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Hyperparameter tuning with Ray Tune

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, there are tools that help with finding the best combination of parameters. Ray Tune is an industry standard tool for distributed hyperparameter tuning. Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine.

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

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


To run this tutorial, please make sure the following packages are installed:

  • ray[tune]: Distributed hyperparameter tuning library

  • torchvision: For the data transformers

Setup / Imports

Let’s start with the imports:

from functools import partial
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import random_split
import torchvision
import torchvision.transforms as transforms
from ray import tune
from ray.air import Checkpoint, session
from ray.tune.schedulers import ASHAScheduler

# TODO: Migrate to ray.train.Checkpoint and remove following line
os.environ["RAY_AIR_NEW_PERSISTENCE_MODE"]="0"

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))]
    )

    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 = torch.flatten(x, 1)  # flatten all dimensions except batch
        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.

We wrap the training script in a function train_cifar(config, data_dir=None). The config parameter will receive the hyperparameters we would like to train with. The data_dir specifies the directory where we load and store the data, so that multiple runs can share the same data source. We also load the model and optimizer state at the start of the run, if a checkpoint is provided. Further down in this tutorial you will find information on how to save the checkpoint and what it is used for.

net = Net(config["l1"], config["l2"])

checkpoint = session.get_checkpoint()

if checkpoint:
    checkpoint_state = checkpoint.to_dict()
    start_epoch = checkpoint_state["epoch"]
    net.load_state_dict(checkpoint_state["net_state_dict"])
    optimizer.load_state_dict(checkpoint_state["optimizer_state_dict"])
else:
    start_epoch = 0

The learning rate of the optimizer is made configurable, too:

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

We also split the training data into a training and validation subset. We thus train on 80% of the data and calculate the validation loss on the remaining 20%. The batch sizes with which we iterate through the training and test sets are configurable as well.

Adding (multi) GPU support with DataParallel

Image classification benefits largely from GPUs. Luckily, we can continue to use PyTorch’s abstractions in Ray Tune. Thus, we can wrap our model in nn.DataParallel to support data parallel training on multiple GPUs:

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

By using a device variable we make sure that training also works when we have no GPUs available. PyTorch requires us to send our data to the GPU memory explicitly, like this:

for i, data in enumerate(trainloader, 0):
    inputs, labels = data
    inputs, labels = inputs.to(device), labels.to(device)

The code now supports training on CPUs, on a single GPU, and on multiple GPUs. Notably, Ray also supports fractional GPUs so we can share GPUs among trials, as long as the model still fits on the GPU memory. We’ll come back to that later.

Communicating with Ray Tune

The most interesting part is the communication with Ray Tune:

checkpoint_data = {
    "epoch": epoch,
    "net_state_dict": net.state_dict(),
    "optimizer_state_dict": optimizer.state_dict(),
}
checkpoint = Checkpoint.from_dict(checkpoint_data)

session.report(
    {"loss": val_loss / val_steps, "accuracy": correct / total},
    checkpoint=checkpoint,
)

Here we first save a checkpoint and then report some metrics back to Ray Tune. Specifically, we send the validation loss and accuracy back to Ray Tune. Ray Tune can then use these metrics to decide which hyperparameter configuration lead to the best results. These metrics can also be used to stop bad performing trials early in order to avoid wasting resources on those trials.

The checkpoint saving is optional, however, it is necessary if we wanted to use advanced schedulers like Population Based Training. Also, by saving the checkpoint we can later load the trained models and validate them on a test set. Lastly, saving checkpoints is useful for fault tolerance, and it allows us to interrupt training and continue training later.

Full training function

The full code example looks like this:

def train_cifar(config, data_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)

    checkpoint = session.get_checkpoint()

    if checkpoint:
        checkpoint_state = checkpoint.to_dict()
        start_epoch = checkpoint_state["epoch"]
        net.load_state_dict(checkpoint_state["net_state_dict"])
        optimizer.load_state_dict(checkpoint_state["optimizer_state_dict"])
    else:
        start_epoch = 0

    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(start_epoch, 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

        checkpoint_data = {
            "epoch": epoch,
            "net_state_dict": net.state_dict(),
            "optimizer_state_dict": optimizer.state_dict(),
        }
        checkpoint = Checkpoint.from_dict(checkpoint_data)

        session.report(
            {"loss": val_loss / val_steps, "accuracy": correct / total},
            checkpoint=checkpoint,
        )
    print("Finished Training")

As you can see, most of the code is adapted directly from the original 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_accuracy(net, device="cpu"):
    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 = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    return correct / total

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 Ray Tune’s search space. Here is an example:

config = {
    "l1": tune.choice([2 ** i for i in range(9)]),
    "l2": tune.choice([2 ** i for i in range(9)]),
    "lr": tune.loguniform(1e-4, 1e-1),
    "batch_size": tune.choice([2, 4, 8, 16])
}

The tune.choice() accepts a list of values that are uniformly sampled from. 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, Ray 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.

We wrap the train_cifar function with functools.partial to set the constant data_dir parameter. We can also tell Ray Tune what resources should be available for each trial:

gpus_per_trial = 2
# ...
result = tune.run(
    partial(train_cifar, data_dir=data_dir),
    resources_per_trial={"cpu": 8, "gpu": gpus_per_trial},
    config=config,
    num_samples=num_samples,
    scheduler=scheduler,
    checkpoint_at_end=True)

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):
    data_dir = os.path.abspath("./data")
    load_data(data_dir)
    config = {
        "l1": tune.choice([2**i for i in range(9)]),
        "l2": tune.choice([2**i for i in range(9)]),
        "lr": tune.loguniform(1e-4, 1e-1),
        "batch_size": tune.choice([2, 4, 8, 16]),
    }
    scheduler = ASHAScheduler(
        metric="loss",
        mode="min",
        max_t=max_num_epochs,
        grace_period=1,
        reduction_factor=2,
    )
    result = tune.run(
        partial(train_cifar, data_dir=data_dir),
        resources_per_trial={"cpu": 2, "gpu": gpus_per_trial},
        config=config,
        num_samples=num_samples,
        scheduler=scheduler,
    )

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

    best_trained_model = Net(best_trial.config["l1"], best_trial.config["l2"])
    device = "cpu"
    if torch.cuda.is_available():
        device = "cuda:0"
        if gpus_per_trial > 1:
            best_trained_model = nn.DataParallel(best_trained_model)
    best_trained_model.to(device)

    best_checkpoint = best_trial.checkpoint.to_air_checkpoint()
    best_checkpoint_data = best_checkpoint.to_dict()

    best_trained_model.load_state_dict(best_checkpoint_data["net_state_dict"])

    test_acc = test_accuracy(best_trained_model, device)
    print("Best trial test set accuracy: {}".format(test_acc))


if __name__ == "__main__":
    # You can change the number of GPUs per trial here:
    main(num_samples=10, max_num_epochs=10, gpus_per_trial=0)
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /var/lib/jenkins/workspace/beginner_source/data/cifar-10-python.tar.gz

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Extracting /var/lib/jenkins/workspace/beginner_source/data/cifar-10-python.tar.gz to /var/lib/jenkins/workspace/beginner_source/data
Files already downloaded and verified
2024-03-18 17:29:02,569 WARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 2147479552 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.
2024-03-18 17:29:02,718 INFO worker.py:1642 -- Started a local Ray instance.
2024-03-18 17:29:03,787 INFO tune.py:228 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run(...)`.
2024-03-18 17:29:03,789 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
+--------------------------------------------------------------------+
| Configuration for experiment     train_cifar_2024-03-18_17-29-03   |
+--------------------------------------------------------------------+
| Search algorithm                 BasicVariantGenerator             |
| Scheduler                        AsyncHyperBandScheduler           |
| Number of trials                 10                                |
+--------------------------------------------------------------------+

View detailed results here: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03
To visualize your results with TensorBoard, run: `tensorboard --logdir /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03`

Trial status: 10 PENDING
Current time: 2024-03-18 17:29:04. Total running time: 0s
Logical resource usage: 0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+-------------------------------------------------------------------------------+
| Trial name                status       l1     l2            lr     batch_size |
+-------------------------------------------------------------------------------+
| train_cifar_040a7_00000   PENDING      16      1   0.00213327               2 |
| train_cifar_040a7_00001   PENDING       1      2   0.013416                 4 |
| train_cifar_040a7_00002   PENDING     256     64   0.0113784                2 |
| train_cifar_040a7_00003   PENDING      64    256   0.0274071                8 |
| train_cifar_040a7_00004   PENDING      16      2   0.056666                 4 |
| train_cifar_040a7_00005   PENDING       8     64   0.000353097              4 |
| train_cifar_040a7_00006   PENDING      16      4   0.000147684              8 |
| train_cifar_040a7_00007   PENDING     256    256   0.00477469               8 |
| train_cifar_040a7_00008   PENDING     128    256   0.0306227                8 |
| train_cifar_040a7_00009   PENDING       2     16   0.0286986                2 |
+-------------------------------------------------------------------------------+
(pid=6796) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
(pid=6796)   _torch_pytree._register_pytree_node(

Trial train_cifar_040a7_00001 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00001 config             |
+--------------------------------------------------+
| batch_size                                     4 |
| l1                                             1 |
| l2                                             2 |
| lr                                       0.01342 |
+--------------------------------------------------+
(ImplicitFunc pid=6796) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ray/train/_internal/syncer.py:95: UserWarning: `SyncConfig(upload_dir)` is a deprecated configuration and will be ignored. Please remove it from your `SyncConfig`, as this will raise an error in a future version of Ray.
(ImplicitFunc pid=6796) Please specify `train.RunConfig(storage_path)` instead.
(ImplicitFunc pid=6796)   warnings.warn(
(ImplicitFunc pid=6796) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ray/train/_internal/syncer.py:95: UserWarning: `SyncConfig(syncer)` is a deprecated configuration and will be ignored. Please remove it from your `SyncConfig`, as this will raise an error in a future version of Ray.
(ImplicitFunc pid=6796) Please implement custom syncing logic with a custom `pyarrow.fs.FileSystem` instead, and pass it into `train.RunConfig(storage_filesystem)`.
(ImplicitFunc pid=6796)   warnings.warn(

Trial train_cifar_040a7_00005 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 config             |
+--------------------------------------------------+
| batch_size                                     4 |
| l1                                             8 |
| l2                                            64 |
| lr                                       0.00035 |
+--------------------------------------------------+

Trial train_cifar_040a7_00004 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00004 config             |
+--------------------------------------------------+
| batch_size                                     4 |
| l1                                            16 |
| l2                                             2 |
| lr                                       0.05667 |
+--------------------------------------------------+

Trial train_cifar_040a7_00003 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00003 config             |
+--------------------------------------------------+
| batch_size                                     8 |
| l1                                            64 |
| l2                                           256 |
| lr                                       0.02741 |
+--------------------------------------------------+

Trial train_cifar_040a7_00002 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00002 config             |
+--------------------------------------------------+
| batch_size                                     2 |
| l1                                           256 |
| l2                                            64 |
| lr                                       0.01138 |
+--------------------------------------------------+

Trial train_cifar_040a7_00000 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00000 config             |
+--------------------------------------------------+
| batch_size                                     2 |
| l1                                            16 |
| l2                                             1 |
| lr                                       0.00213 |
+--------------------------------------------------+

Trial train_cifar_040a7_00007 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 config             |
+--------------------------------------------------+
| batch_size                                     8 |
| l1                                           256 |
| l2                                           256 |
| lr                                       0.00477 |
+--------------------------------------------------+

Trial train_cifar_040a7_00006 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 config             |
+--------------------------------------------------+
| batch_size                                     8 |
| l1                                            16 |
| l2                                             4 |
| lr                                       0.00015 |
+--------------------------------------------------+
(func pid=6803) Files already downloaded and verified
(func pid=6795) [1,  2000] loss: 2.317
(func pid=6817) Files already downloaded and verified [repeated 15x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)

Trial status: 8 RUNNING | 2 PENDING
Current time: 2024-03-18 17:29:34. Total running time: 30s
Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+-------------------------------------------------------------------------------+
| Trial name                status       l1     l2            lr     batch_size |
+-------------------------------------------------------------------------------+
| train_cifar_040a7_00000   RUNNING      16      1   0.00213327               2 |
| train_cifar_040a7_00001   RUNNING       1      2   0.013416                 4 |
| train_cifar_040a7_00002   RUNNING     256     64   0.0113784                2 |
| train_cifar_040a7_00003   RUNNING      64    256   0.0274071                8 |
| train_cifar_040a7_00004   RUNNING      16      2   0.056666                 4 |
| train_cifar_040a7_00005   RUNNING       8     64   0.000353097              4 |
| train_cifar_040a7_00006   RUNNING      16      4   0.000147684              8 |
| train_cifar_040a7_00007   RUNNING     256    256   0.00477469               8 |
| train_cifar_040a7_00008   PENDING     128    256   0.0306227                8 |
| train_cifar_040a7_00009   PENDING       2     16   0.0286986                2 |
+-------------------------------------------------------------------------------+
(func pid=6795) [1,  4000] loss: 1.153 [repeated 8x across cluster]
(func pid=6818) [1,  4000] loss: 0.788 [repeated 6x across cluster]
(func pid=6795) [1,  6000] loss: 0.768 [repeated 2x across cluster]
Trial status: 8 RUNNING | 2 PENDING
Current time: 2024-03-18 17:30:04. Total running time: 1min 0s
Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+-------------------------------------------------------------------------------+
| Trial name                status       l1     l2            lr     batch_size |
+-------------------------------------------------------------------------------+
| train_cifar_040a7_00000   RUNNING      16      1   0.00213327               2 |
| train_cifar_040a7_00001   RUNNING       1      2   0.013416                 4 |
| train_cifar_040a7_00002   RUNNING     256     64   0.0113784                2 |
| train_cifar_040a7_00003   RUNNING      64    256   0.0274071                8 |
| train_cifar_040a7_00004   RUNNING      16      2   0.056666                 4 |
| train_cifar_040a7_00005   RUNNING       8     64   0.000353097              4 |
| train_cifar_040a7_00006   RUNNING      16      4   0.000147684              8 |
| train_cifar_040a7_00007   RUNNING     256    256   0.00477469               8 |
| train_cifar_040a7_00008   PENDING     128    256   0.0306227                8 |
| train_cifar_040a7_00009   PENDING       2     16   0.0286986                2 |
+-------------------------------------------------------------------------------+

Trial train_cifar_040a7_00006 finished iteration 1 at 2024-03-18 17:30:07. Total running time: 1min 3s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         56.8352 |
| time_total_s                             56.8352 |
| training_iteration                             1 |
| accuracy                                  0.0991 |
| loss                                     2.31188 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00007 finished iteration 1 at 2024-03-18 17:30:09. Total running time: 1min 5s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         59.6454 |
| time_total_s                             59.6454 |
| training_iteration                             1 |
| accuracy                                  0.4769 |
| loss                                     1.45169 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00003 finished iteration 1 at 2024-03-18 17:30:10. Total running time: 1min 6s
+--------------------------------------------------+
| Trial train_cifar_040a7_00003 result             |
+--------------------------------------------------+
| time_this_iter_s                         60.4706 |
| time_total_s                             60.4706 |
| training_iteration                             1 |
| accuracy                                  0.2406 |
| loss                                     2.01473 |
+--------------------------------------------------+
Trial train_cifar_040a7_00003 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00003_3_batch_size=8,l1=64,l2=256,lr=0.0274_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00003 completed after 1 iterations at 2024-03-18 17:30:10. Total running time: 1min 6s

Trial train_cifar_040a7_00008 started with configuration:
+--------------------------------------------------+
| Trial train_cifar_040a7_00008 config             |
+--------------------------------------------------+
| batch_size                                     8 |
| l1                                           128 |
| l2                                           256 |
| lr                                       0.03062 |
+--------------------------------------------------+
(func pid=6798) Files already downloaded and verified
(func pid=6797) [1,  6000] loss: 0.728 [repeated 4x across cluster]
(func pid=6798) Files already downloaded and verified
(func pid=6795) [1,  8000] loss: 0.576
(func pid=6808) [1,  8000] loss: 0.496
(func pid=6817) [2,  2000] loss: 2.309 [repeated 4x across cluster]
(func pid=6795) [1, 10000] loss: 0.461 [repeated 2x across cluster]

Trial status: 8 RUNNING | 1 TERMINATED | 1 PENDING
Current time: 2024-03-18 17:30:34. Total running time: 1min 30s
Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00000   RUNNING        16      1   0.00213327               2                                                    |
| train_cifar_040a7_00001   RUNNING         1      2   0.013416                 4                                                    |
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2                                                    |
| train_cifar_040a7_00004   RUNNING        16      2   0.056666                 4                                                    |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4                                                    |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        1            56.8352   2.31188       0.0991 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        1            59.6454   1.45169       0.4769 |
| train_cifar_040a7_00008   RUNNING       128    256   0.0306227                8                                                    |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00009   PENDING         2     16   0.0286986                2                                                    |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6803) [1, 10000] loss: 0.467 [repeated 4x across cluster]
(func pid=6817) [2,  4000] loss: 1.152 [repeated 2x across cluster]

Trial train_cifar_040a7_00005 finished iteration 1 at 2024-03-18 17:30:49. Total running time: 1min 45s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         99.9784 |
| time_total_s                             99.9784 |
| training_iteration                             1 |
| accuracy                                  0.3291 |
| loss                                     1.78989 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00001 finished iteration 1 at 2024-03-18 17:30:50. Total running time: 1min 46s
+--------------------------------------------------+
| Trial train_cifar_040a7_00001 result             |
+--------------------------------------------------+
| time_this_iter_s                         101.118 |
| time_total_s                             101.118 |
| training_iteration                             1 |
| accuracy                                  0.1024 |
| loss                                     2.30967 |
+--------------------------------------------------+
Trial train_cifar_040a7_00001 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00001_1_batch_size=4,l1=1,l2=2,lr=0.0134_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00001 completed after 1 iterations at 2024-03-18 17:30:50. Total running time: 1min 46s

Trial train_cifar_040a7_00009 started with configuration:
+-------------------------------------------------+
| Trial train_cifar_040a7_00009 config            |
+-------------------------------------------------+
| batch_size                                    2 |
| l1                                            2 |
| l2                                           16 |
| lr                                       0.0287 |
+-------------------------------------------------+
(func pid=6796) Files already downloaded and verified
(func pid=6818) [2,  4000] loss: 0.694 [repeated 2x across cluster]
(func pid=6796) Files already downloaded and verified

Trial train_cifar_040a7_00004 finished iteration 1 at 2024-03-18 17:30:53. Total running time: 1min 49s
+--------------------------------------------------+
| Trial train_cifar_040a7_00004 result             |
+--------------------------------------------------+
| time_this_iter_s                          103.28 |
| time_total_s                              103.28 |
| training_iteration                             1 |
| accuracy                                  0.1028 |
| loss                                     2.32868 |
+--------------------------------------------------+
Trial train_cifar_040a7_00004 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00004 completed after 1 iterations at 2024-03-18 17:30:53. Total running time: 1min 49s
(func pid=6797) [1, 12000] loss: 0.355 [repeated 2x across cluster]

Trial train_cifar_040a7_00006 finished iteration 2 at 2024-03-18 17:31:01. Total running time: 1min 57s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         54.5821 |
| time_total_s                             111.417 |
| training_iteration                             2 |
| accuracy                                   0.118 |
| loss                                     2.30271 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 2 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000001

Trial status: 7 RUNNING | 3 TERMINATED
Current time: 2024-03-18 17:31:04. Total running time: 2min 0s
Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00000   RUNNING        16      1   0.00213327               2                                                    |
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2                                                    |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        1            99.9784   1.78989       0.3291 |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        2           111.417    2.30271       0.118  |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        1            59.6454   1.45169       0.4769 |
| train_cifar_040a7_00008   RUNNING       128    256   0.0306227                8                                                    |
| train_cifar_040a7_00009   RUNNING         2     16   0.0286986                2                                                    |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
+------------------------------------------------------------------------------------------------------------------------------------+

Trial train_cifar_040a7_00007 finished iteration 2 at 2024-03-18 17:31:05. Total running time: 2min 1s
+-------------------------------------------------+
| Trial train_cifar_040a7_00007 result            |
+-------------------------------------------------+
| time_this_iter_s                         55.885 |
| time_total_s                             115.53 |
| training_iteration                            2 |
| accuracy                                   0.48 |
| loss                                     1.4338 |
+-------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 2 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000001
(func pid=6808) [2,  2000] loss: 1.753 [repeated 2x across cluster]

Trial train_cifar_040a7_00008 finished iteration 1 at 2024-03-18 17:31:08. Total running time: 2min 4s
+--------------------------------------------------+
| Trial train_cifar_040a7_00008 result             |
+--------------------------------------------------+
| time_this_iter_s                         58.4928 |
| time_total_s                             58.4928 |
| training_iteration                             1 |
| accuracy                                  0.2135 |
| loss                                     2.05511 |
+--------------------------------------------------+
Trial train_cifar_040a7_00008 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-03-18_17-29-03/checkpoint_000000
(func pid=6795) [1, 16000] loss: 0.288 [repeated 2x across cluster]
(func pid=6796) [1,  4000] loss: 1.167 [repeated 4x across cluster]
(func pid=6798) [2,  2000] loss: 2.166 [repeated 2x across cluster]
(func pid=6797) [1, 16000] loss: 0.265 [repeated 2x across cluster]

Trial status: 7 RUNNING | 3 TERMINATED
Current time: 2024-03-18 17:31:34. Total running time: 2min 30s
Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00000   RUNNING        16      1   0.00213327               2                                                    |
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2                                                    |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        1            99.9784   1.78989       0.3291 |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        2           111.417    2.30271       0.118  |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        2           115.53     1.4338        0.48   |
| train_cifar_040a7_00008   RUNNING       128    256   0.0306227                8        1            58.4929   2.05511       0.2135 |
| train_cifar_040a7_00009   RUNNING         2     16   0.0286986                2                                                    |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6818) [3,  4000] loss: 0.636 [repeated 4x across cluster]
(func pid=6798) [2,  4000] loss: 1.081 [repeated 2x across cluster]

Trial train_cifar_040a7_00006 finished iteration 3 at 2024-03-18 17:31:49. Total running time: 2min 45s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         48.0462 |
| time_total_s                             159.464 |
| training_iteration                             3 |
| accuracy                                  0.1227 |
| loss                                     2.28501 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 3 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000002

Trial train_cifar_040a7_00007 finished iteration 3 at 2024-03-18 17:31:54. Total running time: 2min 51s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         49.4507 |
| time_total_s                             164.981 |
| training_iteration                             3 |
| accuracy                                  0.5502 |
| loss                                     1.28403 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 3 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000002

Trial train_cifar_040a7_00008 finished iteration 2 at 2024-03-18 17:32:03. Total running time: 2min 59s
+--------------------------------------------------+
| Trial train_cifar_040a7_00008 result             |
+--------------------------------------------------+
| time_this_iter_s                         54.3386 |
| time_total_s                             112.831 |
| training_iteration                             2 |
| accuracy                                  0.1874 |
| loss                                     2.11202 |
+--------------------------------------------------+
Trial train_cifar_040a7_00008 saved a checkpoint for iteration 2 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-03-18_17-29-03/checkpoint_000001

Trial train_cifar_040a7_00008 completed after 2 iterations at 2024-03-18 17:32:03. Total running time: 2min 59s
(func pid=6808) [2, 10000] loss: 0.305 [repeated 4x across cluster]

Trial train_cifar_040a7_00000 finished iteration 1 at 2024-03-18 17:32:04. Total running time: 3min 0s
+--------------------------------------------------+
| Trial train_cifar_040a7_00000 result             |
+--------------------------------------------------+
| time_this_iter_s                         174.495 |
| time_total_s                             174.495 |
| training_iteration                             1 |
| accuracy                                   0.099 |
| loss                                     2.30433 |
+--------------------------------------------------+

Trial status: 6 RUNNING | 4 TERMINATED
Current time: 2024-03-18 17:32:04. Total running time: 3min 0s
Logical resource usage: 12.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00000   RUNNING        16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2                                                    |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        1            99.9784   1.78989       0.3291 |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        3           159.464    2.28501       0.1227 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        3           164.981    1.28403       0.5502 |
| train_cifar_040a7_00009   RUNNING         2     16   0.0286986                2                                                    |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
+------------------------------------------------------------------------------------------------------------------------------------+

Trial train_cifar_040a7_00000 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-03-18_17-29-03/checkpoint_000000

Trial train_cifar_040a7_00000 completed after 1 iterations at 2024-03-18 17:32:04. Total running time: 3min 0s
(func pid=6818) [4,  2000] loss: 1.171 [repeated 4x across cluster]

Trial train_cifar_040a7_00005 finished iteration 2 at 2024-03-18 17:32:12. Total running time: 3min 8s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         82.3606 |
| time_total_s                             182.339 |
| training_iteration                             2 |
| accuracy                                   0.412 |
| loss                                     1.58349 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 2 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000001
(func pid=6817) [4,  4000] loss: 1.118 [repeated 2x across cluster]

Trial train_cifar_040a7_00002 finished iteration 1 at 2024-03-18 17:32:22. Total running time: 3min 18s
+--------------------------------------------------+
| Trial train_cifar_040a7_00002 result             |
+--------------------------------------------------+
| time_this_iter_s                         192.384 |
| time_total_s                             192.384 |
| training_iteration                             1 |
| accuracy                                  0.1647 |
| loss                                     2.08392 |
+--------------------------------------------------+
Trial train_cifar_040a7_00002 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-03-18_17-29-03/checkpoint_000000
(func pid=6818) [4,  4000] loss: 0.605 [repeated 2x across cluster]

Trial train_cifar_040a7_00006 finished iteration 4 at 2024-03-18 17:32:29. Total running time: 3min 25s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         40.1472 |
| time_total_s                             199.611 |
| training_iteration                             4 |
| accuracy                                  0.1776 |
| loss                                     2.17807 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 4 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000003

Trial status: 5 TERMINATED | 5 RUNNING
Current time: 2024-03-18 17:32:34. Total running time: 3min 30s
Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2        1           192.384    2.08392       0.1647 |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        2           182.339    1.58349       0.412  |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        4           199.611    2.17807       0.1776 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        3           164.981    1.28403       0.5502 |
| train_cifar_040a7_00009   RUNNING         2     16   0.0286986                2                                                    |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [3,  4000] loss: 0.736 [repeated 2x across cluster]

Trial train_cifar_040a7_00007 finished iteration 4 at 2024-03-18 17:32:35. Total running time: 3min 31s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         40.9567 |
| time_total_s                             205.938 |
| training_iteration                             4 |
| accuracy                                  0.5306 |
| loss                                     1.35366 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 4 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000003
(func pid=6817) [5,  2000] loss: 2.142 [repeated 3x across cluster]
(func pid=6818) [5,  2000] loss: 1.110 [repeated 3x across cluster]
(func pid=6817) [5,  4000] loss: 1.038 [repeated 2x across cluster]
(func pid=6818) [5,  4000] loss: 0.566 [repeated 3x across cluster]

Trial status: 5 TERMINATED | 5 RUNNING
Current time: 2024-03-18 17:33:04. Total running time: 4min 0s
Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2        1           192.384    2.08392       0.1647 |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        2           182.339    1.58349       0.412  |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        4           199.611    2.17807       0.1776 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        4           205.938    1.35366       0.5306 |
| train_cifar_040a7_00009   RUNNING         2     16   0.0286986                2                                                    |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
+------------------------------------------------------------------------------------------------------------------------------------+

Trial train_cifar_040a7_00006 finished iteration 5 at 2024-03-18 17:33:05. Total running time: 4min 1s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         35.9771 |
| time_total_s                             235.588 |
| training_iteration                             5 |
| accuracy                                  0.2201 |
| loss                                     2.01999 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 5 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000004
(func pid=6808) [3, 10000] loss: 0.282 [repeated 2x across cluster]

Trial train_cifar_040a7_00007 finished iteration 5 at 2024-03-18 17:33:14. Total running time: 4min 10s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         38.3481 |
| time_total_s                             244.286 |
| training_iteration                             5 |
| accuracy                                   0.556 |
| loss                                     1.28368 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 5 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000004

Trial train_cifar_040a7_00009 finished iteration 1 at 2024-03-18 17:33:14. Total running time: 4min 10s
+--------------------------------------------------+
| Trial train_cifar_040a7_00009 result             |
+--------------------------------------------------+
| time_this_iter_s                          143.43 |
| time_total_s                              143.43 |
| training_iteration                             1 |
| accuracy                                  0.0986 |
| loss                                     2.32247 |
+--------------------------------------------------+
Trial train_cifar_040a7_00009 saved a checkpoint for iteration 1 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00009_9_batch_size=2,l1=2,l2=16,lr=0.0287_2024-03-18_17-29-04/checkpoint_000000

Trial train_cifar_040a7_00009 completed after 1 iterations at 2024-03-18 17:33:14. Total running time: 4min 10s
(func pid=6797) [2,  8000] loss: 0.550

Trial train_cifar_040a7_00005 finished iteration 3 at 2024-03-18 17:33:17. Total running time: 4min 13s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         65.2434 |
| time_total_s                             247.582 |
| training_iteration                             3 |
| accuracy                                   0.476 |
| loss                                     1.42439 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 3 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000002
(func pid=6817) [6,  2000] loss: 1.989
(func pid=6818) [6,  2000] loss: 1.056
(func pid=6808) [4,  2000] loss: 1.392

Trial status: 6 TERMINATED | 4 RUNNING
Current time: 2024-03-18 17:33:34. Total running time: 4min 30s
Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2        1           192.384    2.08392       0.1647 |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        3           247.582    1.42439       0.476  |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        5           235.588    2.01999       0.2201 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        5           244.286    1.28368       0.556  |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [4,  4000] loss: 0.682 [repeated 3x across cluster]

Trial train_cifar_040a7_00006 finished iteration 6 at 2024-03-18 17:33:39. Total running time: 4min 35s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         34.0603 |
| time_total_s                             269.648 |
| training_iteration                             6 |
| accuracy                                  0.2486 |
| loss                                     1.89162 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 6 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000005
(func pid=6808) [4,  6000] loss: 0.442 [repeated 3x across cluster]

Trial train_cifar_040a7_00007 finished iteration 6 at 2024-03-18 17:33:49. Total running time: 4min 45s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         35.3732 |
| time_total_s                             279.659 |
| training_iteration                             6 |
| accuracy                                  0.5553 |
| loss                                     1.31817 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 6 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000005
(func pid=6797) [2, 14000] loss: 0.303 [repeated 2x across cluster]
(func pid=6818) [7,  2000] loss: 1.000 [repeated 2x across cluster]

Trial status: 6 TERMINATED | 4 RUNNING
Current time: 2024-03-18 17:34:04. Total running time: 5min 0s
Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2        1           192.384    2.08392       0.1647 |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        3           247.582    1.42439       0.476  |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        6           269.648    1.89162       0.2486 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        6           279.659    1.31817       0.5553 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [4, 10000] loss: 0.264 [repeated 3x across cluster]

Trial train_cifar_040a7_00006 finished iteration 7 at 2024-03-18 17:34:12. Total running time: 5min 9s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         33.0713 |
| time_total_s                             302.719 |
| training_iteration                             7 |
| accuracy                                  0.3105 |
| loss                                     1.81475 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 7 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000006
(func pid=6818) [7,  4000] loss: 0.531

Trial train_cifar_040a7_00005 finished iteration 4 at 2024-03-18 17:34:16. Total running time: 5min 12s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         59.3739 |
| time_total_s                             306.956 |
| training_iteration                             4 |
| accuracy                                  0.4957 |
| loss                                     1.39076 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 4 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000003
(func pid=6797) [2, 18000] loss: 0.237
(func pid=6817) [8,  2000] loss: 1.786

Trial train_cifar_040a7_00007 finished iteration 7 at 2024-03-18 17:34:24. Total running time: 5min 20s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                          35.284 |
| time_total_s                             314.943 |
| training_iteration                             7 |
| accuracy                                  0.5399 |
| loss                                     1.41932 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 7 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000006
(func pid=6808) [5,  2000] loss: 1.290

Trial status: 6 TERMINATED | 4 RUNNING
Current time: 2024-03-18 17:34:34. Total running time: 5min 30s
Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00002   RUNNING       256     64   0.0113784                2        1           192.384    2.08392       0.1647 |
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        4           306.956    1.39076       0.4957 |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        7           302.719    1.81475       0.3105 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        7           314.943    1.41932       0.5399 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6817) [8,  4000] loss: 0.880 [repeated 2x across cluster]

Trial train_cifar_040a7_00002 finished iteration 2 at 2024-03-18 17:34:45. Total running time: 5min 42s
+--------------------------------------------------+
| Trial train_cifar_040a7_00002 result             |
+--------------------------------------------------+
| time_this_iter_s                         143.712 |
| time_total_s                             336.096 |
| training_iteration                             2 |
| accuracy                                  0.1758 |
| loss                                     2.11569 |
+--------------------------------------------------+
Trial train_cifar_040a7_00002 saved a checkpoint for iteration 2 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-03-18_17-29-03/checkpoint_000001

Trial train_cifar_040a7_00002 completed after 2 iterations at 2024-03-18 17:34:45. Total running time: 5min 42s

Trial train_cifar_040a7_00006 finished iteration 8 at 2024-03-18 17:34:46. Total running time: 5min 42s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         33.3054 |
| time_total_s                             336.025 |
| training_iteration                             8 |
| accuracy                                  0.3428 |
| loss                                     1.74327 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 8 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000007
(func pid=6808) [5,  6000] loss: 0.426 [repeated 3x across cluster]
(func pid=6817) [9,  2000] loss: 1.719 [repeated 2x across cluster]

Trial train_cifar_040a7_00007 finished iteration 8 at 2024-03-18 17:34:58. Total running time: 5min 55s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         34.1198 |
| time_total_s                             349.063 |
| training_iteration                             8 |
| accuracy                                  0.5567 |
| loss                                     1.38891 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 8 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000007

Trial status: 7 TERMINATED | 3 RUNNING
Current time: 2024-03-18 17:35:04. Total running time: 6min 0s
Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        4           306.956    1.39076       0.4957 |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        8           336.025    1.74327       0.3428 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        8           349.063    1.38891       0.5567 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [5, 10000] loss: 0.256 [repeated 2x across cluster]

Trial train_cifar_040a7_00005 finished iteration 5 at 2024-03-18 17:35:14. Total running time: 6min 10s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         57.3245 |
| time_total_s                             364.281 |
| training_iteration                             5 |
| accuracy                                  0.5282 |
| loss                                     1.31321 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 5 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000004

Trial train_cifar_040a7_00006 finished iteration 9 at 2024-03-18 17:35:16. Total running time: 6min 12s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         30.5156 |
| time_total_s                              366.54 |
| training_iteration                             9 |
| accuracy                                  0.3682 |
| loss                                     1.67895 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 9 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000008
(func pid=6818) [9,  4000] loss: 0.513 [repeated 3x across cluster]
(func pid=6817) [10,  2000] loss: 1.661 [repeated 2x across cluster]

Trial train_cifar_040a7_00007 finished iteration 9 at 2024-03-18 17:35:31. Total running time: 6min 27s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         32.5354 |
| time_total_s                             381.598 |
| training_iteration                             9 |
| accuracy                                  0.5448 |
| loss                                     1.44242 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 9 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000008
(func pid=6808) [6,  4000] loss: 0.608

Trial status: 7 TERMINATED | 3 RUNNING
Current time: 2024-03-18 17:35:34. Total running time: 6min 30s
Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        5           364.281    1.31321       0.5282 |
| train_cifar_040a7_00006   RUNNING        16      4   0.000147684              8        9           366.54     1.67895       0.3682 |
| train_cifar_040a7_00007   RUNNING       256    256   0.00477469               8        9           381.598    1.44242       0.5448 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6817) [10,  4000] loss: 0.828
(func pid=6818) [10,  2000] loss: 0.936 [repeated 2x across cluster]

Trial train_cifar_040a7_00006 finished iteration 10 at 2024-03-18 17:35:46. Total running time: 6min 42s
+--------------------------------------------------+
| Trial train_cifar_040a7_00006 result             |
+--------------------------------------------------+
| time_this_iter_s                         30.1098 |
| time_total_s                              396.65 |
| training_iteration                            10 |
| accuracy                                  0.3882 |
| loss                                      1.6355 |
+--------------------------------------------------+
Trial train_cifar_040a7_00006 saved a checkpoint for iteration 10 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-03-18_17-29-03/checkpoint_000009

Trial train_cifar_040a7_00006 completed after 10 iterations at 2024-03-18 17:35:46. Total running time: 6min 42s
(func pid=6808) [6,  8000] loss: 0.308
(func pid=6818) [10,  4000] loss: 0.507
(func pid=6808) [6, 10000] loss: 0.244

Trial train_cifar_040a7_00007 finished iteration 10 at 2024-03-18 17:36:01. Total running time: 6min 57s
+--------------------------------------------------+
| Trial train_cifar_040a7_00007 result             |
+--------------------------------------------------+
| time_this_iter_s                         30.2124 |
| time_total_s                             411.811 |
| training_iteration                            10 |
| accuracy                                  0.5466 |
| loss                                     1.44793 |
+--------------------------------------------------+
Trial train_cifar_040a7_00007 saved a checkpoint for iteration 10 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-03-18_17-29-03/checkpoint_000009

Trial train_cifar_040a7_00007 completed after 10 iterations at 2024-03-18 17:36:01. Total running time: 6min 57s

Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:36:04. Total running time: 7min 0s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        5           364.281    1.31321       0.5282 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+

Trial train_cifar_040a7_00005 finished iteration 6 at 2024-03-18 17:36:06. Total running time: 7min 2s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         51.9989 |
| time_total_s                              416.28 |
| training_iteration                             6 |
| accuracy                                  0.5506 |
| loss                                     1.25131 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 6 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000005
(func pid=6808) [7,  2000] loss: 1.191
(func pid=6808) [7,  4000] loss: 0.592
(func pid=6808) [7,  6000] loss: 0.396

Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:36:34. Total running time: 7min 31s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        6           416.28     1.25131       0.5506 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [7,  8000] loss: 0.301
(func pid=6808) [7, 10000] loss: 0.237

Trial train_cifar_040a7_00005 finished iteration 7 at 2024-03-18 17:36:51. Total running time: 7min 47s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         45.3555 |
| time_total_s                             461.635 |
| training_iteration                             7 |
| accuracy                                  0.5586 |
| loss                                     1.23884 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 7 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000006
(func pid=6808) [8,  2000] loss: 1.163

Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:37:04. Total running time: 8min 1s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        7           461.635    1.23884       0.5586 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [8,  4000] loss: 0.578
(func pid=6808) [8,  6000] loss: 0.388
(func pid=6808) [8,  8000] loss: 0.288
(func pid=6808) [8, 10000] loss: 0.232
Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:37:35. Total running time: 8min 31s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        7           461.635    1.23884       0.5586 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+

Trial train_cifar_040a7_00005 finished iteration 8 at 2024-03-18 17:37:36. Total running time: 8min 32s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         44.7931 |
| time_total_s                             506.428 |
| training_iteration                             8 |
| accuracy                                  0.5686 |
| loss                                     1.21974 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 8 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000007
(func pid=6808) [9,  2000] loss: 1.127
(func pid=6808) [9,  4000] loss: 0.569
(func pid=6808) [9,  6000] loss: 0.373

Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:38:05. Total running time: 9min 1s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        8           506.428    1.21974       0.5686 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [9,  8000] loss: 0.290
(func pid=6808) [9, 10000] loss: 0.230

Trial train_cifar_040a7_00005 finished iteration 9 at 2024-03-18 17:38:22. Total running time: 9min 18s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                         45.8612 |
| time_total_s                              552.29 |
| training_iteration                             9 |
| accuracy                                  0.5602 |
| loss                                     1.23817 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 9 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000008
(func pid=6808) [10,  2000] loss: 1.114

Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:38:35. Total running time: 9min 31s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        9           552.29     1.23817       0.5602 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+
(func pid=6808) [10,  4000] loss: 0.553
(func pid=6808) [10,  6000] loss: 0.371
(func pid=6808) [10,  8000] loss: 0.283
(func pid=6808) [10, 10000] loss: 0.222
Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2024-03-18 17:39:05. Total running time: 10min 1s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00005   RUNNING         8     64   0.000353097              4        9           552.29     1.23817       0.5602 |
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+

Trial train_cifar_040a7_00005 finished iteration 10 at 2024-03-18 17:39:06. Total running time: 10min 2s
+--------------------------------------------------+
| Trial train_cifar_040a7_00005 result             |
+--------------------------------------------------+
| time_this_iter_s                          44.816 |
| time_total_s                             597.106 |
| training_iteration                            10 |
| accuracy                                  0.5875 |
| loss                                     1.17725 |
+--------------------------------------------------+
Trial train_cifar_040a7_00005 saved a checkpoint for iteration 10 at: /var/lib/jenkins/ray_results/train_cifar_2024-03-18_17-29-03/train_cifar_040a7_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-03-18_17-29-03/checkpoint_000009

Trial train_cifar_040a7_00005 completed after 10 iterations at 2024-03-18 17:39:06. Total running time: 10min 2s

Trial status: 10 TERMINATED
Current time: 2024-03-18 17:39:06. Total running time: 10min 2s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+------------------------------------------------------------------------------------------------------------------------------------+
| train_cifar_040a7_00000   TERMINATED     16      1   0.00213327               2        1           174.495    2.30433       0.099  |
| train_cifar_040a7_00001   TERMINATED      1      2   0.013416                 4        1           101.118    2.30967       0.1024 |
| train_cifar_040a7_00002   TERMINATED    256     64   0.0113784                2        2           336.096    2.11569       0.1758 |
| train_cifar_040a7_00003   TERMINATED     64    256   0.0274071                8        1            60.4706   2.01473       0.2406 |
| train_cifar_040a7_00004   TERMINATED     16      2   0.056666                 4        1           103.28     2.32868       0.1028 |
| train_cifar_040a7_00005   TERMINATED      8     64   0.000353097              4       10           597.106    1.17725       0.5875 |
| train_cifar_040a7_00006   TERMINATED     16      4   0.000147684              8       10           396.65     1.6355        0.3882 |
| train_cifar_040a7_00007   TERMINATED    256    256   0.00477469               8       10           411.811    1.44793       0.5466 |
| train_cifar_040a7_00008   TERMINATED    128    256   0.0306227                8        2           112.831    2.11202       0.1874 |
| train_cifar_040a7_00009   TERMINATED      2     16   0.0286986                2        1           143.43     2.32247       0.0986 |
+------------------------------------------------------------------------------------------------------------------------------------+

Best trial config: {'l1': 8, 'l2': 64, 'lr': 0.0003530972286268149, 'batch_size': 4}
Best trial final validation loss: 1.1772535697743296
Best trial final validation accuracy: 0.5875
Files already downloaded and verified
Files already downloaded and verified
Best trial test set accuracy: 0.5868

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

Number of trials: 10/10 (10 TERMINATED)
+-----+--------------+------+------+-------------+--------+---------+------------+
| ... |   batch_size |   l1 |   l2 |          lr |   iter |    loss |   accuracy |
|-----+--------------+------+------+-------------+--------+---------+------------|
| ... |            2 |    1 |  256 | 0.000668163 |      1 | 2.31479 |     0.0977 |
| ... |            4 |   64 |    8 | 0.0331514   |      1 | 2.31605 |     0.0983 |
| ... |            4 |    2 |    1 | 0.000150295 |      1 | 2.30755 |     0.1023 |
| ... |           16 |   32 |   32 | 0.0128248   |     10 | 1.66912 |     0.4391 |
| ... |            4 |    8 |  128 | 0.00464561  |      2 | 1.7316  |     0.3463 |
| ... |            8 |  256 |    8 | 0.00031556  |      1 | 2.19409 |     0.1736 |
| ... |            4 |   16 |  256 | 0.00574329  |      2 | 1.85679 |     0.3368 |
| ... |            8 |    2 |    2 | 0.00325652  |      1 | 2.30272 |     0.0984 |
| ... |            2 |    2 |    2 | 0.000342987 |      2 | 1.76044 |     0.292  |
| ... |            4 |   64 |   32 | 0.003734    |      8 | 1.53101 |     0.4761 |
+-----+--------------+------+------+-------------+--------+---------+------------+

Best trial config: {'l1': 64, 'l2': 32, 'lr': 0.0037339984519545164, 'batch_size': 4}
Best trial final validation loss: 1.5310075663924216
Best trial final validation accuracy: 0.4761
Best trial test set accuracy: 0.4737

Most trials have been stopped early in order to avoid wasting resources. The best performing trial achieved a validation accuracy of about 47%, which could be confirmed on the test set.

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

Total running time of the script: ( 10 minutes 21.671 seconds)

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