mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
8d5c07b781
This PR adds a user guide to AIR for using Ray Train. It provides a high level overview of the trainers and removes redundant sections. The main file to review is here: doc/source/ray-air/trainer.rst. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com> Signed-off-by: Richard Liaw <rliaw@berkeley.edu> Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Kai Fricke <kai@anyscale.com>
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
import ray
|
|
from ray import train
|
|
from ray.air import session, Checkpoint
|
|
from ray.train.torch import TorchTrainer
|
|
from ray.air.config import ScalingConfig
|
|
|
|
input_size = 1
|
|
layer_size = 15
|
|
output_size = 1
|
|
num_epochs = 3
|
|
|
|
|
|
class NeuralNetwork(nn.Module):
|
|
def __init__(self):
|
|
super(NeuralNetwork, self).__init__()
|
|
self.layer1 = nn.Linear(input_size, layer_size)
|
|
self.relu = nn.ReLU()
|
|
self.layer2 = nn.Linear(layer_size, output_size)
|
|
|
|
def forward(self, input):
|
|
return self.layer2(self.relu(self.layer1(input)))
|
|
|
|
|
|
def train_loop_per_worker():
|
|
dataset_shard = session.get_dataset_shard("train")
|
|
model = NeuralNetwork()
|
|
loss_fn = nn.MSELoss()
|
|
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
|
|
|
|
model = train.torch.prepare_model(model)
|
|
|
|
for epoch in range(num_epochs):
|
|
for batches in dataset_shard.iter_torch_batches(
|
|
batch_size=32, dtypes=torch.float
|
|
):
|
|
inputs, labels = torch.unsqueeze(batches["x"], 1), batches["y"]
|
|
output = model(inputs)
|
|
loss = loss_fn(output, labels)
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
print(f"epoch: {epoch}, loss: {loss.item()}")
|
|
|
|
session.report(
|
|
{},
|
|
checkpoint=Checkpoint.from_dict(
|
|
dict(epoch=epoch, model=model.state_dict())
|
|
),
|
|
)
|
|
|
|
|
|
train_dataset = ray.data.from_items([{"x": x, "y": 2 * x + 1} for x in range(200)])
|
|
scaling_config = ScalingConfig(num_workers=3)
|
|
# If using GPUs, use the below scaling config instead.
|
|
# scaling_config = ScalingConfig(num_workers=3, use_gpu=True)
|
|
trainer = TorchTrainer(
|
|
train_loop_per_worker=train_loop_per_worker,
|
|
scaling_config=scaling_config,
|
|
datasets={"train": train_dataset},
|
|
)
|
|
result = trainer.fit()
|