mirror of https://github.com/hpcaitech/ColossalAI
114 lines
3.1 KiB
Python
114 lines
3.1 KiB
Python
import colossalai
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import os
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from colossalai.amp.amp_type import AMP_TYPE
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import torch.nn as nn
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from pathlib import Path
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from torchvision import transforms
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from torch.optim import Adam
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from colossalai.initialize import get_default_parser
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from colossalai.core import global_context as gpc
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from colossalai.logging import get_dist_logger
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from colossalai.trainer import Trainer
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from colossalai.utils import get_dataloader
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from torchvision.models import resnet18
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from torchvision.datasets import CIFAR10
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BATCH_SIZE = 128
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IMG_SIZE = 32
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NUM_EPOCHS = 200
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CONFIG = dict(
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# Config
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fp16=dict(
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mode=AMP_TYPE.TORCH
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)
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)
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def test_trainer():
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parser = get_default_parser()
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args = parser.parse_args()
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colossalai.launch(
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config=CONFIG,
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rank=args.rank,
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world_size=args.world_size,
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host=args.host,
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port=args.port,
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backend=args.backend
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)
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# build model
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model = resnet18(num_classes=10)
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# build dataloaders
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train_dataset = CIFAR10(
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root=Path(os.environ['DATA']),
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download=True,
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transform=transforms.Compose(
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[
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transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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]
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)
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)
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test_dataset = CIFAR10(
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root=Path(os.environ['DATA']),
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train=False,
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download=True,
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transform=transforms.Compose(
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[
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transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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]
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)
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)
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train_dataloader = get_dataloader(dataset=train_dataset,
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shuffle=True,
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batch_size=BATCH_SIZE,
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num_workers=1,
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pin_memory=True,
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drop_last=True)
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test_dataloader = get_dataloader(dataset=test_dataset,
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batch_size=BATCH_SIZE,
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num_workers=1,
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pin_memory=True,
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drop_last=True)
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# build optimizer
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optimizer = Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss()
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engine, train_dataloader, *args = colossalai.initialize(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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train_dataloader=train_dataloader
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)
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logger = get_dist_logger()
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logger.info("engine is built", ranks=[0])
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trainer = Trainer(engine=engine,
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logger=logger)
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logger.info("trainer is built", ranks=[0])
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logger.info("start training", ranks=[0])
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trainer.fit(
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train_dataloader=train_dataloader,
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test_dataloader=test_dataloader,
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epochs=NUM_EPOCHS,
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max_steps=100,
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display_progress=True,
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test_interval=5
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)
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if __name__ == '__main__':
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test_trainer()
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