes = len(episode_source_paths)

    if num_shards <= 0:
        raise ValueError(""num_shards must be positive."")
    if num_shards > num_total_episodes:
        logging.warning(
            f""Warning: num_shards ({num_shards}) is greater than total episodes ({num_total_episodes}). ""
            f""Setting num_shards to {num_total_episodes}.""
        )
        num_shards = num_total_episodes

    logging.info(
        f""Preparing to write {num_total_episodes} episodes to {num_shards} TFRecord shards in {output_dir}...""
    )

    output_filenames = [
        str(output_dir / f""shard-{i:05d}-of-{num_shards:05d}.tfrecord"")
        for i in range(num_shards)
    ]
    writers = [tf.io.TFRecordWriter(filename) for filename in output_filenames]

    writer_idx_for_episode = 0
    try:
        for i, npy_path in enumerate(episode_source_paths):
            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")


if __name__ == ""__main__"":
    if (
        not Path(args.source_data_dir).exists()
        or not (Path(args.source_data_dir) / ""metadata.npy"").exists()
    ):
        logging.error(f""Please generate data in '{args.source_data_dir}' first."")
    else:
        main_preprocess(
            args.source_data_dir, args.output_tfrecords_dir, args.num_shards
        )
",python,tab
327,375399,"sbatch_scripts/preprocess/mp4_to_npy_6xx copy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_6xx_Jun_29
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/all_6xx_Jun_29/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/all_6xx_Jun_29""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


",shellscript,tab
328,388461,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_6xx_Jun_29
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/all_6xx_Jun_29/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/all_6xx_Jun_29""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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571,631428,"sbatch_scripts/preprocess/mp4_to_npy_open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_6xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/6xx/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/6xx""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


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1097,866579,"TERMINAL",0,0,"934",,terminal_output
1098,867625,"TERMINAL",0,0,"4045",,terminal_output
1099,868654,"TERMINAL",0,0,"156",,terminal_output
1100,869699,"TERMINAL",0,0,"278",,terminal_output
1101,870745,"TERMINAL",0,0,"489",,terminal_output
1102,871794,"TERMINAL",0,0,"5910",,terminal_output
1103,872837,"TERMINAL",0,0,"6101",,terminal_output
1104,874839,"TERMINAL",0,0,"712",,terminal_output
1105,875840,"TERMINAL",0,0,"823",,terminal_output
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1107,877844,"TERMINAL",0,0,"5045",,terminal_output
1108,878841,"TERMINAL",0,0,"156",,terminal_output
1109,879842,"TERMINAL",0,0,"267",,terminal_output
1110,880841,"TERMINAL",0,0,"378",,terminal_output
1111,881841,"TERMINAL",0,0,"489",,terminal_output
1112,882840,"TERMINAL",0,0,"5920",,terminal_output
1113,883842,"TERMINAL",0,0,"6201",,terminal_output
1114,884849,"TERMINAL",0,0,"712",,terminal_output
1115,885858,"TERMINAL",0,0,"823",,terminal_output
1116,886840,"TERMINAL",0,0,"934",,terminal_output
1117,887848,"TERMINAL",0,0,"2:0045",,terminal_output
1118,888899,"TERMINAL",0,0,"156",,terminal_output
1119,889847,"TERMINAL",0,0,"267",,terminal_output
1120,890841,"TERMINAL",0,0,"378",,terminal_output
1121,892849,"TERMINAL",0,0,"4930",,terminal_output
1122,892850,"TERMINAL",0,0,"6301",,terminal_output
1123,893841,"TERMINAL",0,0,"712",,terminal_output
1124,894842,"TERMINAL",0,0,"823",,terminal_output
1125,896842,"TERMINAL",0,0,"934",,terminal_output
1126,897847,"TERMINAL",0,0,"1045",,terminal_output
1127,898843,"TERMINAL",0,0,"156",,terminal_output
1128,899842,"TERMINAL",0,0,"267",,terminal_output
1129,900840,"TERMINAL",0,0,"378",,terminal_output
1130,901842,"TERMINAL",0,0,"489",,terminal_output
1131,902842,"TERMINAL",0,0,"5940",,terminal_output
1132,903841,"TERMINAL",0,0,"6401",,terminal_output
1133,904842,"TERMINAL",0,0,"712",,terminal_output
1134,905840,"TERMINAL",0,0,"823",,terminal_output
1135,906843,"TERMINAL",0,0,"934",,terminal_output
1136,907845,"TERMINAL",0,0,"2045",,terminal_output
1137,908841,"TERMINAL",0,0,"156",,terminal_output
1138,909842,"TERMINAL",0,0,"267",,terminal_output
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1140,911842,"TERMINAL",0,0,"489",,terminal_output
1141,912841,"TERMINAL",0,0,"5950",,terminal_output
1142,913841,"TERMINAL",0,0,"6512",,terminal_output
1143,914844,"TERMINAL",0,0,"823",,terminal_output
1144,915845,"TERMINAL",0,0,"934",,terminal_output
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1284,1065006,"TERMINAL",0,0,"621",,terminal_output
1285,1065006,"TERMINAL",0,0,"82",,terminal_output
1286,1065776,"TERMINAL",0,0,"93",,terminal_output
1287,1066820,"TERMINAL",0,0,"5:004",,terminal_output
1288,1067876,"TERMINAL",0,0,"15",,terminal_output
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1291,1071048,"TERMINAL",0,0,"48",,terminal_output
1292,1072102,"TERMINAL",0,0,"59",,terminal_output
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1294,1074200,"TERMINAL",0,0,"71",,terminal_output
1295,1074530,"TERMINAL",0,0,"
>",,terminal_output
1296,1077059,"TERMINAL",0,0,"fsacct_week",,terminal_command
1297,1077109,"TERMINAL",0,0,"",,terminal_output
1298,1077136,"TERMINAL",0,0,"          JobID                        JobName        Partition All        State    Elapsed  Timelimit 
--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- 
        3273476 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:06   00:10:00 
        3273480 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:32   00:10:00 
        3273494 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:40   00:10:00 
        3273503 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273681 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273687 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273795 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273820 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:31   00:15:00 
        3273828 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273829 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273831 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:29   12:00:00 
        3273841 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:13   12:00:00 
        3273846 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:28   12:00:00 
        3274766         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:02   00:10:00 
        3276030 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:01:35   00:10:00 
        3276039 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:00:56   00:10:00 
        3276048 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:10:03   00:10:00 
        3276051 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:30:01   00:30:00 
        3276052 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276053 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276058 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:03   01:00:00 
        3276715         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:13   00:10:00 
        3277318         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   16:00:27   16:00:00 
        3282472      train_dyn_knoms_full-test      accelerated  32      TIMEOUT   00:10:24   00:10:00 
        3282485           train_dyn_knoms_full      accelerated  32       FAILED   00:00:27 1-00:00:00 
        3282489           train_dyn_knoms_full      accelerated  32      TIMEOUT 1-00:00:27 1-00:00:00 
        3285784 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:28   00:10:00 
        3285785  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:32   00:10:00 
        3285797  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:28   00:10:00 
        3285798 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:27   00:10:00 
        3285811 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT   00:10:07   00:10:00 
        3285820  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:06   00:10:00 
        3286113  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286114 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286195   train_dyn_knoms_full_nodes_2      accelerated  64      TIMEOUT   00:10:26   00:10:00 
        3287192                   download_7xx      accelerated  32       FAILED   00:00:27   00:10:00 
        3287194                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287195                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287198                   download_6xx      accelerated  32      TIMEOUT   00:10:18   00:10:00 
        3287213                   download_6xx          cpuonly 152    COMPLETED   01:44:02   06:00:00 
        3287214                   download_7xx          cpuonly 152    COMPLETED   04:27:48   06:00:00 
        3287215                   download_8xx          cpuonly 152    COMPLETED   01:07:53   06:00:00 
        3287216                   download_9xx          cpuonly 152    COMPLETED   02:11:44   06:00:00 
        3287217                  download_10xx          cpuonly 152    COMPLETED   01:10:01   06:00:00 
        3289199                   download_6xx          cpuonly 152    COMPLETED   03:05:47   06:00:00 
        3289204                   download_7xx          cpuonly 152    COMPLETED   05:07:25   06:00:00 
        3289205                   download_8xx          cpuonly 152    COMPLETED   01:27:47   06:00:00 
        3289206                   download_9xx          cpuonly 152    COMPLETED   02:27:37   06:00:00 
        3289207                  download_10xx          cpuonly 152    COMPLETED   01:25:46   06:00:00 
        3289242 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:32   00:10:00 
        3289247 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289322 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:20   00:10:00 
        3289376 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:17   00:10:00 
        3289389 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289394 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:03:42   00:10:00 
        3289433 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:16   00:10:00 
        3289439 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:49   00:10:00 
        3289469 mp4_to_npy_openai_first_try_6+          cpuonly 152    COMPLETED   00:18:29   01:00:00 
        3289483 mp4_to_npy_openai_first_try_7+          cpuonly 152    COMPLETED   00:48:15   01:00:00 
        3289484 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:43   01:00:00 
        3289485 mp4_to_npy_openai_first_try_9+          cpuonly 152    COMPLETED   00:32:34   01:00:00 
        3289486 mp4_to_npy_openai_first_try_1+          cpuonly 152    COMPLETED   00:08:10   01:00:00 
        3289548  train_dyn_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3289577  train_dyn_overfit_tfrecord_10      accelerated  32      RUNNING   14:58:34 1-00:00:00 
        3290215            npy_to_tfrecord_6xx          cpuonly 152    COMPLETED   00:01:01   00:10:00 
",,terminal_output
1299,1088537,"logs/logs_preprocessing/npy_to_tfrecord_6xx_3290215.log",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=00:10:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_6xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


2025-06-25 10:32:04.124461: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-06-25 10:32:05.052422: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1750840325.292673 2999568 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1750840325.333498 2999568 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1750840325.678559 2999568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750840325.678601 2999568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750840325.678606 2999568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750840325.678610 2999568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-06-25 10:32:05.683864: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
ERROR:root:Please generate data in '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx' first.
Time taken: 34 seconds

============================= JOB FEEDBACK =============================

Job ID: 3290215
Cluster: hk
User/Group: tum_ind3695/hk-project-pai00039
Account: hk-project-p0023960
State: COMPLETED (exit code 0)
Partition: cpuonly
Nodes: 1
Cores per node: 152
Nodelist: hkn1244
CPU Utilized: 00:00:05
CPU Efficiency: 0.05% of 02:34:32 core-walltime
Job Wall-clock time: 00:01:01
Starttime: Wed Jun 25 10:31:35 2025
Endtime: Wed Jun 25 10:32:36 2025
Memory Utilized: 421.60 MB
Memory Efficiency: 0.18% of 233.79 GB
Energy Consumed: 12347 Joule / 3.42972222222222 Watthours
Average node power draw: 202.409836065574 Watt
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1427,1677966,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=01:30:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-pai00039
#SBATCH --cpus-per-task=72
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=preprocess_video_to_npy
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

source .venv/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_npy_tmp""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
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1465,1801598,"TERMINAL",0,0,"
>",,terminal_output
1466,1812338,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/mp4_to_npy_open_ai.sbatch",,terminal_command
1467,1812394,"TERMINAL",0,0,"Submitted batch job 3290234
",,terminal_output
1468,1899480,"logs/logs_preprocessing/npy_to_tfrecord_6xx_3290230.log",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=00:10:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_6xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


2025-06-25 10:46:05.648985: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-06-25 10:46:06.662495: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1750841166.941294 2319797 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1750841166.982633 2319797 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1750841167.328623 2319797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841167.328660 2319797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841167.328665 2319797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841167.328669 2319797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-06-25 10:46:07.333840: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
INFO:root:Preparing to write 26960 episodes to 100 TFRecord shards in /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx...
2025-06-25 10:46:34.179331: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
INFO:root:  Processed 100/26960 episodes...
INFO:root:  Processed 200/26960 episodes...
INFO:root:  Processed 300/26960 episodes...
INFO:root:  Processed 400/26960 episodes...
INFO:root:  Processed 500/26960 episodes...
INFO:root:  Processed 600/26960 episodes...
INFO:root:  Processed 700/26960 episodes...
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1499,1899704,"logs/logs_preprocessing/npy_to_tfrecord_6xx_3290230.log",3551,0,"INFO:root:  Processed 800/26960 episodes...
INFO:root:  Processed 900/26960 episodes...
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1518,1913543,"utils/preprocess_dataset.py",3202,1,"
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1519,1913589,"utils/preprocess_dataset.py",3163,39,"            logging.info(f""  {fname}"")
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1520,1913860,"utils/preprocess_dataset.py",3124,78,"        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1521,1913878,"utils/preprocess_dataset.py",3077,125,"        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1522,1913905,"utils/preprocess_dataset.py",3067,135,"        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1523,1913940,"utils/preprocess_dataset.py",2979,223,"            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1524,1913974,"utils/preprocess_dataset.py",2957,245,"        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1525,1914007,"utils/preprocess_dataset.py",2930,272,"            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1526,1914041,"utils/preprocess_dataset.py",2899,303,"        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1527,1914072,"utils/preprocess_dataset.py",2886,316,"    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1528,1914105,"utils/preprocess_dataset.py",2814,388,"                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1529,1914139,"utils/preprocess_dataset.py",2779,423,"            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1530,1914172,"utils/preprocess_dataset.py",2778,424,"
            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1531,1914205,"utils/preprocess_dataset.py",2695,507,"                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1532,1914239,"utils/preprocess_dataset.py",2694,508,"
                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1533,1914272,"utils/preprocess_dataset.py",2625,577,"                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1534,1914305,"utils/preprocess_dataset.py",2560,642,"                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1535,1914340,"utils/preprocess_dataset.py",2559,643,"
                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1536,1914372,"utils/preprocess_dataset.py",2492,710,"                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1537,1914406,"utils/preprocess_dataset.py",2443,759,"                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1538,1914440,"utils/preprocess_dataset.py",2426,776,"            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1539,1914475,"utils/preprocess_dataset.py",2344,858,"                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1540,1914518,"utils/preprocess_dataset.py",2305,897,"            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1541,1914543,"utils/preprocess_dataset.py",2245,957,"        for i, npy_path in enumerate(episode_source_paths):
            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1542,1914577,"utils/preprocess_dataset.py",2236,966,"    try:
        for i, npy_path in enumerate(episode_source_paths):
            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1543,1914610,"utils/preprocess_dataset.py",2205,997,"    writer_idx_for_episode = 0
    try:
        for i, npy_path in enumerate(episode_source_paths):
            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1544,1915038,"utils/preprocess_dataset.py",2236,966,"    try:
        for i, npy_path in enumerate(episode_source_paths):
            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1545,1916014,"utils/preprocess_dataset.py",2236,966,"    try:
        for i, npy_path in enumerate(episode_source_paths):
            if i % 100 == 0 and i > 0:
                logging.info(f""  Processed {i}/{num_total_episodes} episodes..."")
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")
",python,selection_command
1546,1936597,"utils/preprocess_dataset.py",2245,180,"        for i, npy_path in tqdm(enumerate(episode_source_paths), total=num_total_episodes, desc=""Processing episodes""):",python,content
1547,1936598,"utils/preprocess_dataset.py",130,0,"from tqdm import tqdm
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1548,1943664,"utils/preprocess_dataset.py",2655,0,"",python,selection_mouse
1549,1943666,"utils/preprocess_dataset.py",2655,83,"
                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards",python,selection_mouse
1550,1943675,"utils/preprocess_dataset.py",2655,0,"",python,selection_command
1551,1943676,"utils/preprocess_dataset.py",2655,83,"
                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards",python,selection_command
1552,1951908,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",0,0,"",shellscript,tab
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1555,1955089,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",625,0,"",shellscript,selection_command
1556,1955455,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",559,0,"",shellscript,selection_command
1557,1956514,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",625,0,"",shellscript,selection_command
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1559,1956897,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",674,0,"",shellscript,selection_command
1560,1958123,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",818,0,"",shellscript,selection_command
1561,1958365,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",818,0,"
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1562,1958618,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",819,0,"
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1563,1958715,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",820,0,"
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1564,1959143,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",821,0,"
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path",shellscript,content
1565,1959152,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",822,0,"",shellscript,selection_command
1566,1960657,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",913,0,"s_160x90",shellscript,content
1567,1960657,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",910,3,"",shellscript,content
1568,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",909,0,"npy/6xx/10f",shellscript,content
1569,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",907,0,"en_ai_minecraf",shellscript,content
1570,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",906,1,"",shellscript,content
1571,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",905,0,"a/o",shellscript,content
1572,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",904,0,"m_ind3695-jafa_ws_shared/da",shellscript,content
1573,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",903,0,"rk/workspace/scratch/t",shellscript,content
1574,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",902,0,"/hkfs/w",shellscript,content
1575,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",901,1,"",shellscript,content
1576,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",886,0,"/6xx/10fps_160x90",shellscript,content
1577,1960658,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",885,1,"",shellscript,content
1578,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",884,0,"i_minecraf",shellscript,content
1579,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",883,0,"en_",shellscript,content
1580,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",882,0,"ind3695-jafa_ws_shared/data/o",shellscript,content
1581,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",881,0,"ch/tum",shellscript,content
1582,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",880,0,"ace/scra",shellscript,content
1583,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",879,1,"",shellscript,content
1584,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",878,0,"/hkfs/work/works",shellscript,content
1585,1960659,"sbatch_scripts/preprocess/preprocess_video_to_npy.sbatch",875,3,"",shellscript,content
1586,1963450,"TERMINAL",0,0,"python utils/preprocess_video_to_npy.py --input_path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/6xx/10fps_160x90 --output_path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/6xx/10fps_160x90",,terminal_command
1587,1963500,"TERMINAL",0,0,"",,terminal_output
1588,1968028,"TERMINAL",0,0,"^CTraceback (most recent call last):
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/utils/preprocess_video_to_npy.py"", line 4, in <module>
    import tyro
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/tyro/__init__.py"", line 8, in <module>
    from . import extras as extras
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/tyro/extras/__init__.py"", line 9, in <module>
    from .._argparse_formatter import set_accent_color as set_accent_color
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/tyro/_argparse_formatter.py"", line 36, in <module>
    from rich.columns import Columns
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/rich/columns.py"", line 7, in <module>
    from .console import Console, ConsoleOptions, RenderableType, RenderResult
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/rich/console.py"", line 62, in <module>
    from .scope import render_scope
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/rich/scope.py"", line 5, in <module>
    from .panel import Panel
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/rich/panel.py"", line 4, in <module>
    from .box import ROUNDED, Box
  File ""<frozen importlib._bootstrap>"", line 404, in parent
KeyboardInterrupt
",,terminal_output
1589,1968068,"TERMINAL",0,0,"
",,terminal_output
1590,1978978,"TERMINAL",0,0,"python utils/preprocess_video_to_npy.py --input_path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/6xx/10fps_160x90 --output_path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/6xx/10^C",,terminal_command
1591,1978990,"TERMINAL",0,0,"^C


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1592,1980334,"utils/preprocess_dataset.py",0,0,"",python,tab
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1597,1987092,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",909,0,"
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100",shellscript,content
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1624,1988818,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",964,0,"/hkfs/work/workspace/scratch/tum_",shellscript,content
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1628,1991838,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",910,0,"",shellscript,selection_command
1629,1994313,"TERMINAL",0,0,"python utils/preprocess_dataset.py --source_data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/6xx/10fps_160x90 --output_tfrecords_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx/10fps_160x90 --num_shards 100",,terminal_command
1630,1994360,"TERMINAL",0,0,"",,terminal_output
1631,1995927,"TERMINAL",0,0,"2025-06-25 10:50:29.268061: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
",,terminal_output
1632,1996836,"TERMINAL",0,0,"2025-06-25 10:50:30.176474: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
",,terminal_output
1633,1997141,"TERMINAL",0,0,"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1750841430.482566 4076464 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
",,terminal_output
1634,1997222,"TERMINAL",0,0,"E0000 00:00:1750841430.564240 4076464 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
",,terminal_output
1635,1997643,"TERMINAL",0,0,"W0000 00:00:1750841430.973467 4076464 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841430.973542 4076464 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841430.973548 4076464 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841430.973554 4076464 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-06-25 10:50:30.986742: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
",,terminal_output
1636,2014962,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1217,0,"",shellscript,selection_command
1637,2029504,"TERMINAL",0,0,"ERROR:root:Please generate data in '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/6xx/10fps_160x90' first.
",,terminal_output
1638,2030652,"TERMINAL",0,0,"",,terminal_output
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1680,2089429,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1058,0,"x",shellscript,content
1681,2089430,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1059,0,"",shellscript,selection_keyboard
1682,2089715,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1059,0,"/",shellscript,content
1683,2089716,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1060,0,"",shellscript,selection_keyboard
1684,2090292,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1060,0,"10fps_160x90",shellscript,content
1685,2090664,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",1071,0,"",shellscript,selection_command
1686,2093371,"TERMINAL",0,0,"kpython utils/preprocess_dataset.py --source_data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx/10fps_160x90  --output_tfrecords_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx/10fps_160x90 --num_shards 100^C",,terminal_command
1687,2093387,"TERMINAL",0,0,"^C


",,terminal_output
1688,2095180,"TERMINAL",0,0,"python utils/preprocess_dataset.py --source_data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx/10fps_160x90  --output_tfrecords_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx/10fps_160x90 --num_shards 100",,terminal_command
1689,2095230,"TERMINAL",0,0,"",,terminal_output
1690,2097305,"TERMINAL",0,0,"2025-06-25 10:52:10.605677: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
",,terminal_output
1691,2098056,"TERMINAL",0,0,"2025-06-25 10:52:11.400302: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
",,terminal_output
1692,2098490,"TERMINAL",0,0,"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1750841531.780001 4147101 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1750841531.832985 4147101 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
",,terminal_output
1693,2098908,"TERMINAL",0,0,"W0000 00:00:1750841532.234232 4147101 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841532.234312 4147101 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841532.234322 4147101 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841532.234329 4147101 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-06-25 10:52:12.250504: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
",,terminal_output
1694,2130222,"TERMINAL",0,0,"INFO:root:Preparing to write 26960 episodes to 100 TFRecord shards in /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx/10fps_160x90...
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1696,2132559,"TERMINAL",0,0,"2025-06-25 10:52:45.901198: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
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",,terminal_output
1786,2148190,"TERMINAL",0,0,"^CTraceback (most recent call last):
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/utils/preprocess_dataset.py"", line 76, in main_preprocess
    episode_data = np.load(npy_path)
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/numpy/lib/npyio.py"", line 427, in load
    fid = stack.enter_context(open(os_fspath(file), ""rb""))
KeyboardInterrupt

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/utils/preprocess_dataset.py"", line 104, in <module>
    main_preprocess(
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/utils/preprocess_dataset.py"", line 88, in main_preprocess
    writer.close()
  File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar/.venv_jafar/lib/python3.10/site-packages/tensorflow/python/lib/io/tf_record.py"", line 317, in close
    super(TFRecordWriter, self).close()
KeyboardInterrupt
",,terminal_output
1787,2148371,"TERMINAL",0,0,"^C",,terminal_output
1788,2148775,"TERMINAL",0,0,"^C",,terminal_output
1789,2148845,"TERMINAL",0,0,"
",,terminal_output
1790,2148974,"TERMINAL",0,0,"^C",,terminal_command
1791,2148984,"TERMINAL",0,0,"^C


",,terminal_output
1792,2151968,"utils/preprocess_dataset.py",0,0,"",python,tab
1793,2153400,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",0,0,"",shellscript,tab
1794,2160230,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",909,319,"",shellscript,content
1795,2160629,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",908,1,"",shellscript,content
1796,2161187,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",907,1,"",shellscript,content
1797,2168200,"TERMINAL",0,0,"queue",,terminal_command
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1809,2178646,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx copy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=00:10:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_6xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
1810,2178824,"TERMINAL",0,0,"2655",,terminal_output
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1815,2183170,"sbatch_scripts/preprocess/npy_to_tfrecord_7xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=00:10:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_6xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/6xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/6xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
1816,2184117,"TERMINAL",0,0,"716:008:00",,terminal_output
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1910,2216838,"TERMINAL",0,0,"
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1911,2227411,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_7xx.sbatch",,terminal_command
1912,2227459,"TERMINAL",0,0,"Submitted batch job 3290235
",,terminal_output
1913,2229476,"sbatch_scripts/preprocess/npy_to_tfrecord_7xx copy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_7xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/7xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/7xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
1914,2233991,"sbatch_scripts/preprocess/npy_to_tfrecord_8xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_7xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/7xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/7xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
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1945,2243436,"sbatch_scripts/preprocess/npy_to_tfrecord_8xx copy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_8xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/8xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/8xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
1946,2247039,"sbatch_scripts/preprocess/npy_to_tfrecord_9xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_8xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/8xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/8xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
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1973,2255893,"sbatch_scripts/preprocess/npy_to_tfrecord_9xx copy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_9xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/9xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/9xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
1974,2260655,"sbatch_scripts/preprocess/npy_to_tfrecord_10xx.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_9xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/9xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/9xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
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1999,2268535,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_7^C.sbatch",,terminal_command
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2040,2309730,"TERMINAL",0,0,"          JobID                        JobName        Partition All        State    Elapsed  Timelimit 
--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- 
        3273476 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:06   00:10:00 
        3273480 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:32   00:10:00 
        3273494 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:40   00:10:00 
        3273503 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273681 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273687 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273795 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273820 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:31   00:15:00 
        3273828 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273829 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273831 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:29   12:00:00 
        3273841 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:13   12:00:00 
        3273846 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:28   12:00:00 
        3274766         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:02   00:10:00 
        3276030 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:01:35   00:10:00 
        3276039 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:00:56   00:10:00 
        3276048 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:10:03   00:10:00 
        3276051 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:30:01   00:30:00 
        3276052 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276053 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276058 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:03   01:00:00 
        3276715         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:13   00:10:00 
        3277318         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   16:00:27   16:00:00 
        3282472      train_dyn_knoms_full-test      accelerated  32      TIMEOUT   00:10:24   00:10:00 
        3282485           train_dyn_knoms_full      accelerated  32       FAILED   00:00:27 1-00:00:00 
        3282489           train_dyn_knoms_full      accelerated  32      TIMEOUT 1-00:00:27 1-00:00:00 
        3285784 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:28   00:10:00 
        3285785  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:32   00:10:00 
        3285797  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:28   00:10:00 
        3285798 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:27   00:10:00 
        3285811 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT   00:10:07   00:10:00 
        3285820  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:06   00:10:00 
        3286113  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286114 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286195   train_dyn_knoms_full_nodes_2      accelerated  64      TIMEOUT   00:10:26   00:10:00 
        3287192                   download_7xx      accelerated  32       FAILED   00:00:27   00:10:00 
        3287194                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287195                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287198                   download_6xx      accelerated  32      TIMEOUT   00:10:18   00:10:00 
        3287213                   download_6xx          cpuonly 152    COMPLETED   01:44:02   06:00:00 
        3287214                   download_7xx          cpuonly 152    COMPLETED   04:27:48   06:00:00 
        3287215                   download_8xx          cpuonly 152    COMPLETED   01:07:53   06:00:00 
        3287216                   download_9xx          cpuonly 152    COMPLETED   02:11:44   06:00:00 
        3287217                  download_10xx          cpuonly 152    COMPLETED   01:10:01   06:00:00 
        3289199                   download_6xx          cpuonly 152    COMPLETED   03:05:47   06:00:00 
        3289204                   download_7xx          cpuonly 152    COMPLETED   05:07:25   06:00:00 
        3289205                   download_8xx          cpuonly 152    COMPLETED   01:27:47   06:00:00 
        3289206                   download_9xx          cpuonly 152    COMPLETED   02:27:37   06:00:00 
        3289207                  download_10xx          cpuonly 152    COMPLETED   01:25:46   06:00:00 
        3289242 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:32   00:10:00 
        3289247 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289322 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:20   00:10:00 
        3289376 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:17   00:10:00 
        3289389 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289394 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:03:42   00:10:00 
        3289433 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:16   00:10:00 
        3289439 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:49   00:10:00 
        3289469 mp4_to_npy_openai_first_try_6+          cpuonly 152    COMPLETED   00:18:29   01:00:00 
        3289483 mp4_to_npy_openai_first_try_7+          cpuonly 152    COMPLETED   00:48:15   01:00:00 
        3289484 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:43   01:00:00 
        3289485 mp4_to_npy_openai_first_try_9+          cpuonly 152    COMPLETED   00:32:34   01:00:00 
        3289486 mp4_to_npy_openai_first_try_1+          cpuonly 152    COMPLETED   00:08:10   01:00:00 
        3289548  train_dyn_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3289577  train_dyn_overfit_tfrecord_10      accelerated  32      RUNNING   15:19:07 1-00:00:00 
        3290215            npy_to_tfrecord_6xx          cpuonly 152    COMPLETED   00:01:01   00:10:00 
        3290230            npy_to_tfrecord_6xx          cpuonly 152      TIMEOUT   00:10:06   00:10:00 
        3290234          mp4_to_npy_openai_all          cpuonly 152      RUNNING   00:08:06   00:10:00 
        3290235            npy_to_tfrecord_7xx          cpuonly 152      RUNNING   00:00:06   02:00:00 
",,terminal_output
2041,2313350,"TERMINAL",0,0,"fsacct_week",,terminal_command
2042,2313401,"TERMINAL",0,0,"",,terminal_output
2043,2313434,"TERMINAL",0,0,"          JobID                        JobName        Partition All        State    Elapsed  Timelimit 
--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- 
        3273476 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:06   00:10:00 
        3273480 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:32   00:10:00 
        3273494 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:40   00:10:00 
        3273503 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273681 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273687 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273795 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273820 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:31   00:15:00 
        3273828 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273829 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273831 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:29   12:00:00 
        3273841 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:13   12:00:00 
        3273846 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:28   12:00:00 
        3274766         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:02   00:10:00 
        3276030 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:01:35   00:10:00 
        3276039 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:00:56   00:10:00 
        3276048 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:10:03   00:10:00 
        3276051 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:30:01   00:30:00 
        3276052 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276053 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276058 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:03   01:00:00 
        3276715         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:13   00:10:00 
        3277318         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   16:00:27   16:00:00 
        3282472      train_dyn_knoms_full-test      accelerated  32      TIMEOUT   00:10:24   00:10:00 
        3282485           train_dyn_knoms_full      accelerated  32       FAILED   00:00:27 1-00:00:00 
        3282489           train_dyn_knoms_full      accelerated  32      TIMEOUT 1-00:00:27 1-00:00:00 
        3285784 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:28   00:10:00 
        3285785  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:32   00:10:00 
        3285797  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:28   00:10:00 
        3285798 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:27   00:10:00 
        3285811 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT   00:10:07   00:10:00 
        3285820  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:06   00:10:00 
        3286113  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286114 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286195   train_dyn_knoms_full_nodes_2      accelerated  64      TIMEOUT   00:10:26   00:10:00 
        3287192                   download_7xx      accelerated  32       FAILED   00:00:27   00:10:00 
        3287194                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287195                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287198                   download_6xx      accelerated  32      TIMEOUT   00:10:18   00:10:00 
        3287213                   download_6xx          cpuonly 152    COMPLETED   01:44:02   06:00:00 
        3287214                   download_7xx          cpuonly 152    COMPLETED   04:27:48   06:00:00 
        3287215                   download_8xx          cpuonly 152    COMPLETED   01:07:53   06:00:00 
        3287216                   download_9xx          cpuonly 152    COMPLETED   02:11:44   06:00:00 
        3287217                  download_10xx          cpuonly 152    COMPLETED   01:10:01   06:00:00 
        3289199                   download_6xx          cpuonly 152    COMPLETED   03:05:47   06:00:00 
        3289204                   download_7xx          cpuonly 152    COMPLETED   05:07:25   06:00:00 
        3289205                   download_8xx          cpuonly 152    COMPLETED   01:27:47   06:00:00 
        3289206                   download_9xx          cpuonly 152    COMPLETED   02:27:37   06:00:00 
        3289207                  download_10xx          cpuonly 152    COMPLETED   01:25:46   06:00:00 
        3289242 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:32   00:10:00 
        3289247 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289322 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:20   00:10:00 
        3289376 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:17   00:10:00 
        3289389 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289394 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:03:42   00:10:00 
        3289433 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:16   00:10:00 
        3289439 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:49   00:10:00 
        3289469 mp4_to_npy_openai_first_try_6+          cpuonly 152    COMPLETED   00:18:29   01:00:00 
        3289483 mp4_to_npy_openai_first_try_7+          cpuonly 152    COMPLETED   00:48:15   01:00:00 
        3289484 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:43   01:00:00 
        3289485 mp4_to_npy_openai_first_try_9+          cpuonly 152    COMPLETED   00:32:34   01:00:00 
        3289486 mp4_to_npy_openai_first_try_1+          cpuonly 152    COMPLETED   00:08:10   01:00:00 
        3289548  train_dyn_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3289577  train_dyn_overfit_tfrecord_10      accelerated  32      RUNNING   15:19:10 1-00:00:00 
        3290215            npy_to_tfrecord_6xx          cpuonly 152    COMPLETED   00:01:01   00:10:00 
        3290230            npy_to_tfrecord_6xx          cpuonly 152      TIMEOUT   00:10:06   00:10:00 
        3290234          mp4_to_npy_openai_all          cpuonly 152      RUNNING   00:08:09   00:10:00 
        3290235            npy_to_tfrecord_7xx          cpuonly 152      RUNNING   00:00:09   02:00:00 
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2064,2527121,"logs/logs_preprocessing/mp4_to_npy_openai_all_3290234.log",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=00:10:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_all
#SBATCH --mail-type=ALL

cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy//10fps_160x90
Number of processes: 152
Converting mp4 to npy files...
slurmstepd: error: *** JOB 3290234 ON hkn0364 CANCELLED AT 2025-06-25T10:57:43 DUE TO TIME LIMIT ***

============================= JOB FEEDBACK =============================

Job ID: 3290234
Cluster: hk
User/Group: tum_ind3695/hk-project-pai00039
Account: hk-project-p0023960
State: TIMEOUT (exit code 0)
Partition: cpuonly
Nodes: 1
Cores per node: 152
Nodelist: hkn0364
CPU Utilized: 01:05:48
CPU Efficiency: 4.29% of 1-01:35:12 core-walltime
Job Wall-clock time: 00:10:06
Starttime: Wed Jun 25 10:47:37 2025
Endtime: Wed Jun 25 10:57:43 2025
Memory Utilized: 26.06 GB
Memory Efficiency: 11.15% of 233.79 GB
Energy Consumed: 381371 Joule / 105.936388888889 Watthours
Average node power draw: 629.325082508251 Watt
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2184,2587420,"TERMINAL",0,0,"2043",,terminal_output
2185,2588473,"TERMINAL",0,0,"154",,terminal_output
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2189,2591634,"TERMINAL",0,0,"487",,terminal_output
2190,2592239,"TERMINAL",0,0,"
>",,terminal_output
2191,2596375,"TERMINAL",0,0,"fsacct_week",,terminal_command
2192,2596426,"TERMINAL",0,0,"",,terminal_output
2193,2596466,"TERMINAL",0,0,"          JobID                        JobName        Partition All        State    Elapsed  Timelimit 
--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- 
        3273476 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:06   00:10:00 
        3273480 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:32   00:10:00 
        3273494 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:40   00:10:00 
        3273503 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273681 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273687 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273795 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273820 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:31   00:15:00 
        3273828 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273829 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273831 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:29   12:00:00 
        3273841 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:13   12:00:00 
        3273846 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:28   12:00:00 
        3274766         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:02   00:10:00 
        3276030 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:01:35   00:10:00 
        3276039 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:00:56   00:10:00 
        3276048 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:10:03   00:10:00 
        3276051 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:30:01   00:30:00 
        3276052 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276053 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276058 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:03   01:00:00 
        3276715         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:13   00:10:00 
        3277318         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   16:00:27   16:00:00 
        3282472      train_dyn_knoms_full-test      accelerated  32      TIMEOUT   00:10:24   00:10:00 
        3282485           train_dyn_knoms_full      accelerated  32       FAILED   00:00:27 1-00:00:00 
        3282489           train_dyn_knoms_full      accelerated  32      TIMEOUT 1-00:00:27 1-00:00:00 
        3285784 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:28   00:10:00 
        3285785  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:32   00:10:00 
        3285797  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:28   00:10:00 
        3285798 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:27   00:10:00 
        3285811 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT   00:10:07   00:10:00 
        3285820  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:06   00:10:00 
        3286113  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286114 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286195   train_dyn_knoms_full_nodes_2      accelerated  64      TIMEOUT   00:10:26   00:10:00 
        3287192                   download_7xx      accelerated  32       FAILED   00:00:27   00:10:00 
        3287194                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287195                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287198                   download_6xx      accelerated  32      TIMEOUT   00:10:18   00:10:00 
        3287213                   download_6xx          cpuonly 152    COMPLETED   01:44:02   06:00:00 
        3287214                   download_7xx          cpuonly 152    COMPLETED   04:27:48   06:00:00 
        3287215                   download_8xx          cpuonly 152    COMPLETED   01:07:53   06:00:00 
        3287216                   download_9xx          cpuonly 152    COMPLETED   02:11:44   06:00:00 
        3287217                  download_10xx          cpuonly 152    COMPLETED   01:10:01   06:00:00 
        3289199                   download_6xx          cpuonly 152    COMPLETED   03:05:47   06:00:00 
        3289204                   download_7xx          cpuonly 152    COMPLETED   05:07:25   06:00:00 
        3289205                   download_8xx          cpuonly 152    COMPLETED   01:27:47   06:00:00 
        3289206                   download_9xx          cpuonly 152    COMPLETED   02:27:37   06:00:00 
        3289207                  download_10xx          cpuonly 152    COMPLETED   01:25:46   06:00:00 
        3289242 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:32   00:10:00 
        3289247 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289322 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:20   00:10:00 
        3289376 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:17   00:10:00 
        3289389 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289394 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:03:42   00:10:00 
        3289433 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:16   00:10:00 
        3289439 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:49   00:10:00 
        3289469 mp4_to_npy_openai_first_try_6+          cpuonly 152    COMPLETED   00:18:29   01:00:00 
        3289483 mp4_to_npy_openai_first_try_7+          cpuonly 152    COMPLETED   00:48:15   01:00:00 
        3289484 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:43   01:00:00 
        3289485 mp4_to_npy_openai_first_try_9+          cpuonly 152    COMPLETED   00:32:34   01:00:00 
        3289486 mp4_to_npy_openai_first_try_1+          cpuonly 152    COMPLETED   00:08:10   01:00:00 
        3289548  train_dyn_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3289577  train_dyn_overfit_tfrecord_10      accelerated  32      RUNNING   15:23:53 1-00:00:00 
        3290215            npy_to_tfrecord_6xx          cpuonly 152    COMPLETED   00:01:01   00:10:00 
        3290230            npy_to_tfrecord_6xx          cpuonly 152      TIMEOUT   00:10:06   00:10:00 
        3290234          mp4_to_npy_openai_all          cpuonly 152      TIMEOUT   00:10:06   00:10:00 
        3290235            npy_to_tfrecord_7xx          cpuonly 152      RUNNING   00:04:52   02:00:00 
        3290241          mp4_to_npy_openai_all          cpuonly   0      PENDING   00:00:00   12:00:00 
",,terminal_output
2194,2617749,"logs/logs_preprocessing/npy_to_tfrecord_6xx_3290215.log",0,0,"",log,tab
2195,2619780,"logs/logs_preprocessing/npy_to_tfrecord_6xx_3290230.log",0,0,"",log,tab
2196,2619815,"logs/logs_preprocessing/npy_to_tfrecord_6xx_3290230.log",3639,0,"INFO:root:  Processed 1000/26960 episodes...
INFO:root:  Processed 1100/26960 episodes...
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INFO:root:  Processed 4000/26960 episodes...
slurmstepd: error: *** JOB 3290230 ON hkn1023 CANCELLED AT 2025-06-25T10:55:43 DUE TO TIME LIMIT ***

============================= JOB FEEDBACK =============================

Job ID: 3290230
Cluster: hk
User/Group: tum_ind3695/hk-project-pai00039
Account: hk-project-p0023960
State: TIMEOUT (exit code 0)
Partition: cpuonly
Nodes: 1
Cores per node: 152
Nodelist: hkn1023
CPU Utilized: 00:06:48
CPU Efficiency: 0.44% of 1-01:35:12 core-walltime
Job Wall-clock time: 00:10:06
Starttime: Wed Jun 25 10:45:37 2025
Endtime: Wed Jun 25 10:55:43 2025
Memory Utilized: 1.14 GB
Memory Efficiency: 0.49% of 233.79 GB
Energy Consumed: 161940 Joule / 44.9833333333333 Watthours
Average node power draw: 267.227722772277 Watt
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2243,2626532,"logs/logs_preprocessing/npy_to_tfrecord_7xx_3290235.log",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=02:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_7xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/7xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/7xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
2025-06-25 10:56:06.332176: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-06-25 10:56:07.262424: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1750841767.511119 3221520 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1750841767.547736 3221520 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1750841767.922807 3221520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841767.922841 3221520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841767.922846 3221520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1750841767.922850 3221520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-06-25 10:56:07.928143: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
INFO:root:Preparing to write 14769 episodes to 100 TFRecord shards in /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/7xx...

Processing episodes:   0%|          | 0/14769 [00:00<?, ?it/s]2025-06-25 10:56:31.561652: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)

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2245,2629817,"logs/logs_preprocessing/npy_to_tfrecord_7xx_3290235.log",49524,0,"",log,selection_command
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2250,2634661,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",20,0,"",shellscript,selection_command
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2284,2660279,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",,terminal_command
2285,2660327,"TERMINAL",0,0,"Submitted batch job 3290255
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2286,2664271,"TERMINAL",0,0,"fsacct_week ^C",,terminal_command
2287,2664279,"TERMINAL",0,0,"^C


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2289,2665449,"TERMINAL",0,0,"             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
           3289577 accelerat train_dy tum_ind3  R   15:25:02      1 hkn0712
           3290241   cpuonly mp4_to_n tum_ind3  R       0:00      1 hkn0364
           3290255   cpuonly npy_to_t tum_ind3  R       0:00      1 hkn1159
           3290235   cpuonly npy_to_t tum_ind3  R       6:01      1 hkn0138
",,terminal_output
2290,2669958,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch^C",,terminal_command
2291,2669967,"TERMINAL",0,0,"^C


",,terminal_output
2292,2671057,"TERMINAL",0,0,"fqueue",,terminal_command
2293,2671093,"TERMINAL",0,0,"     JOBID        PARTITION                                                         NAME     USER    STATE       TIME TIME_LIMI  NODES NODELIST(REASON)
   3289577      accelerated                                train_dyn_overfit_tfrecord_10 tum_ind3  RUNNING   15:25:08 1-00:00:00      1 hkn0712
   3290241          cpuonly                                        mp4_to_npy_openai_all tum_ind3  RUNNING       0:06  12:00:00      1 hkn0364
   3290255          cpuonly                                          npy_to_tfrecord_6xx tum_ind3  RUNNING       0:06   3:00:00      1 hkn1159
   3290235          cpuonly                                          npy_to_tfrecord_7xx tum_ind3  RUNNING       6:07   2:00:00      1 hkn0138
",,terminal_output
2294,2681264,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_8xx.sbatch",,terminal_command
2295,2681310,"TERMINAL",0,0,"Submitted batch job 3290256
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2296,2684129,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_9xx.sbatch",,terminal_command
2297,2684167,"TERMINAL",0,0,"Submitted batch job 3290257
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2298,2686959,"TERMINAL",0,0,"sbatch sbatch_scripts/preprocess/npy_to_tfrecord_10xx.sbatch",,terminal_command
2299,2687004,"TERMINAL",0,0,"Submitted batch job 3290258
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2300,2703546,"TERMINAL",0,0,"bash",,terminal_focus
2301,2706195,"TERMINAL",0,0,"bash",,terminal_focus
2302,2706198,"utils/preprocess_dataset.py",0,0,"",python,tab
2303,2708712,"utils/preprocess_dataset.py",0,2104,"from dataclasses import dataclass

import tensorflow as tf
import numpy as np
import logging
import tyro
from pathlib import Path
from tqdm import tqdm

logging.basicConfig(level=logging.INFO)


@dataclass
class Args:
    source_data_dir: str = ""data/coinrun_episodes""
    output_tfrecords_dir: str = ""data_tfrecords""
    num_shards: int = 50


args = tyro.cli(Args)


def _bytes_feature(value):
    if isinstance(value, type(tf.constant(0))):
        value = value.numpy()
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def create_tfrecord_example(episode_numpy_array):
    feature = {
        ""height"": _int64_feature(episode_numpy_array.shape[1]),
        ""width"": _int64_feature(episode_numpy_array.shape[2]),
        ""channels"": _int64_feature(episode_numpy_array.shape[3]),
        ""sequence_length"": _int64_feature(episode_numpy_array.shape[0]),
        ""raw_video"": _bytes_feature(episode_numpy_array.tobytes()),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))


def main_preprocess(data_dir_str, output_dir_str, num_shards):
    data_dir = Path(data_dir_str)
    output_dir = Path(output_dir_str)
    output_dir.mkdir(parents=True, exist_ok=True)

    metadata = np.load(data_dir / ""metadata.npy"", allow_pickle=True)
    episode_source_paths = [Path(item[""path""]) for item in metadata]
    num_total_episodes = len(episode_source_paths)

    if num_shards <= 0:
        raise ValueError(""num_shards must be positive."")
    if num_shards > num_total_episodes:
        logging.warning(
            f""Warning: num_shards ({num_shards}) is greater than total episodes ({num_total_episodes}). ""
            f""Setting num_shards to {num_total_episodes}.""
        )
        num_shards = num_total_episodes

    logging.info(
        f""Preparing to write {num_total_episodes} episodes to {num_shards} TFRecord shards in {output_dir}...""
    )

    output_filenames = [
        str(output_dir / f""shard-{i:05d}-of-{num_shards:05d}.tfrecord"")",python,selection_command
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2337,2711041,"utils/preprocess_dataset.py",703,0,"",python,selection_command
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2339,2899815,"sbatch_scripts/preprocess/npy_to_tfrecord_6xx.sbatch",0,0,"",shellscript,tab
2340,2900135,"sbatch_scripts/preprocess/npy_to_tfrecord_10xx.sbatch",0,0,"",shellscript,tab
2341,2904596,"TERMINAL",0,0,"",,terminal_focus
2342,2908042,"TERMINAL",0,0,"gs",,terminal_command
2343,2908093,"TERMINAL",0,0,"",,terminal_output
2344,2908158,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes not staged for commit:
  (use ""git add <file>..."" to update what will be committed)
  (use ""git restore <file>..."" to discard changes in working directory)
	modified:   utils/preprocess_dataset.py
	modified:   utils/preprocess_video_to_npy.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py

no changes added to commit (use ""git add"" and/or ""git commit -a"")
",,terminal_output
2345,2913724,"utils/preprocess_dataset.py",0,0,"",python,tab
2346,2913743,"utils/preprocess_dataset.py",130,0,"",python,selection_command
2347,2927604,"utils/preprocess_video_to_npy.py",0,0,"import ffmpeg
import numpy as np
import os
import tyro
import multiprocessing as mp
from dataclasses import dataclass
import json

@dataclass
class Args:
    target_width, target_height = 160, 90
    target_fps = 10
    input_path: str = (
        ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms/""
    )
    output_path: str = (
        ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_npy""
    )


def preprocess_video(
    idx, in_filename, output_path, target_width, target_height, target_fps
):
    print(f""Processing video {idx}"")
    
    print('filename', in_filename)
    try:
        out, _ = (
            ffmpeg.input(in_filename)
            .filter(""fps"", fps=target_fps, round=""up"")
            .filter(""scale"", target_width, target_height)
            .output(""pipe:"", format=""rawvideo"", pix_fmt=""rgb24"")
            .run(capture_stdout=True, quiet=True)
        )
        
        frame_size = target_height * target_width * 3
        n_frames = len(out) // frame_size
        
        frames = np.frombuffer(out, np.uint8).reshape(
            n_frames, target_height, target_width, 3
        )
        
        output_file = f'{output_path}/{os.path.splitext(os.path.basename(in_filename))[0]}.npy'
        if not os.path.exists(os.path.dirname(output_file)):
            os.makedirs(os.path.dirname(output_file))
        
        np.save(output_file, frames)
        print(f""Saved {n_frames} frames to {output_file} with shape {frames.shape}"")
        return in_filename, True
    except Exception as e:
        print(f""Error processing video {idx} ({in_filename}): {e}"")
        return in_filename, False


def get_meta_data(filename, directory):
    filepath = os.path.join(directory, filename)
    arr = np.load(filepath, mmap_mode=""r"")
    return filepath, arr.shape[0]


def main():
    args = tyro.cli(Args)

    output_path = f""{args.output_path}/{args.target_fps}fps_{args.target_width}x{args.target_height}""
    print(output_path)

    num_processes = mp.cpu_count()
    print(f""Number of processes: {num_processes}"")

    print(""Converting mp4 to npy files..."")
    pool_args = [
        (
            idx,
            args.input_path + in_filename,
            output_path,
            args.target_width,
            args.target_height,
            args.target_fps,
        )
        for idx, in_filename in enumerate(os.listdir(args.input_path))
        if in_filename.endswith("".mp4"") or in_filename.endswith("".webm"")
    ]

    results = []
    with mp.Pool(processes=num_processes) as pool:
        for result in pool.starmap(preprocess_video, pool_args):
            results.append(result)
    print(""Done converting mp4 to npy files"")

    # count the number of failed videos
    failed_videos = [result for result in results if not result[1]]
    print(f""Number of failed videos: {len(failed_videos)}"")
    print(f""Number of successful videos: {len(results) - len(failed_videos)}"")
    print(f""Number of total videos: {len(results)}"")

    json.dump(failed_videos, open(output_path + ""/failed_videos.json"", ""w""))

    print(""Creating metadata file..."")
    metadata = []
    filenames = [
        filename
        for filename in os.listdir(output_path)
        if filename.endswith("".npy"") and filename != ""metadata.npy""
    ]
    pool_args = [(filename, output_path) for filename in filenames]

    with mp.Pool(processes=num_processes) as pool:
        results = list(pool.starmap(get_meta_data, pool_args))
        metadata = [{""path"": path, ""length"": length} for path, length in results]
    np.save(output_path + ""/metadata.npy"", metadata)
    print(f""Saved {len(metadata)} videos to {output_path}"")


if __name__ == ""__main__"":
    main()
",python,tab
2348,2927606,"utils/preprocess_video_to_npy.py",118,0,"",python,selection_command
2349,2939235,"TERMINAL",0,0,"bash",,terminal_focus
2350,2945014,"TERMINAL",0,0,"gs",,terminal_command
2351,2945085,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes not staged for commit:
  (use ""git add <file>..."" to update what will be committed)
  (use ""git restore <file>..."" to discard changes in working directory)
	modified:   utils/preprocess_dataset.py
	modified:   utils/preprocess_video_to_npy.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py

no changes added to commit (use ""git add"" and/or ""git commit -a"")
",,terminal_output
2352,2953745,"TERMINAL",0,0,"git add utils/preprocess_*",,terminal_command
2353,2953794,"TERMINAL",0,0,"",,terminal_output
2354,2962591,"TERMINAL",0,0,"gs",,terminal_command
2355,2962628,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes to be committed:
  (use ""git restore --staged <file>..."" to unstage)
	modified:   utils/preprocess_dataset.py
	modified:   utils/preprocess_video_to_npy.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py

",,terminal_output
2356,2975922,"utils/preprocess_dataset.py",0,0,"from dataclasses import dataclass

import tensorflow as tf
import numpy as np
import logging
import tyro
from pathlib import Path
from tqdm import tqdm

logging.basicConfig(level=logging.INFO)


@dataclass
class Args:
    source_data_dir: str = ""data/coinrun_episodes""
    output_tfrecords_dir: str = ""data_tfrecords""
    num_shards: int = 50


args = tyro.cli(Args)


def _bytes_feature(value):
    if isinstance(value, type(tf.constant(0))):
        value = value.numpy()
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def create_tfrecord_example(episode_numpy_array):
    feature = {
        ""height"": _int64_feature(episode_numpy_array.shape[1]),
        ""width"": _int64_feature(episode_numpy_array.shape[2]),
        ""channels"": _int64_feature(episode_numpy_array.shape[3]),
        ""sequence_length"": _int64_feature(episode_numpy_array.shape[0]),
        ""raw_video"": _bytes_feature(episode_numpy_array.tobytes()),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))


def main_preprocess(data_dir_str, output_dir_str, num_shards):
    data_dir = Path(data_dir_str)
    output_dir = Path(output_dir_str)
    output_dir.mkdir(parents=True, exist_ok=True)

    metadata = np.load(data_dir / ""metadata.npy"", allow_pickle=True)
    episode_source_paths = [Path(item[""path""]) for item in metadata]
    num_total_episodes = len(episode_source_paths)

    if num_shards <= 0:
        raise ValueError(""num_shards must be positive."")
    if num_shards > num_total_episodes:
        logging.warning(
            f""Warning: num_shards ({num_shards}) is greater than total episodes ({num_total_episodes}). ""
            f""Setting num_shards to {num_total_episodes}.""
        )
        num_shards = num_total_episodes

    logging.info(
        f""Preparing to write {num_total_episodes} episodes to {num_shards} TFRecord shards in {output_dir}...""
    )

    output_filenames = [
        str(output_dir / f""shard-{i:05d}-of-{num_shards:05d}.tfrecord"")
        for i in range(num_shards)
    ]
    writers = [tf.io.TFRecordWriter(filename) for filename in output_filenames]

    writer_idx_for_episode = 0
    try:
        for i, npy_path in tqdm(enumerate(episode_source_paths), total=num_total_episodes, desc=""Processing episodes""):
            try:
                episode_data = np.load(npy_path)
                tf_example = create_tfrecord_example(episode_data)

                current_writer = writers[writer_idx_for_episode]
                current_writer.write(tf_example.SerializeToString())

                writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards

            except Exception as e:
                logging.error(f""Skipping {npy_path} due to error: {e}"")
    finally:
        for writer in writers:
            writer.close()
        logging.info(
            f""TFRecord sharding complete. {num_shards} shards written to {output_dir}.""
        )
        logging.info(""Generated shard files:"")
        for fname in output_filenames:
            logging.info(f""  {fname}"")


if __name__ == ""__main__"":
    if (
        not Path(args.source_data_dir).exists()
        or not (Path(args.source_data_dir) / ""metadata.npy"").exists()
    ):
        logging.error(f""Please generate data in '{args.source_data_dir}' first."")
    else:
        main_preprocess(
            args.source_data_dir, args.output_tfrecords_dir, args.num_shards
        )
",python,tab
2357,2975924,"utils/preprocess_dataset.py",130,0,"",python,selection_command
2358,2980004,"utils/preprocess_video_to_npy.py",0,0,"import ffmpeg
import numpy as np
import os
import tyro
import multiprocessing as mp
from dataclasses import dataclass
import json

@dataclass
class Args:
    target_width, target_height = 160, 90
    target_fps = 10
    input_path: str = (
        ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms/""
    )
    output_path: str = (
        ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_npy""
    )


def preprocess_video(
    idx, in_filename, output_path, target_width, target_height, target_fps
):
    print(f""Processing video {idx}"")
    
    print('filename', in_filename)
    try:
        out, _ = (
            ffmpeg.input(in_filename)
            .filter(""fps"", fps=target_fps, round=""up"")
            .filter(""scale"", target_width, target_height)
            .output(""pipe:"", format=""rawvideo"", pix_fmt=""rgb24"")
            .run(capture_stdout=True, quiet=True)
        )
        
        frame_size = target_height * target_width * 3
        n_frames = len(out) // frame_size
        
        frames = np.frombuffer(out, np.uint8).reshape(
            n_frames, target_height, target_width, 3
        )
        
        output_file = f'{output_path}/{os.path.splitext(os.path.basename(in_filename))[0]}.npy'
        if not os.path.exists(os.path.dirname(output_file)):
            os.makedirs(os.path.dirname(output_file))
        
        np.save(output_file, frames)
        print(f""Saved {n_frames} frames to {output_file} with shape {frames.shape}"")
        return in_filename, True
    except Exception as e:
        print(f""Error processing video {idx} ({in_filename}): {e}"")
        return in_filename, False


def get_meta_data(filename, directory):
    filepath = os.path.join(directory, filename)
    arr = np.load(filepath, mmap_mode=""r"")
    return filepath, arr.shape[0]


def main():
    args = tyro.cli(Args)

    output_path = f""{args.output_path}/{args.target_fps}fps_{args.target_width}x{args.target_height}""
    print(output_path)

    num_processes = mp.cpu_count()
    print(f""Number of processes: {num_processes}"")

    print(""Converting mp4 to npy files..."")
    pool_args = [
        (
            idx,
            args.input_path + in_filename,
            output_path,
            args.target_width,
            args.target_height,
            args.target_fps,
        )
        for idx, in_filename in enumerate(os.listdir(args.input_path))
        if in_filename.endswith("".mp4"") or in_filename.endswith("".webm"")
    ]

    results = []
    with mp.Pool(processes=num_processes) as pool:
        for result in pool.starmap(preprocess_video, pool_args):
            results.append(result)
    print(""Done converting mp4 to npy files"")

    # count the number of failed videos
    failed_videos = [result for result in results if not result[1]]
    print(f""Number of failed videos: {len(failed_videos)}"")
    print(f""Number of successful videos: {len(results) - len(failed_videos)}"")
    print(f""Number of total videos: {len(results)}"")

    json.dump(failed_videos, open(output_path + ""/failed_videos.json"", ""w""))

    print(""Creating metadata file..."")
    metadata = []
    filenames = [
        filename
        for filename in os.listdir(output_path)
        if filename.endswith("".npy"") and filename != ""metadata.npy""
    ]
    pool_args = [(filename, output_path) for filename in filenames]

    with mp.Pool(processes=num_processes) as pool:
        results = list(pool.starmap(get_meta_data, pool_args))
        metadata = [{""path"": path, ""length"": length} for path, length in results]
    np.save(output_path + ""/metadata.npy"", metadata)
    print(f""Saved {len(metadata)} videos to {output_path}"")


if __name__ == ""__main__"":
    main()
",python,tab
2359,2980007,"utils/preprocess_video_to_npy.py",118,0,"",python,selection_command
2360,2993349,"utils/preprocess_video_to_npy.py",0,0,"import ffmpeg
import numpy as np
import os
import tyro
import multiprocessing as mp
from dataclasses import dataclass


@dataclass
class Args:
    target_width, target_height = 160, 90
    target_fps = 10
    input_path: str = (
        ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms/""
    )
    output_path: str = (
        ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_npy""
    )


def preprocess_video(
    idx, in_filename, output_path, target_width, target_height, target_fps
):
    print(f""Processing video {idx}"")

    out, _ = (
        ffmpeg.input(in_filename)
        .filter(""fps"", fps=target_fps, round=""up"")
        .filter(""scale"", target_width, target_height)
        .output(""pipe:"", format=""rawvideo"", pix_fmt=""rgb24"")
        .run(capture_stdout=True, quiet=True)
    )

    frame_size = target_height * target_width * 3
    n_frames = len(out) // frame_size

    frames = np.frombuffer(out, np.uint8).reshape(
        n_frames, target_height, target_width, 3
    )

    output_file = f'{output_path}/{in_filename.split(""/"")[-1].split(""."")[0]}.npy'
    if not os.path.exists(os.path.dirname(output_file)):
        os.makedirs(os.path.dirname(output_file))

    np.save(output_file, frames)
    print(f""Saved {n_frames} frames to {output_file} with shape {frames.shape}"")


def get_meta_data(filename, directory):
    filepath = os.path.join(directory, filename)
    arr = np.load(filepath, mmap_mode=""r"")
    return filepath, arr.shape[0]


def main():
    args = tyro.cli(Args)

    output_path = f""{args.output_path}/{args.target_fps}fps_{args.target_width}x{args.target_height}""
    print(output_path)

    print(f""Number of processes: {mp.cpu_count()}"")
    print(""Converting mp4 to npy files..."")
    pool_args = [
        (
            idx,
            args.input_path + in_filename,
            output_path,
            args.target_width,
            args.target_height,
            args.target_fps,
        )
        for idx, in_filename in enumerate(os.listdir(args.input_path))
        if in_filename.endswith("".mp4"") or in_filename.endswith("".webm"")
    ]

    with mp.Pool(processes=mp.cpu_count()) as pool:
        pool.starmap(preprocess_video, pool_args)
    print(""Done converting mp4 to npy files"")

    print(""Creating metadata file..."")
    metadata = []
    filenames = [
        filename
        for filename in os.listdir(output_path)
        if filename.endswith("".npy"") and filename != ""metadata.npy""
    ]
    pool_args = [(filename, output_path) for filename in filenames]

    with mp.Pool(processes=mp.cpu_count()) as pool:
        results = list(pool.starmap(get_meta_data, pool_args))
        metadata = [{""path"": path, ""length"": length} for path, length in results]
    np.save(output_path + ""/metadata.npy"", metadata)
    print(f""Saved {len(metadata)} videos to {output_path}"")


if __name__ == ""__main__"":
    main()
",python,tab
2361,2993350,"utils/preprocess_video_to_npy.py",2912,0,"",python,selection_mouse
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2363,3050319,"utils/preprocess_video_to_npy.py",0,0,"",python,tab
2364,3074575,"TERMINAL",0,0,"git commit -m 'feat: add error handling and logging to dataset preprocessing;
^C",,terminal_command
2365,3074585,"TERMINAL",0,0,"^C


",,terminal_output
2366,3076456,"TERMINAL",0,0,"git commit -m 'feat: add error handling and logging to dataset preprocessing'",,terminal_command
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2368,3076696,"TERMINAL",0,0,"g",,terminal_output
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2376,3082804,"utils/preprocess_video_to_npy.py",0,0,"",python,tab
2377,3083293,"TERMINAL",0,0,"Failed
- hook id: black
- files were modified by this hook

reformatted utils/preprocess_dataset.py
reformatted utils/preprocess_video_to_npy.py

All done! ✨ 🍰 ✨
2 files reformatted.

",,terminal_output
2378,3085795,"TERMINAL",0,0,"git pushgs^C",,terminal_command
2379,3085807,"TERMINAL",0,0,"^C


",,terminal_output
2380,3086264,"TERMINAL",0,0,"gs",,terminal_command
2381,3086293,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes to be committed:
  (use ""git restore --staged <file>..."" to unstage)
	modified:   utils/preprocess_dataset.py
	modified:   utils/preprocess_video_to_npy.py

Changes not staged for commit:
  (use ""git add <file>..."" to update what will be committed)
  (use ""git restore <file>..."" to discard changes in working directory)
	modified:   utils/preprocess_dataset.py
	modified:   utils/preprocess_video_to_npy.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py

",,terminal_output
2382,3092633,"utils/preprocess_dataset.py",0,0,"",python,tab
2383,3092636,"utils/preprocess_dataset.py",2267,0,"",python,selection_command
2384,3094733,"utils/preprocess_video_to_npy.py",0,0,"",python,tab
2385,3094735,"utils/preprocess_video_to_npy.py",130,0,"",python,selection_command
2386,3102392,"TERMINAL",0,0,"git add utils/preprocess_*",,terminal_command
2387,3102419,"TERMINAL",0,0,"",,terminal_output
2388,3104322,"TERMINAL",0,0,"gs",,terminal_command
2389,3104374,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes to be committed:
  (use ""git restore --staged <file>..."" to unstage)
	modified:   utils/preprocess_dataset.py
	modified:   utils/preprocess_video_to_npy.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py

",,terminal_output
2390,3107451,"TERMINAL",0,0,"git commit -m 'feat: add error handling and logging to dataset preprocessing'",,terminal_command
2391,3107501,"TERMINAL",0,0,"",,terminal_output
2392,3108178,"TERMINAL",0,0,"black....................................................................",,terminal_output
2393,3108578,"TERMINAL",0,0,"Passed
",,terminal_output
2394,3108769,"TERMINAL",0,0,"[preprocess_video 4dc91c0] feat: add error handling and logging to dataset preprocessing
 2 files changed, 48 insertions(+), 24 deletions(-)
",,terminal_output
2395,3110105,"TERMINAL",0,0,"git push",,terminal_command
2396,3110123,"TERMINAL",0,0,"",,terminal_output
2397,3111427,"TERMINAL",0,0,"Enumerating objects: 9, done.
Counting objects:  11% (1/9)
Counting objects:  22% (2/9)
Counting objects:  33% (3/9)
Counting objects:  44% (4/9)
Counting objects:  55% (5/9)
Counting objects:  66% (6/9)
Counting objects:  77% (7/9)
Counting objects:  88% (8/9)
Counting objects: 100% (9/9)
Counting objects: 100% (9/9), done.
Delta compression using up to 152 threads
Compressing objects:  20% (1/5)
Compressing objects:  40% (2/5)
Compressing objects:  60% (3/5)
Compressing objects:  80% (4/5)
Compressing objects: 100% (5/5)
Compressing objects: 100% (5/5), done.
Writing objects:  20% (1/5)
Writing objects:  40% (2/5)
Writing objects:  60% (3/5)
Writing objects:  80% (4/5)
Writing objects: 100% (5/5)
Writing objects: 100% (5/5), 1.02 KiB | 524.00 KiB/s, done.
Total 5 (delta 4), reused 0 (delta 0), pack-reused 0
",,terminal_output
2398,3111528,"TERMINAL",0,0,"remote: Resolving deltas:   0% (0/4)
remote: Resolving deltas:  25% (1/4)
remote: Resolving deltas:  50% (2/4)
remote: Resolving deltas:  75% (3/4)
remote: Resolving deltas: 100% (4/4)
remote: Resolving deltas: 100% (4/4), completed with 4 local objects.
",,terminal_output
2399,3111716,"TERMINAL",0,0,"To github.com:p-doom/jafar.git
   6936ce1..4dc91c0  preprocess_video -> preprocess_video
",,terminal_output
2400,3163076,".gitignore",0,0,"*.pyc
*.npy
*.png
*.gif

wandb_key
checkpoints/
wandb/
__pycache__/

logs/
sandbox/
sbatch_scripts/
vs-code-recorder/
data/
data_tfrecords/",ignore,tab
2401,3164223,".gitignore",6,0,"",ignore,selection_command
2402,3164602,".gitignore",24,0,"",ignore,selection_command
2403,3164795,".gitignore",68,0,"",ignore,selection_command
2404,3165197,".gitignore",68,1,"
",ignore,selection_command
2405,3165292,".gitignore",68,2,"
l",ignore,selection_command
2406,3165468,".gitignore",68,8,"
logs/
s",ignore,selection_command
2407,3165733,".gitignore",68,17,"
logs/
sandbox/
s",ignore,selection_command
2408,3165758,".gitignore",68,33,"
logs/
sandbox/
sbatch_scripts/
v",ignore,selection_command
2409,3165785,".gitignore",68,51,"
logs/
sandbox/
sbatch_scripts/
vs-code-recorder/
d",ignore,selection_command
2410,3165826,".gitignore",68,57,"
logs/
sandbox/
sbatch_scripts/
vs-code-recorder/
data/
d",ignore,selection_command
2411,3166218,".gitignore",68,57,"",ignore,content
2412,3166858,".gitignore",67,15,"",ignore,content
2413,3166878,".gitignore",55,0,"",ignore,selection_command
2414,3188693,"TERMINAL",0,0,"mv utils/preprocess_video_splitter.py utils/preprocess_video_splitter_tmp.py",,terminal_command
2415,3188715,"TERMINAL",0,0,"",,terminal_output
2416,3190465,"TERMINAL",0,0,"gs",,terminal_command
2417,3190501,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes not staged for commit:
  (use ""git add/rm <file>..."" to update what will be committed)
  (use ""git restore <file>..."" to discard changes in working directory)
	modified:   .gitignore
	deleted:    utils/preprocess_video_splitter.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	logs/
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	sandbox/
	sbatch_scripts/
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py
	utils/preprocess_video_splitter_tmp.py

no changes added to commit (use ""git add"" and/or ""git commit -a"")
",,terminal_output
2418,3204902,"TERMINAL",0,0,"git add .gitignore utils/preprocess_video_splitter.py",,terminal_command
2419,3204925,"TERMINAL",0,0,"",,terminal_output
2420,3205858,"TERMINAL",0,0,"gs",,terminal_command
2421,3205889,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes to be committed:
  (use ""git restore --staged <file>..."" to unstage)
	modified:   .gitignore
	deleted:    utils/preprocess_video_splitter.py

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	logs/
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	sandbox/
	sbatch_scripts/
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py
	utils/preprocess_video_splitter_tmp.py

",,terminal_output
2422,3290046,"TERMINAL",0,0,"git commmit -m 'chore: remove preprocess_video_splitter.py and local gitignore changes'",,terminal_command
2423,3290074,"TERMINAL",0,0,"git: 'commmit' is not a git command. See 'git --help'.

The most similar command is
	commit
",,terminal_output
2424,3294467,"TERMINAL",0,0,"git commit -m 'chore: remove preprocess_video_splitter.py and local gitignore changes'",,terminal_command
2425,3294518,"TERMINAL",0,0,"",,terminal_output
2426,3297670,"TERMINAL",0,0,"black................................................(no files to check)Skipped
",,terminal_output
2427,3297728,"TERMINAL",0,0,"[preprocess_video 44b5dc6] chore: remove preprocess_video_splitter.py and local gitignore changes
 2 files changed, 1 insertion(+), 74 deletions(-)
 delete mode 100644 utils/preprocess_video_splitter.py
",,terminal_output
2428,3300773,"TERMINAL",0,0,"git push",,terminal_command
2429,3300826,"TERMINAL",0,0,"",,terminal_output
2430,3302102,"TERMINAL",0,0,"Enumerating objects: 7, done.
Counting objects:  14% (1/7)
Counting objects:  28% (2/7)
Counting objects:  42% (3/7)
Counting objects:  57% (4/7)
Counting objects:  71% (5/7)
Counting objects:  85% (6/7)
Counting objects: 100% (7/7)
Counting objects: 100% (7/7), done.
Delta compression using up to 152 threads
Compressing objects:  25% (1/4)
Compressing objects:  50% (2/4)
Compressing objects:  75% (3/4)
Compressing objects: 100% (4/4)
Compressing objects: 100% (4/4), done.
Writing objects:  25% (1/4)
Writing objects:  50% (2/4)
Writing objects:  75% (3/4)
Writing objects: 100% (4/4)
Writing objects: 100% (4/4), 398 bytes | 398.00 KiB/s, done.
Total 4 (delta 3), reused 0 (delta 0), pack-reused 0
",,terminal_output
2431,3302239,"TERMINAL",0,0,"remote: Resolving deltas:   0% (0/3)
remote: Resolving deltas:  33% (1/3)
remote: Resolving deltas:  66% (2/3)
remote: Resolving deltas: 100% (3/3)
remote: Resolving deltas: 100% (3/3), completed with 3 local objects.
",,terminal_output
2432,3302420,"TERMINAL",0,0,"To github.com:p-doom/jafar.git
   4dc91c0..44b5dc6  preprocess_video -> preprocess_video
",,terminal_output
2433,3310266,".gitignore",67,0,"",ignore,selection_mouse
2434,3310275,".gitignore",66,0,"",ignore,selection_command
2435,3325965,".gitignore",67,0,"",ignore,selection_mouse
2436,3325972,".gitignore",66,0,"",ignore,selection_command
2437,3545233,"TERMINAL",0,0,"git checkout origin/main -- .gitignore",,terminal_command
2438,3545288,"TERMINAL",0,0,"",,terminal_output
2439,3545312,"TERMINAL",0,0,"",,terminal_output
2440,3545482,".gitignore",67,0,"
",ignore,content
2441,3549241,"TERMINAL",0,0,"git add .gitignore",,terminal_command
2442,3549276,"TERMINAL",0,0,"",,terminal_output
2443,3552112,"TERMINAL",0,0,"gs",,terminal_command
2444,3552155,"TERMINAL",0,0,"On branch preprocess_video
Your branch is up to date with 'origin/preprocess_video'.

Changes to be committed:
  (use ""git restore --staged <file>..."" to unstage)
	modified:   .gitignore

Untracked files:
  (use ""git add <file>..."" to include in what will be committed)
	logs/
	notes.md
	requirements_franz.txt
	sample_resolution_batches.py
	sandbox/
	sbatch_scripts/
	shell_scripts/
	train_dynamics_single_batch.py
	train_lam_single_batch.py
	train_lam_tf_seeding.py
	train_tokenizer_logging.py
	train_tokenizer_single_batch.py
	utils/clip_checker.py
	utils/dataloader_seeding.py
	utils/preprocess_video_splitter_tmp.py

",,terminal_output
2445,3555358,"TERMINAL",0,0,"git commit -m ""Remove .gitignore change from PR""",,terminal_command
2446,3555409,"TERMINAL",0,0,"",,terminal_output
2447,3558293,"TERMINAL",0,0,"black................................................(no files to check)Skipped
",,terminal_output
2448,3558344,"TERMINAL",0,0,"[preprocess_video 8ad9ce2] Remove .gitignore change from PR
 1 file changed, 1 insertion(+), 1 deletion(-)
",,terminal_output
2449,3559951,"TERMINAL",0,0,"git push",,terminal_command
2450,3560004,"TERMINAL",0,0,"",,terminal_output
2451,3561232,"TERMINAL",0,0,"Enumerating objects: 5, done.
Counting objects:  20% (1/5)
Counting objects:  40% (2/5)
Counting objects:  60% (3/5)
Counting objects:  80% (4/5)
Counting objects: 100% (5/5)
Counting objects: 100% (5/5), done.
Delta compression using up to 152 threads
Compressing objects:  33% (1/3)
Compressing objects:  66% (2/3)
Compressing objects: 100% (3/3)
Compressing objects: 100% (3/3), done.
Writing objects:  33% (1/3)
Writing objects:  66% (2/3)
Writing objects: 100% (3/3)
Writing objects: 100% (3/3), 313 bytes | 313.00 KiB/s, done.
Total 3 (delta 2), reused 0 (delta 0), pack-reused 0
",,terminal_output
2452,3561380,"TERMINAL",0,0,"remote: Resolving deltas:   0% (0/2)
remote: Resolving deltas:  50% (1/2)
remote: Resolving deltas: 100% (2/2)
remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
",,terminal_output
2453,3561592,"TERMINAL",0,0,"To github.com:p-doom/jafar.git
   44b5dc6..8ad9ce2  preprocess_video -> preprocess_video
",,terminal_output
2454,3905452,".gitignore",68,0,"",ignore,selection_mouse
2455,4554198,"TERMINAL",0,0,"git checkout main",,terminal_command
2456,4554251,"TERMINAL",0,0,"",,terminal_output
2457,4554290,"TERMINAL",0,0,"Switched to branch 'main'
Your branch is up to date with 'origin/main'.
",,terminal_output
2458,4556833,"TERMINAL",0,0,"git pull",,terminal_command
2459,4556884,"TERMINAL",0,0,"",,terminal_output
2460,4558360,"TERMINAL",0,0,"Already up to date.
",,terminal_output
2461,4594076,"TERMINAL",0,0,"queue",,terminal_command
2462,4594130,"TERMINAL",0,0,"",,terminal_output
2463,4594188,"TERMINAL",0,0,"(BEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jun 25 11:33:47 2025JOBID PARTITION     NAME     USER ST	TIME  NODES NODELIST(REASON)3289577 accelerat train_dy tum_ind3  R   15:57:11	  1 hkn07123290257   cpuonly npy_to_t tum_ind3  R30:09	  1 hkn02023290241   cpuonly mp4_to_n tum_ind3  R32:09	  1 hkn03643290255   cpuonly npy_to_t tum_ind3  R32:09	  1 hkn11593290235   cpuonly npy_to_t tum_ind3  R38:10	  1 hkn0138",,terminal_output
2464,4595236,"TERMINAL",0,0,"821010101",,terminal_output
2465,4595441,"TERMINAL",0,0,"
>",,terminal_output
2466,4596849,"TERMINAL",0,0,"queue",,terminal_command
2467,4596900,"TERMINAL",0,0,"",,terminal_output
2468,4596954,"TERMINAL",0,0,"(BEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jun 25 11:33:50 2025JOBID PARTITION     NAME     USER ST	TIME  NODES NODELIST(REASON)3289577 accelerat train_dy tum_ind3  R   15:57:14	  1 hkn07123290257   cpuonly npy_to_t tum_ind3  R30:12	  1 hkn02023290241   cpuonly mp4_to_n tum_ind3  R32:12	  1 hkn03643290255   cpuonly npy_to_t tum_ind3  R32:12	  1 hkn11593290235   cpuonly npy_to_t tum_ind3  R38:13	  1 hkn0138",,terminal_output
2469,4598010,"TERMINAL",0,0,"153334",,terminal_output
2470,4599059,"TERMINAL",0,0,"264445",,terminal_output
2471,4600070,"TERMINAL",0,0,"375556
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2472,4602730,"TERMINAL",0,0,"fsacct_week",,terminal_command
2473,4602780,"TERMINAL",0,0,"",,terminal_output
2474,4602805,"TERMINAL",0,0,"          JobID                        JobName        Partition All        State    Elapsed  Timelimit 
--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- 
        3273476 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:06   00:10:00 
        3273480 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:32   00:10:00 
        3273494 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:40   00:10:00 
        3273503 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273681 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273687 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:16   00:10:00 
        3273795 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3273820 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:00:31   00:15:00 
        3273828 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273829 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT 1-00:00:10 1-00:00:00 
        3273831 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:29   12:00:00 
        3273841 train_tokenizer_knoms_overfit+      accelerated  32       FAILED   00:01:13   12:00:00 
        3273846 train_tokenizer_knoms_overfit+      accelerated  32      TIMEOUT   12:00:28   12:00:00 
        3274766         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:02   00:10:00 
        3276030 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:01:35   00:10:00 
        3276039 train_tokenizer_overfit_seedi+      accelerated   8       FAILED   00:00:56   00:10:00 
        3276048 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:10:03   00:10:00 
        3276051 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   00:30:01   00:30:00 
        3276052 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276053 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:02   01:00:00 
        3276058 train_tokenizer_overfit_seedi+      accelerated   8      TIMEOUT   01:00:03   01:00:00 
        3276715         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   00:10:13   00:10:00 
        3277318         train_lam_ful_tfrecord      accelerated  32      TIMEOUT   16:00:27   16:00:00 
        3282472      train_dyn_knoms_full-test      accelerated  32      TIMEOUT   00:10:24   00:10:00 
        3282485           train_dyn_knoms_full      accelerated  32       FAILED   00:00:27 1-00:00:00 
        3282489           train_dyn_knoms_full      accelerated  32      TIMEOUT 1-00:00:27 1-00:00:00 
        3285784 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:28   00:10:00 
        3285785  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:32   00:10:00 
        3285797  train_lam_overfit_tfrecord_10      accelerated  32       FAILED   00:00:28   00:10:00 
        3285798 train_tokenizer_overfit_tfrec+      accelerated  32       FAILED   00:00:27   00:10:00 
        3285811 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT   00:10:07   00:10:00 
        3285820  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:06   00:10:00 
        3286113  train_lam_overfit_tfrecord_10      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286114 train_tokenizer_overfit_tfrec+      accelerated  32      TIMEOUT 1-00:00:09 1-00:00:00 
        3286195   train_dyn_knoms_full_nodes_2      accelerated  64      TIMEOUT   00:10:26   00:10:00 
        3287192                   download_7xx      accelerated  32       FAILED   00:00:27   00:10:00 
        3287194                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287195                   download_7xx      accelerated  32       FAILED   00:00:26   00:10:00 
        3287198                   download_6xx      accelerated  32      TIMEOUT   00:10:18   00:10:00 
        3287213                   download_6xx          cpuonly 152    COMPLETED   01:44:02   06:00:00 
        3287214                   download_7xx          cpuonly 152    COMPLETED   04:27:48   06:00:00 
        3287215                   download_8xx          cpuonly 152    COMPLETED   01:07:53   06:00:00 
        3287216                   download_9xx          cpuonly 152    COMPLETED   02:11:44   06:00:00 
        3287217                  download_10xx          cpuonly 152    COMPLETED   01:10:01   06:00:00 
        3289199                   download_6xx          cpuonly 152    COMPLETED   03:05:47   06:00:00 
        3289204                   download_7xx          cpuonly 152    COMPLETED   05:07:25   06:00:00 
        3289205                   download_8xx          cpuonly 152    COMPLETED   01:27:47   06:00:00 
        3289206                   download_9xx          cpuonly 152    COMPLETED   02:27:37   06:00:00 
        3289207                  download_10xx          cpuonly 152    COMPLETED   01:25:46   06:00:00 
        3289242 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:32   00:10:00 
        3289247 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289322 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:20   00:10:00 
        3289376 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:17   00:10:00 
        3289389 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:00:33   00:10:00 
        3289394 preprocess_video_to_npy_opena+          cpuonly 152    COMPLETED   00:03:42   00:10:00 
        3289433 preprocess_video_to_npy_opena+          cpuonly 152      TIMEOUT   00:10:16   00:10:00 
        3289439 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:49   00:10:00 
        3289469 mp4_to_npy_openai_first_try_6+          cpuonly 152    COMPLETED   00:18:29   01:00:00 
        3289483 mp4_to_npy_openai_first_try_7+          cpuonly 152    COMPLETED   00:48:15   01:00:00 
        3289484 mp4_to_npy_openai_first_try_8+          cpuonly 152    COMPLETED   00:08:43   01:00:00 
        3289485 mp4_to_npy_openai_first_try_9+          cpuonly 152    COMPLETED   00:32:34   01:00:00 
        3289486 mp4_to_npy_openai_first_try_1+          cpuonly 152    COMPLETED   00:08:10   01:00:00 
        3289548  train_dyn_overfit_tfrecord_10      accelerated  32      TIMEOUT   00:10:17   00:10:00 
        3289577  train_dyn_overfit_tfrecord_10      accelerated  32      RUNNING   15:57:20 1-00:00:00 
        3290215            npy_to_tfrecord_6xx          cpuonly 152    COMPLETED   00:01:01   00:10:00 
        3290230            npy_to_tfrecord_6xx          cpuonly 152      TIMEOUT   00:10:06   00:10:00 
        3290234          mp4_to_npy_openai_all          cpuonly 152      TIMEOUT   00:10:06   00:10:00 
        3290235            npy_to_tfrecord_7xx          cpuonly 152      RUNNING   00:38:19   02:00:00 
        3290241          mp4_to_npy_openai_all          cpuonly 152      RUNNING   00:32:18   12:00:00 
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2795,4842759,".gitignore",278,0,"i",ignore,content
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2797,4842850,".gitignore",279,0,"o",ignore,content
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2808,4844103,".gitignore",285,0,"",ignore,selection_keyboard
2809,4844366,".gitignore",285,0,"c",ignore,content
2810,4844368,".gitignore",286,0,"",ignore,selection_keyboard
2811,4844459,".gitignore",286,0,"h",ignore,content
2812,4844460,".gitignore",287,0,"",ignore,selection_keyboard
2813,4845354,".gitignore",287,0,"e",ignore,content
2814,4845410,".gitignore",288,0,"s",ignore,content
2815,4845411,".gitignore",289,0,"",ignore,selection_keyboard
2816,4845555,".gitignore",289,0,".",ignore,content
2817,4845556,".gitignore",290,0,"",ignore,selection_keyboard
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2819,4846177,".gitignore",291,0,"",ignore,selection_keyboard
2820,4846284,".gitignore",291,0,"y",ignore,content
2821,4846284,".gitignore",292,0,"",ignore,selection_keyboard
2822,4847072,".gitignore",290,2,"py",ignore,content
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2836,5681949,".gitignore",299,0,"",ignore,selection_keyboard
2837,5681966,".gitignore",299,0,";",ignore,content
2838,5681967,".gitignore",300,0,"",ignore,selection_keyboard
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2840,5681969,".gitignore",301,0,"",ignore,selection_keyboard
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2842,5681989,".gitignore",302,0,"",ignore,selection_keyboard
2843,5813738,".gitignore",301,0,"",ignore,selection_command
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2852,5815740,".gitignore",292,0,"",ignore,selection_command
2853,5815777,".gitignore",291,0,"",ignore,selection_command
2854,5816580,".gitignore",292,0,"",ignore,selection_command
2855,5816738,".gitignore",293,0,"",ignore,selection_command
2856,5817154,".gitignore",292,0,"",ignore,selection_command
2857,5817417,".gitignore",292,10,"",ignore,content
2858,5817433,".gitignore",291,0,"",ignore,selection_command
2859,5817554,".gitignore",292,0,"",ignore,selection_command
2860,5817902,".gitignore",292,0,"
",ignore,content
2861,5819666,".gitignore",293,0,"t",ignore,content
2862,5819668,".gitignore",294,0,"",ignore,selection_keyboard
2863,5820909,".gitignore",294,0,"r",ignore,content
2864,5820911,".gitignore",295,0,"",ignore,selection_keyboard
2865,5821040,".gitignore",295,0,"a",ignore,content
2866,5821040,".gitignore",296,0,"",ignore,selection_keyboard
2867,5821097,".gitignore",296,0,"i",ignore,content
2868,5821098,".gitignore",297,0,"",ignore,selection_keyboard
2869,5821201,".gitignore",297,0,"n",ignore,content
2870,5821202,".gitignore",298,0,"",ignore,selection_keyboard
2871,5821723,".gitignore",298,0,"_",ignore,content
2872,5821724,".gitignore",299,0,"",ignore,selection_keyboard
2873,5822464,".gitignore",299,0,"d",ignore,content
2874,5822465,".gitignore",300,0,"",ignore,selection_keyboard
2875,5822553,".gitignore",300,0,"y",ignore,content
2876,5822554,".gitignore",301,0,"",ignore,selection_keyboard
2877,5822802,".gitignore",300,0,"ata_generator.p",ignore,content
2878,5822805,".gitignore",315,0,"",ignore,selection_command
2879,5824410,".gitignore",314,1,"",ignore,content
2880,5825519,".gitignore",313,1,"",ignore,content
2881,5826195,".gitignore",313,0,"_",ignore,content
2882,5826195,".gitignore",314,0,"",ignore,selection_keyboard
2883,5827136,".gitignore",315,0,"",ignore,selection_command
2884,5827490,".gitignore",314,1,"",ignore,content
2885,5827968,".gitignore",314,0,"*",ignore,content
2886,5827969,".gitignore",315,0,"",ignore,selection_keyboard
2887,5829330,".gitignore",314,0,"",ignore,selection_command
2888,5830006,".gitignore",315,0,"
train_data_generator_*",ignore,content
2889,5830020,".gitignore",316,0,"",ignore,selection_command
2890,5830636,".gitignore",337,0,"",ignore,selection_command
2891,5831557,".gitignore",315,23,"",ignore,content
2892,5831567,".gitignore",293,0,"",ignore,selection_command
2893,5831641,".gitignore",264,0,"",ignore,selection_command
2894,5832018,".gitignore",293,0,"",ignore,selection_command
2895,5832362,".gitignore",292,23,"",ignore,content
2896,5832375,".gitignore",264,0,"",ignore,selection_command
2897,5832987,".gitignore",292,0,"
",ignore,content
2898,5833360,".gitignore",293,0,"t",ignore,content
2899,5833361,".gitignore",294,0,"",ignore,selection_keyboard
2900,5833414,".gitignore",294,0,"r",ignore,content
2901,5833415,".gitignore",295,0,"",ignore,selection_keyboard
2902,5833480,".gitignore",295,0,"a",ignore,content
2903,5833481,".gitignore",296,0,"",ignore,selection_keyboard
2904,5833516,".gitignore",296,0,"i",ignore,content
2905,5833517,".gitignore",297,0,"",ignore,selection_keyboard
2906,5833607,".gitignore",297,0,"n",ignore,content
2907,5833608,".gitignore",298,0,"",ignore,selection_keyboard
2908,5833886,".gitignore",298,0,"*",ignore,content
2909,5833887,".gitignore",299,0,"",ignore,selection_keyboard
2910,5834322,".gitignore",298,1,"",ignore,content
2911,5834650,".gitignore",298,0,"_",ignore,content
2912,5834651,".gitignore",299,0,"",ignore,selection_keyboard
2913,5835242,".gitignore",299,0,"d",ignore,content
2914,5835243,".gitignore",300,0,"",ignore,selection_keyboard
2915,5835318,".gitignore",300,0,"y",ignore,content
2916,5835319,".gitignore",301,0,"",ignore,selection_keyboard
2917,5835564,".gitignore",301,0,"n",ignore,content
2918,5835565,".gitignore",302,0,"",ignore,selection_keyboard
2919,5836009,".gitignore",302,0,"a",ignore,content
2920,5836290,".gitignore",303,0,"m",ignore,content
2921,5836291,".gitignore",304,0,"",ignore,selection_keyboard
2922,5837699,".gitignore",304,0,"i",ignore,content
2923,5837700,".gitignore",305,0,"",ignore,selection_keyboard
2924,5838309,".gitignore",305,0,"c",ignore,content
2925,5838310,".gitignore",306,0,"",ignore,selection_keyboard
2926,5838692,".gitignore",306,0,"s",ignore,content
2927,5838692,".gitignore",307,0,"",ignore,selection_keyboard
2928,5839651,".gitignore",307,0,")",ignore,content
2929,5839651,".gitignore",308,0,"",ignore,selection_keyboard
2930,5840229,".gitignore",307,1,"",ignore,content
2931,5840406,".gitignore",307,0,"_",ignore,content
2932,5840407,".gitignore",308,0,"",ignore,selection_keyboard
2933,5840720,".gitignore",308,0,"&",ignore,content
2934,5840721,".gitignore",309,0,"",ignore,selection_keyboard
2935,5841235,".gitignore",308,1,"",ignore,content
2936,5841415,".gitignore",308,0,"*",ignore,content
2937,5841416,".gitignore",309,0,"",ignore,selection_keyboard
2938,5843375,".gitignore",309,0,"
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2939,5844087,".gitignore",310,0,"t",ignore,content
2940,5844088,".gitignore",311,0,"",ignore,selection_keyboard
2941,5844169,".gitignore",311,0,"r",ignore,content
2942,5844170,".gitignore",312,0,"",ignore,selection_keyboard
2943,5844245,".gitignore",312,0,"a",ignore,content
2944,5844246,".gitignore",313,0,"",ignore,selection_keyboard
2945,5844338,".gitignore",313,0,"i",ignore,content
2946,5844339,".gitignore",314,0,"",ignore,selection_keyboard
2947,5844379,".gitignore",314,0,"n",ignore,content
2948,5844380,".gitignore",315,0,"",ignore,selection_keyboard
2949,5844896,".gitignore",315,0,"_",ignore,content
2950,5844897,".gitignore",316,0,"",ignore,selection_keyboard
2951,5845437,".gitignore",316,0,"l",ignore,content
2952,5845438,".gitignore",317,0,"",ignore,selection_keyboard
2953,5845578,".gitignore",317,0,"a",ignore,content
2954,5845579,".gitignore",318,0,"",ignore,selection_keyboard
2955,5845690,".gitignore",318,0,"m",ignore,content
2956,5845691,".gitignore",319,0,"",ignore,selection_keyboard
2957,5847704,".gitignore",319,0,"_",ignore,content
2958,5847705,".gitignore",320,0,"",ignore,selection_keyboard
2959,5848258,".gitignore",320,0,"*",ignore,content
2960,5848259,".gitignore",321,0,"",ignore,selection_keyboard
2961,5848685,".gitignore",321,0,"
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2962,5849651,".gitignore",322,0,"t",ignore,content
2963,5849652,".gitignore",323,0,"",ignore,selection_keyboard
2964,5849719,".gitignore",323,0,"r",ignore,content
2965,5849720,".gitignore",324,0,"",ignore,selection_keyboard
2966,5849831,".gitignore",324,0,"a",ignore,content
2967,5849832,".gitignore",325,0,"",ignore,selection_keyboard
2968,5849870,".gitignore",325,0,"i",ignore,content
2969,5849871,".gitignore",326,0,"",ignore,selection_keyboard
2970,5849943,".gitignore",326,0,"n",ignore,content
2971,5849944,".gitignore",327,0,"",ignore,selection_keyboard
2972,5850507,".gitignore",327,0,"_",ignore,content
2973,5850508,".gitignore",328,0,"",ignore,selection_keyboard
2974,5850830,".gitignore",328,0,"t",ignore,content
2975,5850832,".gitignore",329,0,"",ignore,selection_keyboard
2976,5850989,".gitignore",329,0,"o",ignore,content
2977,5850990,".gitignore",330,0,"",ignore,selection_keyboard
2978,5851068,".gitignore",330,0,"k",ignore,content
2979,5851069,".gitignore",331,0,"",ignore,selection_keyboard
2980,5851405,".gitignore",331,0,"e",ignore,content
2981,5851406,".gitignore",332,0,"",ignore,selection_keyboard
2982,5851552,".gitignore",332,0,"n",ignore,content
2983,5851553,".gitignore",333,0,"",ignore,selection_keyboard
2984,5851613,".gitignore",333,0,"i",ignore,content
2985,5851614,".gitignore",334,0,"",ignore,selection_keyboard
2986,5851791,".gitignore",334,0,"z",ignore,content
2987,5852212,".gitignore",335,0,"e",ignore,content
2988,5852213,".gitignore",336,0,"",ignore,selection_keyboard
2989,5852286,".gitignore",336,0,"r",ignore,content
2990,5852287,".gitignore",337,0,"",ignore,selection_keyboard
2991,5852564,".gitignore",337,0,"_",ignore,content
2992,5852565,".gitignore",338,0,"",ignore,selection_keyboard
2993,5853379,".gitignore",338,0,"*",ignore,content
2994,5853863,".gitignore",339,0,"
",ignore,content
2995,5856344,".gitignore",340,0,"n",ignore,content
2996,5856346,".gitignore",341,0,"",ignore,selection_keyboard
2997,5856410,".gitignore",341,0,"o",ignore,content
2998,5856411,".gitignore",342,0,"",ignore,selection_keyboard
2999,5856510,".gitignore",342,0,"t",ignore,content
3000,5856511,".gitignore",343,0,"",ignore,selection_keyboard
3001,5856585,".gitignore",343,0,"e",ignore,content
3002,5856586,".gitignore",344,0,"",ignore,selection_keyboard
3003,5856841,".gitignore",344,0,"s",ignore,content
3004,5856842,".gitignore",345,0,"",ignore,selection_keyboard
3005,5857363,".gitignore",345,0,".",ignore,content
3006,5857364,".gitignore",346,0,"",ignore,selection_keyboard
3007,5857693,".gitignore",346,0,"m",ignore,content
3008,5857694,".gitignore",347,0,"",ignore,selection_keyboard
3009,5857816,".gitignore",347,0,"d",ignore,content
3010,5857817,".gitignore",348,0,"",ignore,selection_keyboard
3011,5859284,".gitignore",348,0,"
",ignore,content
3012,5859706,".gitignore",349,0,"s",ignore,content
3013,5859706,".gitignore",350,0,"",ignore,selection_keyboard
3014,5859779,".gitignore",350,0,"h",ignore,content
3015,5859780,".gitignore",351,0,"",ignore,selection_keyboard
3016,5859923,".gitignore",351,0,"e",ignore,content
3017,5859923,".gitignore",352,0,"",ignore,selection_keyboard
3018,5859996,".gitignore",352,0,"l",ignore,content
3019,5859997,".gitignore",353,0,"",ignore,selection_keyboard
3020,5860126,".gitignore",353,0,"l",ignore,content
3021,5860127,".gitignore",354,0,"",ignore,selection_keyboard
3022,5861071,".gitignore",354,0,"_scripts/",ignore,content
3023,5861505,".gitignore",362,0,"",ignore,selection_command
3024,5864049,"TERMINAL",0,0,"gs",,terminal_command
3025,5864090,"TERMINAL",0,0,"On branch main
Your branch is up to date with 'origin/main'.

Changes not staged for commit:
  (use ""git add <file>..."" to update what will be committed)
  (use ""git restore <file>..."" to discard changes in working directory)
	modified:   .gitignore

no changes added to commit (use ""git add"" and/or ""git commit -a"")
",,terminal_output
3026,5913206,"TERMINAL",0,0,"git update-index --skip-worktree .gitignore",,terminal_command
3027,5913260,"TERMINAL",0,0,"",,terminal_output
3028,5914498,"TERMINAL",0,0,"gs",,terminal_command
3029,5914524,"TERMINAL",0,0,"On branch main
Your branch is up to date with 'origin/main'.

nothing to commit, working tree clean
",,terminal_output
3030,5928713,"TERMINAL",0,0,"git checkout -b ^C",,terminal_command
3031,5928722,"TERMINAL",0,0,"^C


",,terminal_output
3032,5929800,"TERMINAL",0,0,"git pull",,terminal_command
3033,5929850,"TERMINAL",0,0,"",,terminal_output
3034,5931214,"TERMINAL",0,0,"Already up to date.
",,terminal_output
3035,5933206,"TERMINAL",0,0,"git pul^C",,terminal_command
3036,5933217,"TERMINAL",0,0,"^C


",,terminal_output
3037,6023033,"TERMINAL",0,0,"git checkout -b 'infra-slurm-dev-scripts'",,terminal_command
3038,6023084,"TERMINAL",0,0,"Switched to a new branch 'infra-slurm-dev-scripts'
",,terminal_output
3039,6030870,"TERMINAL",0,0,"mkdir slurm",,terminal_command
3040,6030890,"TERMINAL",0,0,"",,terminal_output
3041,6032456,"TERMINAL",0,0,"cd slurm/",,terminal_command
3042,6032468,"TERMINAL",0,0,"",,terminal_output
3043,6040725,"TERMINAL",0,0,"mkdir dev/",,terminal_command
3044,6040742,"TERMINAL",0,0,"",,terminal_output
3045,6050045,"TERMINAL",0,0,"mkdir prod",,terminal_command
3046,6050052,"TERMINAL",0,0,"",,terminal_output
3047,6052217,"TERMINAL",0,0,"cd dev/",,terminal_command
3048,6052228,"TERMINAL",0,0,"",,terminal_output
3049,6056631,"TERMINAL",0,0,"mkdir ali",,terminal_command
3050,6056650,"TERMINAL",0,0,"",,terminal_output
3051,6058615,"TERMINAL",0,0,"ls",,terminal_command
3052,6058635,"TERMINAL",0,0,"ali
",,terminal_output
3053,6076633,"TERMINAL",0,0,"cd ..",,terminal_command
3054,6076644,"TERMINAL",0,0,"",,terminal_output
3055,6078346,"TERMINAL",0,0,"cd ..",,terminal_command
3056,6078353,"TERMINAL",0,0,"",,terminal_output
3057,6085051,"TERMINAL",0,0,"cp -r sbatch_scripts/ slurm/dev/ali/",,terminal_command
3058,6085102,"TERMINAL",0,0,"",,terminal_output
3059,6085186,"TERMINAL",0,0,"",,terminal_output
3060,6094991,"TERMINAL",0,0,"cp -r shell_scripts/ slurm/dev/ali/",,terminal_command
3061,6095009,"TERMINAL",0,0,"",,terminal_output
3062,6110171,".gitignore",347,0,"",ignore,selection_command
3063,6133301,"TERMINAL",0,0,"cd slurm/",,terminal_command
3064,6133304,"TERMINAL",0,0,"",,terminal_output
3065,6133692,"TERMINAL",0,0,"ls",,terminal_command
3066,6133720,"TERMINAL",0,0,"dev_ali  prod
",,terminal_output
3067,6139176,"TERMINAL",0,0,"rm -r dev_ali/",,terminal_command
3068,6139222,"TERMINAL",0,0,"",,terminal_output
3069,6139230,"TERMINAL",0,0,"",,terminal_output
3070,6141694,"TERMINAL",0,0,"cd ..",,terminal_command
3071,6141701,"TERMINAL",0,0,"",,terminal_output
3072,6150862,"TERMINAL",0,0,"cp -r sbatch_scripts/ slurm/",,terminal_command
3073,6150914,"TERMINAL",0,0,"",,terminal_output
3074,6150943,"TERMINAL",0,0,"",,terminal_output
3075,6153062,"TERMINAL",0,0,"cd slurm/",,terminal_command
3076,6153069,"TERMINAL",0,0,"",,terminal_output
3077,6153461,"TERMINAL",0,0,"ls",,terminal_command
3078,6153486,"TERMINAL",0,0,"prod  sbatch_scripts
",,terminal_output
3079,6158325,"TERMINAL",0,0,"ls",,terminal_command
3080,6158343,"TERMINAL",0,0,"prod  sbatch_scripts
",,terminal_output
3081,6168816,"TERMINAL",0,0,"mv sbatch_scripts/ dev",,terminal_command
3082,6168826,"TERMINAL",0,0,"",,terminal_output
3083,6170191,"TERMINAL",0,0,"cd dev/",,terminal_command
3084,6170203,"TERMINAL",0,0,"",,terminal_output
3085,6170624,"TERMINAL",0,0,"ls",,terminal_command
3086,6170651,"TERMINAL",0,0,"coinrun  preprocess  procgen  rsync  sample_jafar  train_dyn  train_dyn_dev  train_lam  train_lam_dev  train_tokenizer  train_tokenizer_dev
",,terminal_output
3087,6209371,"TERMINAL",0,0,"mkdir a^C",,terminal_command
3088,6209392,"TERMINAL",0,0,"^C


",,terminal_output
3089,6212300,"TERMINAL",0,0,"mkdir ali",,terminal_command
3090,6212319,"TERMINAL",0,0,"",,terminal_output
3091,6215959,"TERMINAL",0,0,"mv * ali",,terminal_command
3092,6215979,"TERMINAL",0,0,"mv: cannot move 'ali' to a subdirectory of itself, 'ali/ali'
",,terminal_output
3093,6217049,"TERMINAL",0,0,"ls",,terminal_command
3094,6217060,"TERMINAL",0,0,"ali
",,terminal_output
3095,6221581,"TERMINAL",0,0,"mkdir mihir franz",,terminal_command
3096,6221600,"TERMINAL",0,0,"",,terminal_output
3097,6222662,"TERMINAL",0,0,"cd ..",,terminal_command
3098,6222669,"TERMINAL",0,0,"",,terminal_output
3099,6223185,"TERMINAL",0,0,"ls",,terminal_command
3100,6223214,"TERMINAL",0,0,"dev  prod
",,terminal_output
3101,6231468,"TERMINAL",0,0,"mv prod/ jobs",,terminal_command
3102,6231483,"TERMINAL",0,0,"",,terminal_output
3103,6236586,"TERMINAL",0,0,"mkdir templates",,terminal_command
3104,6236602,"TERMINAL",0,0,"",,terminal_output
3105,6265425,"TERMINAL",0,0,"mkdir utils^C",,terminal_command
3106,6265434,"TERMINAL",0,0,"^C


",,terminal_output
3107,6266760,"TERMINAL",0,0,"ls",,terminal_command
3108,6266785,"TERMINAL",0,0,"dev  jobs  templates
",,terminal_output
3109,6275841,"TERMINAL",0,0,"cd dev/",,terminal_command
3110,6275845,"TERMINAL",0,0,"",,terminal_output
3111,6276241,"TERMINAL",0,0,"ls",,terminal_command
3112,6276259,"TERMINAL",0,0,"ali  franz  mihir
",,terminal_output
3113,6277868,"TERMINAL",0,0,"cd ali/",,terminal_command
3114,6277877,"TERMINAL",0,0,"",,terminal_output
3115,6278534,"TERMINAL",0,0,"ls",,terminal_command
3116,6278557,"TERMINAL",0,0,"coinrun  preprocess  procgen  rsync  sample_jafar  train_dyn  train_dyn_dev  train_lam  train_lam_dev  train_tokenizer  train_tokenizer_dev
",,terminal_output
3117,6283700,"TERMINAL",0,0,"cd ../..",,terminal_command
3118,6283711,"TERMINAL",0,0,"",,terminal_output
3119,6284117,"TERMINAL",0,0,"ls",,terminal_command
3120,6284136,"TERMINAL",0,0,"dev  jobs  templates
",,terminal_output
3121,6285962,"TERMINAL",0,0,"cd jobs/",,terminal_command
3122,6285973,"TERMINAL",0,0,"",,terminal_output
3123,6292533,"TERMINAL",0,0,"",,terminal_focus
3124,6336115,"slurm/jobs/mp4_to_npy_open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=12:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_all
#SBATCH --mail-type=ALL

cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


",shellscript,tab
3125,6339042,"TERMINAL",0,0,"bash",,terminal_focus
3126,6344494,"TERMINAL",0,0,"mkdir preprocess",,terminal_command
3127,6344540,"TERMINAL",0,0,"",,terminal_output
3128,6348166,"TERMINAL",0,0,"mkdir train",,terminal_command
3129,6348187,"TERMINAL",0,0,"",,terminal_output
3130,6353661,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=12:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_all
#SBATCH --mail-type=ALL

cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


",shellscript,tab
3131,6370350,"slurm/dev/ali/preprocess/npy_to_tfrecord_10xx copy.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=03:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_10xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/10xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/10xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
3132,6375720,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=03:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_10xx
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_npy/10xx/10fps_160x90""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_first_try_tfrecord/10xx""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
3133,6377943,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",20,0,"",shellscript,selection_command
3134,6378169,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",21,0,"",shellscript,selection_command
3135,6378192,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",39,0,"",shellscript,selection_command
3136,6378224,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",67,0,"",shellscript,selection_command
3137,6378252,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",91,0,"",shellscript,selection_command
3138,6378285,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",119,0,"",shellscript,selection_command
3139,6378322,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",157,0,"",shellscript,selection_command
3140,6378356,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",208,0,"",shellscript,selection_command
3141,6378390,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",258,0,"",shellscript,selection_command
3142,6378422,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",301,0,"",shellscript,selection_command
3143,6378458,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",341,0,"",shellscript,selection_command
3144,6378985,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",365,0,"",shellscript,selection_command
3145,6379264,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",341,0,"",shellscript,selection_command
3146,6379424,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",301,0,"",shellscript,selection_command
3147,6380649,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",340,0,"",shellscript,selection_command
3148,6380944,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",339,1,"",shellscript,content
3149,6381196,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",338,1,"",shellscript,content
3150,6381223,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",337,1,"",shellscript,content
3151,6381252,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",336,1,"",shellscript,content
3152,6381285,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",335,1,"",shellscript,content
3153,6381320,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",334,1,"",shellscript,content
3154,6381351,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",333,1,"",shellscript,content
3155,6381385,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",332,1,"",shellscript,content
3156,6381417,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",331,1,"",shellscript,content
3157,6381454,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",330,1,"",shellscript,content
3158,6381485,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",329,1,"",shellscript,content
3159,6381626,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",328,1,"",shellscript,content
3160,6381985,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",327,1,"",shellscript,content
3161,6383205,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",327,0,"tfrecord_open_ai_all",shellscript,content
3162,6383462,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",346,0,"",shellscript,selection_command
3163,6383577,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",370,0,"",shellscript,selection_command
3164,6383835,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",390,0,"",shellscript,selection_command
3165,6383864,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",414,0,"",shellscript,selection_command
3166,6383891,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",421,0,"",shellscript,selection_command
3167,6383928,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",423,0,"",shellscript,selection_command
3168,6383957,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",454,0,"",shellscript,selection_command
3169,6383993,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",456,0,"",shellscript,selection_command
3170,6384325,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",502,0,"",shellscript,selection_command
3171,6386802,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=12:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=mp4_to_npy_openai_all
#SBATCH --mail-type=ALL

cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""

start_time=$(date +%s)  
python utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""


",shellscript,tab
3172,6387248,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",20,0,"",shellscript,selection_command
3173,6387473,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",21,0,"",shellscript,selection_command
3174,6387499,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",39,0,"",shellscript,selection_command
3175,6387534,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",67,0,"",shellscript,selection_command
3176,6387568,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",91,0,"",shellscript,selection_command
3177,6387603,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",119,0,"",shellscript,selection_command
3178,6387637,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",157,0,"",shellscript,selection_command
3179,6387672,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",208,0,"",shellscript,selection_command
3180,6387702,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",258,0,"",shellscript,selection_command
3181,6387745,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",301,0,"",shellscript,selection_command
3182,6387769,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",342,0,"",shellscript,selection_command
3183,6387807,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",366,0,"",shellscript,selection_command
3184,6387836,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",367,0,"",shellscript,selection_command
3185,6387873,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",374,0,"",shellscript,selection_command
3186,6387906,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",375,0,"",shellscript,selection_command
3187,6387941,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",407,0,"",shellscript,selection_command
3188,6388092,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",408,0,"",shellscript,selection_command
3189,6388277,"slurm/dev/ali/preprocess/mp4_to_npy_open_ai.sbatch",501,0,"",shellscript,selection_command
3190,6389476,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",0,0,"",shellscript,tab
3191,6390194,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",580,0,"
",shellscript,content
3192,6390484,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",581,0,"
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""",shellscript,content
3193,6390492,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",582,0,"",shellscript,selection_command
3194,6390866,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",581,0,"",shellscript,selection_command
3195,6391256,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",457,0,"",shellscript,selection_command
3196,6391764,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",581,0,"",shellscript,selection_command
3197,6392215,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",457,0,"",shellscript,selection_command
3198,6393301,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",791,4,"",shellscript,content
3199,6393301,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",674,108,"",shellscript,content
3200,6393301,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",580,1,"",shellscript,content
3201,6393301,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",562,17,"",shellscript,content
3202,6393301,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",548,10,"",shellscript,content
3203,6394153,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",554,0,"",shellscript,selection_command
3204,6395190,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",656,0,"",shellscript,selection_command
3205,6395392,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",655,0,"",shellscript,selection_command
3206,6395467,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",654,0,"",shellscript,selection_command
3207,6395820,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",653,0,"",shellscript,selection_command
3208,6395821,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",652,0,"",shellscript,selection_command
3209,6395823,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",651,0,"",shellscript,selection_command
3210,6395824,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",650,0,"",shellscript,selection_command
3211,6395839,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",649,0,"",shellscript,selection_command
3212,6395875,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",648,0,"",shellscript,selection_command
3213,6396961,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",657,0,"",shellscript,selection_command
3214,6397668,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",681,0,"",shellscript,selection_command
3215,6397897,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",777,0,"",shellscript,selection_command
3216,6398145,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",820,0,"",shellscript,selection_command
3217,6398170,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",874,0,"",shellscript,selection_command
3218,6398201,"slurm/dev/ali/preprocess/npy_to_tfrecord__open_ai.sbatch",876,0,"",shellscript,selection_command
3219,6407002,"slurm/jobs/preprocess/npy_to_tfrecord__open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=03:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_open_ai_all
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord/""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
3220,6414927,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=03:00:00
#SBATCH --partition=cpuonly
#SBATCH --account=hk-project-p0023960
#SBATCH --output=logs/logs_preprocessing/%x_%j.log
#SBATCH --error=logs/logs_preprocessing/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=npy_to_tfrecord_open_ai_all
#SBATCH --mail-type=ALL
#!/usr/bin/env bash
# Log the sbatch script
cat $0

source .venv_jafar/bin/activate

input_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/""
output_path=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord/""

start_time=$(date +%s)  
python utils/preprocess_dataset.py --source_data_dir $input_path --output_tfrecords_dir $output_path --num_shards 100
end_time=$(date +%s)
echo ""Time taken: $((end_time - start_time)) seconds""
",shellscript,tab
3221,6428918,"slurm/jobs/train/train_dyn_knoms_full.sbatch",0,0,"#!/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_dyn/%x_%j.log
#SBATCH --error=logs/logs_training_dyn/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_dyn_knoms_full
#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


CHECKPOINT_DIR=$ws_dir/checkpoints/dyn/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR


data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards'


lam_checkpoint='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/lam/3277318/lam_1750444032_10000'
tokenizer_checkpoint='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/3273828/tokenizer_1750269881_15500'

srun python train_dynamics.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --lam_checkpoint $lam_checkpoint \
    --tokenizer_checkpoint $tokenizer_checkpoint \
    --batch_size=48 \
    --log_checkpoint_interval=500 \
    --log_image_interval=50 \
    --seed=0 \
    --min_lr=0.0000866 \
    --max_lr=0.0000866 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar

",shellscript,tab
3222,6436785,"slurm/jobs/train/train_lam_full.sbatch",0,0,"#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=16:00:00
#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_ful_tfrecord
#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

CHECKPOINT_DIR=$ws_dir/checkpoints/lam/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR

data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards'

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --log_checkpoint_interval=500 \
    --log_image_interval=50 \
    --seed=0 \
    --min_lr=0.0000866 \
    --max_lr=0.0000866 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar

",shellscript,tab
3223,6444727,"slurm/jobs/train/train_tokenizer_knoms_overfit_tfrecord_full.sbatch",0,0,"#!/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_tokenizer/%x_%j.log
#SBATCH --error=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_tokenizer_knoms_overfit_tfrecord_full
#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

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


data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards'

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --num_steps=300000 \
    --warmup_steps=10000 \
    --log_checkpoint_interval=500 \
    --seed=0 \
    --min_lr=0.0000866 \
    --max_lr=0.0000866 \
    --log_image_interval=100 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar


",shellscript,tab
3224,6454164,"slurm/jobs/train/train_tokenizer_full.sbatch",0,0,"#!/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_tokenizer/%x_%j.log
#SBATCH --error=logs/logs_training_tokenizer/%x_%j.log
#SBATCH --mail-user=avocadoaling@gmail.com
#SBATCH --job-name=train_tokenizer_knoms_overfit_tfrecord_full
#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

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


data_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards'

srun python train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --num_steps=300000 \
    --warmup_steps=10000 \
    --log_checkpoint_interval=500 \
    --seed=0 \
    --min_lr=0.0000866 \
    --max_lr=0.0000866 \
    --log_image_interval=100 \
    --log \
    --entity instant-uv \
    --data_dir $data_dir \
    --project jafar


",shellscript,tab
3225,6455776,"slurm/jobs/train/train_tokenizer_full.sbatch",20,0,"",shellscript,selection_command
3226,6456003,"slurm/jobs/train/train_tokenizer_full.sbatch",21,0,"",shellscript,selection_command
3227,6456027,"slurm/jobs/train/train_tokenizer_full.sbatch",39,0,"",shellscript,selection_command
3228,6456056,"slurm/jobs/train/train_tokenizer_full.sbatch",67,0,"",shellscript,selection_command
3229,6456089,"slurm/jobs/train/train_tokenizer_full.sbatch",91,0,"",shellscript,selection_command
3230,6456123,"slurm/jobs/train/train_tokenizer_full.sbatch",123,0,"",shellscript,selection_command
3231,6456161,"slurm/jobs/train/train_tokenizer_full.sbatch",161,0,"",shellscript,selection_command
3232,6456189,"slurm/jobs/train/train_tokenizer_full.sbatch",187,0,"",shellscript,selection_command
3233,6456222,"slurm/jobs/train/train_tokenizer_full.sbatch",208,0,"",shellscript,selection_command
3234,6456258,"slurm/jobs/train/train_tokenizer_full.sbatch",264,0,"",shellscript,selection_command
3235,6456289,"slurm/jobs/train/train_tokenizer_full.sbatch",319,0,"",shellscript,selection_command
3236,6456323,"slurm/jobs/train/train_tokenizer_full.sbatch",362,0,"",shellscript,selection_command
3237,6456357,"slurm/jobs/train/train_tokenizer_full.sbatch",425,0,"",shellscript,selection_command
3238,6456392,"slurm/jobs/train/train_tokenizer_full.sbatch",449,0,"",shellscript,selection_command
3239,6466488,"TERMINAL",0,0,"mkdir sample",,terminal_command
3240,6466499,"TERMINAL",0,0,"",,terminal_output
3241,6486724,"slurm/jobs/sample/sample_knoms.sh",0,0,"
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",shellscript,tab
3242,6537153,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",0,0,"",shellscript,tab
3243,6537737,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",20,0,"",shellscript,selection_command
3244,6538345,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",21,0,"",shellscript,selection_command
3245,6538588,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",39,0,"",shellscript,selection_command
3246,6538616,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",67,0,"",shellscript,selection_command
3247,6538648,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",91,0,"",shellscript,selection_command
3248,6538680,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",119,0,"",shellscript,selection_command
3249,6538714,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",157,0,"",shellscript,selection_command
3250,6538748,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",208,0,"",shellscript,selection_command
3251,6538781,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",258,0,"",shellscript,selection_command
3252,6538816,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",301,0,"",shellscript,selection_command
3253,6538994,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",342,0,"",shellscript,selection_command
3254,6539254,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",366,0,"",shellscript,selection_command
3255,6539279,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",367,0,"",shellscript,selection_command
3256,6539306,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",374,0,"",shellscript,selection_command
3257,6539340,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",375,0,"",shellscript,selection_command
3258,6539374,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",407,0,"",shellscript,selection_command
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3261,6541672,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",408,0,"",shellscript,selection_command
3262,6542139,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",500,0,"",shellscript,selection_command
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3265,6542733,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",481,0,"",shellscript,selection_command
3266,6543194,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",481,17,"",shellscript,content
3267,6543488,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",481,0,"k",shellscript,content
3268,6543489,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",482,0,"",shellscript,selection_keyboard
3269,6543583,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",482,0,"n",shellscript,content
3270,6543584,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",483,0,"",shellscript,selection_keyboard
3271,6543674,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",483,0,"o",shellscript,content
3272,6543675,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",484,0,"",shellscript,selection_keyboard
3273,6543794,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",484,0,"m",shellscript,content
3274,6543795,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",485,0,"",shellscript,selection_keyboard
3275,6543902,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",485,0,"s",shellscript,content
3276,6543903,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",486,0,"",shellscript,selection_keyboard
3277,6544440,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",485,0,"",shellscript,selection_command
3278,6544837,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",566,0,"",shellscript,selection_command
3279,6545745,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",580,0,"s",shellscript,content
3280,6545745,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",572,8,"",shellscript,content
3281,6545746,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",564,7,"",shellscript,content
3282,6545746,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",563,0,"kn",shellscript,content
3283,6546995,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",0,0,"",shellscript,tab
3284,6548012,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",20,0,"",shellscript,selection_command
3285,6548255,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",21,0,"",shellscript,selection_command
3286,6548277,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",39,0,"",shellscript,selection_command
3287,6548306,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",67,0,"",shellscript,selection_command
3288,6548339,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",91,0,"",shellscript,selection_command
3289,6548372,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",119,0,"",shellscript,selection_command
3290,6548406,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",157,0,"",shellscript,selection_command
3291,6548439,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",208,0,"",shellscript,selection_command
3292,6548472,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",258,0,"",shellscript,selection_command
3293,6548507,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",301,0,"",shellscript,selection_command
3294,6548539,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",348,0,"",shellscript,selection_command
3295,6548573,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",372,0,"",shellscript,selection_command
3296,6548608,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",392,0,"",shellscript,selection_command
3297,6548643,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",416,0,"",shellscript,selection_command
3298,6548674,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",423,0,"",shellscript,selection_command
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3300,6548741,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",456,0,"",shellscript,selection_command
3301,6548980,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",457,0,"",shellscript,selection_command
3302,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",645,0,"s",shellscript,content
3303,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",637,8,"",shellscript,content
3304,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",629,7,"",shellscript,content
3305,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",628,0,"kn",shellscript,content
3306,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",547,0,"s",shellscript,content
3307,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",539,8,"",shellscript,content
3308,6550265,"slurm/jobs/preprocess/npy_to_tfrecord_open_ai.sbatch",531,7,"",shellscript,content
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3333,6556161,"slurm/jobs/preprocess/mp4_to_npy_open_ai.sbatch",337,0,"ms",shellscript,content
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