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\r[23;2t[23;1t\r\r\n[?1004l[?2004l[?1l>[?25h[>4;m[?1049l[23;0;0t\r[KMerge made by the 'ort' strategy.\r\n dev/alfred/coinrun/latent_action_ablation/train_dynamics_coinrun.sbatch | 54 [32m++++++++++++++++++++++++++++++++++++++++++++++++++++++[m\r\n dev/alfred/coinrun/latent_action_ablation/train_lam_12.sbatch | 47 [32m+++++++++++++++++++++++++++++++++++++++++++++++[m\r\n dev/alfred/coinrun/latent_action_ablation/train_lam_24.sbatch | 47 [32m+++++++++++++++++++++++++++++++++++++++++++++++[m\r\n dev/alfred/coinrun/latent_action_ablation/train_lam_48.sbatch | 47 [32m+++++++++++++++++++++++++++++++++++++++++++++++[m\r\n dev/alfred/coinrun/latent_action_ablation/train_lam_6.sbatch | 47 [32m+++++++++++++++++++++++++++++++++++++++++++++++[m\r\n dev/alfred/coinrun/latent_action_ablation/train_tokenizer_coinrun copy.sbatch | 46 [32m++++++++++++++++++++++++++++++++++++++++++++++[m\r\n dev/alfred/coinrun/{ => latent_action_ablation}/train_tokenizer_coinrun.sbatch | 8 [32m++++[m[31m----[m\r\n dev/alfred/coinrun/train_tokenizer_coinrun.sh | 53 [31m-----------------------------------------------------[m\r\n 8 files changed, 292 insertions(+), 57 deletions(-)\r\n create mode 100644 dev/alfred/coinrun/latent_action_ablation/train_dynamics_coinrun.sbatch\r\n create mode 100644 dev/alfred/coinrun/latent_action_ablation/train_lam_12.sbatch\r\n create mode 100644 dev/alfred/coinrun/latent_action_ablation/train_lam_24.sbatch\r\n create mode 100644 dev/alfred/coinrun/latent_action_ablation/train_lam_48.sbatch\r\n create mode 100644 dev/alfred/coinrun/latent_action_ablation/train_lam_6.sbatch\r\n create mode 100644 dev/alfred/coinrun/latent_action_ablation/train_tokenizer_coinrun copy.sbatch\r\n rename dev/alfred/coinrun/{ => latent_action_ablation}/train_tokenizer_coinrun.sbatch (86%)\r\n delete mode 100755 dev/alfred/coinrun/train_tokenizer_coinrun.sh\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;0
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]633;E;2025-06-27 17:23:33 git pulls;dcbf4775-e574-4e9d-b507-67c2737583de]633;Cgit: 'pulls' is not a git command. See 'git --help'.\r\n\r\nThe most similar command is\r\n\tpull\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;1
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]633;E;2025-06-27 17:23:35 git pull;dcbf4775-e574-4e9d-b507-67c2737583de]633;C
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Already up to date.\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;0
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]633;E;2025-06-27 17:23:39 gs;dcbf4775-e574-4e9d-b507-67c2737583de]633;COn branch main\r\nYour branch is ahead of 'origin/main' by 2 commits.\r\n (use "git push" to publish your local commits)\r\n\r\nnothing to commit, working tree clean\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;0
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Enumerating objects: 26, done.\r\nCounting objects: 4% (1/24)\rCounting objects: 8% (2/24)\rCounting objects: 12% (3/24)\rCounting objects: 16% (4/24)\rCounting objects: 20% (5/24)\rCounting objects: 25% (6/24)\rCounting objects: 29% (7/24)\rCounting objects: 33% (8/24)\rCounting objects: 37% (9/24)\rCounting objects: 41% (10/24)\rCounting objects: 45% (11/24)\rCounting objects: 50% (12/24)\rCounting objects: 54% (13/24)\rCounting objects: 58% (14/24)\rCounting objects: 62% (15/24)\rCounting objects: 66% (16/24)\rCounting objects: 70% (17/24)\rCounting objects: 75% (18/24)\rCounting objects: 79% (19/24)\rCounting objects: 83% (20/24)\rCounting objects: 87% (21/24)\rCounting objects: 91% (22/24)\rCounting objects: 95% (23/24)\rCounting objects: 100% (24/24)\rCounting objects: 100% (24/24), done.\r\nDelta compression using up to 152 threads\r\nCompressing objects: 6% (1/16)\rCompressing objects: 12% (2/16)\rCompressing objects: 18% (3/16)\rCompressing objects: 25% (4/16)\rCompressing objects: 31% (5/16)\rCompressing objects: 37% (6/16)\rCompressing objects: 43% (7/16)\rCompressing objects: 50% (8/16)\rCompressing objects: 56% (9/16)\rCompressing objects: 62% (10/16)\rCompressing objects: 68% (11/16)\rCompressing objects: 75% (12/16)\rCompressing objects: 81% (13/16)\rCompressing objects: 87% (14/16)\rCompressing objects: 93% (15/16)\rCompressing objects: 100% (16/16)\rCompressing objects: 100% (16/16), done.\r\nWriting objects: 6% (1/16)\rWriting objects: 12% (2/16)\rWriting objects: 18% (3/16)\rWriting objects: 25% (4/16)\rWriting objects: 31% (5/16)\rWriting objects: 37% (6/16)\rWriting objects: 43% (7/16)\rWriting objects: 56% (9/16)\rWriting objects: 62% (10/16)\rWriting objects: 68% (11/16)\rWriting objects: 75% (12/16)\rWriting objects: 81% (13/16)\rWriting objects: 87% (14/16)\rWriting objects: 93% (15/16)\rWriting objects: 100% (16/16)\rWriting objects: 100% (16/16), 1.99 KiB | 509.00 KiB/s, done.\r\nTotal 16 (delta 10), reused 0 (delta 0), pack-reused 0\r\n
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remote: Resolving deltas: 0% (0/10)[K\rremote: Resolving deltas: 10% (1/10)[K\rremote: Resolving deltas: 20% (2/10)[K\rremote: Resolving deltas: 30% (3/10)[K\rremote: Resolving deltas: 40% (4/10)[K\rremote: Resolving deltas: 50% (5/10)[K\rremote: Resolving deltas: 60% (6/10)[K\rremote: Resolving deltas: 70% (7/10)[K\rremote: Resolving deltas: 80% (8/10)[K\rremote: Resolving deltas: 90% (9/10)[K\rremote: Resolving deltas: 100% (10/10)[K\rremote: Resolving deltas: 100% (10/10), completed with 3 local objects.[K\r\n
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To github.com:p-doom/slurm.git\r\n 06dec29..0e07bf2 main -> main\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;0
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slurm/dev/alfred/overfit_sample/train_tokenizer_overfit_sample_size_0.6_mio.sbatch
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#!/usr/bin/env bash\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=5:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_overfit_sample_size_0.6_mio\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv_jafar/bin/activate\n\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\ntf_records_dir=$ws_dir/data/knoms_tfrecords_200_shards\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=("overfit_sample", "tokenizer", "model_scaling", "alfred")\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer_single_sample.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=4.3e-5 \\n --max_lr=4.3e-4 \\n --log_image_interval=10 \\n --log \\n --entity instant-uv \\n --project jafar \\n --name $job_name \\n --tags $tags \\n --model_dim 64 \\n --data_dir $tf_records_dir\n\n
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hk-project-p0023960\n\n\n\nhow do I move a lot of files from one directory to another?\n\nsrc: /hkfs/work/workspace/scratch/tum_ind3695-jafar_ws\ndst: /hkfs/work/workspace/scratch/tum_ind3695-jafar_data\n\n\n\nDIR=$(ws_find jafar_data)\nsetfacl -Rm g:hk-project-p0023960:rX,d:g:hk-project-p0023960:rX $DIR\n\nDIR=$(ws_find jafar_workspace)\n\n\nDIR=$(ws_find jafar_workspace)\ngetfacl $DIR\n\ngetfacl $(ws_find jafar_workspace)\n\n\nsetfacl -Rm g:hk-project-p0023960:rX,d:g:hk-project-p0023960:rX $DIR\n\nws_access_ls='getfacl $(ws_find jafar_workspace)'\nws_access_granter='setfacl -Rm g:hk-project-p0023960:rX,d:g:hk-project-p0023960:rX $DIR'\n\nhk-project-p0023960\n\n\ncp \n\ncp data_gen_gym_coinrun.sbatch data_gen_gym_bigfish.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_bossfight.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_caveflyer.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_chaser.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_climber.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_dodgeball.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_fruitbot.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_heist.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_jumper.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_leaper.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_maze.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_miner.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_ninja.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_plunder.sbatch\ncp data_gen_gym_coinrun.sbatch data_gen_gym_starpilot.sbatch\n\n\n\n\ncurl -O https://omnomnom.vision.rwth-aachen.de/data/atari_v1_release/spaceinvaders.tar.gz\ncurl -O https://omnomnom.vision.rwth-aachen.de/data/atari_v1_release/qbert.tar.gz\ncurl -O https://omnomnom.vision.rwth-aachen.de/data/atari_v1_release/mspacman.tar.gz\ncurl -O https://omnomnom.vision.rwth-aachen.de/data/atari_v1_release/pinball.tar.gz\ncurl -O https://omnomnom.vision.rwth-aachen.de/data/atari_v1_release/revenge.tar.gz\n\n\n\n\n## mp4 to jafar format\n\n- make it k fps\n- crop it/downsample it\n- chunk it into n frames\n- convert to npy\n- save it to data/atari_v1_release/spaceinvaders/spaceinvaders.npy\n\n```bash\nsbatch --time=04:00:00 --partition=cpuonly --account=hk-project-pai00039 --cpus-per-task=64 --ntasks=1 --job-name=aws_download --wrap="aws s3 cp s3://minescrape-bucket/videos/knoms/ . --recursive"\n\npython utils/preprocess_dataset.py --source_data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafar_ws/minecraft_videos/preprocessed/10fps_160x90/ --output_tfrecords_dir /hkfs/work/workspace/scratch/tum_ind3695-jafar_ws/minecraft_videos/preprocessed/10fps_160x90/tfrecords/ \n\n```\n1,6b: 80 nodes\n\n\n```\nsalloc --time=01:00:00 --partition=accelerated-h100 --nodes=1 --gres=gpu:4 --cpus-per-task=8 --ntasks-per-node=4\naccelerated-h100\n\nsrun python train_tokenizer.py --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafar_ws/data/tf_records --image_height 160 --image_width 90\n\n/hkfs/work/workspace/scratch/tum_ind3695-jafar_ws/data/knoms\n\n\n\n\n\n\n```\n\n{'streams': [{'index': 0, 'codec_name': 'vp9', 'codec_long_name': 'Google VP9', 'profile': 'Profile 0', 'codec_type': 'video', 'codec_tag_string': '[0][0][0][0]', 'codec_tag': '0x0000', 'width': 640, 'height': 360, 'coded_width': 640, 'coded_height': 360, 'closed_captions': 0, 'film_grain': 0, 'has_b_frames': 0, 'sample_aspect_ratio': '1:1', 'display_aspect_ratio': '16:9', 'pix_fmt': 'yuv420p', 'level': -99, 'color_range': 'tv', 'color_space': 'bt709', 'color_transfer': 'bt709', 'color_primaries': 'bt709', 'refs': 1, 'r_frame_rate': '30/1', 'avg_frame_rate': '30/1', 'time_base': '1/1000', 'start_pts': 0, 'start_time': '0.000000', 'disposition': {'default': 1, 'dub': 0, 'original': 0, 'comment': 0, 'lyrics': 0, 'karaoke': 0, 'forced': 0, 'hearing_impaired': 0, 'visual_impaired': 0, 'clean_effects': 0, 'attached_pic': 0, 'timed_thumbnails': 0, 'non_diegetic': 0, 'captions': 0, 'descriptions': 0, 'metadata': 0, 'dependent': 0, 'still_image': 0}, 'tags': {'language': 'eng'}}], 'format': {'filename': '/hkfs/work/workspace/scratch/tum_ind3695-jafar_ws/data/knoms/293_KxCeK_wk-KA.webm', 'nb_streams': 1, 'nb_programs': 0, 'nb_stream_groups': 0, 'format_name': 'matroska,webm', 'format_long_name': 'Matroska / WebM', 'start_time': '0.000000', 'duration': '1155.767000', 'size': '50776376', 'bit_rate': '351464', 'probe_score': 100, 'tags': {'encoder': 'google/video-file'}}}\n\n(Pdb) \n\n{'index': 0, 'codec_name': 'vp9', 'codec_long_name': 'Google VP9', 'profile': 'Profile 0', 'codec_type': 'video', 'codec_tag_string': '[0][0][0][0]', 'codec_tag': '0x0000', 'width': 640, 'height': 360, 'coded_width': 640, 'coded_height': 360, 'closed_captions': 0, 'film_grain': 0, 'has_b_frames': 0, 'sample_aspect_ratio': '1:1', 'display_aspect_ratio': '16:9', 'pix_fmt': 'yuv420p', 'level': -99, 'color_range': 'tv', 'color_space': 'bt709', 'color_transfer': 'bt709', 'color_primaries': 'bt709', 'refs': 1, 'r_frame_rate': '30/1', 'avg_frame_rate': '30/1', 'time_base': '1/1000', 'start_pts': 0, 'start_time': '0.000000', 'disposition': {'default': 1, 'dub': 0, 'original': 0, 'comment': 0, 'lyrics': 0, 'karaoke': 0, 'forced': 0, 'hearing_impaired': 0, 'visual_impaired': 0, 'clean_effects': 0, 'attached_pic': 0, 'timed_thumbnails': 0, 'non_diegetic': 0, 'captions': 0, 'descriptions': 0, 'metadata': 0, 'dependent': 0, 'still_image': 0}, 'tags': {'language': 'eng'}}\n\n\n\n\n\nuid=996262(tum_dbd0378) gid=502226(hk-project-p0023960) groups=502226(hk-project-p0023960),501163(hk-access-ext),501959(tum_tu\n\n\n12min 39s\n12min 54s\ndelta: 15s\n\n12*60* 10 = 7200\n39*10 = 390\nsum: 7590\n\n```\n 3237740 preprocess_video_splitter cpuonly 152 COMPLETED 00:47:47 01:30:00 \n 3238237 preprocess_video_splitter cpuonly 152 TIMEOUT 01:30:11 01:30:00 \n 3238924 preprocess_video_splitter_cop+ cpuonly 152 COMPLETED 00:47:40 01:30:00 \n 3239678 preprocess_video_to_npy cpuonly 152 TIMEOUT 01:30:25 01:30:00 \n 3251122 preprocess_video_to_npy cpuonly 152 COMPLETED 00:00:28 01:30:00 \n 3251189 rsync_knoms_npy_to_shared cpuonly 152 COMPLETED 00:16:50 01:30:00 \n 3251202 preprocess_video_to_npy cpuonly 152 COMPLETED 00:04:39 01:30:00 \n 3251494 preprocess_video_to_npy cpuonly 152 COMPLETED 00:19:45 01:30:00 \n 3255047 train_tokenizer_knoms accelerated 0 FAILED 00:00:00 00:30:00 \n 3255048 train_tokenizer_knoms accelerated 0 FAILED 00:00:00 00:30:00 \n 3255049 train_tokenizer_knoms accelerated 0 FAILED 00:00:00 00:30:00 \n 3255050 train_tokenizer_knoms accelerated 32 FAILED 00:05:08 00:30:00 \n 3255482 train_tokenizer_knoms accelerated-h100 32 FAILED 00:00:44 00:15:00 \n 3255483 train_tokenizer_knoms accelerated-h100 32 FAILED 00:03:53 00:15:00 \n 3255493 data_gen_coinrun-test cpuonly 152 TIMEOUT 00:30:17 00:30:00 \n 3255506 data_gen_coinrun-test cpuonly 152 TIMEOUT 01:30:12 01:30:00 \n 3256929 train_tokenizer_coinrun accelerated 32 FAILED 00:01:46 01:00:00 \n 3257751 train_tokenizer_coinrun accelerated 0 CANCELLED b+ 00:00:00 06:00:00 \n 3257924 train_tokenizer_knoms_overfit accelerated 0 CANCELLED b+ 00:00:00 04:00:00 \n 3258011 train_tokenizer_knoms_overfit accelerated 0 CANCELLED b+ 00:00:00 06:00:00 \n 3258018 train_tokenizer_coinrun accelerated 0 CANCELLED b+ 00:00:00 06:00:00 \n 3258057 train_tokenizer_knoms_overfit accelerated 0 CANCELLED b+ 00:00:00 06:00:00 \n 3258278 train_tokenizer_knoms_overfit accelerated 32 CANCELLED b+ 01:54:35 06:00:00 \n 3258283 train_tokenizer_coinrun accelerated 32 TIMEOUT 06:00:19 06:00:00 \n 3259422 train_tokenizer_knoms_overfit accelerated-h100 32 TIMEOUT 06:00:12 06:00:00 \n```\n\n\n\n```\n00:47:47\n01:30:11\n00:47:40\n01:30:25\n00:00:28\n00:16:50\n00:04:39\n00:19:45\n00:00:00\n00:00:00\n00:00:00\n00:05:08\n00:00:44\n00:03:53\n00:30:17\n01:30:12\n00:01:46\n00:00:00\n00:00:00\n00:00:00\n00:00:00\n00:00:00\n01:54:35\n06:00:19\n06:00:12\n\n```\n\n{'encoder': 18.978.432, 'vq': 32.768, 'decoder': 18.978.416, 'total': 37.989.616}\n{'encoder': 4.770.688, 'vq': 32.768, 'decoder': 4.770.672, 'total': 9.574.128}\n{'encoder': 10.694.912, 'vq': 32.768, 'decoder': 10.694.896, 'total': 21.422.576}\n{'encoder': 8029184, 'vq': 32768, 'decoder': 8029168, 'total': 16091120}\n\n\n**BASELINE 37.9mio**\nmodel_dim = 512\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n{'encoder': 18.978.432, 'vq': 32.768, 'decoder': 18.978.416, 'total': 37.989.616}\n\n**BASELINE 9.6mio**\nmodel_dim = 256\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n{'encoder': 18.978.432, 'vq': 32.768, 'decoder': 18.978.416, 'total': 37.989.616}\n\n\n\n**2.4 mio**\nmodel_dim = 128\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 1205760, 'vq': 32768, 'decoder': 1205744, 'total': 2.444.272}\n\n**648k**\nmodel_dim = 64\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 308032, 'vq': 32768, 'decoder': 308016, 'total': 648.816}\n\n\n\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n\n--model_dim 384 \\n--num_blocks 6 \\n{'encoder': 8029184, 'vq': 32768, 'decoder': 8029168, 'total': 16091120}\n\n\n--model_dim 384 \\n{'encoder': 10.694.912, 'vq': 32.768, 'decoder': 10.694.896, 'total': 21.422.576}\n\n--model_dim 256\\n{'encoder': 4770688, 'vq': 32768, 'decoder': 4770672, 'total': 9574128}\n\n\n**MINIMAL**\nmodel_dim = 128\nnum_blocks = 2\nlatent_dim = 8\nnum_latents = 256\npatch_size = 16\n\nscancel 3297722 3297723 3297726 \n
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markdown
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notes.md
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\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n
|
markdown
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selection_command
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55
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notes.md
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\n{'encoder': 308032, 'vq': 32768, 'decoder': 308016, 'total': 648.816}\n\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n
|
markdown
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selection_command
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notes.md
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\n**648k**\nmodel_dim = 64\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 308032, 'vq': 32768, 'decoder': 308016, 'total': 648.816}\n\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n
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markdown
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selection_command
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notes.md
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\n{'encoder': 1205760, 'vq': 32768, 'decoder': 1205744, 'total': 2.444.272}\n\n**648k**\nmodel_dim = 64\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 308032, 'vq': 32768, 'decoder': 308016, 'total': 648.816}\n\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n
|
markdown
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selection_command
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58
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notes.md
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\n**2.4 mio**\nmodel_dim = 128\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 1205760, 'vq': 32768, 'decoder': 1205744, 'total': 2.444.272}\n\n**648k**\nmodel_dim = 64\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 308032, 'vq': 32768, 'decoder': 308016, 'total': 648.816}\n\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n
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markdown
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selection_command
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59
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notes.md
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\n**BASELINE 9.6mio**\nmodel_dim = 256\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n{'encoder': 18.978.432, 'vq': 32.768, 'decoder': 18.978.416, 'total': 37.989.616}\n\n\n\n**2.4 mio**\nmodel_dim = 128\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 1205760, 'vq': 32768, 'decoder': 1205744, 'total': 2.444.272}\n\n**648k**\nmodel_dim = 64\nlatent_dim = 32\nnum_latents = 1024\npatch_size = 4\nnum_blocks = 8\nnum_heads = 8\n\n{'encoder': 308032, 'vq': 32768, 'decoder': 308016, 'total': 648.816}\n\n**179k**\nmodel_dim = 64 # drastically smaller transformer hidden size\nlatent_dim = 8 # low latent space\nnum_latents = 256 # smaller codebook\npatch_size = 8 # shorter sequences\nnum_blocks = 2 # shallow encoder and decoder\n{'encoder': 89032, 'vq': 2048, 'decoder': 88848, 'total': 179.928}\n
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salloc --account=hk-project-p0023960 --time=00:30:00 --partition=accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8 --ntasks-per-node=1
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]633;E;2025-06-27 17:25:03 salloc --account=hk-project-p0023960 --time=00:30:00 --partition=accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8 --ntasks-per-node=1;c14815de-3769-4833-8fae-1a69327311a6]633;Csalloc: Pending job allocation 3299578\r\nsalloc: job 3299578 queued and waiting for resources\r\n
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]633;E;2025-06-27 17:25:05 queue;dcbf4775-e574-4e9d-b507-67c2737583de]633;C
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[?1049h[22;0;0t[1;24r(B[m[4l[?7h[H[2JEvery 1.0s: squeue --me[1;131Hhkn1993.localdomain: Fri Jun 27 17:25:05 2025[3;14HJOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)[4;12H3299271 accelerat train_dy tum_ind3 PD\t0:00\t 1 (Priority)[5;12H3299578 accelerat interact tum_ind3 PD\t0:00\t 1 (Priority)[24;175H
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[1;170H6[24;175H
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salloc
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^Csalloc: Job allocation 3299578 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit]633;D;1
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