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8fe439998e
Signed-off-by: Kai Fricke coding@kaifricke.com Why are these changes needed? Splitting up #26884: This PR includes changes to use Tuner() instead of tune.run() for most docs files (rst and py), and a change to move reuse_actors to the TuneConfig
121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
# flake8: noqa
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import os
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from filelock import FileLock
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import torch.nn as nn
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import torch.nn.functional as F
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from torchvision import datasets, transforms
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EPOCH_SIZE = 512
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TEST_SIZE = 256
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def train(model, optimizer, train_loader, device=None):
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device = device or torch.device("cpu")
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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if batch_idx * len(data) > EPOCH_SIZE:
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return
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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def test(model, data_loader, device=None):
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device = device or torch.device("cpu")
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model.eval()
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correct = 0
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total = 0
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with torch.no_grad():
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for batch_idx, (data, target) in enumerate(data_loader):
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if batch_idx * len(data) > TEST_SIZE:
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break
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data, target = data.to(device), target.to(device)
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outputs = model(data)
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_, predicted = torch.max(outputs.data, 1)
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total += target.size(0)
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correct += (predicted == target).sum().item()
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return correct / total
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def load_data():
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mnist_transforms = transforms.Compose(
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[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
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)
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with FileLock(os.path.expanduser("~/data.lock")):
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train_loader = torch.utils.data.DataLoader(
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datasets.MNIST(
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"~/data", train=True, download=True, transform=mnist_transforms
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),
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batch_size=64,
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shuffle=True,
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)
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test_loader = torch.utils.data.DataLoader(
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datasets.MNIST(
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"~/data", train=False, download=True, transform=mnist_transforms
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),
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batch_size=64,
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shuffle=True,
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)
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return train_loader, test_loader
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class ConvNet(nn.Module):
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def __init__(self):
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super(ConvNet, self).__init__()
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self.conv1 = nn.Conv2d(1, 3, kernel_size=3)
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self.fc = nn.Linear(192, 10)
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def forward(self, x):
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x = F.relu(F.max_pool2d(self.conv1(x), 3))
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x = x.view(-1, 192)
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x = self.fc(x)
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return F.log_softmax(x, dim=1)
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# __pytorch_optuna_start__
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# 1. Wrap your PyTorch model in an objective function.
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import torch
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from ray import tune, air
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from ray.air import session
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from ray.tune.search.optuna import OptunaSearch
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# 1. Wrap a PyTorch model in an objective function.
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def objective(config):
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train_loader, test_loader = load_data() # Load some data
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model = ConvNet().to("cpu") # Create a PyTorch conv net
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optimizer = torch.optim.SGD( # Tune the optimizer
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model.parameters(), lr=config["lr"], momentum=config["momentum"]
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)
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while True:
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train(model, optimizer, train_loader) # Train the model
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acc = test(model, test_loader) # Compute test accuracy
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session.report({"mean_accuracy": acc}) # Report to Tune
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# 2. Define a search space and initialize the search algorithm.
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search_space = {"lr": tune.loguniform(1e-4, 1e-2), "momentum": tune.uniform(0.1, 0.9)}
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algo = OptunaSearch()
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# 3. Start a Tune run that maximizes mean accuracy and stops after 5 iterations.
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tuner = tune.Tuner(
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objective,
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tune_config=tune.TuneConfig(
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metric="mean_accuracy",
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mode="max",
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search_alg=algo,
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),
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run_config=air.RunConfig(
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stop={"training_iteration": 5},
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),
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param_space=search_space,
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)
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results = tuner.fit()
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print("Best config is:", results.get_best_result().config)
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# __pytorch_optuna_end__
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