mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
862d10c162
Removes all ML related code from `ray.util` Removes: - `ray.util.xgboost` - `ray.util.lightgbm` - `ray.util.horovod` - `ray.util.ray_lightning` Moves `ray.util.ml_utils` to other locations Closes #23900 Signed-off-by: Amog Kamsetty <amogkamsetty@yahoo.com> Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: Kai Fricke <kai@anyscale.com>
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
# This file is duplicated in ray/tests/ray_lightning
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import argparse
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import os
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import torch
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from torch import nn
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import torch.nn.functional as F
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from torchvision.datasets import MNIST
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from torch.utils.data import DataLoader, random_split
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from torchvision import transforms
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import pytorch_lightning as pl
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from ray_lightning import RayPlugin
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class LitAutoEncoder(pl.LightningModule):
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def __init__(self):
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super().__init__()
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self.encoder = nn.Sequential(
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nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3)
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)
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self.decoder = nn.Sequential(
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nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28)
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)
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def forward(self, x):
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# in lightning, forward defines the prediction/inference actions
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embedding = self.encoder(x)
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return embedding
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def training_step(self, batch, batch_idx):
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# training_step defines the train loop. It is independent of forward
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x, y = batch
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x = x.view(x.size(0), -1)
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z = self.encoder(x)
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x_hat = self.decoder(z)
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loss = F.mse_loss(x_hat, x)
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self.log("train_loss", loss)
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return loss
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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def main(num_workers: int = 2, use_gpu: bool = False, max_steps: int = 10):
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dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
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train, val = random_split(dataset, [55000, 5000])
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autoencoder = LitAutoEncoder()
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trainer = pl.Trainer(
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plugins=[RayPlugin(num_workers=num_workers, use_gpu=use_gpu)],
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max_steps=max_steps,
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)
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trainer.fit(autoencoder, DataLoader(train), DataLoader(val))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Ray Lightning Example",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--num-workers",
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type=int,
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default=2,
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help="Number of workers to use for training.",
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)
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parser.add_argument(
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"--max-steps",
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type=int,
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default=10,
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help="Maximum number of steps to run for training.",
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)
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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="Whether to enable GPU training.",
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)
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args = parser.parse_args()
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main(num_workers=args.num_workers, max_steps=args.max_steps, use_gpu=args.use_gpu)
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