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Richard LiawandGitHub
037aa2b961
* Refactor documentation and directory structurre * update loss * ,ore examples * fix comments * more code * svgs * formatting * more_docs * more writing * comments ready * move * whitespace * examples * fix * bold * pytorch * batch * fix * fix test * Apply suggestions from code review * quarantinegp * tests/ * fix missing
97 lines
2.6 KiB
Python
97 lines
2.6 KiB
Python
# yapf: disable
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"""
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This file holds code for a Distributed Pytorch + Tune page in the docs.
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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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# __torch_tune_example__
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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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import ray
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from ray import tune
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from ray.experimental.sgd.pytorch.pytorch_trainer import PyTorchTrainable
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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=config.get("lr", 1e-4))
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def data_creator(config):
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"""Returns training dataloader, validation dataloader."""
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return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
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def tune_example(num_replicas=1, use_gpu=False):
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config = {
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"model_creator": tune.function(model_creator),
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"data_creator": tune.function(data_creator),
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"optimizer_creator": tune.function(optimizer_creator),
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"loss_creator": tune.function(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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}
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analysis = tune.run(
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PyTorchTrainable,
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num_samples=12,
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config=config,
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stop={"training_iteration": 2},
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verbose=1)
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return analysis.get_best_config(metric="validation_loss", mode="min")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--address",
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type=str,
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help="the address to use for Ray")
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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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ray.init(address=args.address)
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tune_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)
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