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 numpy as np
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.tune import CLIReporter
from ray.tune.schedulers import ASHAScheduler

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

Data loaders

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

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

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

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

    return trainset, testset

Configurable neural network

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

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

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

The train function

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

We wrap the training script in a function train_cifar(config, checkpoint_dir=None, data_dir=None). As you can guess, the config parameter will receive the hyperparameters we would like to train with. The checkpoint_dir parameter is used to restore checkpoints. The data_dir specifies the directory where we load and store the data, so multiple runs can share the same data source.

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

if checkpoint_dir:
    model_state, optimizer_state = torch.load(
        os.path.join(checkpoint_dir, "checkpoint"))
    net.load_state_dict(model_state)
    optimizer.load_state_dict(optimizer_state)

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:

with tune.checkpoint_dir(epoch) as checkpoint_dir:
    path = os.path.join(checkpoint_dir, "checkpoint")
    torch.save((net.state_dict(), optimizer.state_dict()), path)

tune.report(loss=(val_loss / val_steps), accuracy=correct / total)

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.

Full training function

The full code example looks like this:

def train_cifar(config, checkpoint_dir=None, 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)

    if checkpoint_dir:
        model_state, optimizer_state = torch.load(
            os.path.join(checkpoint_dir, "checkpoint"))
        net.load_state_dict(model_state)
        optimizer.load_state_dict(optimizer_state)

    trainset, testset = load_data(data_dir)

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

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

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

            # zero the parameter gradients
            optimizer.zero_grad()

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

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

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

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

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

        with tune.checkpoint_dir(epoch) as checkpoint_dir:
            path = os.path.join(checkpoint_dir, "checkpoint")
            torch.save((net.state_dict(), optimizer.state_dict()), path)

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

As you can see, most of the code is adapted directly from the 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.sample_from(lambda _: 2**np.random.randint(2, 9)),
    "l2": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),
    "lr": tune.loguniform(1e-4, 1e-1),
    "batch_size": tune.choice([2, 4, 8, 16])
}

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

At each trial, 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,
    progress_reporter=reporter,
    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.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
        "l2": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
        "lr": tune.loguniform(1e-4, 1e-1),
        "batch_size": tune.choice([2, 4, 8, 16])
    }
    scheduler = ASHAScheduler(
        metric="loss",
        mode="min",
        max_t=max_num_epochs,
        grace_period=1,
        reduction_factor=2)
    reporter = CLIReporter(
        # parameter_columns=["l1", "l2", "lr", "batch_size"],
        metric_columns=["loss", "accuracy", "training_iteration"])
    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,
        progress_reporter=reporter)

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

    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_dir = best_trial.checkpoint.value
    model_state, optimizer_state = torch.load(os.path.join(
        best_checkpoint_dir, "checkpoint"))
    best_trained_model.load_state_dict(model_state)

    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)

Out:

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /workspace/ko-latest/beginner_source/data/cifar-10-python.tar.gz
Extracting /workspace/ko-latest/beginner_source/data/cifar-10-python.tar.gz to /workspace/ko-latest/beginner_source/data
Files already downloaded and verified
== Status ==
Current time: 2022-07-10 22:43:53 (running for 00:00:00.26)
Memory usage on this node: 6.8/62.7 GiB
Using AsyncHyperBand: num_stopped=0
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: None
Resources requested: 2.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (9 PENDING, 1 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |
|-------------------------+----------+------------------+--------------+------+------+-------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 |
| train_cifar_56838_00001 | PENDING  |                  |           16 |   16 |  256 | 0.0328464   |
| train_cifar_56838_00002 | PENDING  |                  |            8 |    8 |  256 | 0.00224488  |
| train_cifar_56838_00003 | PENDING  |                  |            8 |    4 |   32 | 0.000122311 |
| train_cifar_56838_00004 | PENDING  |                  |            8 |   32 |  256 | 0.000414364 |
| train_cifar_56838_00005 | PENDING  |                  |            8 |  256 |   16 | 0.000280282 |
| train_cifar_56838_00006 | PENDING  |                  |            4 |   16 |    8 | 0.0314931   |
| train_cifar_56838_00007 | PENDING  |                  |            2 |    8 |    4 | 0.00769353  |
| train_cifar_56838_00008 | PENDING  |                  |            4 |   16 |    8 | 0.00532825  |
| train_cifar_56838_00009 | PENDING  |                  |            8 |   16 |   32 | 0.00482255  |
+-------------------------+----------+------------------+--------------+------+------+-------------+


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== Status ==
Current time: 2022-07-10 22:44:01 (running for 00:00:07.61)
Memory usage on this node: 12.1/62.7 GiB
Using AsyncHyperBand: num_stopped=0
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: None
Resources requested: 20.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |
|-------------------------+----------+------------------+--------------+------+------+-------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 |
| train_cifar_56838_00001 | RUNNING  | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   |
| train_cifar_56838_00002 | RUNNING  | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  |
| train_cifar_56838_00003 | RUNNING  | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 |
| train_cifar_56838_00004 | RUNNING  | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 |
| train_cifar_56838_00005 | RUNNING  | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 |
| train_cifar_56838_00006 | RUNNING  | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |
| train_cifar_56838_00007 | RUNNING  | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |
| train_cifar_56838_00008 | RUNNING  | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |
| train_cifar_56838_00009 | RUNNING  | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  |
+-------------------------+----------+------------------+--------------+------+------+-------------+


== Status ==
Current time: 2022-07-10 22:44:06 (running for 00:00:12.63)
Memory usage on this node: 12.4/62.7 GiB
Using AsyncHyperBand: num_stopped=0
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: None
Resources requested: 20.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |
|-------------------------+----------+------------------+--------------+------+------+-------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 |
| train_cifar_56838_00001 | RUNNING  | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   |
| train_cifar_56838_00002 | RUNNING  | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  |
| train_cifar_56838_00003 | RUNNING  | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 |
| train_cifar_56838_00004 | RUNNING  | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 |
| train_cifar_56838_00005 | RUNNING  | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 |
| train_cifar_56838_00006 | RUNNING  | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |
| train_cifar_56838_00007 | RUNNING  | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |
| train_cifar_56838_00008 | RUNNING  | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |
| train_cifar_56838_00009 | RUNNING  | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  |
+-------------------------+----------+------------------+--------------+------+------+-------------+


(func pid=16075) [1,  2000] loss: 2.261
(func pid=16078) [1,  2000] loss: 2.081
(func pid=16073) [1,  2000] loss: 2.320
== Status ==
Current time: 2022-07-10 22:44:11 (running for 00:00:17.64)
Memory usage on this node: 12.4/62.7 GiB
Using AsyncHyperBand: num_stopped=0
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: None
Resources requested: 20.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |
|-------------------------+----------+------------------+--------------+------+------+-------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 |
| train_cifar_56838_00001 | RUNNING  | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   |
| train_cifar_56838_00002 | RUNNING  | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  |
| train_cifar_56838_00003 | RUNNING  | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 |
| train_cifar_56838_00004 | RUNNING  | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 |
| train_cifar_56838_00005 | RUNNING  | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 |
| train_cifar_56838_00006 | RUNNING  | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |
| train_cifar_56838_00007 | RUNNING  | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |
| train_cifar_56838_00008 | RUNNING  | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |
| train_cifar_56838_00009 | RUNNING  | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  |
+-------------------------+----------+------------------+--------------+------+------+-------------+


(func pid=16035) [1,  2000] loss: 2.292
(func pid=16067) [1,  2000] loss: 2.309
(func pid=16065) [1,  2000] loss: 2.003
(func pid=16069) [1,  2000] loss: 2.287
(func pid=16079) [1,  2000] loss: 1.922
(func pid=16071) [1,  2000] loss: 2.309
(func pid=16063) [1,  2000] loss: 2.090
(func pid=16075) [1,  4000] loss: 1.117
== Status ==
Current time: 2022-07-10 22:44:16 (running for 00:00:22.65)
Memory usage on this node: 12.4/62.7 GiB
Using AsyncHyperBand: num_stopped=0
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: None
Resources requested: 20.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |
|-------------------------+----------+------------------+--------------+------+------+-------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 |
| train_cifar_56838_00001 | RUNNING  | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   |
| train_cifar_56838_00002 | RUNNING  | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  |
| train_cifar_56838_00003 | RUNNING  | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 |
| train_cifar_56838_00004 | RUNNING  | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 |
| train_cifar_56838_00005 | RUNNING  | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 |
| train_cifar_56838_00006 | RUNNING  | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |
| train_cifar_56838_00007 | RUNNING  | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |
| train_cifar_56838_00008 | RUNNING  | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |
| train_cifar_56838_00009 | RUNNING  | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  |
+-------------------------+----------+------------------+--------------+------+------+-------------+


Result for train_cifar_56838_00000:
  accuracy: 0.1795
  date: 2022-07-10_22-44-17
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 2.1979861251831054
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 20.82857584953308
  time_this_iter_s: 20.82857584953308
  time_total_s: 20.82857584953308
  timestamp: 1657460657
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

(func pid=16078) [1,  4000] loss: 0.937
(func pid=16073) [1,  4000] loss: 1.160
Result for train_cifar_56838_00001:
  accuracy: 0.2186
  date: 2022-07-10_22-44-20
  done: false
  experiment_id: 2695b7fbd447401482e07d4c41087a6d
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 1.9839304121017456
  node_ip: 172.17.0.2
  pid: 16063
  should_checkpoint: true
  time_since_restore: 21.64501452445984
  time_this_iter_s: 21.64501452445984
  time_total_s: 21.64501452445984
  timestamp: 1657460660
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00001'
  warmup_time: 0.004156827926635742

(func pid=16067) [1,  4000] loss: 1.152
(func pid=16065) [1,  4000] loss: 0.817
(func pid=16069) [1,  4000] loss: 1.026
(func pid=16079) [1,  4000] loss: 0.815
(func pid=16075) [1,  6000] loss: 0.770
(func pid=16071) [1,  4000] loss: 1.150
== Status ==
Current time: 2022-07-10 22:44:25 (running for 00:00:31.95)
Memory usage on this node: 12.4/62.7 GiB
Using AsyncHyperBand: num_stopped=0
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: -2.0909582686424253
Resources requested: 20.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+----------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 2.19799 |     0.1795 |                    1 |
| train_cifar_56838_00001 | RUNNING  | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 1.98393 |     0.2186 |                    1 |
| train_cifar_56838_00002 | RUNNING  | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  |         |            |                      |
| train_cifar_56838_00003 | RUNNING  | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 |         |            |                      |
| train_cifar_56838_00004 | RUNNING  | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 |         |            |                      |
| train_cifar_56838_00005 | RUNNING  | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 |         |            |                      |
| train_cifar_56838_00006 | RUNNING  | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |         |            |                      |
| train_cifar_56838_00007 | RUNNING  | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING  | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |         |            |                      |
| train_cifar_56838_00009 | RUNNING  | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  |         |            |                      |
+-------------------------+----------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16078) [1,  6000] loss: 0.612
(func pid=16073) [1,  6000] loss: 0.773
Result for train_cifar_56838_00002:
  accuracy: 0.4429
  date: 2022-07-10_22-44-30
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 1.5160775324821472
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 31.435377836227417
  time_this_iter_s: 31.435377836227417
  time_total_s: 31.435377836227417
  timestamp: 1657460670
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

(func pid=16035) [2,  2000] loss: 2.095
Result for train_cifar_56838_00003:
  accuracy: 0.0991
  date: 2022-07-10_22-44-30
  done: true
  experiment_id: 8c92d76a260c4f088d720b06a660148b
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 2.3025425796508787
  node_ip: 172.17.0.2
  pid: 16067
  should_checkpoint: true
  time_since_restore: 31.941344022750854
  time_this_iter_s: 31.941344022750854
  time_total_s: 31.941344022750854
  timestamp: 1657460670
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00003'
  warmup_time: 0.003927946090698242

== Status ==
Current time: 2022-07-10 22:44:30 (running for 00:00:37.25)
Memory usage on this node: 12.2/62.7 GiB
Using AsyncHyperBand: num_stopped=1
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: -2.0909582686424253
Resources requested: 20.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 RUNNING)
+-------------------------+----------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status   | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+----------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING  | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 2.19799 |     0.1795 |                    1 |
| train_cifar_56838_00001 | RUNNING  | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 1.98393 |     0.2186 |                    1 |
| train_cifar_56838_00002 | RUNNING  | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.51608 |     0.4429 |                    1 |
| train_cifar_56838_00003 | RUNNING  | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00004 | RUNNING  | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 |         |            |                      |
| train_cifar_56838_00005 | RUNNING  | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 |         |            |                      |
| train_cifar_56838_00006 | RUNNING  | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |         |            |                      |
| train_cifar_56838_00007 | RUNNING  | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING  | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |         |            |                      |
| train_cifar_56838_00009 | RUNNING  | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  |         |            |                      |
+-------------------------+----------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00009:
  accuracy: 0.4602
  date: 2022-07-10_22-44-31
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 1.4845350241661073
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 32.477478981018066
  time_this_iter_s: 32.477478981018066
  time_total_s: 32.477478981018066
  timestamp: 1657460671
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00004:
  accuracy: 0.3054
  date: 2022-07-10_22-44-31
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 1.8610201303482055
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 32.60156440734863
  time_this_iter_s: 32.60156440734863
  time_total_s: 32.60156440734863
  timestamp: 1657460671
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

(func pid=16075) [1,  8000] loss: 0.578
Result for train_cifar_56838_00005:
  accuracy: 0.1098
  date: 2022-07-10_22-44-34
  done: true
  experiment_id: e5530312d715420480690396146f8f6a
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 2.2834343837738036
  node_ip: 172.17.0.2
  pid: 16071
  should_checkpoint: true
  time_since_restore: 35.010396242141724
  time_this_iter_s: 35.010396242141724
  time_total_s: 35.010396242141724
  timestamp: 1657460674
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00005'
  warmup_time: 0.004166364669799805

(func pid=16063) [2,  2000] loss: 2.020
(func pid=16078) [1,  8000] loss: 0.449
Result for train_cifar_56838_00000:
  accuracy: 0.2417
  date: 2022-07-10_22-44-36
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 2
  loss: 1.991692670249939
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 40.07335877418518
  time_this_iter_s: 19.2447829246521
  time_total_s: 40.07335877418518
  timestamp: 1657460676
  timesteps_since_restore: 0
  training_iteration: 2
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

== Status ==
Current time: 2022-07-10 22:44:36 (running for 00:00:42.62)
Memory usage on this node: 11.0/62.7 GiB
Using AsyncHyperBand: num_stopped=2
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: -1.991692670249939 | Iter 1.000: -1.9839304121017456
Resources requested: 16.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (8 RUNNING, 2 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.99169 |     0.2417 |                    2 |
| train_cifar_56838_00001 | RUNNING    | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 1.98393 |     0.2186 |                    1 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.51608 |     0.4429 |                    1 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.86102 |     0.3054 |                    1 |
| train_cifar_56838_00006 | RUNNING    | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |         |            |                      |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |         |            |                      |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.48454 |     0.4602 |                    1 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16073) [1,  8000] loss: 0.580
(func pid=16075) [1, 10000] loss: 0.462
Result for train_cifar_56838_00001:
  accuracy: 0.2047
  date: 2022-07-10_22-44-39
  done: true
  experiment_id: 2695b7fbd447401482e07d4c41087a6d
  hostname: 5686ae09a02b
  iterations_since_restore: 2
  loss: 2.0411721435546877
  node_ip: 172.17.0.2
  pid: 16063
  should_checkpoint: true
  time_since_restore: 40.697744846343994
  time_this_iter_s: 19.052730321884155
  time_total_s: 40.697744846343994
  timestamp: 1657460679
  timesteps_since_restore: 0
  training_iteration: 2
  trial_id: '56838_00001'
  warmup_time: 0.004156827926635742

(func pid=16065) [2,  2000] loss: 1.456
(func pid=16079) [2,  2000] loss: 1.516
(func pid=16069) [2,  2000] loss: 1.743
(func pid=16078) [1, 10000] loss: 0.353
(func pid=16073) [1, 10000] loss: 0.464
== Status ==
Current time: 2022-07-10 22:44:44 (running for 00:00:51.03)
Memory usage on this node: 10.5/62.7 GiB
Using AsyncHyperBand: num_stopped=3
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: -2.016432406902313 | Iter 1.000: -1.9839304121017456
Resources requested: 14.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (7 RUNNING, 3 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.99169 |     0.2417 |                    2 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.51608 |     0.4429 |                    1 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.86102 |     0.3054 |                    1 |
| train_cifar_56838_00006 | RUNNING    | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   |         |            |                      |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  |         |            |                      |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.48454 |     0.4602 |                    1 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16075) [1, 12000] loss: 0.385
Result for train_cifar_56838_00008:
  accuracy: 0.3427
  date: 2022-07-10_22-44-48
  done: false
  experiment_id: 5f6c1e5cc5df4dbd89517a0c1809e32d
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 1.7398582818984984
  node_ip: 172.17.0.2
  pid: 16078
  should_checkpoint: true
  time_since_restore: 49.40703058242798
  time_this_iter_s: 49.40703058242798
  time_total_s: 49.40703058242798
  timestamp: 1657460688
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00008'
  warmup_time: 0.004579305648803711

(func pid=16035) [3,  2000] loss: 1.886
(func pid=16065) [2,  4000] loss: 0.716
(func pid=16079) [2,  4000] loss: 0.743
Result for train_cifar_56838_00006:
  accuracy: 0.1033
  date: 2022-07-10_22-44-50
  done: true
  experiment_id: da615e620a8848738d40edb00735aaa8
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 2.3036576274871825
  node_ip: 172.17.0.2
  pid: 16073
  should_checkpoint: true
  time_since_restore: 51.20533299446106
  time_this_iter_s: 51.20533299446106
  time_total_s: 51.20533299446106
  timestamp: 1657460690
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00006'
  warmup_time: 0.004386186599731445

== Status ==
Current time: 2022-07-10 22:44:50 (running for 00:00:56.49)
Memory usage on this node: 10.4/62.7 GiB
Using AsyncHyperBand: num_stopped=4
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: -2.016432406902313 | Iter 1.000: -1.9839304121017456
Resources requested: 14.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (7 RUNNING, 3 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.99169 |     0.2417 |                    2 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.51608 |     0.4429 |                    1 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.86102 |     0.3054 |                    1 |
| train_cifar_56838_00006 | RUNNING    | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.48454 |     0.4602 |                    1 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [2,  4000] loss: 0.822
(func pid=16075) [1, 14000] loss: 0.330
Result for train_cifar_56838_00000:
  accuracy: 0.3336
  date: 2022-07-10_22-44-53
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 3
  loss: 1.767630652999878
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 57.53247427940369
  time_this_iter_s: 17.459115505218506
  time_total_s: 57.53247427940369
  timestamp: 1657460693
  timesteps_since_restore: 0
  training_iteration: 3
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

(func pid=16078) [2,  2000] loss: 1.736
Result for train_cifar_56838_00002:
  accuracy: 0.4722
  date: 2022-07-10_22-44-57
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 2
  loss: 1.4643251877784729
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 58.40793561935425
  time_this_iter_s: 26.97255778312683
  time_total_s: 58.40793561935425
  timestamp: 1657460697
  timesteps_since_restore: 0
  training_iteration: 2
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

== Status ==
Current time: 2022-07-10 22:44:57 (running for 00:01:03.83)
Memory usage on this node: 9.8/62.7 GiB
Using AsyncHyperBand: num_stopped=4
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: -1.991692670249939 | Iter 1.000: -1.9839304121017456
Resources requested: 12.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (6 RUNNING, 4 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.76763 |     0.3336 |                    3 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.46433 |     0.4722 |                    2 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.86102 |     0.3054 |                    1 |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.48454 |     0.4602 |                    1 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00009:
  accuracy: 0.4901
  date: 2022-07-10_22-44-57
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 2
  loss: 1.4002712901592254
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 58.75606441497803
  time_this_iter_s: 26.27858543395996
  time_total_s: 58.75606441497803
  timestamp: 1657460697
  timesteps_since_restore: 0
  training_iteration: 2
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00004:
  accuracy: 0.4226
  date: 2022-07-10_22-44-58
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 2
  loss: 1.5639259339809417
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 59.510462045669556
  time_this_iter_s: 26.908897638320923
  time_total_s: 59.510462045669556
  timestamp: 1657460698
  timesteps_since_restore: 0
  training_iteration: 2
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

(func pid=16075) [1, 16000] loss: 0.289
(func pid=16078) [2,  4000] loss: 0.880
== Status ==
Current time: 2022-07-10 22:45:03 (running for 00:01:09.88)
Memory usage on this node: 10.0/62.7 GiB
Using AsyncHyperBand: num_stopped=4
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: -1.5639259339809417 | Iter 1.000: -1.9839304121017456
Resources requested: 12.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (6 RUNNING, 4 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.76763 |     0.3336 |                    3 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.46433 |     0.4722 |                    2 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.56393 |     0.4226 |                    2 |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.40027 |     0.4901 |                    2 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16075) [1, 18000] loss: 0.257
(func pid=16035) [4,  2000] loss: 1.716
(func pid=16065) [3,  2000] loss: 1.341
(func pid=16079) [3,  2000] loss: 1.435
(func pid=16069) [3,  2000] loss: 1.520
== Status ==
Current time: 2022-07-10 22:45:08 (running for 00:01:14.90)
Memory usage on this node: 10.0/62.7 GiB
Using AsyncHyperBand: num_stopped=4
Bracket: Iter 8.000: None | Iter 4.000: None | Iter 2.000: -1.5639259339809417 | Iter 1.000: -1.9839304121017456
Resources requested: 12.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (6 RUNNING, 4 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.76763 |     0.3336 |                    3 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.46433 |     0.4722 |                    2 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.56393 |     0.4226 |                    2 |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.40027 |     0.4901 |                    2 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16078) [2,  6000] loss: 0.575
Result for train_cifar_56838_00000:
  accuracy: 0.3655
  date: 2022-07-10_22-45-11
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 4
  loss: 1.6717940900802613
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 74.95665144920349
  time_this_iter_s: 17.424177169799805
  time_total_s: 74.95665144920349
  timestamp: 1657460711
  timesteps_since_restore: 0
  training_iteration: 4
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

(func pid=16075) [1, 20000] loss: 0.231
(func pid=16065) [3,  4000] loss: 0.667
(func pid=16079) [3,  4000] loss: 0.718
== Status ==
Current time: 2022-07-10 22:45:16 (running for 00:01:22.50)
Memory usage on this node: 10.0/62.7 GiB
Using AsyncHyperBand: num_stopped=4
Bracket: Iter 8.000: None | Iter 4.000: -1.6717940900802613 | Iter 2.000: -1.5639259339809417 | Iter 1.000: -1.9839304121017456
Resources requested: 12.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (6 RUNNING, 4 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.67179 |     0.3655 |                    4 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.46433 |     0.4722 |                    2 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.56393 |     0.4226 |                    2 |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.40027 |     0.4901 |                    2 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [3,  4000] loss: 0.739
(func pid=16078) [2,  8000] loss: 0.429
== Status ==
Current time: 2022-07-10 22:45:21 (running for 00:01:27.52)
Memory usage on this node: 9.9/62.7 GiB
Using AsyncHyperBand: num_stopped=4
Bracket: Iter 8.000: None | Iter 4.000: -1.6717940900802613 | Iter 2.000: -1.5639259339809417 | Iter 1.000: -1.9839304121017456
Resources requested: 12.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (6 RUNNING, 4 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.67179 |     0.3655 |                    4 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.46433 |     0.4722 |                    2 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.56393 |     0.4226 |                    2 |
| train_cifar_56838_00007 | RUNNING    | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  |         |            |                      |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.40027 |     0.4901 |                    2 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00007:
  accuracy: 0.1023
  date: 2022-07-10_22-45-22
  done: true
  experiment_id: 88d5671d66c84a008f8c0b23996abf54
  hostname: 5686ae09a02b
  iterations_since_restore: 1
  loss: 2.3122479389190675
  node_ip: 172.17.0.2
  pid: 16075
  should_checkpoint: true
  time_since_restore: 83.7643940448761
  time_this_iter_s: 83.7643940448761
  time_total_s: 83.7643940448761
  timestamp: 1657460722
  timesteps_since_restore: 0
  training_iteration: 1
  trial_id: '56838_00007'
  warmup_time: 0.004323244094848633

(func pid=16035) [5,  2000] loss: 1.640
Result for train_cifar_56838_00002:
  accuracy: 0.5124
  date: 2022-07-10_22-45-23
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 3
  loss: 1.374449992275238
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 84.27105474472046
  time_this_iter_s: 25.86311912536621
  time_total_s: 84.27105474472046
  timestamp: 1657460723
  timesteps_since_restore: 0
  training_iteration: 3
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

Result for train_cifar_56838_00009:
  accuracy: 0.4821
  date: 2022-07-10_22-45-23
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 3
  loss: 1.439400708436966
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 84.41391801834106
  time_this_iter_s: 25.657853603363037
  time_total_s: 84.41391801834106
  timestamp: 1657460723
  timesteps_since_restore: 0
  training_iteration: 3
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00004:
  accuracy: 0.4654
  date: 2022-07-10_22-45-24
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 3
  loss: 1.4511688560724259
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 85.2825973033905
  time_this_iter_s: 25.772135257720947
  time_total_s: 85.2825973033905
  timestamp: 1657460724
  timesteps_since_restore: 0
  training_iteration: 3
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

(func pid=16078) [2, 10000] loss: 0.348
Result for train_cifar_56838_00000:
  accuracy: 0.4036
  date: 2022-07-10_22-45-28
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 5
  loss: 1.5952760919570923
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 92.26456785202026
  time_this_iter_s: 17.307916402816772
  time_total_s: 92.26456785202026
  timestamp: 1657460728
  timesteps_since_restore: 0
  training_iteration: 5
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

== Status ==
Current time: 2022-07-10 22:45:28 (running for 00:01:34.81)
Memory usage on this node: 9.3/62.7 GiB
Using AsyncHyperBand: num_stopped=5
Bracket: Iter 8.000: None | Iter 4.000: -1.6717940900802613 | Iter 2.000: -1.5639259339809417 | Iter 1.000: -2.0909582686424253
Resources requested: 10.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (5 RUNNING, 5 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.59528 |     0.4036 |                    5 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.37445 |     0.5124 |                    3 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.45117 |     0.4654 |                    3 |
| train_cifar_56838_00008 | RUNNING    | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.73986 |     0.3427 |                    1 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.4394  |     0.4821 |                    3 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00008:
  accuracy: 0.3832
  date: 2022-07-10_22-45-30
  done: true
  experiment_id: 5f6c1e5cc5df4dbd89517a0c1809e32d
  hostname: 5686ae09a02b
  iterations_since_restore: 2
  loss: 1.713135867524147
  node_ip: 172.17.0.2
  pid: 16078
  should_checkpoint: true
  time_since_restore: 91.32331967353821
  time_this_iter_s: 41.91628909111023
  time_total_s: 91.32331967353821
  timestamp: 1657460730
  timesteps_since_restore: 0
  training_iteration: 2
  trial_id: '56838_00008'
  warmup_time: 0.004579305648803711

(func pid=16079) [4,  2000] loss: 1.394
(func pid=16065) [4,  2000] loss: 1.281
(func pid=16069) [4,  2000] loss: 1.385
== Status ==
Current time: 2022-07-10 22:45:35 (running for 00:01:41.75)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.6717940900802613 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.59528 |     0.4036 |                    5 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.37445 |     0.5124 |                    3 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.45117 |     0.4654 |                    3 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.4394  |     0.4821 |                    3 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


== Status ==
Current time: 2022-07-10 22:45:40 (running for 00:01:46.76)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.6717940900802613 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.59528 |     0.4036 |                    5 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.37445 |     0.5124 |                    3 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.45117 |     0.4654 |                    3 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.4394  |     0.4821 |                    3 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16035) [6,  2000] loss: 1.566
(func pid=16079) [4,  4000] loss: 0.699
(func pid=16065) [4,  4000] loss: 0.645
(func pid=16069) [4,  4000] loss: 0.684
== Status ==
Current time: 2022-07-10 22:45:45 (running for 00:01:51.77)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.6717940900802613 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.59528 |     0.4036 |                    5 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.37445 |     0.5124 |                    3 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.45117 |     0.4654 |                    3 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.4394  |     0.4821 |                    3 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00000:
  accuracy: 0.4212
  date: 2022-07-10_22-45-46
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 6
  loss: 1.535954684638977
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 110.03145003318787
  time_this_iter_s: 17.766882181167603
  time_total_s: 110.03145003318787
  timestamp: 1657460746
  timesteps_since_restore: 0
  training_iteration: 6
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

Result for train_cifar_56838_00009:
  accuracy: 0.4816
  date: 2022-07-10_22-45-49
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 4
  loss: 1.4396800416231155
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 110.01181650161743
  time_this_iter_s: 25.597898483276367
  time_total_s: 110.01181650161743
  timestamp: 1657460749
  timesteps_since_restore: 0
  training_iteration: 4
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00002:
  accuracy: 0.538
  date: 2022-07-10_22-45-49
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 4
  loss: 1.2983079232215882
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 110.21407890319824
  time_this_iter_s: 25.943024158477783
  time_total_s: 110.21407890319824
  timestamp: 1657460749
  timesteps_since_restore: 0
  training_iteration: 4
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

Result for train_cifar_56838_00004:
  accuracy: 0.494
  date: 2022-07-10_22-45-50
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 4
  loss: 1.3866686725378037
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 111.56446027755737
  time_this_iter_s: 26.28186297416687
  time_total_s: 111.56446027755737
  timestamp: 1657460750
  timesteps_since_restore: 0
  training_iteration: 4
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

== Status ==
Current time: 2022-07-10 22:45:50 (running for 00:01:56.93)
Memory usage on this node: 8.7/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.53595 |     0.4212 |                    6 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.29831 |     0.538  |                    4 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.38667 |     0.494  |                    4 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.43968 |     0.4816 |                    4 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


== Status ==
Current time: 2022-07-10 22:45:55 (running for 00:02:01.96)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.53595 |     0.4212 |                    6 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.29831 |     0.538  |                    4 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.38667 |     0.494  |                    4 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.43968 |     0.4816 |                    4 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16079) [5,  2000] loss: 1.366
(func pid=16035) [7,  2000] loss: 1.510
(func pid=16065) [5,  2000] loss: 1.239
(func pid=16069) [5,  2000] loss: 1.303
== Status ==
Current time: 2022-07-10 22:46:00 (running for 00:02:06.97)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.53595 |     0.4212 |                    6 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.29831 |     0.538  |                    4 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.38667 |     0.494  |                    4 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.43968 |     0.4816 |                    4 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00000:
  accuracy: 0.4397
  date: 2022-07-10_22-46-04
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 7
  loss: 1.4841811712265014
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 127.94247055053711
  time_this_iter_s: 17.911020517349243
  time_total_s: 127.94247055053711
  timestamp: 1657460764
  timesteps_since_restore: 0
  training_iteration: 7
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

(func pid=16079) [5,  4000] loss: 0.680
(func pid=16065) [5,  4000] loss: 0.624
(func pid=16069) [5,  4000] loss: 0.639
== Status ==
Current time: 2022-07-10 22:46:09 (running for 00:02:15.49)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.48418 |     0.4397 |                    7 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.29831 |     0.538  |                    4 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.38667 |     0.494  |                    4 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.43968 |     0.4816 |                    4 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


== Status ==
Current time: 2022-07-10 22:46:14 (running for 00:02:20.50)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.48418 |     0.4397 |                    7 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.29831 |     0.538  |                    4 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.38667 |     0.494  |                    4 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.43968 |     0.4816 |                    4 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00009:
  accuracy: 0.5258
  date: 2022-07-10_22-46-14
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 5
  loss: 1.3628854699850081
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 135.30842113494873
  time_this_iter_s: 25.2966046333313
  time_total_s: 135.30842113494873
  timestamp: 1657460774
  timesteps_since_restore: 0
  training_iteration: 5
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00002:
  accuracy: 0.5601
  date: 2022-07-10_22-46-15
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 5
  loss: 1.2476169444322587
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 136.4897665977478
  time_this_iter_s: 26.27568769454956
  time_total_s: 136.4897665977478
  timestamp: 1657460775
  timesteps_since_restore: 0
  training_iteration: 5
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

Result for train_cifar_56838_00004:
  accuracy: 0.5458
  date: 2022-07-10_22-46-16
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 5
  loss: 1.2629864408731462
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 137.3824725151062
  time_this_iter_s: 25.818012237548828
  time_total_s: 137.3824725151062
  timestamp: 1657460776
  timesteps_since_restore: 0
  training_iteration: 5
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

(func pid=16035) [8,  2000] loss: 1.463
== Status ==
Current time: 2022-07-10 22:46:21 (running for 00:02:27.77)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: None | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.48418 |     0.4397 |                    7 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.24762 |     0.5601 |                    5 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.26299 |     0.5458 |                    5 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.36289 |     0.5258 |                    5 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00000:
  accuracy: 0.4506
  date: 2022-07-10_22-46-21
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 8
  loss: 1.4564062212944031
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 145.36241030693054
  time_this_iter_s: 17.419939756393433
  time_total_s: 145.36241030693054
  timestamp: 1657460781
  timesteps_since_restore: 0
  training_iteration: 8
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

(func pid=16079) [6,  2000] loss: 1.346
(func pid=16065) [6,  2000] loss: 1.203
(func pid=16069) [6,  2000] loss: 1.245
== Status ==
Current time: 2022-07-10 22:46:26 (running for 00:02:32.91)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.45641 |     0.4506 |                    8 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.24762 |     0.5601 |                    5 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.26299 |     0.5458 |                    5 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.36289 |     0.5258 |                    5 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


== Status ==
Current time: 2022-07-10 22:46:31 (running for 00:02:37.93)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.45641 |     0.4506 |                    8 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.24762 |     0.5601 |                    5 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.26299 |     0.5458 |                    5 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.36289 |     0.5258 |                    5 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16079) [6,  4000] loss: 0.680
(func pid=16035) [9,  2000] loss: 1.424
(func pid=16065) [6,  4000] loss: 0.619
(func pid=16069) [6,  4000] loss: 0.609
== Status ==
Current time: 2022-07-10 22:46:36 (running for 00:02:42.94)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.45641 |     0.4506 |                    8 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.24762 |     0.5601 |                    5 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.26299 |     0.5458 |                    5 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.36289 |     0.5258 |                    5 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00000:
  accuracy: 0.4767
  date: 2022-07-10_22-46-38
  done: false
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 9
  loss: 1.4082407505035401
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 162.4210286140442
  time_this_iter_s: 17.058618307113647
  time_total_s: 162.4210286140442
  timestamp: 1657460798
  timesteps_since_restore: 0
  training_iteration: 9
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

Result for train_cifar_56838_00009:
  accuracy: 0.5429
  date: 2022-07-10_22-46-39
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 6
  loss: 1.32848661339283
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 160.85404992103577
  time_this_iter_s: 25.545628786087036
  time_total_s: 160.85404992103577
  timestamp: 1657460799
  timesteps_since_restore: 0
  training_iteration: 6
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00002:
  accuracy: 0.5624
  date: 2022-07-10_22-46-41
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 6
  loss: 1.236316787004471
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 161.8820824623108
  time_this_iter_s: 25.39231586456299
  time_total_s: 161.8820824623108
  timestamp: 1657460801
  timesteps_since_restore: 0
  training_iteration: 6
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

Result for train_cifar_56838_00004:
  accuracy: 0.5539
  date: 2022-07-10_22-46-42
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 6
  loss: 1.270205118751526
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 163.53253483772278
  time_this_iter_s: 26.150062322616577
  time_total_s: 163.53253483772278
  timestamp: 1657460802
  timesteps_since_restore: 0
  training_iteration: 6
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

== Status ==
Current time: 2022-07-10 22:46:42 (running for 00:02:48.91)
Memory usage on this node: 8.7/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.40824 |     0.4767 |                    9 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23632 |     0.5624 |                    6 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.27021 |     0.5539 |                    6 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.32849 |     0.5429 |                    6 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


== Status ==
Current time: 2022-07-10 22:46:47 (running for 00:02:53.92)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.40824 |     0.4767 |                    9 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23632 |     0.5624 |                    6 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.27021 |     0.5539 |                    6 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.32849 |     0.5429 |                    6 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16065) [7,  2000] loss: 1.199
(func pid=16079) [7,  2000] loss: 1.325
(func pid=16035) [10,  2000] loss: 1.379
(func pid=16069) [7,  2000] loss: 1.188
== Status ==
Current time: 2022-07-10 22:46:52 (running for 00:02:58.94)
Memory usage on this node: 8.8/62.7 GiB
Using AsyncHyperBand: num_stopped=6
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 8.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (4 RUNNING, 6 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | RUNNING    | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.40824 |     0.4767 |                    9 |
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23632 |     0.5624 |                    6 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.27021 |     0.5539 |                    6 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.32849 |     0.5429 |                    6 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00000:
  accuracy: 0.4889
  date: 2022-07-10_22-46-55
  done: true
  experiment_id: a41c4004031948dbb188518d989774e1
  hostname: 5686ae09a02b
  iterations_since_restore: 10
  loss: 1.3861094895362853
  node_ip: 172.17.0.2
  pid: 16035
  should_checkpoint: true
  time_since_restore: 179.23221969604492
  time_this_iter_s: 16.811191082000732
  time_total_s: 179.23221969604492
  timestamp: 1657460815
  timesteps_since_restore: 0
  training_iteration: 10
  trial_id: '56838_00000'
  warmup_time: 0.00463104248046875

(func pid=16065) [7,  4000] loss: 0.605
(func pid=16079) [7,  4000] loss: 0.676
== Status ==
Current time: 2022-07-10 22:47:00 (running for 00:03:06.79)
Memory usage on this node: 8.2/62.7 GiB
Using AsyncHyperBand: num_stopped=7
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 6.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (3 RUNNING, 7 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23632 |     0.5624 |                    6 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.27021 |     0.5539 |                    6 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.32849 |     0.5429 |                    6 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [7,  4000] loss: 0.583
== Status ==
Current time: 2022-07-10 22:47:05 (running for 00:03:11.81)
Memory usage on this node: 8.2/62.7 GiB
Using AsyncHyperBand: num_stopped=7
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 6.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (3 RUNNING, 7 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23632 |     0.5624 |                    6 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.27021 |     0.5539 |                    6 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.32849 |     0.5429 |                    6 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00002:
  accuracy: 0.5231
  date: 2022-07-10_22-47-05
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 7
  loss: 1.3272357536792756
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 186.79223775863647
  time_this_iter_s: 24.910155296325684
  time_total_s: 186.79223775863647
  timestamp: 1657460825
  timesteps_since_restore: 0
  training_iteration: 7
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

Result for train_cifar_56838_00009:
  accuracy: 0.529
  date: 2022-07-10_22-47-06
  done: false
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 7
  loss: 1.3492257507801055
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 187.8302516937256
  time_this_iter_s: 26.97620177268982
  time_total_s: 187.8302516937256
  timestamp: 1657460826
  timesteps_since_restore: 0
  training_iteration: 7
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00004:
  accuracy: 0.5697
  date: 2022-07-10_22-47-10
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 7
  loss: 1.2133433543801309
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 190.97661209106445
  time_this_iter_s: 27.444077253341675
  time_total_s: 190.97661209106445
  timestamp: 1657460830
  timesteps_since_restore: 0
  training_iteration: 7
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

(func pid=16065) [8,  2000] loss: 1.181
== Status ==
Current time: 2022-07-10 22:47:15 (running for 00:03:21.35)
Memory usage on this node: 8.2/62.7 GiB
Using AsyncHyperBand: num_stopped=7
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 6.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (3 RUNNING, 7 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.32724 |     0.5231 |                    7 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.21334 |     0.5697 |                    7 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.34923 |     0.529  |                    7 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16079) [8,  2000] loss: 1.323
(func pid=16069) [8,  2000] loss: 1.130
== Status ==
Current time: 2022-07-10 22:47:20 (running for 00:03:26.38)
Memory usage on this node: 8.2/62.7 GiB
Using AsyncHyperBand: num_stopped=7
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 6.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (3 RUNNING, 7 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.32724 |     0.5231 |                    7 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.21334 |     0.5697 |                    7 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.34923 |     0.529  |                    7 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16065) [8,  4000] loss: 0.596
== Status ==
Current time: 2022-07-10 22:47:25 (running for 00:03:31.40)
Memory usage on this node: 8.2/62.7 GiB
Using AsyncHyperBand: num_stopped=7
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 6.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (3 RUNNING, 7 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.32724 |     0.5231 |                    7 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.21334 |     0.5697 |                    7 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.34923 |     0.529  |                    7 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16079) [8,  4000] loss: 0.673
== Status ==
Current time: 2022-07-10 22:47:30 (running for 00:03:36.41)
Memory usage on this node: 8.2/62.7 GiB
Using AsyncHyperBand: num_stopped=7
Bracket: Iter 8.000: -1.4564062212944031 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 6.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (3 RUNNING, 7 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.32724 |     0.5231 |                    7 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.21334 |     0.5697 |                    7 |
| train_cifar_56838_00009 | RUNNING    | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.34923 |     0.529  |                    7 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [8,  4000] loss: 0.566
Result for train_cifar_56838_00002:
  accuracy: 0.5668
  date: 2022-07-10_22-47-32
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 8
  loss: 1.2393127641439439
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 213.24754667282104
  time_this_iter_s: 26.45530891418457
  time_total_s: 213.24754667282104
  timestamp: 1657460852
  timesteps_since_restore: 0
  training_iteration: 8
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

Result for train_cifar_56838_00009:
  accuracy: 0.5168
  date: 2022-07-10_22-47-33
  done: true
  experiment_id: 4095c21ecbe046d88541a61aa1a3f975
  hostname: 5686ae09a02b
  iterations_since_restore: 8
  loss: 1.3837481220006942
  node_ip: 172.17.0.2
  pid: 16079
  should_checkpoint: true
  time_since_restore: 214.15919280052185
  time_this_iter_s: 26.328941106796265
  time_total_s: 214.15919280052185
  timestamp: 1657460853
  timesteps_since_restore: 0
  training_iteration: 8
  trial_id: '56838_00009'
  warmup_time: 0.0041849613189697266

Result for train_cifar_56838_00004:
  accuracy: 0.5798
  date: 2022-07-10_22-47-38
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 8
  loss: 1.1878560908555984
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 219.15277433395386
  time_this_iter_s: 28.176162242889404
  time_total_s: 219.15277433395386
  timestamp: 1657460858
  timesteps_since_restore: 0
  training_iteration: 8
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

== Status ==
Current time: 2022-07-10 22:47:38 (running for 00:03:44.52)
Memory usage on this node: 7.5/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23931 |     0.5668 |                    8 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.18786 |     0.5798 |                    8 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16065) [9,  2000] loss: 1.157
== Status ==
Current time: 2022-07-10 22:47:43 (running for 00:03:49.55)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23931 |     0.5668 |                    8 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.18786 |     0.5798 |                    8 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


== Status ==
Current time: 2022-07-10 22:47:48 (running for 00:03:54.56)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23931 |     0.5668 |                    8 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.18786 |     0.5798 |                    8 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [9,  2000] loss: 1.095
(func pid=16065) [9,  4000] loss: 0.594
== Status ==
Current time: 2022-07-10 22:47:53 (running for 00:03:59.58)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23931 |     0.5668 |                    8 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.18786 |     0.5798 |                    8 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [9,  4000] loss: 0.549
== Status ==
Current time: 2022-07-10 22:47:58 (running for 00:04:04.59)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23931 |     0.5668 |                    8 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.18786 |     0.5798 |                    8 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00002:
  accuracy: 0.5404
  date: 2022-07-10_22-47-58
  done: false
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 9
  loss: 1.330999031496048
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 239.7574098110199
  time_this_iter_s: 26.509863138198853
  time_total_s: 239.7574098110199
  timestamp: 1657460878
  timesteps_since_restore: 0
  training_iteration: 9
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

== Status ==
Current time: 2022-07-10 22:48:03 (running for 00:04:10.18)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.331   |     0.5404 |                    9 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.18786 |     0.5798 |                    8 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00004:
  accuracy: 0.5963
  date: 2022-07-10_22-48-05
  done: false
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 9
  loss: 1.164097112059593
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 246.76238989830017
  time_this_iter_s: 27.609615564346313
  time_total_s: 246.76238989830017
  timestamp: 1657460885
  timesteps_since_restore: 0
  training_iteration: 9
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

(func pid=16065) [10,  2000] loss: 1.151
== Status ==
Current time: 2022-07-10 22:48:10 (running for 00:04:17.13)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.331   |     0.5404 |                    9 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.1641  |     0.5963 |                    9 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16069) [10,  2000] loss: 1.066
== Status ==
Current time: 2022-07-10 22:48:15 (running for 00:04:22.16)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.331   |     0.5404 |                    9 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.1641  |     0.5963 |                    9 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


(func pid=16065) [10,  4000] loss: 0.585
== Status ==
Current time: 2022-07-10 22:48:20 (running for 00:04:27.17)
Memory usage on this node: 7.6/62.7 GiB
Using AsyncHyperBand: num_stopped=8
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 4.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (2 RUNNING, 8 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00002 | RUNNING    | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.331   |     0.5404 |                    9 |
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.1641  |     0.5963 |                    9 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00002:
  accuracy: 0.5689
  date: 2022-07-10_22-48-23
  done: true
  experiment_id: 7623bcb1ab0f40fb936c7b0f8965a482
  hostname: 5686ae09a02b
  iterations_since_restore: 10
  loss: 1.2367445414543152
  node_ip: 172.17.0.2
  pid: 16065
  should_checkpoint: true
  time_since_restore: 264.78081727027893
  time_this_iter_s: 25.023407459259033
  time_total_s: 264.78081727027893
  timestamp: 1657460903
  timesteps_since_restore: 0
  training_iteration: 10
  trial_id: '56838_00002'
  warmup_time: 0.0038442611694335938

(func pid=16069) [10,  4000] loss: 0.535
== Status ==
Current time: 2022-07-10 22:48:28 (running for 00:04:35.23)
Memory usage on this node: 7.0/62.7 GiB
Using AsyncHyperBand: num_stopped=9
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 2.0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (1 RUNNING, 9 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00004 | RUNNING    | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.1641  |     0.5963 |                    9 |
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00002 | TERMINATED | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23674 |     0.5689 |                   10 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Result for train_cifar_56838_00004:
  accuracy: 0.5999
  date: 2022-07-10_22-48-31
  done: true
  experiment_id: 0fb2802d6b8d434bbdf6305678a70a8c
  hostname: 5686ae09a02b
  iterations_since_restore: 10
  loss: 1.1392601644277573
  node_ip: 172.17.0.2
  pid: 16069
  should_checkpoint: true
  time_since_restore: 272.8836762905121
  time_this_iter_s: 26.121286392211914
  time_total_s: 272.8836762905121
  timestamp: 1657460911
  timesteps_since_restore: 0
  training_iteration: 10
  trial_id: '56838_00004'
  warmup_time: 0.00504755973815918

== Status ==
Current time: 2022-07-10 22:48:32 (running for 00:04:38.27)
Memory usage on this node: 6.7/62.7 GiB
Using AsyncHyperBand: num_stopped=10
Bracket: Iter 8.000: -1.3115304430723191 | Iter 4.000: -1.4131743570804596 | Iter 2.000: -1.6385309007525444 | Iter 1.000: -2.0909582686424253
Resources requested: 0/32 CPUs, 0/2 GPUs, 0.0/42.84 GiB heap, 0.0/9.31 GiB objects (0.0/1.0 accelerator_type:P100)
Result logdir: /root/ray_results/train_cifar_2022-07-10_22-43-53
Number of trials: 10/10 (10 TERMINATED)
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+
| Trial name              | status     | loc              |   batch_size |   l1 |   l2 |          lr |    loss |   accuracy |   training_iteration |
|-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------|
| train_cifar_56838_00000 | TERMINATED | 172.17.0.2:16035 |           16 |    8 |  256 | 0.000284896 | 1.38611 |     0.4889 |                   10 |
| train_cifar_56838_00001 | TERMINATED | 172.17.0.2:16063 |           16 |   16 |  256 | 0.0328464   | 2.04117 |     0.2047 |                    2 |
| train_cifar_56838_00002 | TERMINATED | 172.17.0.2:16065 |            8 |    8 |  256 | 0.00224488  | 1.23674 |     0.5689 |                   10 |
| train_cifar_56838_00003 | TERMINATED | 172.17.0.2:16067 |            8 |    4 |   32 | 0.000122311 | 2.30254 |     0.0991 |                    1 |
| train_cifar_56838_00004 | TERMINATED | 172.17.0.2:16069 |            8 |   32 |  256 | 0.000414364 | 1.13926 |     0.5999 |                   10 |
| train_cifar_56838_00005 | TERMINATED | 172.17.0.2:16071 |            8 |  256 |   16 | 0.000280282 | 2.28343 |     0.1098 |                    1 |
| train_cifar_56838_00006 | TERMINATED | 172.17.0.2:16073 |            4 |   16 |    8 | 0.0314931   | 2.30366 |     0.1033 |                    1 |
| train_cifar_56838_00007 | TERMINATED | 172.17.0.2:16075 |            2 |    8 |    4 | 0.00769353  | 2.31225 |     0.1023 |                    1 |
| train_cifar_56838_00008 | TERMINATED | 172.17.0.2:16078 |            4 |   16 |    8 | 0.00532825  | 1.71314 |     0.3832 |                    2 |
| train_cifar_56838_00009 | TERMINATED | 172.17.0.2:16079 |            8 |   16 |   32 | 0.00482255  | 1.38375 |     0.5168 |                    8 |
+-------------------------+------------+------------------+--------------+------+------+-------------+---------+------------+----------------------+


Best trial config: {'l1': 32, 'l2': 256, 'lr': 0.0004143640950626381, 'batch_size': 8}
Best trial final validation loss: 1.1392601644277573
Best trial final validation accuracy: 0.5999
Files already downloaded and verified
Files already downloaded and verified
Best trial test set accuracy: 0.602

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

Number of trials: 10 (10 TERMINATED)
+-----+------+------+-------------+--------------+---------+------------+--------------------+
| ... |   l1 |   l2 |          lr |   batch_size |    loss |   accuracy | training_iteration |
|-----+------+------+-------------+--------------+---------+------------+--------------------|
| ... |   64 |    4 | 0.00011629  |            2 | 1.87273 |     0.244  |                  2 |
| ... |   32 |   64 | 0.000339763 |            8 | 1.23603 |     0.567  |                  8 |
| ... |    8 |   16 | 0.00276249  |           16 | 1.1815  |     0.5836 |                 10 |
| ... |    4 |   64 | 0.000648721 |            4 | 1.31131 |     0.5224 |                  8 |
| ... |   32 |   16 | 0.000340753 |            8 | 1.26454 |     0.5444 |                  8 |
| ... |    8 |    4 | 0.000699775 |            8 | 1.99594 |     0.1983 |                  2 |
| ... |  256 |    8 | 0.0839654   |           16 | 2.3119  |     0.0993 |                  1 |
| ... |   16 |  128 | 0.0758154   |           16 | 2.33575 |     0.1327 |                  1 |
| ... |   16 |    8 | 0.0763312   |           16 | 2.31129 |     0.1042 |                  4 |
| ... |  128 |   16 | 0.000124903 |            4 | 2.26917 |     0.1945 |                  1 |
+-----+------+------+-------------+--------------+---------+------------+--------------------+


Best trial config: {'l1': 8, 'l2': 16, 'lr': 0.00276249, 'batch_size': 16, 'data_dir': '...'}
Best trial final validation loss: 1.181501
Best trial final validation accuracy: 0.5836
Best trial test set accuracy: 0.5806

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

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