Add files using upload-large-folder tool

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-5612308a-14dd-4f05-ae65-c6cd496f68351752499707410-2025_07_14-15.29.02.547/source.csv

@@ -0,0 +1,127 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,206,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"3:29:02 PM [info] Activating crowd-code\n3:29:02 PM [info] Recording started\n3:29:02 PM [info] Initializing git provider using file system watchers...\n",Log,tab
+3,365,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"3:29:02 PM [info] Git repository found\n3:29:02 PM [info] Git provider initialized successfully\n3:29:02 PM [info] Initial git state: [object Object]\n",Log,content
+4,2862,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command
+5,2909,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:05 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;96649176-7412-48ad-98ea-7639505dc671]633;C",,terminal_output
+6,2952,"TERMINAL",0,0,"]0;tum_cte0515@hkn1990:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output
+7,7671,"TERMINAL",0,0,"watch",,terminal_focus
+8,24040,"TERMINAL",0,0,"salloc --time=10:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command
+9,24098,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:26 salloc --time=10:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;ba61a590-3751-4127-93b1-5716be1c7bd0]633;Csalloc: Pending job allocation 3344502\r\nsalloc: job 3344502 queued and waiting for resources\r\n",,terminal_output
+10,25688,"TERMINAL",0,0,"^Csalloc: Job allocation 3344502 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;1]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output
+11,30368,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command
+12,30400,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:32 salloc --time=01:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;ba61a590-3751-4127-93b1-5716be1c7bd0]633;Csalloc: Pending job allocation 3344503\r\nsalloc: job 3344503 queued and waiting for resources\r\n",,terminal_output
+13,31244,"TERMINAL",0,0,"^Csalloc: Job allocation 3344503 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;1",,terminal_output
+14,35141,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command
+15,35153,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:37 salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;ba61a590-3751-4127-93b1-5716be1c7bd0]633;Csalloc: Granted job allocation 3344505\r\n",,terminal_output
+16,35274,"TERMINAL",0,0,"salloc: Waiting for resource configuration\r\n",,terminal_output
+17,37228,"TERMINAL",0,0,"bash",,terminal_focus
+18,38854,"TERMINAL",0,0,"queue",,terminal_command
+19,38926,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:41 queue;450fbccd-48d1-432b-b44b-06394616d158]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1990.localdomain: Mon Jul 14 15:29:41 2025JOBID PARTITION     NAME     USER ST\tTIME  NODES NODELIST(REASON)3341079 accelerat train_to tum_cte0  R 1-04:59:15\t  2 hkn[0628-0629]3341080 accelerat train_to tum_cte0  R 1-04:59:15\t  2 hkn[0631-0632]3344246 accelerat interact tum_cte0  R    1:13:32\t  2 hkn[0626,0630]3344505 dev_accel interact tum_cte0  R\t0:04\t  1 hkn0402",,terminal_output
+20,39972,"TERMINAL",0,0,"26635",,terminal_output
+21,41004,"TERMINAL",0,0,"37746",,terminal_output
+22,42041,"TERMINAL",0,0,"48857",,terminal_output
+23,43101,"TERMINAL",0,0,"59968",,terminal_output
+24,44132,"TERMINAL",0,0,"6202079",,terminal_output
+25,45165,"TERMINAL",0,0,"711810",,terminal_output
+26,46236,"TERMINAL",0,0,"82291",,terminal_output
+27,46520,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output
+28,49617,"TERMINAL",0,0,"idling",,terminal_command
+29,49671,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:52 idling;450fbccd-48d1-432b-b44b-06394616d158]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1990.localdomain: Mon Jul 14 15:29:52 2025Partition dev_cpuonly:     10 nodes idle\rPartition cpuonly:     42 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated:     40 nodes idle\rPartition dev_accelerated-h100    :\t 1 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 7 nodes idle",,terminal_output
+30,50700,"TERMINAL",0,0,"3",,terminal_output
+31,51749,"TERMINAL",0,0,"4",,terminal_output
+32,52802,"TERMINAL",0,0,"5",,terminal_output
+33,53446,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output
+34,62317,"TERMINAL",0,0,"salloc: Nodes hkn0402 are ready for job\r\n",,terminal_output
+35,63118,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h[tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+36,84352,"TERMINAL",0,0,"srun",,terminal_focus
+37,85382,"TERMINAL",0,0,"[?25lso[?25h[?25lo[?25h",,terminal_output
+38,85520,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output
+39,85615,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output
+40,85840,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output
+41,85947,"TERMINAL",0,0,"[?25le[?25h",,terminal_output
+42,86009,"TERMINAL",0,0,"[?25l [?25h",,terminal_output
+43,86141,"TERMINAL",0,0,"[?25l.[?25h[?25lv[?25h",,terminal_output
+44,86295,"TERMINAL",0,0,"env/",,terminal_output
+45,86587,"TERMINAL",0,0,"",,terminal_output
+46,86945,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output
+47,87054,"TERMINAL",0,0,"in/",,terminal_output
+48,87522,"TERMINAL",0,0,"[?25la[?25h",,terminal_output
+49,87637,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output
+50,87784,"TERMINAL",0,0,"tivate",,terminal_output
+51,88103,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+52,94820,"sample.py",0,0,"from dataclasses import dataclass\nimport time\nimport os\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nfrom orbax.checkpoint import PyTreeCheckpointer\nfrom flax.training.train_state import TrainState\nimport grain\nimport orbax.checkpoint as ocp\nimport optax\nfrom PIL import Image, ImageDraw\nimport tyro\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n    # Experiment\n    seed: int = 0\n    seq_len: int = 16\n    image_channels: int = 3\n    image_height: int = 90\n    image_width: int = 160\n    data_dir: str = ""data/coinrun_episodes""\n    checkpoint: str = """"\n    # Sampling\n    batch_size: int = 1\n    maskgit_steps: int = 25\n    temperature: float = 1.0\n    sample_argmax: bool = True\n    start_frame: int = 0\n    # Tokenizer checkpoint\n    tokenizer_dim: int = 512\n    latent_patch_dim: int = 32\n    num_patch_latents: int = 1024\n    patch_size: int = 4\n    tokenizer_num_blocks: int = 8\n    tokenizer_num_heads: int = 8\n    # LAM checkpoint\n    lam_dim: int = 512\n    latent_action_dim: int = 32\n    num_latent_actions: int = 6\n    lam_patch_size: int = 16\n    lam_num_blocks: int = 8\n    lam_num_heads: int = 8\n    # Dynamics checkpoint\n    dyna_dim: int = 512\n    dyna_num_blocks: int = 12\n    dyna_num_heads: int = 8\n\n\nargs = tyro.cli(Args)\nrng = jax.random.PRNGKey(args.seed)\n\n# --- Load Genie checkpoint ---\ngenie = Genie(\n    # Tokenizer\n    in_dim=args.image_channels,\n    tokenizer_dim=args.tokenizer_dim,\n    latent_patch_dim=args.latent_patch_dim,\n    num_patch_latents=args.num_patch_latents,\n    patch_size=args.patch_size,\n    tokenizer_num_blocks=args.tokenizer_num_blocks,\n    tokenizer_num_heads=args.tokenizer_num_heads,\n    # LAM\n    lam_dim=args.lam_dim,\n    latent_action_dim=args.latent_action_dim,\n    num_latent_actions=args.num_latent_actions,\n    lam_patch_size=args.lam_patch_size,\n    lam_num_blocks=args.lam_num_blocks,\n    lam_num_heads=args.lam_num_heads,\n    lam_co_train=True,\n    # Dynamics\n    dyna_dim=args.dyna_dim,\n    dyna_num_blocks=args.dyna_num_blocks,\n    dyna_num_heads=args.dyna_num_heads,\n)\nrng, _rng = jax.random.split(rng)\nimage_shape = (args.image_height, args.image_width, args.image_channels)\ndummy_inputs = dict(\n    videos=jnp.zeros((args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32),\n    mask_rng=_rng,\n)\nrng, _rng = jax.random.split(rng)\nparams = genie.init(_rng, dummy_inputs)\n\n# ckpt = PyTreeCheckpointer().restore(args.checkpoint)[""model""][""params""][""params""]\n# Create a dummy TrainState for checkpoint structure\nlr_schedule = optax.warmup_cosine_decay_schedule(\n    0.0, 3e-4, 1000, 300000\n)\ntx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\ndummy_train_state = TrainState.create(\n    apply_fn=genie.apply,\n    params=params,  # or params if your model expects that\n    tx=tx,  # No optimizer needed for eval\n)\n\nhandler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\nhandler_registry.add('model_state', ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler)\nhandler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n\n\n# Set up Orbax CheckpointManager\ncheckpoint_manager = ocp.CheckpointManager(\n    args.checkpoint,  # Directory containing the checkpoint\n    options=ocp.CheckpointManagerOptions(max_to_keep=1, step_format_fixed_length=6),\n    handler_registry=handler_registry\n)\n\n# Prepare abstract state for restore\nabstract_train_state = jax.tree_util.tree_map(\n    ocp.utils.to_shape_dtype_struct, dummy_train_state\n)\n\n# Restore the checkpoint (only model_state needed for sampling)\nrestored = checkpoint_manager.restore(\n    checkpoint_manager.latest_step(),\n    args=ocp.args.Composite(\n        model_state=ocp.args.StandardRestore(abstract_train_state),\n    ),\n)\nrestored_train_state = restored[""model_state""]\n\n# Extract model parameters for inference\nparams = restored_train_state.params\n\n\n# params[""params""].update(ckpt)\n\n\ndef _sampling_wrapper(module, batch):\n    return module.sample(batch, args.seq_len, args.maskgit_steps, args.temperature, args.sample_argmax)\n\n# --- Define autoregressive sampling loop ---\ndef _autoreg_sample(rng, video_batch, action_batch):\n    vid = video_batch[:, : args.start_frame + 1]\n    sampling_fn = jax.jit(nn.apply(_sampling_wrapper, genie)) \n    rng, _rng = jax.random.split(rng)\n    batch = dict(videos=vid, latent_actions=action_batch, rng=_rng)\n    generated_vid = sampling_fn(\n        params,\n        batch\n    )\n    return generated_vid\n\n# # --- Get video + latent actions ---\narray_record_files = [\n    os.path.join(args.data_dir, x)\n    for x in os.listdir(args.data_dir)\n    if x.endswith("".array_record"")\n]\ngrain_dataloader = get_dataloader(\n    array_record_files,\n    args.seq_len,\n    # NOTE: We deliberately pass the global batch size\n    # The dataloader shards the dataset across all processes\n    args.batch_size,\n    *image_shape,\n    num_workers=8,\n    prefetch_buffer_size=1,\n    seed=args.seed,\n)\ninitial_state = grain_dataloader._create_initial_state()\ngrain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\njax.debug.breakpoint()\nvideo_batch = next(grain_iterator)\njax.debug.breakpoint()\n# video_batch = np.load(""overfit_dir/single_sample_corner.npy"")\n# Get latent actions for all videos in the batch\nbatch = dict(videos=video_batch)\naction_batch = genie.apply(params, batch, False, method=Genie.vq_encode)\naction_batch = action_batch.reshape(video_batch.shape[0], args.seq_len - 1, 1)\n\n# --- Sample + evaluate video ---\nvid = _autoreg_sample(rng, video_batch, action_batch)\ngt = video_batch[:, : vid.shape[1]].clip(0, 1).reshape(-1, *video_batch.shape[2:])\nrecon = vid.clip(0, 1).reshape(-1, *vid.shape[2:])\nssim = pix.ssim(gt[:, args.start_frame + 1 :], recon[:, args.start_frame + 1 :]).mean()\nprint(f""SSIM: {ssim}"")\n\n# --- Construct video ---\ntrue_videos = (video_batch * 255).astype(np.uint8)\npred_videos = (vid * 255).astype(np.uint8)\nvideo_comparison = np.zeros((2, *vid.shape), dtype=np.uint8)\nvideo_comparison[0] = true_videos[:, :args.seq_len]\nvideo_comparison[1] = pred_videos\nframes = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n# --- Save video --- \nimgs = [Image.fromarray(img) for img in frames]\n# Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\nfor t, img in enumerate(imgs[1:]):\n    d = ImageDraw.Draw(img)\n    for row in range(action_batch.shape[0]):\n        action = action_batch[row, t, 0]\n        y_offset = row * video_batch.shape[2] + 2\n        d.text((2, y_offset), f""{action}"", fill=255)\nimgs[0].save(\n    f""generation_{time.time()}.gif"",\n    save_all=True,\n    append_images=imgs[1:],\n    duration=250,\n    loop=0,\n)\n",python,tab
+53,94915,"TERMINAL",0,0,"\r(jafar) [tum_cte0515@hkn0402 jafar]$ \r(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+54,148636,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output
+55,148874,"TERMINAL",0,0,"idling",,terminal_output
+56,149041,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+57,160599,"TERMINAL",0,0,"bash",,terminal_focus
+58,164468,"TERMINAL",0,0,"tmux a",,terminal_command
+59,164479,"TERMINAL",0,0,"]633;E;2025-07-14 15:31:46 tmux a;450fbccd-48d1-432b-b44b-06394616d158]633;Cno sessions\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;1",,terminal_output
+60,176523,"TERMINAL",0,0,"srun",,terminal_focus
+61,181253,"TERMINAL",0,0,"idling\r\n\r\r\n\r\r\n\r",,terminal_output
+62,182032,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output
+63,182304,"TERMINAL",0,0,"",,terminal_output
+64,182514,"TERMINAL",0,0,"",,terminal_output
+65,183774,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output
+66,184082,"TERMINAL",0,0,"m': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+67,184283,"TERMINAL",0,0,"\ro': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+68,184445,"TERMINAL",0,0,"\rd': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+69,184620,"TERMINAL",0,0,"[?25lm\ru': module unload devel/cuda/12.4\r\n\r\r\n\r\r\n\r[?25h",,terminal_output
+70,184813,"TERMINAL",0,0,"[1@l': modul",,terminal_output
+71,184920,"TERMINAL",0,0,"[?25lm[1@e': module[?25h",,terminal_output
+72,186275,"TERMINAL",0,0,"[?25l\r[9@jafar) [tum_cte0515@hkn0402 jafar]$ module\r\n[?2004l\r[?25h",,terminal_output
+73,186368,"TERMINAL",0,0,"Lmod Warning: \r\n----------------------------------------------------------------------------------------------------\r\nThe following dependent module(s) are not currently loaded: devel/cuda/12.4 (required by:\r\nmpi/openmpi/5.0)\r\n----------------------------------------------------------------------------------------------------\r\n\r\n\r\n\r\n]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+74,187458,"TERMINAL",0,0,"m",,terminal_output
+75,188318,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output
+76,188537,"TERMINAL",0,0,"[?25lm': module unload devel/cuda/12.4\r[?25h",,terminal_output
+77,188712,"TERMINAL",0,0,"[?25lm[1@o': mo[?25h",,terminal_output
+78,188827,"TERMINAL",0,0,"[?25lm[1@d': mod[?25h",,terminal_output
+79,188885,"TERMINAL",0,0,"[?25lme': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked[?25h",,terminal_output
+80,189124,"TERMINAL",0,0,"\rfailed reverse-i-search)`modeu': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsizescaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+81,189331,"TERMINAL",0,0,"[?25lm\rl': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked[?25h",,terminal_output
+82,190338,"TERMINAL",0,0,"^C\r\n\r\n\r[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+83,190778,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output
+84,190999,"TERMINAL",0,0,"m': module unload devel/cuda/12.4\r",,terminal_output
+85,191308,"TERMINAL",0,0,"[?25lm[1@o': mo[?25h",,terminal_output
+86,192625,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+87,193357,"TERMINAL",0,0,"modelsize-scaling/train_dynamics_modelsize_scaling_3",,terminal_output
+88,193987,"TERMINAL",0,0,"modelsize-scaling/train_dynamics_modelsize_scaling_3087000 --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+89,194447,"TERMINAL",0,0,"modelsize-scaling/train_dynamics_modelsize_scaling_3",,terminal_output
+90,194807,"TERMINAL",0,0,"\rmodule unload devel/cuda/12.4\r\n\r\r\n\r\r\n\r",,terminal_output
+91,195611,"TERMINAL",0,0,"mpi/openmpi/5.0\r",,terminal_output
+92,196151,"TERMINAL",0,0,"[?25l\r[13@jafar) [tum_cte0515@hkn0402 jafar]$ mo\r\n[?2004l\r[?25h",,terminal_output
+93,196276,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+94,197041,"TERMINAL",0,0,"module unload mpi/openmpi/5.0",,terminal_output
+95,197244,"TERMINAL",0,0,"devel/cuda/12.4",,terminal_output
+96,197809,"TERMINAL",0,0,"[?25l[?2004l\r[?25h\r\nNote: the module ""devel/cuda/12.4"" cannot be unloaded because it was not loaded.\r\n\r\n]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output
+97,202765,"TERMINAL",0,0,"module unload devel/cuda/12.4",,terminal_output
+98,202990,"TERMINAL",0,0,"mpi/openmpi/5.0",,terminal_output
+99,203154,"TERMINAL",0,0,"devel/cuda/12.4",,terminal_output
+100,203381,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output
+101,203782,"TERMINAL",0,0,"idling",,terminal_output
+102,204328,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output
+103,205187,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output
+104,206994,"sample.py",0,0,"",python,tab
+105,206997,"sample.py",5254,0,"",python,selection_mouse
+106,207025,"sample.py",5253,0,"",python,selection_command
+107,218430,"TERMINAL",0,0,"2025-07-14 15:32:40.851942: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output
+108,231780,"TERMINAL",0,0,"2025-07-14 15:32:54.216693: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output
+109,248304,"TERMINAL",0,0,"2025-07-14 15:33:10.775327: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output
+110,252736,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 91000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/091000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 89000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/089000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 90000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/090000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/080000/metrics/metrics not found.\r\n",,terminal_output
+111,273123,"sample.py",5019,0,"",python,selection_mouse
+112,273126,"sample.py",5018,0,"",python,selection_command
+113,273749,"sample.py",5254,0,"",python,selection_mouse
+114,273751,"sample.py",5253,0,"",python,selection_command
+115,277876,"TERMINAL",0,0,"bash",,terminal_focus
+116,283470,"TERMINAL",0,0,"cd $ws_dir",,terminal_command
+117,284396,"TERMINAL",0,0,"cd logs/",,terminal_command
+118,284816,"TERMINAL",0,0,"ls",,terminal_command
+119,284828,"TERMINAL",0,0,"]633;E;2025-07-14 15:33:47 ls;450fbccd-48d1-432b-b44b-06394616d158]633;C3306965  logs_alfred  logs_franz  logs_mihir\r\n]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs]633;D;0",,terminal_output
+120,287852,"TERMINAL",0,0,"Entering jdb:\r\n(jdb) ",,terminal_output
+121,287863,"TERMINAL",0,0,"cd logs_mihir/",,terminal_command
+122,287879,"TERMINAL",0,0,"]633;E;2025-07-14 15:33:50 cd logs_mihir/;450fbccd-48d1-432b-b44b-06394616d158]633;C]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output
+123,288184,"TERMINAL",0,0,"ls",,terminal_command
+124,288235,"TERMINAL",0,0,"]633;E;2025-07-14 15:33:50 ls;450fbccd-48d1-432b-b44b-06394616d158]633;C",,terminal_output
+125,288365,"TERMINAL",0,0,"big_run                                        train_lam_action_space_scaling_20_3329788.log   train_lam_model_size_scaling_38M_3317098.log            train_tokenizer_model_size_scaling_140M_3316019.log\r\nbig-runs                                       train_lam_action_space_scaling_20_3329803.log   train_lam_model_size_scaling_38M_3317115.log            train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_dyn_yolorun_3333026.log                  train_lam_action_space_scaling_20_3331285.log   train_lam_model_size_scaling_38M_3317231.log            train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_dyn_yolorun_3333448.log                  train_lam_action_space_scaling_50_3320180.log   train_tokenizer_batch_size_scaling_16_node_3321526.log  train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_dyn_yolorun_3335345.log                  train_lam_action_space_scaling_50_3329789.log   train_tokenizer_batch_size_scaling_1_node_3318551.log   train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_dyn_yolorun_3335362.log                  train_lam_action_space_scaling_50_3329804.log   train_tokenizer_batch_size_scaling_2_node_3318552.log   train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_10_3320179.log  train_lam_action_space_scaling_50_3331286.log   train_tokenizer_batch_size_scaling_2_node_3330806.log   train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_10_3321529.log  train_lam_action_space_scaling_6_3318549.log    train_tokenizer_batch_size_scaling_2_node_3330848.log   train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_10_3329786.log  train_lam_action_space_scaling_6_3320178.log    train_tokenizer_batch_size_scaling_2_node_3331282.log   train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_10_3329801.log  train_lam_action_space_scaling_6_3321528.log    train_tokenizer_batch_size_scaling_4_node_3318553.log   train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_10_3331283.log  train_lam_action_space_scaling_6_3329790.log    train_tokenizer_batch_size_scaling_4_node_3320175.log   train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3318546.log  train_lam_action_space_scaling_6_3329805.log    train_tokenizer_batch_size_scaling_4_node_3321524.log   train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_12_3320177.log  train_lam_action_space_scaling_6_3331287.log    train_tokenizer_batch_size_scaling_8_node_3320176.log   train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_12_3321527.log  train_lam_action_space_scaling_8_3318550.log    train_tokenizer_batch_size_scaling_8_node_3321525.log   train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_12_3329787.log  train_lam_action_space_scaling_8_3329791.log    train_tokenizer_minecraft_overfit_sample_3309656.log    train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_12_3329802.log  train_lam_action_space_scaling_8_3329806.log    train_tokenizer_model_size_scaling_127M_3317233.log     train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_12_3331284.log  train_lam_action_space_scaling_8_3331288.log    train_tokenizer_model_size_scaling_127M_3318554.log     train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_lam_action_space_scaling_20_3318547.log  train_lam_minecraft_overfit_sample_3309655.log  train_tokenizer_model_size_scaling_140M_3313562.log\r\n]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output
+126,291553,"TERMINAL",0,0,"cd big-runs/",,terminal_command
+127,292418,"TERMINAL",0,0,"ls",,terminal_command

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-7f667af1-e8dc-4534-8563-a4ef7be9de461754942097068-2025_08_11-21.55.24.740/source.csv

@@ -0,0 +1,22 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,461,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:55:24 PM [info] Activating crowd-code\n9:55:24 PM [info] Recording started\n9:55:24 PM [info] Initializing git provider using file system watchers...\n9:55:24 PM [info] Git repository found\n9:55:24 PM [info] Git provider initialized successfully\n",Log,tab
+3,483,"TERMINAL",0,0,"git branch",,terminal_command
+4,537,"TERMINAL",0,0,"]633;E;2025-08-11 21:55:24 git branch;686e78ec-8807-43f4-8f60-628f6dc2af90]633;C[?1h=\r",,terminal_output
+5,732,"TERMINAL",0,0,"* (HEAD detached at ab43d16)\r\n  add-wandb-name-and-tags\r\n  before-nnx\r\n  causal-mem-reduce\r\n  causal-spatiotemporal-kv-cache\r\n  causal-st-transformer\r\n  causal-transformer-dynamics-model\r\n  causal-transformer-nnx-no-kv-cache\r\n  convert-to-jax-array-in-iter\r\n  correct-batched-sampling\r\n  dev\r\n  dont-let-tf-see-gpu\r\n  feat/explicit-image-dims\r\n  fix-action-padding-lam-future-information-access\r\n  fix-sampling\r\n  fix-transformer-forwardpass\r\n  fix/spatiotemporal-pe-once-in-STTransformer\r\n  grad-norm-log-and-clip\r\n  grain-dataloader\r\n  input_pipeline/add-npy2array_record\r\n  logging-variants\r\n  lr-schedules\r\n  main\r\n  maskgit-different-maskprob-per-sample\r\n:",,terminal_output
+6,2249,"TERMINAL",0,0,"\r  maskgit-sampling-iterative-unmasking-fix\r\n:",,terminal_output
+7,2427,"TERMINAL",0,0,"\r  metrics-logging-for-dynamics-model\r\n:",,terminal_output
+8,2573,"TERMINAL",0,0,"\r  monkey-patch\r\n:",,terminal_output
+9,2811,"TERMINAL",0,0,"\r  new-arch-sampling\r\n:",,terminal_output
+10,2907,"TERMINAL",0,0,"\r  preprocess_video\r\n:",,terminal_output
+11,2986,"TERMINAL",0,0,"\r  refactor-tmp\r\n:",,terminal_output
+12,3470,"TERMINAL",0,0,"\rM  causal-st-transformer\r\n\r:",,terminal_output
+13,4286,"TERMINAL",0,0,"\rM  causal-spatiotemporal-kv-cache\r\n\r:\rM  causal-mem-reduce\r\n\r:\rM  before-nnx\r\n\r:\rM  add-wandb-name-and-tags\r\n\r:\rM* (HEAD detached at ab43d16)\r\n\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:",,terminal_output
+14,4417,"TERMINAL",0,0,"\r\r:\r\r:\r\r:\r\r:\r\r:",,terminal_output
+15,4470,"TERMINAL",0,0,"\r\r:\r\r:",,terminal_output
+16,4524,"TERMINAL",0,0,"\r\r:",,terminal_output
+17,4848,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output
+18,7630,"TERMINAL",0,0,"git checkout main",,terminal_command
+19,7730,"TERMINAL",0,0,"]633;E;2025-08-11 21:55:32 git checkout main;686e78ec-8807-43f4-8f60-628f6dc2af90]633;Cerror: Your local changes to the following files would be overwritten by checkout:\r\n\ttrain_dynamics.py\r\nPlease commit your changes or stash them before you switch branches.\r\nAborting\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;1",,terminal_output
+20,11378,"TERMINAL",0,0,"git stash",,terminal_command
+21,11436,"TERMINAL",0,0,"]633;E;2025-08-11 21:55:36 git stash;686e78ec-8807-43f4-8f60-628f6dc2af90]633;C",,terminal_output
+22,11610,"TERMINAL",0,0,"Saved working directory and index state WIP on (no branch): ab43d16 fix: missing reshape and shape suffixes in `sample.py` (#134)\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-9995c396-b28a-4817-9971-5d6e293ae32a1753180233918-2025_07_22-12.30.43.584/source.csv

@@ -0,0 +1,2 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,58754,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:30:43 PM [info] Activating crowd-code\n12:30:43 PM [info] Recording started\n12:30:43 PM [info] Initializing git provider using file system watchers...\n12:30:45 PM [info] Retrying git provider initialization...\n12:30:46 PM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4/.git'\n12:30:47 PM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4/.git'\n",Log,tab

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-b85ae4be-e003-4b5f-b92d-85f73081a0e71757580020189-2025_09_11-10.41.19.400/source.csv

@@ -0,0 +1,4 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,15,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\nimport optax\nimport orbax\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n    # Experiment\n    num_steps: int = 200_000\n    seed: int = 0\n    seq_len: int = 16\n    image_channels: int = 3\n    image_resolution: int = 64\n    data_dir: str = ""data/coinrun_episodes""\n    # Optimization\n    batch_size: int = 36\n    min_lr: float = 3e-6\n    max_lr: float = 3e-5\n    warmup_steps: int = 5000\n    # Tokenizer\n    tokenizer_dim: int = 512\n    latent_patch_dim: int = 32\n    num_patch_latents: int = 1024\n    patch_size: int = 4\n    tokenizer_num_blocks: int = 8\n    tokenizer_num_heads: int = 8\n    tokenizer_checkpoint: str = """"\n    # LAM\n    lam_dim: int = 512\n    latent_action_dim: int = 32\n    num_latent_actions: int = 6\n    lam_patch_size: int = 16\n    lam_num_blocks: int = 8\n    lam_num_heads: int = 8\n    lam_checkpoint: str = """"\n    # Dynamics\n    dyna_dim: int = 512\n    dyna_num_blocks: int = 12\n    dyna_num_heads: int = 8\n    dropout: float = 0.0\n    mask_limit: float = 0.5\n    # Logging\n    log: bool = False\n    entity: str = """"\n    project: str = """"\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 25000\n    log_gradients: bool = False\n    name: str = """"\n    tags: list[str] = field(default_factory=lambda: [""dynamics""])\n    wandb_id: str = """"\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n    """"""Compute masked dynamics loss""""""\n    outputs = state.apply_fn(\n        params, inputs, training=True, rngs={""dropout"": inputs[""dropout_rng""]}\n    )\n    mask = outputs[""mask""]\n    ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n        outputs[""token_logits""], outputs[""video_tokens""]\n    )\n    ce_loss = (mask * ce_loss).sum() / mask.sum()\n    acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n    acc = (mask * acc).sum() / mask.sum()\n    select_probs = jax.nn.softmax(outputs[""token_logits""])\n    metrics = dict(\n        cross_entropy_loss=ce_loss,\n        masked_token_accuracy=acc,\n        select_logit=outputs[""token_logits""].max(-1).mean(),\n        select_p=select_probs.max(-1).mean(),\n        entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n    )\n    return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n    """"""Update state and compute metrics""""""\n    grad_fn = jax.value_and_grad(dynamics_loss_fn, has_aux=True, allow_int=True)\n    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n    state = state.apply_gradients(grads=grads)\n    if args.log_gradients:\n        metrics[""gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""dynamics""]\n        )\n    return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n    rng = jax.random.PRNGKey(args.seed)\n    if args.log:\n        wandb_init_kwargs = {\n            ""entity"": args.entity,\n            ""project"": args.project,\n            ""name"": args.name,\n            ""tags"": args.tags,\n            ""group"": ""debug"",\n            ""config"": args,\n        }\n        if args.wandb_id:\n            wandb_init_kwargs.update(\n                {\n                    ""id"": args.wandb_id,\n                    ""resume"": ""allow"",\n                }\n            )\n        wandb.init(**wandb_init_kwargs)\n\n    # --- Initialize model ---\n    genie = Genie(\n        # Tokenizer\n        in_dim=args.image_channels,\n        tokenizer_dim=args.tokenizer_dim,\n        latent_patch_dim=args.latent_patch_dim,\n        num_patch_latents=args.num_patch_latents,\n        patch_size=args.patch_size,\n        tokenizer_num_blocks=args.tokenizer_num_blocks,\n        tokenizer_num_heads=args.tokenizer_num_heads,\n        # LAM\n        lam_dim=args.lam_dim,\n        latent_action_dim=args.latent_action_dim,\n        num_latent_actions=args.num_latent_actions,\n        lam_patch_size=args.lam_patch_size,\n        lam_num_blocks=args.lam_num_blocks,\n        lam_num_heads=args.lam_num_heads,\n        # Dynamics\n        dyna_dim=args.dyna_dim,\n        dyna_num_blocks=args.dyna_num_blocks,\n        dyna_num_heads=args.dyna_num_heads,\n        dropout=args.dropout,\n        mask_limit=args.mask_limit,\n    )\n    rng, _rng = jax.random.split(rng)\n    image_shape = (args.image_resolution, args.image_resolution, args.image_channels)\n    dummy_inputs = dict(\n        videos=jnp.zeros(\n            (args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32\n        ),\n        mask_rng=_rng,\n    )\n    rng, _rng = jax.random.split(rng)\n    init_params = genie.init(_rng, dummy_inputs)\n    init_params = restore_genie_components(\n        init_params, args.tokenizer_checkpoint, args.lam_checkpoint\n    )\n\n    # --- Initialize optimizer ---\n    lr_schedule = optax.warmup_cosine_decay_schedule(\n        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n    )\n    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n    train_state = TrainState.create(apply_fn=genie.apply, params=init_params, tx=tx)\n\n    # --- TRAIN LOOP ---\n    dataloader = get_dataloader(args.data_dir, args.seq_len, args.batch_size)\n    step = 0\n    while step < args.num_steps:\n        for videos in dataloader:\n            # --- Train step ---\n            rng, _rng, _mask_rng = jax.random.split(rng, 3)\n            inputs = dict(\n                videos=videos,\n                action=jnp.zeros((args.batch_size, args.seq_len), dtype=jnp.float32),\n                dropout_rng=_rng,\n                mask_rng=_mask_rng,\n            )\n            train_state, loss, recon, metrics = train_step(train_state, inputs)\n            print(f""Step {step}, loss: {loss}"")\n            step += 1\n\n            # --- Logging ---\n            if args.log:\n                if step % args.log_interval == 0:\n                    wandb.log({""loss"": loss, ""step"": step, **metrics})\n                if step % args.log_image_interval == 0:\n                    gt_seq = inputs[""videos""][0]\n                    recon_seq = recon[0].clip(0, 1)\n                    comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n                    comparison_seq = einops.rearrange(\n                        comparison_seq * 255, ""t h w c -> h (t w) c""\n                    )\n                    log_images = dict(\n                        image=wandb.Image(np.asarray(gt_seq[15])),\n                        recon=wandb.Image(np.asarray(recon_seq[15])),\n                        true_vs_recon=wandb.Image(\n                            np.asarray(comparison_seq.astype(np.uint8))\n                        ),\n                    )\n                    wandb.log(log_images)\n                if step % args.log_checkpoint_interval == 0:\n                    ckpt = {""model"": train_state}\n                    orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n                    save_args = orbax_utils.save_args_from_target(ckpt)\n                    orbax_checkpointer.save(\n                        os.path.join(os.getcwd(), args.ckpt_dir, f""genie_{ts}_{step}""),\n                        ckpt,\n                        save_args=save_args,\n                    )\n            if step >= args.num_steps:\n                break\n",python,tab
+2,910,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:41:19 AM [info] Activating crowd-code\n10:41:19 AM [info] Recording started\n10:41:19 AM [info] Initializing git provider using file system watchers...\n",Log,tab
+3,1419,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"10:41:19 AM [info] Git repository found\n10:41:19 AM [info] Git provider initialized successfully\n10:41:19 AM [info] Initial git state: [object Object]\n",Log,content

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-f514f25a-b687-4125-bb3e-747ec98309d21750930471988-2025_06_26-11.34.36.685/source.csv

@@ -0,0 +1,4 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,377,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"11:34:36 AM [info] Activating crowd-code\n11:34:36 AM [info] Recording started\n11:34:36 AM [info] Initializing git provider using file system watchers...\n11:34:36 AM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3291405/.git'\n",Log,tab
+3,2083,"extension-output-pdoom-org.crowd-code-#1-crowd-code",358,0,"11:34:38 AM [info] Retrying git provider initialization...\n",Log,content
+4,2162,"extension-output-pdoom-org.crowd-code-#1-crowd-code",417,0,"11:34:38 AM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3291405/.git'\n",Log,content