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utils/dataloader_new.py
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train_tokenizer.py
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from dataclasses import dataclass\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax\nfrom orbax.checkpoint import PyTreeCheckpointer\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 300_000\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_tfrecords/coinrun"\n checkpoint: str = ""\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 3e-4\n max_lr: float = 3e-4\n warmup_steps: int = 10000\n # Tokenizer\n model_dim: int = 512\n latent_dim: int = 32\n num_latents: int = 1024\n patch_size: int = 4\n num_blocks: int = 8\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.01\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 = 10000\n log_gradients: bool = False\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(params, state, inputs):\n # --- Compute loss ---\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={"dropout": inputs["rng"]}\n )\n mse = jnp.square(inputs["videos"] - outputs["recon"]).mean()\n q_loss = jnp.square(jax.lax.stop_gradient(outputs["emb"]) - outputs["z"]).mean()\n commitment_loss = jnp.square(\n outputs["emb"] - jax.lax.stop_gradient(outputs["z"])\n ).mean()\n loss = mse + q_loss + args.vq_beta * commitment_loss\n\n # --- Compute validation metrics ---\n gt = inputs["videos"].clip(0, 1).reshape(-1, *inputs["videos"].shape[2:])\n recon = outputs["recon"].clip(0, 1).reshape(-1, *outputs["recon"].shape[2:])\n psnr = pix.psnr(gt, recon).mean()\n ssim = pix.ssim(gt, recon).mean()\n _, index_counts = jnp.unique_counts(\n jnp.ravel(outputs["indices"]), size=args.num_latents, fill_value=0\n )\n codebook_usage = (index_counts != 0).mean()\n metrics = dict(\n loss=loss,\n mse=mse,\n q_loss=q_loss,\n commitment_loss=commitment_loss,\n psnr=psnr,\n ssim=ssim,\n codebook_usage=codebook_usage,\n )\n return loss, (outputs["recon"], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n grad_fn = jax.value_and_grad(tokenizer_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["encoder_gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["encoder"]\n )\n metrics["vq_gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["vq"]\n )\n metrics["decoder_gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["decoder"]\n )\n return state, loss, recon, metrics\n\n\nif __name__ == "__main__":\n jax.distributed.initialize()\n num_devices = jax.device_count()\n if num_devices == 0:\n raise ValueError("No JAX devices found.")\n print(f"Running on {num_devices} devices.")\n\n if args.batch_size % num_devices != 0:\n raise ValueError(\n f"Global batch size {args.batch_size} must be divisible by "\n f"number of devices {num_devices}."\n )\n\n per_device_batch_size_for_init = args.batch_size // num_devices\n\n rng = jax.random.PRNGKey(args.seed)\n if args.log and jax.process_index() == 0:\n wandb.init(entity=args.entity, project=args.project, group="debug", config=args)\n\n # --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_height, args.image_width, args.image_channels)\n inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=jnp.float32,\n ),\n )\n init_params = tokenizer.init(_rng, inputs)\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=tokenizer.apply, params=init_params, tx=tx)\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=("data",))\n\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Load checkpoint ---\n step = 0\n if args.checkpoint:\n restore_target = {"model": train_state}\n restore_args = orbax_utils.restore_args_from_target(restore_target)\n train_state.params["params"].update(\n PyTreeCheckpointer()\n .restore(args.checkpoint, item=restore_target, restore_args=restore_args)[\n "model"\n ]\n .params["params"]\n )\n # Assume checkpoint is of the form tokenizer_<timestamp>_<step>\n step += int(args.checkpoint.split("_")[-1])\n\n # --- TRAIN LOOP ---\n tfrecord_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith(".tfrecord")\n ]\n dataloader = get_dataloader(\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n tfrecord_files,\n args.seq_len,\n args.batch_size,\n *image_shape,\n )\n print(f"Starting training from step {step}...")\n while step < args.num_steps:\n print("Starting epoch...")\n # for videos in dataloader:\n videos = next(dataloader)\n while(True):\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n\n videos_sharding = NamedSharding(\n mesh, PartitionSpec("data", None, None, None, None)\n )\n if not hasattr(videos, 'sharding'):\n videos = jax.make_array_from_process_local_data(videos_sharding, videos)\n\n inputs = dict(videos=videos, rng=_rng)\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 # jax.lax.all_gather(inputs, 'data')\n # jax.lax.all_gather(recon, 'data')\n if args.log and jax.process_index() == 0:\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[0])),\n recon=wandb.Image(np.asarray(recon_seq[0])),\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"tokenizer_{ts}_{step}"),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n
|
python
|
tab
|
2
| 378
|
extension-output-pdoom-org.crowd-code-#1-crowd-code
| 0
| 0
|
12:59:14 PM [info] Activating crowd-code\n12:59:14 PM [info] Welcome back mahajanm. Your user-id is 'e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4'. Happy coding!\n12:59:14 PM [info] Recording started\n
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|
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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)
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python
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selection_mouse
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train_tokenizer.py
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|
\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n
|
python
|
selection_mouse
|
14
| 265,930
|
train_tokenizer.py
| 4,207
| 189
|
\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n
|
python
|
selection_mouse
|
15
| 265,931
|
train_tokenizer.py
| 4,169
| 227
|
\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n
|
python
|
selection_mouse
|
16
| 265,931
|
train_tokenizer.py
| 4,133
| 263
|
\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n
|
python
|
selection_mouse
|
17
| 265,931
|
train_tokenizer.py
| 4,099
| 297
|
\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n
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python
|
selection_mouse
|
18
| 265,964
|
train_tokenizer.py
| 4,397
| 0
| null |
python
|
selection_command
|
19
| 265,964
|
train_tokenizer.py
| 4,099
| 298
|
\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
20
| 266,311
|
train_tokenizer.py
| 4,063
| 334
|
\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
21
| 266,686
|
train_tokenizer.py
| 4,031
| 366
|
\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
22
| 267,781
|
train_tokenizer.py
| 4,063
| 334
|
\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
23
| 268,084
|
train_tokenizer.py
| 4,000
| 397
|
\n # --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
24
| 268,103
|
train_tokenizer.py
| 3,919
| 478
|
wandb.init(entity=args.entity, project=args.project, group="debug", config=args)\n\n # --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
25
| 268,104
|
train_tokenizer.py
| 3,915
| 482
|
wandb.init(entity=args.entity, project=args.project, group="debug", config=args)\n\n # --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
26
| 268,118
|
train_tokenizer.py
| 4,000
| 397
|
\n # --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
27
| 268,405
|
train_tokenizer.py
| 4,001
| 396
|
# --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
28
| 268,406
|
train_tokenizer.py
| 4,032
| 365
|
tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
29
| 268,454
|
train_tokenizer.py
| 4,064
| 333
|
in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
30
| 268,907
|
train_tokenizer.py
| 4,032
| 365
|
tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
31
| 270,869
|
train_tokenizer.py
| 4,074
| 0
| null |
python
|
selection_mouse
|
32
| 270,870
|
train_tokenizer.py
| 4,072
| 6
|
in_dim
|
python
|
selection_mouse
|
33
| 271,024
|
train_tokenizer.py
| 4,064
| 36
|
in_dim=args.image_channels,\n
|
python
|
selection_mouse
|
34
| 271,637
|
train_tokenizer.py
| 4,292
| 0
|
in_dim=args.image_channels,\n
|
python
|
content
|
35
| 271,637
|
train_tokenizer.py
| 4,064
| 36
| null |
python
|
content
|
36
| 272,749
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train_tokenizer.py
| 4,304
| 0
| null |
python
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selection_mouse
|
37
| 273,012
|
train_tokenizer.py
| 4,304
| 1
|
u
|
python
|
selection_mouse
|
38
| 273,012
|
train_tokenizer.py
| 4,304
| 58
|
um_heads,\n dropout=args.dropout,\n codebook_d
|
python
|
selection_mouse
|
39
| 273,012
|
train_tokenizer.py
| 4,304
| 93
|
um_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )
|
python
|
selection_mouse
|
40
| 273,217
|
train_tokenizer.py
| 4,397
| 0
| null |
python
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selection_mouse
|
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| 273,235
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train_tokenizer.py
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| null |
python
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selection_command
|
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| 274,575
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train_tokenizer.py
| 4,256
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| null |
python
|
content
|
43
| 274,575
|
train_tokenizer.py
| 4,064
| 0
|
in_dim=args.image_channels,\n
|
python
|
content
|
44
| 289,787
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train_tokenizer.py
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| null |
python
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selection_mouse
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TERMINAL
| 0
| 0
| null | null |
terminal_focus
|
46
| 302,051
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TERMINAL
| 0
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|
source .venv/bin/activate
| null |
terminal_command
|
47
| 302,085
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TERMINAL
| 0
| 0
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[?25l[?2004l\r]633;E;source .venv/bin/activate;eb7c5413-c8c9-4ed8-a628-a8dc283c2a03]633;C[?25h]0;mahajanm@atcremers51: /usr/stud/mahajanm/Projects/jafar]633;D;0]633;P;Cwd=/usr/stud/mahajanm/Projects/jafar
| null |
terminal_output
|
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| 303,869
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train_tokenizer.py
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python
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tab
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train_tokenizer.py
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python
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selection_mouse
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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train_tokenizer.py
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scripts/train_tokenizer_overfit_batch.sbatch
| 0
| 0
|
#!/bin/bash\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=2\n#SBATCH --time=10:00:00\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:2,VRAM:12G\n#SBATCH --mem=50G\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --job-name=train_tokenizer_minecraft_overfit_batch\n\n# Log the sbatch script\ncat $0\n\ntf_records_dir="/storage/user/mahajanm/Projects/world-modeling/knoms_tfrecords_500/"\nws_dir='/storage/user/mahajanm/Projects/world-modeling'\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nSLURM_STEP_NODELIST=$SLURM_NODELIST srun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=6 \\n --min_lr=1e-4 \\n --max_lr=1e-4 \\n --log_image_interval=3 \\n --log \\n --entity instant-uv \\n --project jafar \\n --data_dir $tf_records_dir\n
|
shellscript
|
tab
|
87
| 466,219
|
TERMINAL
| 0
| 0
|
sbatch scripts/train_tokenizer_overfit_batch.sbatch
| null |
terminal_command
|
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