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Sven
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
119 lines
3.4 KiB
Python
119 lines
3.4 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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from torch import distributed
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from torch.utils.data.distributed import DistributedSampler
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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(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 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(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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}
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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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