我编写了一个简单的 pytorch 脚本来训练 MNIST，它运行良好。我重新实现了我的脚本以使用 Trainable 类：
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F
import ray
from ray import tune
# Change these values if you want the training to run quicker or slower.
EPOCH_SIZE = 512
TEST_SIZE = 256
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
# In this example, we don't change the model architecture
# due to simplicity.
self.conv1 = nn.Conv2d(1, 3, kernel_size=3)
self.fc = nn.Linear(192, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 3))
x = x.view(-1, 192)
x = self.fc(x)
return F.log_softmax(x, dim=1)
class AlexTrainer(tune.Trainable):
def setup(self, config):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Data Setup
mnist_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
self.train_loader = DataLoader(
datasets.MNIST("~/data", train=True, download=True, transform=mnist_transforms),
batch_size=64,
shuffle=True)
self.test_loader = DataLoader(
datasets.MNIST("~/data", train=False, transform=mnist_transforms),
batch_size=64,
shuffle=True)
self.model = ConvNet()
self.optimizer = optim.SGD(self.model.parameters(), lr=config["lr"], momentum=config["momentum"])
print('finished setup')
def step(self):
self.train()
print("after train")
acc = self.test()
return {'acc': acc}
def train(self):
print("in train")
self.model.train()
for batch_idx, (data, target) in enumerate(self.train_loader):
# We set this just for the example to run quickly.
if batch_idx * len(data) > EPOCH_SIZE:
return
data, target = data.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
print(type(data))
output = self.model(data)
loss = F.nll_loss(output, target)
loss.backward()
self.optimizer.step()
def test(self):
self.model.eval()
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (data, target) in enumerate(self.test_loader):
# We set this just for the example to run quickly.
if batch_idx * len(data) > TEST_SIZE:
break
data, target = data.to(self.device), target.to(self.device)
outputs = self.model(data)
_, predicted = torch.max(outputs.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
return correct / total
if __name__ == '__main__':
ray.init()
analysis = tune.run(
AlexTrainer,
stop={"training_iteration": 2},
# verbose=1,
config={
"lr": tune.sample_from(lambda spec: 10 ** (-10 * np.random.rand())),
"momentum": tune.uniform(0.1, 0.9)
}
)
但是，当我尝试运行时，这次它失败了：
Traceback (most recent call last):
File "/hdd/raytune/venv/lib/python3.6/site-packages/ray/tune/trial_runner.py", line 473, in _process_trial
is_duplicate = RESULT_DUPLICATE in result
TypeError: argument of type 'NoneType' is not iterable
Traceback (most recent call last):
File "/hdd/raytune/test_3.py", line 116, in <module>
"momentum": tune.uniform(0.1, 0.9)
File "/hdd/raytune/venv/lib/python3.6/site-packages/ray/tune/tune.py", line 356, in run
raise TuneError("Trials did not complete", incomplete_trials)
ray.tune.error.TuneError: ('Trials did not complete', [AlexTrainer_9b3cd_00000])
这可能是什么原因？