mirror of
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daiyaanarfeen
8f6d73a93a
* raysgd * apply fn * double quotes * removed duplicate TimerStat * removed duplicate find_free_port * imports in pytorch_trainer * init doc * ray.experimental * remove resize example * resnet example * cifar * Fix up after kwargs * data_dir and dataloader_workers args * formatting * loss * init * update code * lint * smoketest * better_configs * fix * fix * fix * train_loader * fixdocs * ok * ok * fix * fix_update * fix * fix * done * fix * fix * fix * small * lint * fix * fix * fix_test * fix * validate * fix * fi
118 lines
3.2 KiB
Python
118 lines
3.2 KiB
Python
"""
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This file holds code for a Training guide for PytorchSGD in the documentation.
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It ignores yapf because yapf doesn't allow comments right after code blocks,
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but we put comments right after code blocks to prevent large white spaces
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in the documentation.
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"""
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# yapf: disable
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# __torch_train_example__
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import numpy as np
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import torch
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import torch.nn as nn
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from torch import distributed
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from torch.utils.data.distributed import DistributedSampler
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from ray.experimental.sgd.pytorch.pytorch_trainer import PyTorchTrainer
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class LinearDataset(torch.utils.data.Dataset):
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"""y = a * x + b"""
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def __init__(self, a, b, size=1000):
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x = np.random.random(size).astype(np.float32) * 10
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x = np.arange(0, 10, 10 / size, dtype=np.float32)
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self.x = torch.from_numpy(x)
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self.y = torch.from_numpy(a * x + b)
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def __getitem__(self, index):
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return self.x[index, None], self.y[index, None]
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def __len__(self):
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return len(self.x)
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def model_creator(config):
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return nn.Linear(1, 1)
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def optimizer_creator(model, config):
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"""Returns optimizer."""
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return torch.optim.SGD(model.parameters(), lr=1e-4)
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def data_creator(batch_size, config):
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"""Returns training dataloader, validation dataloader."""
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train_dataset = LinearDataset(2, 5)
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validation_dataset = LinearDataset(2, 5, size=400)
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train_sampler = None
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if distributed.is_initialized():
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train_sampler = DistributedSampler(train_dataset)
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train_loader = torch.utils.data.DataLoader(
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train_dataset,
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batch_size=batch_size,
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shuffle=(train_sampler is None),
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sampler=train_sampler)
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validation_sampler = None
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if distributed.is_initialized():
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validation_sampler = DistributedSampler(validation_dataset)
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validation_loader = torch.utils.data.DataLoader(
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validation_dataset,
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batch_size=batch_size,
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shuffle=(validation_sampler is None),
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sampler=validation_sampler)
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return train_loader, validation_loader
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def train_example(num_replicas=1, use_gpu=False):
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trainer1 = PyTorchTrainer(
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model_creator,
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data_creator,
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optimizer_creator,
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loss_creator=lambda config: nn.MSELoss(),
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num_replicas=num_replicas,
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use_gpu=use_gpu,
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batch_size=512,
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backend="gloo")
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trainer1.train()
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trainer1.shutdown()
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print("success!")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--redis-address",
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required=False,
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type=str,
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help="the address to use for Redis")
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parser.add_argument(
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"--num-replicas",
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"-n",
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type=int,
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default=1,
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help="Sets number of replicas for training.")
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parser.add_argument(
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"--use-gpu",
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action="store_true",
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default=False,
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help="Enables GPU training")
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parser.add_argument(
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"--tune", action="store_true", default=False, help="Tune training")
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args, _ = parser.parse_known_args()
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import ray
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ray.init(redis_address=args.redis_address)
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train_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)
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