How to use Tune with PyTorch

In this walkthrough, we will show you how to integrate Tune into your PyTorch training workflow. We will follow this tutorial from the PyTorch documentation for training a CIFAR10 image classifier.

../../_images/pytorch_logo.png

Hyperparameter tuning can make the difference between an average model and a highly accurate one. Often simple things like choosing a different learning rate or changing a network layer size can have a dramatic impact on your model performance. Fortunately, Tune makes exploring these optimal parameter combinations easy - and works nicely together with PyTorch.

As you will see, we only need to add some slight modifications. In particular, we need to

  1. wrap data loading and training in functions,

  2. make some network parameters configurable,

  3. add checkpointing (optional),

  4. and define the search space for the model tuning

Note

To run this example, you will need to install the following:

$ pip install ray torch torchvision

Setup / Imports

Let’s start with the imports:

import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from filelock import FileLock
from torch.utils.data import random_split
import torchvision
import torchvision.transforms as transforms
import ray
from ray import tune
from ray.tune.schedulers import ASHAScheduler

Most of the imports are needed for building the PyTorch model. Only the last three imports are for Ray Tune.

Data loaders

We wrap the data loaders in their own function and pass a global data directory. This way we can share a data directory between different trials.

def load_data(data_dir="./data"):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    # We add FileLock here because multiple workers will want to
    # download data, and this may cause overwrites since
    # DataLoader is not threadsafe.
    with FileLock(os.path.expanduser("~/.data.lock")):
        trainset = torchvision.datasets.CIFAR10(
            root=data_dir, train=True, download=True, transform=transform)

        testset = torchvision.datasets.CIFAR10(
            root=data_dir, train=False, download=True, transform=transform)

    return trainset, testset

Configurable neural network

We can only tune those parameters that are configurable. In this example, we can specify the layer sizes of the fully connected layers:

class Net(nn.Module):
    def __init__(self, l1=120, l2=84):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, l1)
        self.fc2 = nn.Linear(l1, l2)
        self.fc3 = nn.Linear(l2, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

The train function

Now it gets interesting, because we introduce some changes to the example from the PyTorch documentation.

The full code example looks like this:

def train_cifar(config, checkpoint_dir=None):
    net = Net(config["l1"], config["l2"])

    device = "cpu"
    if torch.cuda.is_available():
        device = "cuda:0"
        if torch.cuda.device_count() > 1:
            net = nn.DataParallel(net)
    net.to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9)

    # The `checkpoint_dir` parameter gets passed by Ray Tune when a checkpoint
    # should be restored.
    if checkpoint_dir:
        checkpoint = os.path.join(checkpoint_dir, "checkpoint")
        model_state, optimizer_state = torch.load(checkpoint)
        net.load_state_dict(model_state)
        optimizer.load_state_dict(optimizer_state)

    data_dir = os.path.abspath("./data")
    trainset, testset = load_data(data_dir)

    test_abs = int(len(trainset) * 0.8)
    train_subset, val_subset = random_split(
        trainset, [test_abs, len(trainset) - test_abs])

    trainloader = torch.utils.data.DataLoader(
        train_subset,
        batch_size=int(config["batch_size"]),
        shuffle=True,
        num_workers=8)
    valloader = torch.utils.data.DataLoader(
        val_subset,
        batch_size=int(config["batch_size"]),
        shuffle=True,
        num_workers=8)

    for epoch in range(10):  # loop over the dataset multiple times
        running_loss = 0.0
        epoch_steps = 0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            epoch_steps += 1
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1,
                                                running_loss / epoch_steps))
                running_loss = 0.0

        # Validation loss
        val_loss = 0.0
        val_steps = 0
        total = 0
        correct = 0
        for i, data in enumerate(valloader, 0):
            with torch.no_grad():
                inputs, labels = data
                inputs, labels = inputs.to(device), labels.to(device)

                outputs = net(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()

                loss = criterion(outputs, labels)
                val_loss += loss.cpu().numpy()
                val_steps += 1

        # Here we save a checkpoint. It is automatically registered with
        # Ray Tune and will potentially be passed as the `checkpoint_dir`
        # parameter in future iterations.
        with tune.checkpoint_dir(step=epoch) as checkpoint_dir:
            path = os.path.join(checkpoint_dir, "checkpoint")
            torch.save(
                (net.state_dict(), optimizer.state_dict()), path)

        tune.report(loss=(val_loss / val_steps), accuracy=correct / total)
    print("Finished Training")

As you can see, most of the code is adapted directly from the example.

Test set accuracy

Commonly the performance of a machine learning model is tested on a hold-out test set with data that has not been used for training the model. We also wrap this in a function:

def test_best_model(best_trial):
    best_trained_model = Net(best_trial.config["l1"], best_trial.config["l2"])
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    best_trained_model.to(device)

    checkpoint_path = os.path.join(best_trial.checkpoint.value, "checkpoint")

    model_state, optimizer_state = torch.load(checkpoint_path)
    best_trained_model.load_state_dict(model_state)

    trainset, testset = load_data()

    testloader = torch.utils.data.DataLoader(
        testset, batch_size=4, shuffle=False, num_workers=2)

    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = best_trained_model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()


    print("Best trial test set accuracy: {}".format(correct / total))

As you can see, the function also expects a device parameter, so we can do the test set validation on a GPU.

Configuring the search space

Lastly, we need to define Tune’s search space. Here is an example:

config = {
    "l1": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),
    "l2": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),
    "lr": tune.loguniform(1e-4, 1e-1),
    "batch_size": tune.choice([2, 4, 8, 16]),
}

The tune.sample_from() function makes it possible to define your own sample methods to obtain hyperparameters. In this example, the l1 and l2 parameters should be powers of 2 between 4 and 256, so either 4, 8, 16, 32, 64, 128, or 256. The lr (learning rate) should be uniformly sampled between 0.0001 and 0.1. Lastly, the batch size is a choice between 2, 4, 8, and 16.

At each trial, Tune will now randomly sample a combination of parameters from these search spaces. It will then train a number of models in parallel and find the best performing one among these. We also use the ASHAScheduler which will terminate bad performing trials early.

You can specify the number of CPUs, which are then available e.g. to increase the num_workers of the PyTorch DataLoader instances. The selected number of GPUs are made visible to PyTorch in each trial. Trials do not have access to GPUs that haven’t been requested for them - so you don’t have to care about two trials using the same set of resources.

Here we can also specify fractional GPUs, so something like gpus_per_trial=0.5 is completely valid. The trials will then share GPUs among each other. You just have to make sure that the models still fit in the GPU memory.

After training the models, we will find the best performing one and load the trained network from the checkpoint file. We then obtain the test set accuracy and report everything by printing.

The full main function looks like this:

def main(num_samples=10, max_num_epochs=10, gpus_per_trial=2):
    config = {
        "l1": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
        "l2": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
        "lr": tune.loguniform(1e-4, 1e-1),
        "batch_size": tune.choice([2, 4, 8, 16])
    }
    scheduler = ASHAScheduler(
        max_t=max_num_epochs,
        grace_period=1,
        reduction_factor=2)
    result = tune.run(
        tune.with_parameters(train_cifar),
        resources_per_trial={"cpu": 2, "gpu": gpus_per_trial},
        config=config,
        metric="loss",
        mode="min",
        num_samples=num_samples,
        scheduler=scheduler
    )

    best_trial = result.get_best_trial("loss", "min", "last")
    print("Best trial config: {}".format(best_trial.config))
    print("Best trial final validation loss: {}".format(
        best_trial.last_result["loss"]))
    print("Best trial final validation accuracy: {}".format(
        best_trial.last_result["accuracy"]))

    if ray.util.client.ray.is_connected():
        # If using Ray Client, we want to make sure checkpoint access
        # happens on the server. So we wrap `test_best_model` in a Ray task.
        # We have to make sure it gets executed on the same node that
        # ``tune.run`` is called on.
        from ray.util.ml_utils.node import force_on_current_node
        remote_fn = force_on_current_node(ray.remote(test_best_model))
        ray.get(remote_fn.remote(best_trial))
    else:
        test_best_model(best_trial)

main(num_samples=2, max_num_epochs=2, gpus_per_trial=0)

If you run the code, an example output could look like this:

  Number of trials: 10 (10 TERMINATED)
  +-------------------------+------------+-------+------+------+-------------+--------------+---------+------------+----------------------+
  | Trial name              | status     | loc   |   l1 |   l2 |          lr |   batch_size |    loss |   accuracy |   training_iteration |
  |-------------------------+------------+-------+------+------+-------------+--------------+---------+------------+----------------------|
  | train_cifar_87d1f_00000 | TERMINATED |       |   64 |    4 | 0.00011629  |            2 | 1.87273 |     0.244  |                    2 |
  | train_cifar_87d1f_00001 | TERMINATED |       |   32 |   64 | 0.000339763 |            8 | 1.23603 |     0.567  |                    8 |
  | train_cifar_87d1f_00002 | TERMINATED |       |    8 |   16 | 0.00276249  |           16 | 1.1815  |     0.5836 |                   10 |
  | train_cifar_87d1f_00003 | TERMINATED |       |    4 |   64 | 0.000648721 |            4 | 1.31131 |     0.5224 |                    8 |
  | train_cifar_87d1f_00004 | TERMINATED |       |   32 |   16 | 0.000340753 |            8 | 1.26454 |     0.5444 |                    8 |
  | train_cifar_87d1f_00005 | TERMINATED |       |    8 |    4 | 0.000699775 |            8 | 1.99594 |     0.1983 |                    2 |
  | train_cifar_87d1f_00006 | TERMINATED |       |  256 |    8 | 0.0839654   |           16 | 2.3119  |     0.0993 |                    1 |
  | train_cifar_87d1f_00007 | TERMINATED |       |   16 |  128 | 0.0758154   |           16 | 2.33575 |     0.1327 |                    1 |
  | train_cifar_87d1f_00008 | TERMINATED |       |   16 |    8 | 0.0763312   |           16 | 2.31129 |     0.1042 |                    4 |
  | train_cifar_87d1f_00009 | TERMINATED |       |  128 |   16 | 0.000124903 |            4 | 2.26917 |     0.1945 |                    1 |
  +-------------------------+------------+-------+------+------+-------------+--------------+---------+------------+----------------------+


  Best trial config: {'l1': 8, 'l2': 16, 'lr': 0.0027624906698231976, 'batch_size': 16, 'data_dir': '...'}
  Best trial final validation loss: 1.1815014744281769
  Best trial final validation accuracy: 0.5836
  Best trial test set accuracy: 0.5806

As you can see, most trials have been stopped early in order to avoid wasting resources. The best performing trial achieved a validation accuracy of about 58%, which could be confirmed on the test set.

So that’s it! You can now tune the parameters of your PyTorch models.

See More PyTorch Examples

  • MNIST PyTorch Example: Converts the PyTorch MNIST example to use Tune with the function-based API. Also shows how to easily convert something relying on argparse to use Tune.

  • PBT ConvNet Example: Example training a ConvNet with checkpointing in function API.

  • MNIST PyTorch Trainable Example: Converts the PyTorch MNIST example to use Tune with Trainable API. Also uses the HyperBandScheduler and checkpoints the model at the end.