138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Here is an example of `Lightning Fault Tolerant Automatic`.
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Find the documentation: https://pytorch-lightning.readthedocs.io/en/stable/advanced/fault_tolerant_training.html
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RUN WITHOUT FAILURE:
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1. Launch `python pl_examples/fault_tolerant/automatic.py`.
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- You should see `[-1.1343, 0.0186]` in the logs.
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RUN WITH SIMULATED FAILURE:
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1. Launch `python pl_examples/fault_tolerant/automatic.py --emulate_kill_signal`.
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- You should see `kill -SIGTERM {PID}` in the logs.
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2. Run this command within another terminal.
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- You should see `Received signal 15. Saving a fault-tolerant checkpoint and terminating.` in the logs.
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3. Launch `python pl_examples/fault_tolerant/automatic.py --emulate_kill_signal` again.
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- You should see `Restored all states from the checkpoint file at ./.pl_auto_save.ckpt`
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- And you should see `[-1.1343, 0.0186]` in the logs.
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To restart the process, just run `rm .pl_auto_save.ckpt` to delete the auto restart checkpoint.
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This example shows that the weights trained with failure matches the weight trained without failure,
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thus the training has been properly resumed whilst being fully reproducible.
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Used PyTorch 1.7.1.
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"""
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import os
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import random as python_random
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from argparse import ArgumentParser
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from time import sleep
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, Dataset
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from pytorch_lightning import _logger as log
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from pytorch_lightning import LightningModule, seed_everything, Trainer
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class RandomGetItemDataset(Dataset):
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"""A dataset with random elements generated using global rng from torch, numpy and python."""
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def __init__(self, length, size):
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self.size = size
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self.len = length
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def __getitem__(self, index):
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t = torch.rand(self.size)
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n = torch.from_numpy(np.random.rand(self.size))
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p = torch.tensor([python_random.random() for _ in range(self.size)])
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sample = (index + (t + n + p) / 10).float()
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return sample
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def __len__(self):
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return self.len
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class SimpleMLP(LightningModule):
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def __init__(self, fail_on_step: int = -1):
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super().__init__()
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self.layer = torch.nn.Linear(1, 2)
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self.seen_batches = []
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self.fail_on_step = fail_on_step
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def training_step(self, batch, batch_idx):
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if self.global_step == self.fail_on_step:
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log.info(
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f"READY TO BE KILLED WITH SIGTERM SIGNAL. " f"Run `kill -SIGTERM {os.getpid()}` in another terminal."
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)
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# this line is used to wait for you to send the signal to exit gracefully.
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while not self.trainer._terminate_gracefully:
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sleep(0.1)
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batch = batch["data"] if isinstance(batch, dict) else batch
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self.seen_batches.append(torch.stack(batch) if isinstance(batch, list) else batch)
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loss = sum(self.layer(b).sum() for b in batch)
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return loss
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def configure_optimizers(self):
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return torch.optim.SGD(self.layer.parameters(), lr=0.1)
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def train_dataloader(self):
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return DataLoader(RandomGetItemDataset(3, 1))
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def _run_training(default_root_dir=".", max_epochs=3, fail_on_step: int = -1, ckpt_path=None):
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model = SimpleMLP(fail_on_step=fail_on_step)
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trainer = Trainer(default_root_dir=default_root_dir, max_epochs=max_epochs)
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trainer.fit(model, ckpt_path=ckpt_path)
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return model.seen_batches, model.parameters()
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def main(args):
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seed_everything(42)
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os.environ["PL_FAULT_TOLERANT_TRAINING"] = "automatic" # active fault tolerant automatic
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ckpt_path = ".pl_auto_save.ckpt"
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auto_restart_ckpt_path_exists = os.path.exists(ckpt_path)
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if args.emulate_kill_signal:
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fail_on_step = -1 if auto_restart_ckpt_path_exists else 4
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completed_batches = 4 if auto_restart_ckpt_path_exists else 5
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else:
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fail_on_step = -1
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completed_batches = 9
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complete_batches, weights = _run_training(fail_on_step=fail_on_step)
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assert len(complete_batches) == completed_batches
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if not auto_restart_ckpt_path_exists and args.emulate_kill_signal:
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assert os.path.exists(ckpt_path)
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if auto_restart_ckpt_path_exists or not args.emulate_kill_signal:
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log.info([w for w in weights])
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if __name__ == "__main__":
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parser = ArgumentParser(description="Fault Tolerant Under Signal Example")
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parser.add_argument(
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"--emulate_kill_signal",
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action="store_true",
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help="Whether you should gracefully kill the process with a `SIGTERM` signal.",
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)
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main(parser.parse_args())
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