Note
Go to the end to download the full example code.
Hyperparameter tuning with Ray Tune¶
Created On: Aug 31, 2020 | Last Updated: Jun 24, 2025 | Last Verified: Nov 05, 2024
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 various 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
wrap data loading and training in functions,
make some network parameters configurable,
add checkpointing (optional),
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 librarytorchvision: For the data transformers
Setup / Imports¶
Let’s start with the imports:
from functools import partial
import os
import tempfile
from pathlib import Path
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 import train
from ray.train import Checkpoint, get_checkpoint
from ray.tune.schedulers import ASHAScheduler
import ray.cloudpickle as pickle
Most of the imports are needed for building the PyTorch model. Only the last imports are for Ray Tune.
Data loaders¶
We wrap the data loaders in their own function and pass a global data directory. This way we can share a data directory between different trials.
def load_data(data_dir="./data"):
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
trainset = torchvision.datasets.CIFAR10(
root=data_dir, train=True, download=True, transform=transform
)
testset = torchvision.datasets.CIFAR10(
root=data_dir, train=False, download=True, transform=transform
)
return trainset, testset
Configurable neural network¶
We can only tune those parameters that are configurable. In this example, we can specify the layer sizes of the fully connected layers:
class Net(nn.Module):
def __init__(self, l1=120, l2=84):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, l1)
self.fc2 = nn.Linear(l1, l2)
self.fc3 = nn.Linear(l2, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
The train function¶
Now it gets interesting, because we introduce some changes to the example from the PyTorch documentation.
We wrap the training script in a function train_cifar(config, data_dir=None).
The config parameter will receive the hyperparameters we would like to
train with. The data_dir specifies the directory where we load and store the data,
so that multiple runs can share the same data source.
We also load the model and optimizer state at the start of the run, if a checkpoint
is provided. Further down in this tutorial you will find information on how
to save the checkpoint and what it is used for.
net = Net(config["l1"], config["l2"])
checkpoint = get_checkpoint()
if checkpoint:
with checkpoint.as_directory() as checkpoint_dir:
data_path = Path(checkpoint_dir) / "data.pkl"
with open(data_path, "rb") as fp:
checkpoint_state = pickle.load(fp)
start_epoch = checkpoint_state["epoch"]
net.load_state_dict(checkpoint_state["net_state_dict"])
optimizer.load_state_dict(checkpoint_state["optimizer_state_dict"])
else:
start_epoch = 0
The learning rate of the optimizer is made configurable, too:
optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9)
We also split the training data into a training and validation subset. We thus train on 80% of the data and calculate the validation loss on the remaining 20%. The batch sizes with which we iterate through the training and test sets are configurable as well.
Adding (multi) GPU support with DataParallel¶
Image classification benefits largely from GPUs. Luckily, we can continue to use
PyTorch’s abstractions in Ray Tune. Thus, we can wrap our model in nn.DataParallel
to support data parallel training on multiple GPUs:
device = "cpu"
if torch.cuda.is_available():
device = "cuda:0"
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
net.to(device)
By using a device variable we make sure that training also works when we have
no GPUs available. PyTorch requires us to send our data to the GPU memory explicitly,
like this:
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
The code now supports training on CPUs, on a single GPU, and on multiple GPUs. Notably, Ray also supports fractional GPUs so we can share GPUs among trials, as long as the model still fits on the GPU memory. We’ll come back to that later.
Communicating with Ray Tune¶
The most interesting part is the communication with Ray Tune:
checkpoint_data = {
"epoch": epoch,
"net_state_dict": net.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
}
with tempfile.TemporaryDirectory() as checkpoint_dir:
data_path = Path(checkpoint_dir) / "data.pkl"
with open(data_path, "wb") as fp:
pickle.dump(checkpoint_data, fp)
checkpoint = Checkpoint.from_directory(checkpoint_dir)
train.report(
{"loss": val_loss / val_steps, "accuracy": correct / total},
checkpoint=checkpoint,
)
Here we first save a checkpoint and then report some metrics back to Ray Tune. Specifically, we send the validation loss and accuracy back to Ray Tune. Ray Tune can then use these metrics to decide which hyperparameter configuration lead to the best results. These metrics can also be used to stop bad performing trials early in order to avoid wasting resources on those trials.
The checkpoint saving is optional, however, it is necessary if we wanted to use advanced schedulers like Population Based Training. Also, by saving the checkpoint we can later load the trained models and validate them on a test set. Lastly, saving checkpoints is useful for fault tolerance, and it allows us to interrupt training and continue training later.
Full training function¶
The full code example looks like this:
def train_cifar(config, data_dir=None):
net = Net(config["l1"], config["l2"])
device = "cpu"
if torch.cuda.is_available():
device = "cuda:0"
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9)
checkpoint = get_checkpoint()
if checkpoint:
with checkpoint.as_directory() as checkpoint_dir:
data_path = Path(checkpoint_dir) / "data.pkl"
with open(data_path, "rb") as fp:
checkpoint_state = pickle.load(fp)
start_epoch = checkpoint_state["epoch"]
net.load_state_dict(checkpoint_state["net_state_dict"])
optimizer.load_state_dict(checkpoint_state["optimizer_state_dict"])
else:
start_epoch = 0
trainset, testset = load_data(data_dir)
test_abs = int(len(trainset) * 0.8)
train_subset, val_subset = random_split(
trainset, [test_abs, len(trainset) - test_abs]
)
trainloader = torch.utils.data.DataLoader(
train_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8
)
valloader = torch.utils.data.DataLoader(
val_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8
)
for epoch in range(start_epoch, 10): # loop over the dataset multiple times
running_loss = 0.0
epoch_steps = 0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
epoch_steps += 1
if i % 2000 == 1999: # print every 2000 mini-batches
print(
"[%d, %5d] loss: %.3f"
% (epoch + 1, i + 1, running_loss / epoch_steps)
)
running_loss = 0.0
# Validation loss
val_loss = 0.0
val_steps = 0
total = 0
correct = 0
for i, data in enumerate(valloader, 0):
with torch.no_grad():
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
loss = criterion(outputs, labels)
val_loss += loss.cpu().numpy()
val_steps += 1
checkpoint_data = {
"epoch": epoch,
"net_state_dict": net.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
}
with tempfile.TemporaryDirectory() as checkpoint_dir:
data_path = Path(checkpoint_dir) / "data.pkl"
with open(data_path, "wb") as fp:
pickle.dump(checkpoint_data, fp)
checkpoint = Checkpoint.from_directory(checkpoint_dir)
train.report(
{"loss": val_loss / val_steps, "accuracy": correct / total},
checkpoint=checkpoint,
)
print("Finished Training")
As you can see, most of the code is adapted directly from the original example.
Test set accuracy¶
Commonly the performance of a machine learning model is tested on a hold-out test set with data that has not been used for training the model. We also wrap this in a function:
def test_accuracy(net, device="cpu"):
trainset, testset = load_data()
testloader = torch.utils.data.DataLoader(
testset, batch_size=4, shuffle=False, num_workers=2
)
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
return correct / total
The function also expects a device parameter, so we can do the
test set validation on a GPU.
Configuring the search space¶
Lastly, we need to define Ray Tune’s search space. Here is an example:
config = {
"l1": tune.choice([2 ** i for i in range(9)]),
"l2": tune.choice([2 ** i for i in range(9)]),
"lr": tune.loguniform(1e-4, 1e-1),
"batch_size": tune.choice([2, 4, 8, 16])
}
The tune.choice() accepts a list of values that are uniformly sampled from.
In this example, the l1 and l2 parameters
should be powers of 2 between 4 and 256, so either 4, 8, 16, 32, 64, 128, or 256.
The lr (learning rate) should be uniformly sampled between 0.0001 and 0.1. Lastly,
the batch size is a choice between 2, 4, 8, and 16.
At each trial, Ray Tune will now randomly sample a combination of parameters from these
search spaces. It will then train a number of models in parallel and find the best
performing one among these. We also use the ASHAScheduler which will terminate bad
performing trials early.
We wrap the train_cifar function with functools.partial to set the constant
data_dir parameter. We can also tell Ray Tune what resources should be
available for each trial:
gpus_per_trial = 2
# ...
result = tune.run(
partial(train_cifar, data_dir=data_dir),
resources_per_trial={"cpu": 8, "gpu": gpus_per_trial},
config=config,
num_samples=num_samples,
scheduler=scheduler,
checkpoint_at_end=True)
You can specify the number of CPUs, which are then available e.g.
to increase the num_workers of the PyTorch DataLoader instances. The selected
number of GPUs are made visible to PyTorch in each trial. Trials do not have access to
GPUs that haven’t been requested for them - so you don’t have to care about two trials
using the same set of resources.
Here we can also specify fractional GPUs, so something like gpus_per_trial=0.5 is
completely valid. The trials will then share GPUs among each other.
You just have to make sure that the models still fit in the GPU memory.
After training the models, we will find the best performing one and load the trained network from the checkpoint file. We then obtain the test set accuracy and report everything by printing.
The full main function looks like this:
def main(num_samples=10, max_num_epochs=10, gpus_per_trial=2):
data_dir = os.path.abspath("./data")
load_data(data_dir)
config = {
"l1": tune.choice([2**i for i in range(9)]),
"l2": tune.choice([2**i for i in range(9)]),
"lr": tune.loguniform(1e-4, 1e-1),
"batch_size": tune.choice([2, 4, 8, 16]),
}
scheduler = ASHAScheduler(
metric="loss",
mode="min",
max_t=max_num_epochs,
grace_period=1,
reduction_factor=2,
)
result = tune.run(
partial(train_cifar, data_dir=data_dir),
resources_per_trial={"cpu": 2, "gpu": gpus_per_trial},
config=config,
num_samples=num_samples,
scheduler=scheduler,
)
best_trial = result.get_best_trial("loss", "min", "last")
print(f"Best trial config: {best_trial.config}")
print(f"Best trial final validation loss: {best_trial.last_result['loss']}")
print(f"Best trial final validation accuracy: {best_trial.last_result['accuracy']}")
best_trained_model = Net(best_trial.config["l1"], best_trial.config["l2"])
device = "cpu"
if torch.cuda.is_available():
device = "cuda:0"
if gpus_per_trial > 1:
best_trained_model = nn.DataParallel(best_trained_model)
best_trained_model.to(device)
best_checkpoint = result.get_best_checkpoint(trial=best_trial, metric="accuracy", mode="max")
with best_checkpoint.as_directory() as checkpoint_dir:
data_path = Path(checkpoint_dir) / "data.pkl"
with open(data_path, "rb") as fp:
best_checkpoint_data = pickle.load(fp)
best_trained_model.load_state_dict(best_checkpoint_data["net_state_dict"])
test_acc = test_accuracy(best_trained_model, device)
print("Best trial test set accuracy: {}".format(test_acc))
if __name__ == "__main__":
# You can change the number of GPUs per trial here:
main(num_samples=10, max_num_epochs=10, gpus_per_trial=0)
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2025-07-18 23:02:04,050 WARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 2147467264 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.
2025-07-18 23:02:04,107 INFO worker.py:1642 -- Started a local Ray instance.
2025-07-18 23:02:05,102 INFO tune.py:228 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run(...)`.
2025-07-18 23:02:05,104 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
╭────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment train_cifar_2025-07-18_23-02-05 │
├────────────────────────────────────────────────────────────────────┤
│ Search algorithm BasicVariantGenerator │
│ Scheduler AsyncHyperBandScheduler │
│ Number of trials 10 │
╰────────────────────────────────────────────────────────────────────╯
View detailed results here: /var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05
To visualize your results with TensorBoard, run: `tensorboard --logdir /var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05`
Trial status: 10 PENDING
Current time: 2025-07-18 23:02:05. Total running time: 0s
Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭───────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size │
├───────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00000 PENDING 32 64 0.000290926 8 │
│ train_cifar_39009_00001 PENDING 128 4 0.00625436 4 │
│ train_cifar_39009_00002 PENDING 16 8 0.00277484 16 │
│ train_cifar_39009_00003 PENDING 2 4 0.010569 8 │
│ train_cifar_39009_00004 PENDING 256 128 0.000420041 8 │
│ train_cifar_39009_00005 PENDING 64 1 0.00270748 4 │
│ train_cifar_39009_00006 PENDING 8 256 0.000161847 16 │
│ train_cifar_39009_00007 PENDING 2 4 0.0113812 16 │
│ train_cifar_39009_00008 PENDING 128 128 0.000457012 16 │
│ train_cifar_39009_00009 PENDING 32 4 0.050788 2 │
╰───────────────────────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00003 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00003 config │
├──────────────────────────────────────────────────┤
│ batch_size 8 │
│ l1 2 │
│ l2 4 │
│ lr 0.01057 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 config │
├──────────────────────────────────────────────────┤
│ batch_size 16 │
│ l1 16 │
│ l2 8 │
│ lr 0.00277 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00006 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00006 config │
├──────────────────────────────────────────────────┤
│ batch_size 16 │
│ l1 8 │
│ l2 256 │
│ lr 0.00016 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00007 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00007 config │
├──────────────────────────────────────────────────┤
│ batch_size 16 │
│ l1 2 │
│ l2 4 │
│ lr 0.01138 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00004 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00004 config │
├──────────────────────────────────────────────────┤
│ batch_size 8 │
│ l1 256 │
│ l2 128 │
│ lr 0.00042 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00000 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00000 config │
├──────────────────────────────────────────────────┤
│ batch_size 8 │
│ l1 32 │
│ l2 64 │
│ lr 0.00029 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00001 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00001 config │
├──────────────────────────────────────────────────┤
│ batch_size 4 │
│ l1 128 │
│ l2 4 │
│ lr 0.00625 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00005 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00005 config │
├──────────────────────────────────────────────────┤
│ batch_size 4 │
│ l1 64 │
│ l2 1 │
│ lr 0.00271 │
╰──────────────────────────────────────────────────╯
(func pid=3489) [1, 2000] loss: 2.301
Trial status: 8 RUNNING | 2 PENDING
Current time: 2025-07-18 23:02:35. Total running time: 30s
Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭───────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size │
├───────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00000 RUNNING 32 64 0.000290926 8 │
│ train_cifar_39009_00001 RUNNING 128 4 0.00625436 4 │
│ train_cifar_39009_00002 RUNNING 16 8 0.00277484 16 │
│ train_cifar_39009_00003 RUNNING 2 4 0.010569 8 │
│ train_cifar_39009_00004 RUNNING 256 128 0.000420041 8 │
│ train_cifar_39009_00005 RUNNING 64 1 0.00270748 4 │
│ train_cifar_39009_00006 RUNNING 8 256 0.000161847 16 │
│ train_cifar_39009_00007 RUNNING 2 4 0.0113812 16 │
│ train_cifar_39009_00008 PENDING 128 128 0.000457012 16 │
│ train_cifar_39009_00009 PENDING 32 4 0.050788 2 │
╰───────────────────────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 finished iteration 1 at 2025-07-18 23:02:35. Total running time: 30s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 26.01155 │
│ time_total_s 26.01155 │
│ training_iteration 1 │
│ accuracy 0.2914 │
│ loss 1.80341 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000000
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000000)
Trial train_cifar_39009_00006 finished iteration 1 at 2025-07-18 23:02:36. Total running time: 31s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00006 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 26.53592 │
│ time_total_s 26.53592 │
│ training_iteration 1 │
│ accuracy 0.1009 │
│ loss 2.3014 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00006 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00006_6_batch_size=16,l1=8,l2=256,lr=0.0002_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00006 completed after 1 iterations at 2025-07-18 23:02:36. Total running time: 31s
Trial train_cifar_39009_00008 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00008 config │
├──────────────────────────────────────────────────┤
│ batch_size 16 │
│ l1 128 │
│ l2 128 │
│ lr 0.00046 │
╰──────────────────────────────────────────────────╯
Trial train_cifar_39009_00007 finished iteration 1 at 2025-07-18 23:02:36. Total running time: 31s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00007 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 26.51947 │
│ time_total_s 26.51947 │
│ training_iteration 1 │
│ accuracy 0.2494 │
│ loss 1.93476 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00007 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00007_7_batch_size=16,l1=2,l2=4,lr=0.0114_2025-07-18_23-02-05/checkpoint_000000
(func pid=3494) [1, 4000] loss: 1.012 [repeated 8x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
(func pid=3494) [1, 6000] loss: 0.655 [repeated 5x across cluster]
Trial train_cifar_39009_00003 finished iteration 1 at 2025-07-18 23:02:54. Total running time: 49s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00003 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 44.95452 │
│ time_total_s 44.95452 │
│ training_iteration 1 │
│ accuracy 0.1797 │
│ loss 2.00948 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00003 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00003_3_batch_size=8,l1=2,l2=4,lr=0.0106_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00003 completed after 1 iterations at 2025-07-18 23:02:54. Total running time: 49s
Trial train_cifar_39009_00009 started with configuration:
╭──────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00009 config │
├──────────────────────────────────────────────────┤
│ batch_size 2 │
│ l1 32 │
│ l2 4 │
│ lr 0.05079 │
╰──────────────────────────────────────────────────╯
(func pid=3492) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00003_3_batch_size=8,l1=2,l2=4,lr=0.0106_2025-07-18_23-02-05/checkpoint_000000) [repeated 3x across cluster]
Trial train_cifar_39009_00000 finished iteration 1 at 2025-07-18 23:02:55. Total running time: 50s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00000 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 45.5723 │
│ time_total_s 45.5723 │
│ training_iteration 1 │
│ accuracy 0.2273 │
│ loss 2.12843 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00000 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00000_0_batch_size=8,l1=32,l2=64,lr=0.0003_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00000 completed after 1 iterations at 2025-07-18 23:02:55. Total running time: 50s
Trial train_cifar_39009_00004 finished iteration 1 at 2025-07-18 23:02:55. Total running time: 50s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00004 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 45.8067 │
│ time_total_s 45.8067 │
│ training_iteration 1 │
│ accuracy 0.3433 │
│ loss 1.78927 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00004 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00002 finished iteration 2 at 2025-07-18 23:02:58. Total running time: 53s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000001 │
│ time_this_iter_s 22.85803 │
│ time_total_s 48.86958 │
│ training_iteration 2 │
│ accuracy 0.4781 │
│ loss 1.44052 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000001
Trial train_cifar_39009_00007 finished iteration 2 at 2025-07-18 23:03:00. Total running time: 54s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00007 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000001 │
│ time_this_iter_s 23.51036 │
│ time_total_s 50.02983 │
│ training_iteration 2 │
│ accuracy 0.2971 │
│ loss 1.7995 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00007 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00007_7_batch_size=16,l1=2,l2=4,lr=0.0114_2025-07-18_23-02-05/checkpoint_000001
Trial train_cifar_39009_00007 completed after 2 iterations at 2025-07-18 23:03:00. Total running time: 54s
(func pid=3496) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00007_7_batch_size=16,l1=2,l2=4,lr=0.0114_2025-07-18_23-02-05/checkpoint_000001) [repeated 4x across cluster]
Trial train_cifar_39009_00008 finished iteration 1 at 2025-07-18 23:03:00. Total running time: 55s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00008 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 24.25614 │
│ time_total_s 24.25614 │
│ training_iteration 1 │
│ accuracy 0.2384 │
│ loss 2.15216 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00008 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00008_8_batch_size=16,l1=128,l2=128,lr=0.0005_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00008 completed after 1 iterations at 2025-07-18 23:03:00. Total running time: 55s
(func pid=3494) [1, 8000] loss: 0.483 [repeated 5x across cluster]
Trial status: 5 TERMINATED | 5 RUNNING
Current time: 2025-07-18 23:03:05. Total running time: 1min 0s
Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size iter total time (s) loss accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00001 RUNNING 128 4 0.00625436 4 │
│ train_cifar_39009_00002 RUNNING 16 8 0.00277484 16 2 48.8696 1.44052 0.4781 │
│ train_cifar_39009_00004 RUNNING 256 128 0.000420041 8 1 45.8067 1.78927 0.3433 │
│ train_cifar_39009_00005 RUNNING 64 1 0.00270748 4 │
│ train_cifar_39009_00009 RUNNING 32 4 0.050788 2 │
│ train_cifar_39009_00000 TERMINATED 32 64 0.000290926 8 1 45.5723 2.12843 0.2273 │
│ train_cifar_39009_00003 TERMINATED 2 4 0.010569 8 1 44.9545 2.00948 0.1797 │
│ train_cifar_39009_00006 TERMINATED 8 256 0.000161847 16 1 26.5359 2.3014 0.1009 │
│ train_cifar_39009_00007 TERMINATED 2 4 0.0113812 16 2 50.0298 1.7995 0.2971 │
│ train_cifar_39009_00008 TERMINATED 128 128 0.000457012 16 1 24.2561 2.15216 0.2384 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
(func pid=3492) [1, 2000] loss: 2.378 [repeated 2x across cluster]
(func pid=3492) [1, 4000] loss: 1.181 [repeated 5x across cluster]
Trial train_cifar_39009_00002 finished iteration 3 at 2025-07-18 23:03:14. Total running time: 1min 9s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000002 │
│ time_this_iter_s 15.77238 │
│ time_total_s 64.64196 │
│ training_iteration 3 │
│ accuracy 0.5111 │
│ loss 1.34966 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000002
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000002) [repeated 2x across cluster]
Trial train_cifar_39009_00005 finished iteration 1 at 2025-07-18 23:03:16. Total running time: 1min 11s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00005 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 65.69786 │
│ time_total_s 65.69786 │
│ training_iteration 1 │
│ accuracy 0.179 │
│ loss 1.99781 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00005 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00005_5_batch_size=4,l1=64,l2=1,lr=0.0027_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00001 finished iteration 1 at 2025-07-18 23:03:17. Total running time: 1min 12s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00001 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 67.05515 │
│ time_total_s 67.05515 │
│ training_iteration 1 │
│ accuracy 0.2994 │
│ loss 1.85281 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00001 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00001_1_batch_size=4,l1=128,l2=4,lr=0.0063_2025-07-18_23-02-05/checkpoint_000000
(func pid=3492) [1, 6000] loss: 0.786 [repeated 2x across cluster]
Trial train_cifar_39009_00004 finished iteration 2 at 2025-07-18 23:03:25. Total running time: 1min 20s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00004 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000001 │
│ time_this_iter_s 30.10023 │
│ time_total_s 75.90693 │
│ training_iteration 2 │
│ accuracy 0.4415 │
│ loss 1.53227 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00004 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000001
(func pid=3493) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000001) [repeated 3x across cluster]
(func pid=3492) [1, 8000] loss: 0.589 [repeated 4x across cluster]
Trial train_cifar_39009_00002 finished iteration 4 at 2025-07-18 23:03:29. Total running time: 1min 24s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000003 │
│ time_this_iter_s 15.40417 │
│ time_total_s 80.04613 │
│ training_iteration 4 │
│ accuracy 0.5385 │
│ loss 1.30166 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000003
(func pid=3490) [2, 4000] loss: 0.906 [repeated 2x across cluster]
Trial status: 5 TERMINATED | 5 RUNNING
Current time: 2025-07-18 23:03:35. Total running time: 1min 30s
Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size iter total time (s) loss accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00001 RUNNING 128 4 0.00625436 4 1 67.0552 1.85281 0.2994 │
│ train_cifar_39009_00002 RUNNING 16 8 0.00277484 16 4 80.0461 1.30166 0.5385 │
│ train_cifar_39009_00004 RUNNING 256 128 0.000420041 8 2 75.9069 1.53227 0.4415 │
│ train_cifar_39009_00005 RUNNING 64 1 0.00270748 4 1 65.6979 1.99781 0.179 │
│ train_cifar_39009_00009 RUNNING 32 4 0.050788 2 │
│ train_cifar_39009_00000 TERMINATED 32 64 0.000290926 8 1 45.5723 2.12843 0.2273 │
│ train_cifar_39009_00003 TERMINATED 2 4 0.010569 8 1 44.9545 2.00948 0.1797 │
│ train_cifar_39009_00006 TERMINATED 8 256 0.000161847 16 1 26.5359 2.3014 0.1009 │
│ train_cifar_39009_00007 TERMINATED 2 4 0.0113812 16 2 50.0298 1.7995 0.2971 │
│ train_cifar_39009_00008 TERMINATED 128 128 0.000457012 16 1 24.2561 2.15216 0.2384 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
(func pid=3491) [5, 2000] loss: 1.272 [repeated 3x across cluster]
Trial train_cifar_39009_00002 finished iteration 5 at 2025-07-18 23:03:45. Total running time: 1min 40s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000004 │
│ time_this_iter_s 15.55407 │
│ time_total_s 95.6002 │
│ training_iteration 5 │
│ accuracy 0.5002 │
│ loss 1.42006 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000004
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000004) [repeated 2x across cluster]
(func pid=3493) [3, 4000] loss: 0.728 [repeated 4x across cluster]
(func pid=3490) [2, 8000] loss: 0.448 [repeated 2x across cluster]
Trial train_cifar_39009_00004 finished iteration 3 at 2025-07-18 23:03:54. Total running time: 1min 49s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00004 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000002 │
│ time_this_iter_s 28.58838 │
│ time_total_s 104.49532 │
│ training_iteration 3 │
│ accuracy 0.4859 │
│ loss 1.4162 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00004 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000002
(func pid=3493) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000002)
(func pid=3491) [6, 2000] loss: 1.218 [repeated 2x across cluster]
Trial train_cifar_39009_00002 finished iteration 6 at 2025-07-18 23:04:01. Total running time: 1min 55s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000005 │
│ time_this_iter_s 15.62631 │
│ time_total_s 111.22652 │
│ training_iteration 6 │
│ accuracy 0.5578 │
│ loss 1.24678 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000005
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000005)
Trial train_cifar_39009_00005 finished iteration 2 at 2025-07-18 23:04:04. Total running time: 1min 59s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00005 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000001 │
│ time_this_iter_s 48.37254 │
│ time_total_s 114.0704 │
│ training_iteration 2 │
│ accuracy 0.2424 │
│ loss 1.87435 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00005 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00005_5_batch_size=4,l1=64,l2=1,lr=0.0027_2025-07-18_23-02-05/checkpoint_000001
Trial train_cifar_39009_00005 completed after 2 iterations at 2025-07-18 23:04:04. Total running time: 1min 59s
(func pid=3493) [4, 2000] loss: 1.384 [repeated 4x across cluster]
Trial status: 6 TERMINATED | 4 RUNNING
Current time: 2025-07-18 23:04:05. Total running time: 2min 0s
Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size iter total time (s) loss accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00001 RUNNING 128 4 0.00625436 4 1 67.0552 1.85281 0.2994 │
│ train_cifar_39009_00002 RUNNING 16 8 0.00277484 16 6 111.227 1.24678 0.5578 │
│ train_cifar_39009_00004 RUNNING 256 128 0.000420041 8 3 104.495 1.4162 0.4859 │
│ train_cifar_39009_00009 RUNNING 32 4 0.050788 2 │
│ train_cifar_39009_00000 TERMINATED 32 64 0.000290926 8 1 45.5723 2.12843 0.2273 │
│ train_cifar_39009_00003 TERMINATED 2 4 0.010569 8 1 44.9545 2.00948 0.1797 │
│ train_cifar_39009_00005 TERMINATED 64 1 0.00270748 4 2 114.07 1.87435 0.2424 │
│ train_cifar_39009_00006 TERMINATED 8 256 0.000161847 16 1 26.5359 2.3014 0.1009 │
│ train_cifar_39009_00007 TERMINATED 2 4 0.0113812 16 2 50.0298 1.7995 0.2971 │
│ train_cifar_39009_00008 TERMINATED 128 128 0.000457012 16 1 24.2561 2.15216 0.2384 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00001 finished iteration 2 at 2025-07-18 23:04:06. Total running time: 2min 1s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00001 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000001 │
│ time_this_iter_s 49.001 │
│ time_total_s 116.05615 │
│ training_iteration 2 │
│ accuracy 0.3787 │
│ loss 1.6952 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00001 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00001_1_batch_size=4,l1=128,l2=4,lr=0.0063_2025-07-18_23-02-05/checkpoint_000001
Trial train_cifar_39009_00001 completed after 2 iterations at 2025-07-18 23:04:06. Total running time: 2min 1s
(func pid=3490) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00001_1_batch_size=4,l1=128,l2=4,lr=0.0063_2025-07-18_23-02-05/checkpoint_000001) [repeated 2x across cluster]
(func pid=3491) [7, 2000] loss: 1.192 [repeated 2x across cluster]
Trial train_cifar_39009_00002 finished iteration 7 at 2025-07-18 23:04:15. Total running time: 2min 10s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000006 │
│ time_this_iter_s 14.23226 │
│ time_total_s 125.45878 │
│ training_iteration 7 │
│ accuracy 0.5719 │
│ loss 1.1931 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000006
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000006)
Trial train_cifar_39009_00004 finished iteration 4 at 2025-07-18 23:04:20. Total running time: 2min 15s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00004 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000003 │
│ time_this_iter_s 25.92476 │
│ time_total_s 130.42008 │
│ training_iteration 4 │
│ accuracy 0.5058 │
│ loss 1.37831 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00004 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000003
Trial train_cifar_39009_00004 completed after 4 iterations at 2025-07-18 23:04:20. Total running time: 2min 15s
(func pid=3493) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00004_4_batch_size=8,l1=256,l2=128,lr=0.0004_2025-07-18_23-02-05/checkpoint_000003)
Trial train_cifar_39009_00009 finished iteration 1 at 2025-07-18 23:04:22. Total running time: 2min 17s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00009 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000000 │
│ time_this_iter_s 88.15188 │
│ time_total_s 88.15188 │
│ training_iteration 1 │
│ accuracy 0.0992 │
│ loss 2.34015 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00009 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00009_9_batch_size=2,l1=32,l2=4,lr=0.0508_2025-07-18_23-02-05/checkpoint_000000
Trial train_cifar_39009_00009 completed after 1 iterations at 2025-07-18 23:04:22. Total running time: 2min 17s
(func pid=3491) [8, 2000] loss: 1.162 [repeated 3x across cluster]
Trial train_cifar_39009_00002 finished iteration 8 at 2025-07-18 23:04:27. Total running time: 2min 22s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000007 │
│ time_this_iter_s 12.3716 │
│ time_total_s 137.83038 │
│ training_iteration 8 │
│ accuracy 0.5474 │
│ loss 1.26927 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000007
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000007) [repeated 2x across cluster]
(func pid=3491) [9, 2000] loss: 1.132
Trial status: 9 TERMINATED | 1 RUNNING
Current time: 2025-07-18 23:04:35. Total running time: 2min 30s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size iter total time (s) loss accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00002 RUNNING 16 8 0.00277484 16 8 137.83 1.26927 0.5474 │
│ train_cifar_39009_00000 TERMINATED 32 64 0.000290926 8 1 45.5723 2.12843 0.2273 │
│ train_cifar_39009_00001 TERMINATED 128 4 0.00625436 4 2 116.056 1.6952 0.3787 │
│ train_cifar_39009_00003 TERMINATED 2 4 0.010569 8 1 44.9545 2.00948 0.1797 │
│ train_cifar_39009_00004 TERMINATED 256 128 0.000420041 8 4 130.42 1.37831 0.5058 │
│ train_cifar_39009_00005 TERMINATED 64 1 0.00270748 4 2 114.07 1.87435 0.2424 │
│ train_cifar_39009_00006 TERMINATED 8 256 0.000161847 16 1 26.5359 2.3014 0.1009 │
│ train_cifar_39009_00007 TERMINATED 2 4 0.0113812 16 2 50.0298 1.7995 0.2971 │
│ train_cifar_39009_00008 TERMINATED 128 128 0.000457012 16 1 24.2561 2.15216 0.2384 │
│ train_cifar_39009_00009 TERMINATED 32 4 0.050788 2 1 88.1519 2.34015 0.0992 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 finished iteration 9 at 2025-07-18 23:04:38. Total running time: 2min 33s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000008 │
│ time_this_iter_s 11.10743 │
│ time_total_s 148.93781 │
│ training_iteration 9 │
│ accuracy 0.5808 │
│ loss 1.19041 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000008
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000008)
(func pid=3491) [10, 2000] loss: 1.127
Trial train_cifar_39009_00002 finished iteration 10 at 2025-07-18 23:04:49. Total running time: 2min 44s
╭────────────────────────────────────────────────────────────╮
│ Trial train_cifar_39009_00002 result │
├────────────────────────────────────────────────────────────┤
│ checkpoint_dir_name checkpoint_000009 │
│ time_this_iter_s 11.00284 │
│ time_total_s 159.94064 │
│ training_iteration 10 │
│ accuracy 0.5465 │
│ loss 1.27502 │
╰────────────────────────────────────────────────────────────╯
Trial train_cifar_39009_00002 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000009
Trial train_cifar_39009_00002 completed after 10 iterations at 2025-07-18 23:04:49. Total running time: 2min 44s
Trial status: 10 TERMINATED
Current time: 2025-07-18 23:04:49. Total running time: 2min 44s
Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:A10G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name status l1 l2 lr batch_size iter total time (s) loss accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ train_cifar_39009_00000 TERMINATED 32 64 0.000290926 8 1 45.5723 2.12843 0.2273 │
│ train_cifar_39009_00001 TERMINATED 128 4 0.00625436 4 2 116.056 1.6952 0.3787 │
│ train_cifar_39009_00002 TERMINATED 16 8 0.00277484 16 10 159.941 1.27502 0.5465 │
│ train_cifar_39009_00003 TERMINATED 2 4 0.010569 8 1 44.9545 2.00948 0.1797 │
│ train_cifar_39009_00004 TERMINATED 256 128 0.000420041 8 4 130.42 1.37831 0.5058 │
│ train_cifar_39009_00005 TERMINATED 64 1 0.00270748 4 2 114.07 1.87435 0.2424 │
│ train_cifar_39009_00006 TERMINATED 8 256 0.000161847 16 1 26.5359 2.3014 0.1009 │
│ train_cifar_39009_00007 TERMINATED 2 4 0.0113812 16 2 50.0298 1.7995 0.2971 │
│ train_cifar_39009_00008 TERMINATED 128 128 0.000457012 16 1 24.2561 2.15216 0.2384 │
│ train_cifar_39009_00009 TERMINATED 32 4 0.050788 2 1 88.1519 2.34015 0.0992 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Best trial config: {'l1': 16, 'l2': 8, 'lr': 0.0027748384350715634, 'batch_size': 16}
Best trial final validation loss: 1.2750194970130921
Best trial final validation accuracy: 0.5465
(func pid=3491) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2025-07-18_23-02-05/train_cifar_39009_00002_2_batch_size=16,l1=16,l2=8,lr=0.0028_2025-07-18_23-02-05/checkpoint_000009)
Best trial test set accuracy: 0.5853
If you run the code, an example output could look like this:
Number of trials: 10/10 (10 TERMINATED)
+-----+--------------+------+------+-------------+--------+---------+------------+
| ... | batch_size | l1 | l2 | lr | iter | loss | accuracy |
|-----+--------------+------+------+-------------+--------+---------+------------|
| ... | 2 | 1 | 256 | 0.000668163 | 1 | 2.31479 | 0.0977 |
| ... | 4 | 64 | 8 | 0.0331514 | 1 | 2.31605 | 0.0983 |
| ... | 4 | 2 | 1 | 0.000150295 | 1 | 2.30755 | 0.1023 |
| ... | 16 | 32 | 32 | 0.0128248 | 10 | 1.66912 | 0.4391 |
| ... | 4 | 8 | 128 | 0.00464561 | 2 | 1.7316 | 0.3463 |
| ... | 8 | 256 | 8 | 0.00031556 | 1 | 2.19409 | 0.1736 |
| ... | 4 | 16 | 256 | 0.00574329 | 2 | 1.85679 | 0.3368 |
| ... | 8 | 2 | 2 | 0.00325652 | 1 | 2.30272 | 0.0984 |
| ... | 2 | 2 | 2 | 0.000342987 | 2 | 1.76044 | 0.292 |
| ... | 4 | 64 | 32 | 0.003734 | 8 | 1.53101 | 0.4761 |
+-----+--------------+------+------+-------------+--------+---------+------------+
Best trial config: {'l1': 64, 'l2': 32, 'lr': 0.0037339984519545164, 'batch_size': 4}
Best trial final validation loss: 1.5310075663924216
Best trial final validation accuracy: 0.4761
Best trial test set accuracy: 0.4737
Most trials have been stopped early in order to avoid wasting resources. The best performing trial achieved a validation accuracy of about 47%, which could be confirmed on the test set.
So that’s it! You can now tune the parameters of your PyTorch models.
Total running time of the script: (2 minutes 58.391 seconds)
