File size: 54,027 Bytes
7decf4c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"id": "9074d4b4",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import scipy.io\n",
"from torchvision import transforms, utils\n",
"import os\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c85ff158",
"metadata": {},
"outputs": [],
"source": [
"class GravityDataset(Dataset):\n",
" def __init__(self, folder, image_size, exts=['mat']):\n",
" super().__init__()\n",
" self.folder = folder\n",
" self.image_size = image_size\n",
" self.paths = [os.path.join(folder, f) for f in os.listdir(folder) if f.endswith('.mat')]\n",
"\n",
" # Define transformations that are independent of scaling\n",
" self.transform = transforms.Compose([\n",
" transforms.Resize((int(image_size * 1.12), int(image_size * 1.12))), # Resize slightly larger\n",
" transforms.RandomCrop(image_size), # Then crop to the target size\n",
" transforms.RandomHorizontalFlip(), # Random horizontal flip\n",
" transforms.ToTensor() # Convert to tensor\n",
" ])\n",
"\n",
" def scale_to_minus1_1(self, tensor):\n",
" \"\"\"Dynamically scale the tensor to the range [-1, 1] based on its own min and max values.\"\"\"\n",
" min_val = tensor.min()\n",
" max_val = tensor.max()\n",
" # Avoid division by zero if min and max are the same\n",
" if max_val > min_val:\n",
" return 2 * ((tensor - min_val) / (max_val - min_val)) - 1\n",
" else:\n",
" return tensor # If min and max are the same, return tensor as is.\n",
" # tensor = tensor\n",
" return tensor # If min and max are the same, return tensor as is.\n",
"\n",
" def __len__(self):\n",
" return len(self.paths)\n",
"\n",
" def __getitem__(self, index):\n",
" file_path = self.paths[index]\n",
" # Load the .mat file\n",
" data_loc = scipy.io.loadmat(file_path)\n",
" data_val = data_loc['d']\n",
"\n",
" data_val = data_val.reshape(32, 32)\n",
" # Convert numpy array as a PIL image\n",
" img = Image.fromarray(data_val)\n",
"\n",
" # Apply transformations\n",
" if self.transform:\n",
" img = self.transform(img)\n",
" \n",
" # Scale the image to [-1, 1] based on its own min and max values\n",
" img = self.scale_to_minus1_1(img)\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c16dc0d1",
"metadata": {},
"outputs": [],
"source": [
"data = '/mnt/drive/adarsh/DC_cold3/Experiment-14/neg_int'\n",
"dataset = GravityDataset(data, image_size=32)\n",
"dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a8bd15c0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 28.56it/s]\n"
]
},
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from diffusers import StableDiffusionPipeline\n",
"\n",
"pipeline = StableDiffusionPipeline.from_pretrained(\"stable-diffusion-v1-5/stable-diffusion-v1-5\", use_safetensors=True)\n",
"pipeline.unet.config[\"in_channels\"]\n",
"4"
]
},
{
"cell_type": "markdown",
"id": "72b20338",
"metadata": {},
"source": [
"**Training Configuration**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e01c3fae",
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"\n",
"@dataclass\n",
"class TrainingConfig:\n",
" image_size = 32 # the generated image resolution\n",
" train_batch_size = 32\n",
" eval_batch_size = 16 # how many images to sample during evaluation\n",
" num_epochs = 50\n",
" gradient_accumulation_steps = 1\n",
" learning_rate = 1e-4\n",
" lr_warmup_steps = 500\n",
" save_image_epochs = 10\n",
" save_model_epochs = 30\n",
" mixed_precision = \"fp16\" # `no` for float32, `fp16` for automatic mixed precision\n",
" output_dir = \"ddpm-butterflies-128\" # the model name locally and on the HF Hub\n",
"\n",
" push_to_hub = True # whether to upload the saved model to the HF Hub\n",
" hub_model_id = \"jainadarsh/trial\" # the name of the repository to create on the HF Hub\n",
" hub_private_repo = None\n",
" overwrite_output_dir = True # overwrite the old model when re-running the notebook\n",
" seed = 0\n",
"\n",
"\n",
"config = TrainingConfig()"
]
},
{
"cell_type": "markdown",
"id": "9f52a27e",
"metadata": {},
"source": [
"**Load Dataset**"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "b4a98567",
"metadata": {},
"outputs": [],
"source": [
"# from datasets import load_dataset\n",
"\n",
"# config.dataset_name = \"huggan/smithsonian_butterflies_subset\"\n",
"# dataset = load_dataset(config.dataset_name, split=\"train\")\n",
"\n",
"data = '/mnt/drive/adarsh/DC_cold3/Experiment-14/neg_int'\n",
"dataset = GravityDataset(data, image_size=32)\n",
"train_dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "e3aed193",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import torch\n",
"\n",
"batch = next(iter(dataloader)) # batch shape (B, C, H, W)\n",
"imgs = batch[:4] # take first 4\n",
"\n",
"fig, axs = plt.subplots(1, 4, figsize=(16, 4))\n",
"for i, img in enumerate(imgs):\n",
" img = img.detach().cpu()\n",
" if img.ndim == 3: # (C,H,W) -> (H,W,C)\n",
" img = img.permute(1, 2, 0)\n",
" img = (img + 1) / 2 # convert from [-1,1] to [0,1]\n",
" arr = img.numpy().squeeze()\n",
" axs[i].imshow(arr, cmap='gray' if arr.ndim == 2 else None)\n",
" axs[i].axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "06c1dff6",
"metadata": {},
"source": [
"**Create a Architecture**"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "3ee088b3",
"metadata": {},
"outputs": [],
"source": [
"from diffusers import UNet2DModel\n",
"\n",
"model = UNet2DModel(\n",
" sample_size=config.image_size, # the target image resolution\n",
" in_channels=1, # the number of input channels, 3 for RGB images\n",
" out_channels=1, # the number of output channels\n",
" layers_per_block=2, # how many ResNet layers to use per UNet block\n",
" block_out_channels=(128, 128, 256, 256, 512, 512), # the number of output channels for each UNet block\n",
" down_block_types=(\n",
" \"DownBlock2D\", # a regular ResNet downsampling block\n",
" \"DownBlock2D\",\n",
" \"DownBlock2D\",\n",
" \"DownBlock2D\",\n",
" \"AttnDownBlock2D\", # a ResNet downsampling block with spatial self-attention\n",
" \"DownBlock2D\",\n",
" ),\n",
" up_block_types=(\n",
" \"UpBlock2D\", # a regular ResNet upsampling block\n",
" \"AttnUpBlock2D\", # a ResNet upsampling block with spatial self-attention\n",
" \"UpBlock2D\",\n",
" \"UpBlock2D\",\n",
" \"UpBlock2D\",\n",
" \"UpBlock2D\",\n",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "8a703029",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input shape: torch.Size([1, 1, 32, 32])\n",
"Output shape: torch.Size([1, 1, 32, 32])\n"
]
}
],
"source": [
"sample_image = dataset.__getitem__(0).unsqueeze(0) # add batch dimension\n",
"print(\"Input shape:\", sample_image.shape)\n",
"print(\"Output shape:\", model(sample_image, timestep=0).sample.shape)"
]
},
{
"cell_type": "markdown",
"id": "8d3d9af1",
"metadata": {},
"source": [
"**Create a scheduler**"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "22c2ce21",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import torch\n",
"from PIL import Image\n",
"from diffusers import DDPMScheduler\n",
"\n",
"noise_scheduler = DDPMScheduler(num_train_timesteps = 1000)\n",
"noise = torch.randn(sample_image.shape)\n",
"timesteps = torch.LongTensor([50]) # example timestep\n",
"noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n",
"\n",
"# print(\"Noisy image shape:\", noisy_image.shape)\n",
"# Image.fromarray(noisy_image.numpy().squeeze())\n",
"plt.imshow(noisy_image.detach().cpu().numpy().squeeze(), cmap='gray')\n",
"plt.colorbar()\n",
"# plt.axis('off')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "4e878652",
"metadata": {},
"outputs": [],
"source": [
"import torch.nn.functional as F\n",
"\n",
"noise_pred = model(noisy_image, timesteps).sample\n",
"loss = F.mse_loss(noise_pred, noise)"
]
},
{
"cell_type": "markdown",
"id": "7dffb56e",
"metadata": {},
"source": [
"**Train the model**"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "62915913",
"metadata": {},
"outputs": [],
"source": [
"from diffusers.optimization import get_cosine_schedule_with_warmup\n",
"\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n",
"lr_scheduler = get_cosine_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=config.lr_warmup_steps,\n",
" num_training_steps=(len(train_dataloader) * config.num_epochs),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "f2f3427a",
"metadata": {},
"outputs": [],
"source": [
"from diffusers import DDPMPipeline\n",
"from diffusers.utils import make_image_grid\n",
"import os\n",
"\n",
"def evaluate(config, epoch, pipeline):\n",
" # Sample some images from random noise (this is the backward diffusion process).\n",
" # The default pipeline output type is `List[PIL.Image]`\n",
" images = pipeline(\n",
" batch_size=config.eval_batch_size,\n",
" generator=torch.Generator(device='cpu').manual_seed(config.seed), # Use a separate torch generator to avoid rewinding the random state of the main training loop\n",
" ).images\n",
"\n",
" # Make a grid out of the images\n",
" image_grid = make_image_grid(images, rows=4, cols=4)\n",
"\n",
" # Save the images\n",
" test_dir = os.path.join(config.output_dir, \"samples\")\n",
" os.makedirs(test_dir, exist_ok=True)\n",
" image_grid.save(f\"{test_dir}/{epoch:04d}.png\")"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "4fe935dd",
"metadata": {},
"outputs": [],
"source": [
"from accelerate import Accelerator\n",
"from huggingface_hub import create_repo, upload_folder\n",
"from tqdm.auto import tqdm\n",
"from pathlib import Path\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "07df8a4c",
"metadata": {},
"outputs": [],
"source": [
"def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):\n",
" # Initialize accelerator and tensorboard logging\n",
" accelerator = Accelerator(\n",
" mixed_precision=config.mixed_precision,\n",
" gradient_accumulation_steps=config.gradient_accumulation_steps,\n",
" log_with=\"tensorboard\",\n",
" project_dir=os.path.join(config.output_dir, \"logs\"),\n",
" )\n",
" if accelerator.is_main_process:\n",
" if config.output_dir is not None:\n",
" os.makedirs(config.output_dir, exist_ok=True)\n",
" if config.push_to_hub:\n",
" repo_id = create_repo(\n",
" repo_id=config.hub_model_id or Path(config.output_dir).name, exist_ok=True\n",
" ).repo_id\n",
" accelerator.init_trackers(\"train_example\")\n",
"\n",
" # Prepare everything\n",
" # There is no specific order to remember, you just need to unpack the\n",
" # objects in the same order you gave them to the prepare method.\n",
" model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n",
" model, optimizer, train_dataloader, lr_scheduler\n",
" )\n",
"\n",
" global_step = 0\n",
"\n",
" # Now you train the model\n",
" for epoch in range(config.num_epochs):\n",
" progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\n",
" progress_bar.set_description(f\"Epoch {epoch}\")\n",
"\n",
" for step, batch in enumerate(train_dataloader):\n",
" clean_images = batch[\"images\"]\n",
" # Sample noise to add to the images\n",
" noise = torch.randn(clean_images.shape, device=clean_images.device)\n",
" bs = clean_images.shape[0]\n",
"\n",
" # Sample a random timestep for each image\n",
" timesteps = torch.randint(\n",
" 0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,\n",
" dtype=torch.int64\n",
" )\n",
"\n",
" # Add noise to the clean images according to the noise magnitude at each timestep\n",
" # (this is the forward diffusion process)\n",
" noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n",
"\n",
" with accelerator.accumulate(model):\n",
" # Predict the noise residual\n",
" noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n",
" loss = F.mse_loss(noise_pred, noise)\n",
" accelerator.backward(loss)\n",
"\n",
" if accelerator.sync_gradients:\n",
" accelerator.clip_grad_norm_(model.parameters(), 1.0)\n",
" optimizer.step()\n",
" lr_scheduler.step()\n",
" optimizer.zero_grad()\n",
"\n",
" progress_bar.update(1)\n",
" logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n",
" progress_bar.set_postfix(**logs)\n",
" accelerator.log(logs, step=global_step)\n",
" global_step += 1\n",
"\n",
" # After each epoch you optionally sample some demo images with evaluate() and save the model\n",
" if accelerator.is_main_process:\n",
" pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\n",
"\n",
" if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:\n",
" evaluate(config, epoch, pipeline)\n",
"\n",
" if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:\n",
" if config.push_to_hub:\n",
" upload_folder(\n",
" repo_id=repo_id,\n",
" folder_path=config.output_dir,\n",
" commit_message=f\"Epoch {epoch}\",\n",
" ignore_patterns=[\"step_*\", \"epoch_*\"],\n",
" )\n",
" else:\n",
" pipeline.save_pretrained(config.output_dir)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "5d820f05",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Launching training on one GPU.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/drive/adarsh/DC_cold3/my-env/lib/python3.12/site-packages/accelerate/accelerator.py:529: UserWarning: `log_with=tensorboard` was passed but no supported trackers are currently installed.\n",
" warnings.warn(f\"`log_with={log_with}` was passed but no supported trackers are currently installed.\")\n",
"Epoch 0: 0%| | 0/344 [00:00<?, ?it/s]/tmp/ipykernel_288020/2880730127.py:33: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n",
" clean_images = batch[\"images\"]\n"
]
},
{
"ename": "IndexError",
"evalue": "too many indices for tensor of dimension 4",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mIndexError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[88]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01maccelerate\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m notebook_launcher\n\u001b[32m 2\u001b[39m args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mnotebook_launcher\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_loop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_processes\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m/mnt/drive/adarsh/DC_cold3/my-env/lib/python3.12/site-packages/accelerate/launchers.py:270\u001b[39m, in \u001b[36mnotebook_launcher\u001b[39m\u001b[34m(function, args, num_processes, mixed_precision, use_port, master_addr, node_rank, num_nodes, rdzv_backend, rdzv_endpoint, rdzv_conf, rdzv_id, max_restarts, monitor_interval, log_line_prefix_template)\u001b[39m\n\u001b[32m 268\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 269\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mLaunching training on CPU.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m270\u001b[39m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[86]\u001b[39m\u001b[32m, line 33\u001b[39m, in \u001b[36mtrain_loop\u001b[39m\u001b[34m(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\u001b[39m\n\u001b[32m 30\u001b[39m progress_bar.set_description(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 32\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m step, batch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(train_dataloader):\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m clean_images = \u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mimages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 34\u001b[39m \u001b[38;5;66;03m# Sample noise to add to the images\u001b[39;00m\n\u001b[32m 35\u001b[39m noise = torch.randn(clean_images.shape, device=clean_images.device)\n",
"\u001b[31mIndexError\u001b[39m: too many indices for tensor of dimension 4"
]
}
],
"source": [
"from accelerate import notebook_launcher\n",
"args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n",
"notebook_launcher(train_loop, args, num_processes=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "my-env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|