CIFAR10 Classifier: PyTorch + Ray Tune Edition
Objective
Explore Ray Tune and in the process attempt to to find a better set of hyperparameters that can beat the best test acurracy:
- Resnet 50 as backbone
- Minimal augmentation
- Tracking on Tensorboard
Most of the content is similar to the TensorFlow version of Ray Tune.
Date: 24-Oct-2020 | Author: Katnoria
1. Setup Imports
import os
from datetime import datetime
from time import time
import matplotlib.pyplot as plt
import numpy as np
from tqdm.notebook import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, models, transforms, utils
from torch.utils.data import DataLoader, random_split
# Ray
import ray
from ray import tune
# from ray.tune.integration.torch import
from ray.tune.schedulers import ASHAScheduler
torch.manual_seed(42)
np.random.seed(42)
def version_info(cls):
print(f"{cls.__name__}: {cls.__version__}")
print("Version Used in this Notebook:")
version_info(torch)
version_info(np)
import matplotlib as mpl
version_info(mpl)
import tqdm as tq
version_info(tq)
version_info(ray)
Version Used in this Notebook:
torch: 1.6.0
numpy: 1.18.5
matplotlib: 3.3.2
tqdm: 4.48.2
ray: 1.0.0
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
device
device(type='cuda', index=0)
2. Dataset
In this section, we setup the train and test dataloaders. The Trainable class will make use of the data loaders defined here.
# Hyper params
BATCH_SIZE=128
NUM_WORKERS=12
# We are not using any augmentations here
# Uncomment Random*
def load_data(batch_size=32, num_workers=1):
# Setup transforms
transform = transforms.Compose([
# transforms.RandomHorizontalFlip(),
# transforms.RandomRotation(0.2),
transforms.ToTensor(),
transforms.Normalize(tuple([0.5]*3), tuple([0.5]*3))
])
# Load the dataset
train_ds = datasets.CIFAR10(
root="./data", train=True,
download=True, transform=transform
)
# Create train and validation splits
train, val = random_split(train_ds, [45000, 5000])
# Create data loaders
train_loader = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=num_workers)
test_ds = datasets.CIFAR10(
root="./data", train=False,
download=True, transform=transform
)
test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
train_loader, test_loader = load_data(batch_size=BATCH_SIZE, num_workers=NUM_WORKERS)
Files already downloaded and verified
Files already downloaded and verified
2.1 Review Data
We plot some images from the training set.
# Display images
images, labels = iter(train_loader).next()
# see: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.figure(figsize=(10, 10))
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.axis('off')
plt.show()
imshow(utils.make_grid(images))
3. RAY Tune
In the previous few notebooks, we manually tried different hyperparameters to get the best results. We will now explore Ray Tune and how it can help us speeding up the search for best hyperparameters.
We are going to define the following:
- Objective to maximise - which is test accuracy in our case
- Hyperparam search space
- Search algorithm to find best hyperparams
Source: https://docs.ray.io/en/latest/tune/key-concepts.html
4. Use Pretrained Models
Instead of training the full model, it is generally a good practice to use a pretrained network as a base model and add your layers on top. This allows us to reduce the training times and leverage on what base model has learned.
After you’re done with this notebook, you will be able to use Ray Tune to design the network from scratch.
4.1. Define Model
We will use imagenet pre-trained ResNet50 model. You can swap out the base model with others such as ResNet 18 or ResNet 110. Just make sure the input features of the final layer matches with the out features of your base model.
class SimpleNet(nn.Module):
"""Simple Neural Network"""
def __init__(self, base_model, base_fc_out, num_units, drop_rate, activation):
"""
Parameters:
base_model: Backbone/Pretrained Neural Network
base_fc_out: Output unit of the base model
num_units: Number of Input units of the hidden layer
drop_rate: Dropout rate
activation: Activation of hidden unit
"""
super(SimpleNet, self).__init__()
self.base_model = base_model
# FC will be set as requires_grad=True by default
self.base_model.fc = nn.Linear(base_fc_out, num_units)
self.drop1 = nn.Dropout(p=drop_rate)
self.fc1 = nn.Linear(num_units, 10)
self.model = nn.Sequential(
self.base_model,
activation,
self.fc1
)
def forward(self, x):
x = self.model(x)
return x
4.2 Trainable
As with TensorFlow Ray-Tune notebook, we will make use of Trainable API. You can also use the Functional API to create the trainables.
We use the following functions from tune.Trainable:
- setup: invoked once when training begins
- step: called interatively
- cleanup: called when training ends
class PyTorchCIFAR10Trainable(tune.Trainable):
"""CIFAR10 Trainable"""
def setup(self, config):
"""Set the network for training
Parameters
----------
config: Ray config object that contains the hyperparams
"""
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(self.device)
# load data
self.train_loader, self.test_loader = load_data(BATCH_SIZE, NUM_WORKERS)
# create model
base_model = models.resnet50(pretrained=True)
for param in base_model.parameters():
param.requires_grad = False
# NN config
num_units = config.get("hidden_units", 128)
drop_rate = config.get("drop_rate", 0.0)
activation = config.get("activation", nn.ReLU(True))
self.model = SimpleNet(base_model, 2048, num_units, drop_rate, activation)
self.model.to(self.device)
# optimizer & loss
self.criterion = nn.CrossEntropyLoss()
self.optimizer = optim.SGD(
self.model.parameters(),
lr=config.get("learning_rate", 1e-4),
momentum=config.get("momentum", 0.9)
)
def _train_step(self):
"""Single training loop
"""
# set to the model train mode
self.model.train()
epoch_loss = 0
running_corrects = 0
for images, labels in self.train_loader:
images = images.to(self.device)
labels = labels.to(self.device)
self.optimizer.zero_grad()
with torch.set_grad_enabled(True):
preds = self.model(images)
loss = self.criterion(preds, labels)
self.optimizer.step()
# track losses
epoch_loss += loss.item()
_, predicted = torch.max(preds.data, 1)
running_corrects += torch.sum(predicted == labels).item()
loss = epoch_loss/len(self.train_loader)
corrects = running_corrects/len(self.train_loader)
return loss, corrects
def _test_step(self):
"""Single test loop
"""
# set to model to eval mode
self.model.eval()
running_corrects = 0
for images, labels in self.train_loader:
images = images.to(self.device)
labels = labels.to(self.device)
preds = self.model(images)
loss = self.criterion(preds, labels)
_, predicted = torch.max(preds.data, 1)
running_corrects += torch.sum(predicted == labels).item()
corrects = running_corrects/len(self.test_loader)
return corrects
def step(self):
"""Single training step
"""
train_loss, train_acc = self._train_step()
test_acc = self._test_step()
return {
"train_loss": train_loss,
"train_accuracy": train_acc,
"mean_accuracy": test_acc
}
def save_checkpoint(self, dirname):
"""Saves the model
Parameters
----------
dirname: directory to save the model
"""
checkpoint_path = os.path.join(dirname, "pytorch-resnet50-raytune.pth")
torch.save(self.model.state_dict(), checkpoint_path)
return checkpoint_path
def load_checkpoint(self, checkpoint_path):
"""Loads the model
Parameters
----------
checkpoint_path: load the model from this path
"""
self.model.load_state_dict(torch.load(checkpoint_path))
4.3 Setup Config
We now setup the search space for Ray Tune to find an optimal model for us
config = {
"hidden_units": tune.grid_search([32, 64, 128, 256]),
"drop_rate": tune.uniform(0.0, 0.8),
"activation": tune.choice([nn.ReLU(True), nn.ELU(True), nn.SELU(True)]),
"learning_rate": tune.loguniform(1e-4, 1e-1),
"momentum": tune.uniform(0.1, 0.9)
}
# Terminate less promising trials using early stopping
scheduler = ASHAScheduler(metric="mean_accuracy", mode="max")
5. Run Trials
We are now ready to run the trials. You can comment the first two lines. I am doing it in order to access the dashboard over the network.
# shutdown currently running instance
ray.shutdown()
# initialize with the new param
ray.init(dashboard_host="0.0.0.0")
start = time()
# run trials
analysis = tune.run(
PyTorchCIFAR10Trainable,
config=config,
num_samples=15, # runs 15 jobs with separate sample from the search space
checkpoint_at_end=True,
checkpoint_freq=3,
scheduler=scheduler,
stop={"training_iteration": 50},
resources_per_trial={"cpu": 2, "gpu": 1},
ray_auto_init=False
)
stop = time()
2020-10-25 10:22:13,856 INFO services.py:1166 -- View the Ray dashboard at [1m[32mhttp://192.168.86.61:8265[39m[22m
== Status ==Memory usage on this node: 4.4/125.8 GiBUsing AsyncHyperBand: num_stopped=0 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.89873417721519Resources requested: 2/12 CPUs, 1/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (59 PENDING, 1 RUNNING)
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[2m[36m(pid=14472)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
[2m[36m(pid=14476)[0m cpu
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[2m[36m(pid=14476)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14472)[0m 2020-10-25 10:22:59,225 INFO trainable.py:255 -- Trainable.setup took 43.540 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
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[2m[36m(pid=14476)[0m 2020-10-25 10:23:01,702 INFO trainable.py:255 -- Trainable.setup took 46.017 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00001:
date: 2020-10-25_10-23-14
done: true
experiment_id: ac1bfde6cac24d93aebfdf6e2da5ce31
experiment_tag: 1_activation=ReLU(inplace=True),drop_rate=0.29613,hidden_units=64,learning_rate=0.00011127,momentum=0.84265
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 64.9620253164557
node_ip: 192.168.86.61
pid: 14472
time_since_restore: 15.299389123916626
time_this_iter_s: 15.299389123916626
time_total_s: 15.299389123916626
timestamp: 1603592594
timesteps_since_restore: 0
train_accuracy: 14.610795454545455
train_loss: 2.303268365561962
training_iteration: 1
trial_id: e5a1b_00001
== Status ==Memory usage on this node: 6.1/125.8 GiBUsing AsyncHyperBand: num_stopped=1 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.66455696202532Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (58 PENDING, 2 RUNNING)
2020-10-25 10:23:14,692 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s]4)[0m
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[2m[36m(pid=14474)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14474)[0m Files already downloaded and verified
[2m[36m(pid=14474)[0m 2020-10-25 10:23:49,242 INFO trainable.py:255 -- Trainable.setup took 33.942 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00002:
date: 2020-10-25_10-24-04
done: true
experiment_id: 3e374555dd1941b1a08ba08fdc0382b7
experiment_tag: 2_activation=ReLU(inplace=True),drop_rate=0.34255,hidden_units=128,learning_rate=0.079426,momentum=0.8709
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.55696202531646
node_ip: 192.168.86.61
pid: 14474
time_since_restore: 15.394063949584961
time_this_iter_s: 15.394063949584961
time_total_s: 15.394063949584961
timestamp: 1603592644
timesteps_since_restore: 0
train_accuracy: 12.803977272727273
train_loss: 2.3184037296609445
training_iteration: 1
trial_id: e5a1b_00002
== Status ==Memory usage on this node: 6.0/125.8 GiBUsing AsyncHyperBand: num_stopped=2 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (57 PENDING, 2 RUNNING, 1 TERMINATED)
2020-10-25 10:24:04,798 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=14471)[0m cuda:0
[2m[36m(pid=14471)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=14471)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14471)[0m Files already downloaded and verified
[2m[36m(pid=14471)[0m 2020-10-25 10:24:53,735 INFO trainable.py:255 -- Trainable.setup took 48.331 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00003:
date: 2020-10-25_10-25-08
done: true
experiment_id: 7a2df26a53d241adb3d7588624114d88
experiment_tag: 3_activation=SELU(inplace=True),drop_rate=0.68241,hidden_units=256,learning_rate=0.00076445,momentum=0.40808
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 62.835443037974684
node_ip: 192.168.86.61
pid: 14471
time_since_restore: 15.197495460510254
time_this_iter_s: 15.197495460510254
time_total_s: 15.197495460510254
timestamp: 1603592708
timesteps_since_restore: 0
train_accuracy: 13.798295454545455
train_loss: 2.3423713852058756
training_iteration: 1
trial_id: e5a1b_00003
== Status ==Memory usage on this node: 6.0/125.8 GiBUsing AsyncHyperBand: num_stopped=3 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (56 PENDING, 2 RUNNING, 2 TERMINATED)
2020-10-25 10:25:09,111 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=14469)[0m cuda:0
[2m[36m(pid=14469)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=14469)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14469)[0m Files already downloaded and verified
[2m[36m(pid=14469)[0m 2020-10-25 10:25:46,998 INFO trainable.py:255 -- Trainable.setup took 37.322 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00004:
date: 2020-10-25_10-26-02
done: true
experiment_id: 9f330c1c06024f4f999891ce975a7980
experiment_tag: 4_activation=ReLU(inplace=True),drop_rate=0.68091,hidden_units=32,learning_rate=0.00089282,momentum=0.23559
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 58.734177215189874
node_ip: 192.168.86.61
pid: 14469
time_since_restore: 15.128427505493164
time_this_iter_s: 15.128427505493164
time_total_s: 15.128427505493164
timestamp: 1603592762
timesteps_since_restore: 0
train_accuracy: 13.082386363636363
train_loss: 2.319947162135081
training_iteration: 1
trial_id: e5a1b_00004
== Status ==Memory usage on this node: 6.0/125.8 GiBUsing AsyncHyperBand: num_stopped=4 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (55 PENDING, 2 RUNNING, 3 TERMINATED)
2020-10-25 10:26:02,295 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=14468)[0m cuda:0
[2m[36m(pid=14468)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=14468)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14468)[0m Files already downloaded and verified
[2m[36m(pid=14468)[0m 2020-10-25 10:26:40,704 INFO trainable.py:255 -- Trainable.setup took 37.824 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00005:
date: 2020-10-25_10-26-56
done: true
experiment_id: fd2d0fd6f39248d28b105c0d5ebb5bcf
experiment_tag: 5_activation=SELU(inplace=True),drop_rate=0.44544,hidden_units=64,learning_rate=0.064338,momentum=0.65682
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 51.936708860759495
node_ip: 192.168.86.61
pid: 14468
time_since_restore: 15.418859243392944
time_this_iter_s: 15.418859243392944
time_total_s: 15.418859243392944
timestamp: 1603592816
timesteps_since_restore: 0
train_accuracy: 12.011363636363637
train_loss: 2.3522712507031183
training_iteration: 1
trial_id: e5a1b_00005
== Status ==Memory usage on this node: 6.0/125.8 GiBUsing AsyncHyperBand: num_stopped=5 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (54 PENDING, 2 RUNNING, 4 TERMINATED)
2020-10-25 10:26:56,286 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=14480)[0m cuda:0
[2m[36m(pid=14480)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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170500096it [00:38, 4429440.41it/s]
[2m[36m(pid=14480)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14480)[0m Files already downloaded and verified
[2m[36m(pid=14480)[0m 2020-10-25 10:27:39,923 INFO trainable.py:255 -- Trainable.setup took 43.052 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00006:
date: 2020-10-25_10-27-55
done: true
experiment_id: 7d862626eb5240a890ec86f2a12f7395
experiment_tag: 6_activation=SELU(inplace=True),drop_rate=0.45605,hidden_units=128,learning_rate=0.00019567,momentum=0.59201
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 62.68354430379747
node_ip: 192.168.86.61
pid: 14480
time_since_restore: 15.3145911693573
time_this_iter_s: 15.3145911693573
time_total_s: 15.3145911693573
timestamp: 1603592875
timesteps_since_restore: 0
train_accuracy: 13.934659090909092
train_loss: 2.3309759104793724
training_iteration: 1
trial_id: e5a1b_00006
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 63.89873417721519Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (53 PENDING, 2 RUNNING, 5 TERMINATED)
2020-10-25 10:27:55,406 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=14470)[0m cuda:0
[2m[36m(pid=14470)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=14470)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14470)[0m Files already downloaded and verified
[2m[36m(pid=14470)[0m 2020-10-25 10:28:34,464 INFO trainable.py:255 -- Trainable.setup took 38.484 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-28-49
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 67.51898734177215
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 15.369166612625122
time_this_iter_s: 15.369166612625122
time_total_s: 15.369166612625122
timestamp: 1603592929
timesteps_since_restore: 0
train_accuracy: 15.315340909090908
train_loss: 2.3130011944608255
training_iteration: 1
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-29-05
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 67.22784810126582
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 30.54696750640869
time_this_iter_s: 15.17780089378357
time_total_s: 30.54696750640869
timestamp: 1603592945
timesteps_since_restore: 0
train_accuracy: 14.778409090909092
train_loss: 2.3139084415002302
training_iteration: 2
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-29-20
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 66.9493670886076
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 45.79553246498108
time_this_iter_s: 15.248564958572388
time_total_s: 45.79553246498108
timestamp: 1603592960
timesteps_since_restore: 0
train_accuracy: 14.838068181818182
train_loss: 2.313623205504634
training_iteration: 3
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: None | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-29-35
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 67.0253164556962
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 61.04997253417969
time_this_iter_s: 15.254440069198608
time_total_s: 61.04997253417969
timestamp: 1603592975
timesteps_since_restore: 0
train_accuracy: 15.068181818181818
train_loss: 2.31322764266621
training_iteration: 4
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-29-50
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 5
mean_accuracy: 66.87341772151899
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 76.28490781784058
time_this_iter_s: 15.234935283660889
time_total_s: 76.28490781784058
timestamp: 1603592990
timesteps_since_restore: 0
train_accuracy: 15.178977272727273
train_loss: 2.3134158091111616
training_iteration: 5
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-30-06
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 6
mean_accuracy: 67.13924050632912
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 91.52554655075073
time_this_iter_s: 15.240638732910156
time_total_s: 91.52554655075073
timestamp: 1603593006
timesteps_since_restore: 0
train_accuracy: 15.196022727272727
train_loss: 2.3136083355004136
training_iteration: 6
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-30-21
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 7
mean_accuracy: 67.60759493670886
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 106.75112700462341
time_this_iter_s: 15.22558045387268
time_total_s: 106.75112700462341
timestamp: 1603593021
timesteps_since_restore: 0
train_accuracy: 15.164772727272727
train_loss: 2.3132713159376923
training_iteration: 7
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=6 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 65.19620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00000:
date: 2020-10-25_10-30-31
done: true
experiment_id: e7c0c5f1e288451fa663952badef3474
experiment_tag: 0_activation=ReLU(inplace=True),drop_rate=0.75237,hidden_units=32,learning_rate=0.072742,momentum=0.83189
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 61.25316455696203
node_ip: 192.168.86.61
pid: 14476
time_since_restore: 449.46822690963745
time_this_iter_s: 449.46822690963745
time_total_s: 449.46822690963745
timestamp: 1603593031
timesteps_since_restore: 0
train_accuracy: 13.769886363636363
train_loss: 2.3410877829248253
training_iteration: 1
trial_id: e5a1b_00000
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (52 PENDING, 2 RUNNING, 6 TERMINATED)
2020-10-25 10:30:31,274 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s]7)[0m
[2m[36m(pid=14477)[0m cpu
[2m[36m(pid=14477)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-30-36
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 8
mean_accuracy: 66.46835443037975
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 122.02254104614258
time_this_iter_s: 15.271414041519165
time_total_s: 122.02254104614258
timestamp: 1603593036
timesteps_since_restore: 0
train_accuracy: 15.045454545454545
train_loss: 2.3133237687024204
training_iteration: 8
trial_id: e5a1b_00007
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== Status ==Memory usage on this node: 5.3/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
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Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-30-51
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 9
mean_accuracy: 67.84810126582279
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 137.01360845565796
time_this_iter_s: 14.99106740951538
time_total_s: 137.01360845565796
timestamp: 1603593051
timesteps_since_restore: 0
train_accuracy: 14.965909090909092
train_loss: 2.3134234554388304
training_iteration: 9
trial_id: e5a1b_00007
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== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
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170500096it [00:30, 5669202.18it/s]
[2m[36m(pid=14477)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14477)[0m Files already downloaded and verified
[2m[36m(pid=14477)[0m 2020-10-25 10:31:05,187 INFO trainable.py:255 -- Trainable.setup took 33.301 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-31-07
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 10
mean_accuracy: 67.56962025316456
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 152.09481954574585
time_this_iter_s: 15.08121109008789
time_total_s: 152.09481954574585
timestamp: 1603593067
timesteps_since_restore: 0
train_accuracy: 15.017045454545455
train_loss: 2.3132145499641243
training_iteration: 10
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-31-22
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 11
mean_accuracy: 67.41772151898734
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 167.36763167381287
time_this_iter_s: 15.272812128067017
time_total_s: 167.36763167381287
timestamp: 1603593082
timesteps_since_restore: 0
train_accuracy: 15.215909090909092
train_loss: 2.313178800046444
training_iteration: 11
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-31-37
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 12
mean_accuracy: 68.07594936708861
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 182.6762433052063
time_this_iter_s: 15.308611631393433
time_total_s: 182.6762433052063
timestamp: 1603593097
timesteps_since_restore: 0
train_accuracy: 15.096590909090908
train_loss: 2.314235373654149
training_iteration: 12
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-31-53
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 13
mean_accuracy: 67.17721518987342
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 197.966628074646
time_this_iter_s: 15.290384769439697
time_total_s: 197.966628074646
timestamp: 1603593113
timesteps_since_restore: 0
train_accuracy: 14.974431818181818
train_loss: 2.3140658898787065
training_iteration: 13
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-32-08
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 14
mean_accuracy: 67.12658227848101
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 213.26823782920837
time_this_iter_s: 15.301609754562378
time_total_s: 213.26823782920837
timestamp: 1603593128
timesteps_since_restore: 0
train_accuracy: 15.008522727272727
train_loss: 2.314019265499982
training_iteration: 14
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-32-23
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 15
mean_accuracy: 67.9367088607595
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 228.5724549293518
time_this_iter_s: 15.304217100143433
time_total_s: 228.5724549293518
timestamp: 1603593143
timesteps_since_restore: 0
train_accuracy: 15.207386363636363
train_loss: 2.313645559955727
training_iteration: 15
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: None | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-32-39
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 16
mean_accuracy: 66.9493670886076
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 243.883722782135
time_this_iter_s: 15.311267852783203
time_total_s: 243.883722782135
timestamp: 1603593159
timesteps_since_restore: 0
train_accuracy: 15.042613636363637
train_loss: 2.3140709196979348
training_iteration: 16
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-32-54
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 17
mean_accuracy: 68.15189873417721
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 259.18340134620667
time_this_iter_s: 15.299678564071655
time_total_s: 259.18340134620667
timestamp: 1603593174
timesteps_since_restore: 0
train_accuracy: 15.019886363636363
train_loss: 2.3141826580871236
training_iteration: 17
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-33-09
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 18
mean_accuracy: 66.83544303797468
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 274.52326250076294
time_this_iter_s: 15.339861154556274
time_total_s: 274.52326250076294
timestamp: 1603593189
timesteps_since_restore: 0
train_accuracy: 15.295454545454545
train_loss: 2.314132568511096
training_iteration: 18
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-33-25
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 19
mean_accuracy: 67.62025316455696
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 289.9005379676819
time_this_iter_s: 15.377275466918945
time_total_s: 289.9005379676819
timestamp: 1603593205
timesteps_since_restore: 0
train_accuracy: 15.315340909090908
train_loss: 2.314288057386875
training_iteration: 19
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-33-40
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 20
mean_accuracy: 66.84810126582279
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 305.18147706985474
time_this_iter_s: 15.280939102172852
time_total_s: 305.18147706985474
timestamp: 1603593220
timesteps_since_restore: 0
train_accuracy: 15.34375
train_loss: 2.3138568089766935
training_iteration: 20
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-33-55
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 21
mean_accuracy: 67.35443037974683
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 320.4068293571472
time_this_iter_s: 15.22535228729248
time_total_s: 320.4068293571472
timestamp: 1603593235
timesteps_since_restore: 0
train_accuracy: 15.090909090909092
train_loss: 2.314199538393454
training_iteration: 21
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-34-11
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 22
mean_accuracy: 66.9873417721519
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 335.77283787727356
time_this_iter_s: 15.366008520126343
time_total_s: 335.77283787727356
timestamp: 1603593251
timesteps_since_restore: 0
train_accuracy: 14.863636363636363
train_loss: 2.3145367076451127
training_iteration: 22
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-34-26
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 23
mean_accuracy: 67.78481012658227
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 351.12976241111755
time_this_iter_s: 15.356924533843994
time_total_s: 351.12976241111755
timestamp: 1603593266
timesteps_since_restore: 0
train_accuracy: 15.0625
train_loss: 2.3129126212813635
training_iteration: 23
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-34-42
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 24
mean_accuracy: 67.41772151898734
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 366.460791349411
time_this_iter_s: 15.331028938293457
time_total_s: 366.460791349411
timestamp: 1603593282
timesteps_since_restore: 0
train_accuracy: 15.107954545454545
train_loss: 2.313200368122621
training_iteration: 24
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-34-57
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 25
mean_accuracy: 67.18987341772151
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 381.71275568008423
time_this_iter_s: 15.251964330673218
time_total_s: 381.71275568008423
timestamp: 1603593297
timesteps_since_restore: 0
train_accuracy: 14.928977272727273
train_loss: 2.313616208732128
training_iteration: 25
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-35-12
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 26
mean_accuracy: 66.83544303797468
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 396.99172854423523
time_this_iter_s: 15.278972864151001
time_total_s: 396.99172854423523
timestamp: 1603593312
timesteps_since_restore: 0
train_accuracy: 14.721590909090908
train_loss: 2.3143216730518774
training_iteration: 26
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-35-28
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 27
mean_accuracy: 66.9113924050633
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 412.28532242774963
time_this_iter_s: 15.293593883514404
time_total_s: 412.28532242774963
timestamp: 1603593328
timesteps_since_restore: 0
train_accuracy: 15.201704545454545
train_loss: 2.3139414685693653
training_iteration: 27
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-35-43
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 28
mean_accuracy: 67.9493670886076
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 427.6444728374481
time_this_iter_s: 15.359150409698486
time_total_s: 427.6444728374481
timestamp: 1603593343
timesteps_since_restore: 0
train_accuracy: 14.974431818181818
train_loss: 2.3148262351751328
training_iteration: 28
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-35-59
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 29
mean_accuracy: 67.16455696202532
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 443.07418751716614
time_this_iter_s: 15.429714679718018
time_total_s: 443.07418751716614
timestamp: 1603593359
timesteps_since_restore: 0
train_accuracy: 15.167613636363637
train_loss: 2.3138717291030018
training_iteration: 29
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-36-14
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 30
mean_accuracy: 67.60759493670886
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 458.351598739624
time_this_iter_s: 15.277411222457886
time_total_s: 458.351598739624
timestamp: 1603593374
timesteps_since_restore: 0
train_accuracy: 14.90625
train_loss: 2.3137975998900155
training_iteration: 30
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-36-29
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 31
mean_accuracy: 66.70886075949367
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 473.7059943675995
time_this_iter_s: 15.354395627975464
time_total_s: 473.7059943675995
timestamp: 1603593389
timesteps_since_restore: 0
train_accuracy: 15.144886363636363
train_loss: 2.31350699338046
training_iteration: 31
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-36-45
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 32
mean_accuracy: 67.26582278481013
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 489.01561737060547
time_this_iter_s: 15.309623003005981
time_total_s: 489.01561737060547
timestamp: 1603593405
timesteps_since_restore: 0
train_accuracy: 15.136363636363637
train_loss: 2.313723498447375
training_iteration: 32
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-37-00
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 33
mean_accuracy: 67.21518987341773
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 504.4433002471924
time_this_iter_s: 15.427682876586914
time_total_s: 504.4433002471924
timestamp: 1603593420
timesteps_since_restore: 0
train_accuracy: 15.241477272727273
train_loss: 2.3141609958627005
training_iteration: 33
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-37-16
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 34
mean_accuracy: 66.89873417721519
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 519.7542536258698
time_this_iter_s: 15.310953378677368
time_total_s: 519.7542536258698
timestamp: 1603593436
timesteps_since_restore: 0
train_accuracy: 15.198863636363637
train_loss: 2.312824868343093
training_iteration: 34
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-37-31
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 35
mean_accuracy: 67.12658227848101
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 535.0282328128815
time_this_iter_s: 15.273979187011719
time_total_s: 535.0282328128815
timestamp: 1603593451
timesteps_since_restore: 0
train_accuracy: 15.125
train_loss: 2.3140529969876464
training_iteration: 35
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-37-46
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 36
mean_accuracy: 66.43037974683544
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 550.3408923149109
time_this_iter_s: 15.312659502029419
time_total_s: 550.3408923149109
timestamp: 1603593466
timesteps_since_restore: 0
train_accuracy: 15.139204545454545
train_loss: 2.313925429501317
training_iteration: 36
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-38-02
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 37
mean_accuracy: 66.60759493670886
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 565.6509900093079
time_this_iter_s: 15.310097694396973
time_total_s: 565.6509900093079
timestamp: 1603593482
timesteps_since_restore: 0
train_accuracy: 15.392045454545455
train_loss: 2.313574742864479
training_iteration: 37
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-38-17
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 38
mean_accuracy: 66.54430379746836
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 580.9884912967682
time_this_iter_s: 15.337501287460327
time_total_s: 580.9884912967682
timestamp: 1603593497
timesteps_since_restore: 0
train_accuracy: 14.965909090909092
train_loss: 2.3141091005368666
training_iteration: 38
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-38-32
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 39
mean_accuracy: 66.9493670886076
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 596.3374865055084
time_this_iter_s: 15.348995208740234
time_total_s: 596.3374865055084
timestamp: 1603593512
timesteps_since_restore: 0
train_accuracy: 15.252840909090908
train_loss: 2.313800624825738
training_iteration: 39
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=7 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.9620253164557Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00008:
date: 2020-10-25_10-38-43
done: true
experiment_id: 852004158b2f4ee1a851b11b8567541d
experiment_tag: 8_activation=ELU(alpha=True),drop_rate=0.7019,hidden_units=32,learning_rate=0.016684,momentum=0.65761
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 58.36708860759494
node_ip: 192.168.86.61
pid: 14477
time_since_restore: 458.59377360343933
time_this_iter_s: 458.59377360343933
time_total_s: 458.59377360343933
timestamp: 1603593523
timesteps_since_restore: 0
train_accuracy: 13.153409090909092
train_loss: 2.3343163566155867
training_iteration: 1
trial_id: e5a1b_00008
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (51 PENDING, 2 RUNNING, 7 TERMINATED)
2020-10-25 10:38:43,893 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s]3)[0m
[2m[36m(pid=14473)[0m cpu
[2m[36m(pid=14473)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-38-48
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 40
mean_accuracy: 67.60759493670886
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 611.5593600273132
time_this_iter_s: 15.22187352180481
time_total_s: 611.5593600273132
timestamp: 1603593528
timesteps_since_restore: 0
train_accuracy: 15.329545454545455
train_loss: 2.3134726502678613
training_iteration: 40
trial_id: e5a1b_00007
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Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-39-03
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 41
mean_accuracy: 66.82278481012658
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 626.6218528747559
time_this_iter_s: 15.062492847442627
time_total_s: 626.6218528747559
timestamp: 1603593543
timesteps_since_restore: 0
train_accuracy: 15.252840909090908
train_loss: 2.3133508265018463
training_iteration: 41
trial_id: e5a1b_00007
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== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
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170500096it [00:33, 5069001.23it/s]
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-39-18
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 42
mean_accuracy: 67.43037974683544
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 641.7041301727295
time_this_iter_s: 15.082277297973633
time_total_s: 641.7041301727295
timestamp: 1603593558
timesteps_since_restore: 0
train_accuracy: 14.880681818181818
train_loss: 2.313970850272612
training_iteration: 42
trial_id: e5a1b_00007
[2m[36m(pid=14473)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
[2m[36m(pid=14473)[0m Files already downloaded and verified
[2m[36m(pid=14473)[0m 2020-10-25 10:39:21,385 INFO trainable.py:255 -- Trainable.setup took 36.935 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-39-33
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 43
mean_accuracy: 67.11392405063292
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 656.9879372119904
time_this_iter_s: 15.283807039260864
time_total_s: 656.9879372119904
timestamp: 1603593573
timesteps_since_restore: 0
train_accuracy: 15.014204545454545
train_loss: 2.3136963065374982
training_iteration: 43
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-39-49
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 44
mean_accuracy: 67.30379746835443
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 672.2752244472504
time_this_iter_s: 15.28728723526001
time_total_s: 672.2752244472504
timestamp: 1603593589
timesteps_since_restore: 0
train_accuracy: 15.0625
train_loss: 2.3140315907922657
training_iteration: 44
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-40-04
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 45
mean_accuracy: 66.55696202531645
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 687.5008211135864
time_this_iter_s: 15.22559666633606
time_total_s: 687.5008211135864
timestamp: 1603593604
timesteps_since_restore: 0
train_accuracy: 14.991477272727273
train_loss: 2.3140226114879954
training_iteration: 45
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-40-19
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 46
mean_accuracy: 67.59493670886076
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 702.7828109264374
time_this_iter_s: 15.281989812850952
time_total_s: 702.7828109264374
timestamp: 1603593619
timesteps_since_restore: 0
train_accuracy: 15.153409090909092
train_loss: 2.3137594623999163
training_iteration: 46
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-40-35
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 47
mean_accuracy: 67.63291139240506
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 718.0186862945557
time_this_iter_s: 15.235875368118286
time_total_s: 718.0186862945557
timestamp: 1603593635
timesteps_since_restore: 0
train_accuracy: 15.025568181818182
train_loss: 2.3138251704248516
training_iteration: 47
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-40-50
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 48
mean_accuracy: 68.39240506329114
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 733.3843989372253
time_this_iter_s: 15.365712642669678
time_total_s: 733.3843989372253
timestamp: 1603593650
timesteps_since_restore: 0
train_accuracy: 14.982954545454545
train_loss: 2.3133950064128097
training_iteration: 48
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-41-06
done: false
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 49
mean_accuracy: 68.87341772151899
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 748.7407147884369
time_this_iter_s: 15.356315851211548
time_total_s: 748.7407147884369
timestamp: 1603593666
timesteps_since_restore: 0
train_accuracy: 15.326704545454545
train_loss: 2.313831564377655
training_iteration: 49
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00007:
date: 2020-10-25_10-41-21
done: true
experiment_id: 0292f7c20f02475b80a79e4385c5fc81
experiment_tag: 7_activation=ELU(alpha=True),drop_rate=0.79204,hidden_units=256,learning_rate=0.00026318,momentum=0.51466
hostname: ironman
iterations_since_restore: 50
mean_accuracy: 67.37974683544304
node_ip: 192.168.86.61
pid: 14470
time_since_restore: 764.090473651886
time_this_iter_s: 15.349758863449097
time_total_s: 764.090473651886
timestamp: 1603593681
timesteps_since_restore: 0
train_accuracy: 15.099431818181818
train_loss: 2.31364706158638
training_iteration: 50
trial_id: e5a1b_00007
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=8 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 64.43037974683544Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (50 PENDING, 2 RUNNING, 8 TERMINATED)
2020-10-25 10:41:21,565 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s]8)[0m
[2m[36m(pid=14478)[0m cuda:0
[2m[36m(pid=14478)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=14478)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14478)[0m Files already downloaded and verified
[2m[36m(pid=14478)[0m 2020-10-25 10:42:00,872 INFO trainable.py:255 -- Trainable.setup took 38.724 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00010:
date: 2020-10-25_10-42-16
done: true
experiment_id: e384f637820a40cf892edf66ce7cf640
experiment_tag: 10_activation=SELU(inplace=True),drop_rate=0.64749,hidden_units=128,learning_rate=0.026936,momentum=0.79366
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 57.721518987341774
node_ip: 192.168.86.61
pid: 14478
time_since_restore: 15.366005420684814
time_this_iter_s: 15.366005420684814
time_total_s: 15.366005420684814
timestamp: 1603593736
timesteps_since_restore: 0
train_accuracy: 12.880681818181818
train_loss: 2.326554910703139
training_iteration: 1
trial_id: e5a1b_00010
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=9 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 63.89873417721519Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (49 PENDING, 2 RUNNING, 9 TERMINATED)
2020-10-25 10:42:16,411 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=14467)[0m cuda:0
[2m[36m(pid=14467)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=14467)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=14467)[0m Files already downloaded and verified
[2m[36m(pid=14467)[0m 2020-10-25 10:42:47,887 INFO trainable.py:255 -- Trainable.setup took 30.883 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00011:
date: 2020-10-25_10-43-03
done: true
experiment_id: 8636d3e808d74a82ad863c8882cd3cd9
experiment_tag: 11_activation=SELU(inplace=True),drop_rate=0.73059,hidden_units=256,learning_rate=0.00342,momentum=0.50121
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.11392405063291
node_ip: 192.168.86.61
pid: 14467
time_since_restore: 15.263139009475708
time_this_iter_s: 15.263139009475708
time_total_s: 15.263139009475708
timestamp: 1603593783
timesteps_since_restore: 0
train_accuracy: 12.539772727272727
train_loss: 2.3437967896461487
training_iteration: 1
trial_id: e5a1b_00011
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=10 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 63.36708860759494Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (48 PENDING, 2 RUNNING, 10 TERMINATED)
2020-10-25 10:43:03,320 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
0it [00:00, ?it/s]5)[0m
[2m[36m(pid=22135)[0m cuda:0
[2m[36m(pid=22135)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=22135)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=22135)[0m Files already downloaded and verified
[2m[36m(pid=22135)[0m 2020-10-25 10:43:35,229 INFO trainable.py:255 -- Trainable.setup took 31.038 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00012:
date: 2020-10-25_10-43-50
done: true
experiment_id: cf6cc3258a5e4bb3ad6cd841865a8802
experiment_tag: 12_activation=ReLU(inplace=True),drop_rate=0.63864,hidden_units=32,learning_rate=0.0089103,momentum=0.66157
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.58227848101266
node_ip: 192.168.86.61
pid: 22135
time_since_restore: 15.35981273651123
time_this_iter_s: 15.35981273651123
time_total_s: 15.35981273651123
timestamp: 1603593830
timesteps_since_restore: 0
train_accuracy: 12.707386363636363
train_loss: 2.331516767090017
training_iteration: 1
trial_id: e5a1b_00012
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=11 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 62.835443037974684Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (47 PENDING, 2 RUNNING, 11 TERMINATED)
2020-10-25 10:43:50,753 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s]3)[0m
[2m[36m(pid=22363)[0m cuda:0
[2m[36m(pid=22363)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=22363)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=22363)[0m Files already downloaded and verified
[2m[36m(pid=22363)[0m 2020-10-25 10:44:30,395 INFO trainable.py:255 -- Trainable.setup took 38.761 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00013:
date: 2020-10-25_10-44-45
done: true
experiment_id: 1c71743802d94d20bdec5d91a75aff56
experiment_tag: 13_activation=SELU(inplace=True),drop_rate=0.63663,hidden_units=64,learning_rate=0.046775,momentum=0.3704
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 52.53164556962025
node_ip: 192.168.86.61
pid: 22363
time_since_restore: 15.342734575271606
time_this_iter_s: 15.342734575271606
time_total_s: 15.342734575271606
timestamp: 1603593885
timesteps_since_restore: 0
train_accuracy: 11.903409090909092
train_loss: 2.3453086573969233
training_iteration: 1
trial_id: e5a1b_00013
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=12 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 62.79746835443038Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (46 PENDING, 2 RUNNING, 12 TERMINATED)
2020-10-25 10:44:45,900 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=22553)[0m cuda:0
[2m[36m(pid=22553)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=22553)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=22553)[0m Files already downloaded and verified
[2m[36m(pid=22553)[0m 2020-10-25 10:45:18,823 INFO trainable.py:255 -- Trainable.setup took 32.052 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00014:
date: 2020-10-25_10-45-34
done: true
experiment_id: eb8a26188f3c45b1afff9820c1bf0c62
experiment_tag: 14_activation=ReLU(inplace=True),drop_rate=0.30047,hidden_units=128,learning_rate=0.0001914,momentum=0.56262
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 53.11392405063291
node_ip: 192.168.86.61
pid: 22553
time_since_restore: 15.277311086654663
time_this_iter_s: 15.277311086654663
time_total_s: 15.277311086654663
timestamp: 1603593934
timesteps_since_restore: 0
train_accuracy: 11.448863636363637
train_loss: 2.3165191012349995
training_iteration: 1
trial_id: e5a1b_00014
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=13 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 62.75949367088607Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (45 PENDING, 2 RUNNING, 13 TERMINATED)
2020-10-25 10:45:34,272 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=22736)[0m cuda:0
[2m[36m(pid=22736)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=22736)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=22736)[0m Files already downloaded and verified
[2m[36m(pid=22736)[0m 2020-10-25 10:46:05,157 INFO trainable.py:255 -- Trainable.setup took 30.032 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00015:
date: 2020-10-25_10-46-20
done: true
experiment_id: ff43b56a4138404cb4a16ff008cc42d4
experiment_tag: 15_activation=SELU(inplace=True),drop_rate=0.028754,hidden_units=256,learning_rate=0.0024934,momentum=0.53412
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 55.51898734177215
node_ip: 192.168.86.61
pid: 22736
time_since_restore: 15.206795692443848
time_this_iter_s: 15.206795692443848
time_total_s: 15.206795692443848
timestamp: 1603593980
timesteps_since_restore: 0
train_accuracy: 12.340909090909092
train_loss: 2.362239279530265
training_iteration: 1
trial_id: e5a1b_00015
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=14 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 62.72151898734177Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (44 PENDING, 2 RUNNING, 14 TERMINATED)
2020-10-25 10:46:20,530 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=22914)[0m cuda:0
[2m[36m(pid=22914)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=22914)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=22914)[0m Files already downloaded and verified
[2m[36m(pid=22914)[0m 2020-10-25 10:46:50,232 INFO trainable.py:255 -- Trainable.setup took 28.856 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00009:
date: 2020-10-25_10-46-53
done: true
experiment_id: 4f1738f2e11a4d758c90e986d172a936
experiment_tag: 9_activation=ReLU(inplace=True),drop_rate=0.56199,hidden_units=64,learning_rate=0.001198,momentum=0.33487
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 58.35443037974684
node_ip: 192.168.86.61
pid: 14473
time_since_restore: 451.76224517822266
time_this_iter_s: 451.76224517822266
time_total_s: 451.76224517822266
timestamp: 1603594013
timesteps_since_restore: 0
train_accuracy: 13.071022727272727
train_loss: 2.3146613748236136
training_iteration: 1
trial_id: e5a1b_00009
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=15 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 62.68354430379747Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (43 PENDING, 2 RUNNING, 15 TERMINATED)
2020-10-25 10:46:53,259 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=23029)[0m cpu
[2m[36m(pid=23029)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Result for PyTorchCIFAR10Trainable_e5a1b_00016:
date: 2020-10-25_10-47-05
done: true
experiment_id: 4112ab48cb4d4f3e9b4add8cff386956
experiment_tag: 16_activation=ReLU(inplace=True),drop_rate=0.22923,hidden_units=32,learning_rate=0.0059224,momentum=0.1244
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.848101265822784
node_ip: 192.168.86.61
pid: 22914
time_since_restore: 15.293168544769287
time_this_iter_s: 15.293168544769287
time_total_s: 15.293168544769287
timestamp: 1603594025
timesteps_since_restore: 0
train_accuracy: 12.894886363636363
train_loss: 2.324842843142423
training_iteration: 1
trial_id: e5a1b_00016
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== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=16 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 62.325949367088604Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (42 PENDING, 2 RUNNING, 16 TERMINATED)
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2020-10-25 10:47:05,683 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
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[2m[36m(pid=23114)[0m Files already downloaded and verified
[2m[36m(pid=23114)[0m 2020-10-25 10:47:41,591 INFO trainable.py:255 -- Trainable.setup took 34.972 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00018:
date: 2020-10-25_10-47-56
done: true
experiment_id: 00a8de99951e40129a2594c22458f96c
experiment_tag: 18_activation=ELU(alpha=True),drop_rate=0.10165,hidden_units=128,learning_rate=0.0036875,momentum=0.71599
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.74683544303797
node_ip: 192.168.86.61
pid: 23114
time_since_restore: 15.380659818649292
time_this_iter_s: 15.380659818649292
time_total_s: 15.380659818649292
timestamp: 1603594076
timesteps_since_restore: 0
train_accuracy: 12.951704545454545
train_loss: 2.3295809640125795
training_iteration: 1
trial_id: e5a1b_00018
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=17 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 61.96835443037975Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (41 PENDING, 2 RUNNING, 17 TERMINATED)
2020-10-25 10:47:57,158 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
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[2m[36m(pid=23523)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=23523)[0m Files already downloaded and verified
[2m[36m(pid=23523)[0m 2020-10-25 10:49:30,615 INFO trainable.py:255 -- Trainable.setup took 45.984 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00020:
date: 2020-10-25_10-49-45
done: true
experiment_id: 4f41fc56524f4b20aa1a3ead71aaf6a8
experiment_tag: 20_activation=ReLU(inplace=True),drop_rate=0.041345,hidden_units=32,learning_rate=0.003927,momentum=0.53251
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 54.58227848101266
node_ip: 192.168.86.61
pid: 23523
time_since_restore: 15.305274724960327
time_this_iter_s: 15.305274724960327
time_total_s: 15.305274724960327
timestamp: 1603594185
timesteps_since_restore: 0
train_accuracy: 12.625
train_loss: 2.34310289404609
training_iteration: 1
trial_id: e5a1b_00020
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=19 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (39 PENDING, 2 RUNNING, 19 TERMINATED)
2020-10-25 10:49:46,083 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s]7)[0m
[2m[36m(pid=23717)[0m cuda:0
[2m[36m(pid=23717)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=23717)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=23717)[0m Files already downloaded and verified
[2m[36m(pid=23717)[0m 2020-10-25 10:50:27,194 INFO trainable.py:255 -- Trainable.setup took 40.270 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00021:
date: 2020-10-25_10-50-42
done: true
experiment_id: 0f20a0985b494a98a267233cd2c277d8
experiment_tag: 21_activation=SELU(inplace=True),drop_rate=0.50994,hidden_units=64,learning_rate=0.015076,momentum=0.88068
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 55.69620253164557
node_ip: 192.168.86.61
pid: 23717
time_since_restore: 15.290067911148071
time_this_iter_s: 15.290067911148071
time_total_s: 15.290067911148071
timestamp: 1603594242
timesteps_since_restore: 0
train_accuracy: 12.303977272727273
train_loss: 2.342213223603639
training_iteration: 1
trial_id: e5a1b_00021
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=20 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (38 PENDING, 2 RUNNING, 20 TERMINATED)
2020-10-25 10:50:42,651 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=23916)[0m cuda:0
[2m[36m(pid=23916)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=23916)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=23916)[0m Files already downloaded and verified
[2m[36m(pid=23916)[0m 2020-10-25 10:51:19,914 INFO trainable.py:255 -- Trainable.setup took 36.422 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00022:
date: 2020-10-25_10-51-35
done: true
experiment_id: 4d4431bad5d74ea090bd42391f04304a
experiment_tag: 22_activation=ReLU(inplace=True),drop_rate=0.41304,hidden_units=128,learning_rate=0.00093083,momentum=0.73615
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.20253164556962
node_ip: 192.168.86.61
pid: 23916
time_since_restore: 15.260676860809326
time_this_iter_s: 15.260676860809326
time_total_s: 15.260676860809326
timestamp: 1603594295
timesteps_since_restore: 0
train_accuracy: 12.596590909090908
train_loss: 2.3132240169427614
training_iteration: 1
trial_id: e5a1b_00022
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 59.99367088607595Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (37 PENDING, 2 RUNNING, 21 TERMINATED)
2020-10-25 10:51:35,341 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=24101)[0m cuda:0
[2m[36m(pid=24101)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=24101)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=24101)[0m Files already downloaded and verified
[2m[36m(pid=24101)[0m 2020-10-25 10:52:07,735 INFO trainable.py:255 -- Trainable.setup took 31.503 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-52-23
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 66.89873417721519
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 15.282578229904175
time_this_iter_s: 15.282578229904175
time_total_s: 15.282578229904175
timestamp: 1603594343
timesteps_since_restore: 0
train_accuracy: 15.139204545454545
train_loss: 2.3132558275352824
training_iteration: 1
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-52-38
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 67.81012658227849
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 30.5079402923584
time_this_iter_s: 15.225362062454224
time_total_s: 30.5079402923584
timestamp: 1603594358
timesteps_since_restore: 0
train_accuracy: 15.153409090909092
train_loss: 2.3136926124041732
training_iteration: 2
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-52-53
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 67.48101265822785
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 45.74336004257202
time_this_iter_s: 15.235419750213623
time_total_s: 45.74336004257202
timestamp: 1603594373
timesteps_since_restore: 0
train_accuracy: 15.383522727272727
train_loss: 2.3128427057103678
training_iteration: 3
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.0253164556962 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-53-08
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 67.37974683544304
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 60.937007188797
time_this_iter_s: 15.193647146224976
time_total_s: 60.937007188797
timestamp: 1603594388
timesteps_since_restore: 0
train_accuracy: 15.201704545454545
train_loss: 2.3129563189365645
training_iteration: 4
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-53-24
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 5
mean_accuracy: 68.51898734177215
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 76.1317412853241
time_this_iter_s: 15.1947340965271
time_total_s: 76.1317412853241
timestamp: 1603594404
timesteps_since_restore: 0
train_accuracy: 15.147727272727273
train_loss: 2.3135988895188677
training_iteration: 5
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-53-39
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 6
mean_accuracy: 67.40506329113924
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 91.3788492679596
time_this_iter_s: 15.247107982635498
time_total_s: 91.3788492679596
timestamp: 1603594419
timesteps_since_restore: 0
train_accuracy: 14.946022727272727
train_loss: 2.3135218999602576
training_iteration: 6
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-53-54
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 7
mean_accuracy: 67.25316455696202
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 106.6475477218628
time_this_iter_s: 15.268698453903198
time_total_s: 106.6475477218628
timestamp: 1603594434
timesteps_since_restore: 0
train_accuracy: 15.213068181818182
train_loss: 2.3132244009863245
training_iteration: 7
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-54-09
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 8
mean_accuracy: 67.70886075949367
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 121.89685368537903
time_this_iter_s: 15.249305963516235
time_total_s: 121.89685368537903
timestamp: 1603594449
timesteps_since_restore: 0
train_accuracy: 15.252840909090908
train_loss: 2.3134750297123734
training_iteration: 8
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-54-25
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 9
mean_accuracy: 68.41772151898734
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 137.27477025985718
time_this_iter_s: 15.37791657447815
time_total_s: 137.27477025985718
timestamp: 1603594465
timesteps_since_restore: 0
train_accuracy: 15.386363636363637
train_loss: 2.3137014962055464
training_iteration: 9
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-54-40
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 10
mean_accuracy: 68.26582278481013
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 152.59181809425354
time_this_iter_s: 15.317047834396362
time_total_s: 152.59181809425354
timestamp: 1603594480
timesteps_since_restore: 0
train_accuracy: 15.323863636363637
train_loss: 2.312798674133691
training_iteration: 10
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-54-56
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 11
mean_accuracy: 68.0506329113924
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 167.75966930389404
time_this_iter_s: 15.167851209640503
time_total_s: 167.75966930389404
timestamp: 1603594496
timesteps_since_restore: 0
train_accuracy: 15.142045454545455
train_loss: 2.3127939342097803
training_iteration: 11
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=21 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (36 PENDING, 2 RUNNING, 22 TERMINATED)
2020-10-25 10:55:00,421 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
Result for PyTorchCIFAR10Trainable_e5a1b_00017:
date: 2020-10-25_10-55-00
done: true
experiment_id: bc70567a853b4a55ba58333f9110c4be
experiment_tag: 17_activation=ELU(alpha=True),drop_rate=0.029879,hidden_units=64,learning_rate=0.029363,momentum=0.38815
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 57.164556962025316
node_ip: 192.168.86.61
pid: 23029
time_since_restore: 452.0925886631012
time_this_iter_s: 452.0925886631012
time_total_s: 452.0925886631012
timestamp: 1603594500
timesteps_since_restore: 0
train_accuracy: 12.815340909090908
train_loss: 2.3263147784905
training_iteration: 1
trial_id: e5a1b_00017
0it [00:00, ?it/s]2)[0m
[2m[36m(pid=25522)[0m cpu
[2m[36m(pid=25522)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-55-11
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 12
mean_accuracy: 68.15189873417721
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 182.9172899723053
time_this_iter_s: 15.157620668411255
time_total_s: 182.9172899723053
timestamp: 1603594511
timesteps_since_restore: 0
train_accuracy: 15.042613636363637
train_loss: 2.313210465691306
training_iteration: 12
trial_id: e5a1b_00023
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== Status ==Memory usage on this node: 5.1/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
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[2m[36m(pid=25522)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=25522)[0m Files already downloaded and verified
[2m[36m(pid=25522)[0m 2020-10-25 10:55:24,541 INFO trainable.py:255 -- Trainable.setup took 23.276 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-55-26
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 13
mean_accuracy: 67.79746835443038
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 198.01703572273254
time_this_iter_s: 15.099745750427246
time_total_s: 198.01703572273254
timestamp: 1603594526
timesteps_since_restore: 0
train_accuracy: 15.170454545454545
train_loss: 2.3132674118334595
training_iteration: 13
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-55-41
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 14
mean_accuracy: 67.25316455696202
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 213.32692098617554
time_this_iter_s: 15.309885263442993
time_total_s: 213.32692098617554
timestamp: 1603594541
timesteps_since_restore: 0
train_accuracy: 14.977272727272727
train_loss: 2.3137322759086434
training_iteration: 14
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-55-56
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 15
mean_accuracy: 67.74683544303798
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 228.53451251983643
time_this_iter_s: 15.207591533660889
time_total_s: 228.53451251983643
timestamp: 1603594556
timesteps_since_restore: 0
train_accuracy: 15.196022727272727
train_loss: 2.3132733526554974
training_iteration: 15
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 66.9493670886076 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-56-12
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 16
mean_accuracy: 68.30379746835443
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 243.82693696022034
time_this_iter_s: 15.292424440383911
time_total_s: 243.82693696022034
timestamp: 1603594572
timesteps_since_restore: 0
train_accuracy: 15.545454545454545
train_loss: 2.313650876960971
training_iteration: 16
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-56-27
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 17
mean_accuracy: 68.50632911392405
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 259.0681838989258
time_this_iter_s: 15.241246938705444
time_total_s: 259.0681838989258
timestamp: 1603594587
timesteps_since_restore: 0
train_accuracy: 15.144886363636363
train_loss: 2.313972217115489
training_iteration: 17
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-56-43
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 18
mean_accuracy: 67.69620253164557
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 274.4091281890869
time_this_iter_s: 15.340944290161133
time_total_s: 274.4091281890869
timestamp: 1603594603
timesteps_since_restore: 0
train_accuracy: 15.096590909090908
train_loss: 2.3136918531222777
training_iteration: 18
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-56-58
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 19
mean_accuracy: 68.62025316455696
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 289.5901982784271
time_this_iter_s: 15.18107008934021
time_total_s: 289.5901982784271
timestamp: 1603594618
timesteps_since_restore: 0
train_accuracy: 15.377840909090908
train_loss: 2.31315995210951
training_iteration: 19
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-57-13
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 20
mean_accuracy: 68.9746835443038
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 304.8860363960266
time_this_iter_s: 15.295838117599487
time_total_s: 304.8860363960266
timestamp: 1603594633
timesteps_since_restore: 0
train_accuracy: 15.255681818181818
train_loss: 2.3129124370488254
training_iteration: 20
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-57-28
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 21
mean_accuracy: 67.9873417721519
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 320.0944390296936
time_this_iter_s: 15.208402633666992
time_total_s: 320.0944390296936
timestamp: 1603594648
timesteps_since_restore: 0
train_accuracy: 15.034090909090908
train_loss: 2.313699021935463
training_iteration: 21
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-57-44
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 22
mean_accuracy: 68.87341772151899
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 335.3825697898865
time_this_iter_s: 15.288130760192871
time_total_s: 335.3825697898865
timestamp: 1603594664
timesteps_since_restore: 0
train_accuracy: 15.275568181818182
train_loss: 2.312699069353667
training_iteration: 22
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-57-59
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 23
mean_accuracy: 67.82278481012658
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 350.6575222015381
time_this_iter_s: 15.274952411651611
time_total_s: 350.6575222015381
timestamp: 1603594679
timesteps_since_restore: 0
train_accuracy: 14.997159090909092
train_loss: 2.313123720613393
training_iteration: 23
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-58-14
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 24
mean_accuracy: 66.9367088607595
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 365.95018315315247
time_this_iter_s: 15.29266095161438
time_total_s: 365.95018315315247
timestamp: 1603594694
timesteps_since_restore: 0
train_accuracy: 15.329545454545455
train_loss: 2.3137358230623333
training_iteration: 24
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-58-30
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 25
mean_accuracy: 68.34177215189874
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 381.18161511421204
time_this_iter_s: 15.23143196105957
time_total_s: 381.18161511421204
timestamp: 1603594710
timesteps_since_restore: 0
train_accuracy: 14.863636363636363
train_loss: 2.3144176588817076
training_iteration: 25
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-58-45
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 26
mean_accuracy: 67.9873417721519
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 396.4829852581024
time_this_iter_s: 15.30137014389038
time_total_s: 396.4829852581024
timestamp: 1603594725
timesteps_since_restore: 0
train_accuracy: 15.21875
train_loss: 2.312857945534316
training_iteration: 26
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-59-00
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 27
mean_accuracy: 67.55696202531645
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 411.7385709285736
time_this_iter_s: 15.255585670471191
time_total_s: 411.7385709285736
timestamp: 1603594740
timesteps_since_restore: 0
train_accuracy: 15.360795454545455
train_loss: 2.312673983926123
training_iteration: 27
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-59-16
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 28
mean_accuracy: 66.9367088607595
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 426.9771647453308
time_this_iter_s: 15.238593816757202
time_total_s: 426.9771647453308
timestamp: 1603594756
timesteps_since_restore: 0
train_accuracy: 15.125
train_loss: 2.314179627732797
training_iteration: 28
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-59-31
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 29
mean_accuracy: 68.21518987341773
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 442.3133509159088
time_this_iter_s: 15.336186170578003
time_total_s: 442.3133509159088
timestamp: 1603594771
timesteps_since_restore: 0
train_accuracy: 15.556818181818182
train_loss: 2.312748197127472
training_iteration: 29
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_10-59-46
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 30
mean_accuracy: 68.34177215189874
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 457.5120451450348
time_this_iter_s: 15.198694229125977
time_total_s: 457.5120451450348
timestamp: 1603594786
timesteps_since_restore: 0
train_accuracy: 15.321022727272727
train_loss: 2.3130059933120553
training_iteration: 30
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-00-02
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 31
mean_accuracy: 68.35443037974683
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 472.7358250617981
time_this_iter_s: 15.223779916763306
time_total_s: 472.7358250617981
timestamp: 1603594802
timesteps_since_restore: 0
train_accuracy: 15.323863636363637
train_loss: 2.3128031349994917
training_iteration: 31
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-00-17
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 32
mean_accuracy: 68.56962025316456
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 488.0167467594147
time_this_iter_s: 15.280921697616577
time_total_s: 488.0167467594147
timestamp: 1603594817
timesteps_since_restore: 0
train_accuracy: 15.235795454545455
train_loss: 2.312500224194743
training_iteration: 32
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-00-32
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 33
mean_accuracy: 68.51898734177215
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 503.25849413871765
time_this_iter_s: 15.241747379302979
time_total_s: 503.25849413871765
timestamp: 1603594832
timesteps_since_restore: 0
train_accuracy: 15.051136363636363
train_loss: 2.3135474656115878
training_iteration: 33
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-00-48
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 34
mean_accuracy: 68.25316455696202
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 518.5486698150635
time_this_iter_s: 15.290175676345825
time_total_s: 518.5486698150635
timestamp: 1603594848
timesteps_since_restore: 0
train_accuracy: 15.213068181818182
train_loss: 2.312729985876517
training_iteration: 34
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-01-03
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 35
mean_accuracy: 67.64556962025317
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 533.7697687149048
time_this_iter_s: 15.221098899841309
time_total_s: 533.7697687149048
timestamp: 1603594863
timesteps_since_restore: 0
train_accuracy: 15.525568181818182
train_loss: 2.312719862569462
training_iteration: 35
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-01-18
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 36
mean_accuracy: 68.59493670886076
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 549.0623691082001
time_this_iter_s: 15.292600393295288
time_total_s: 549.0623691082001
timestamp: 1603594878
timesteps_since_restore: 0
train_accuracy: 15.244318181818182
train_loss: 2.313355115326968
training_iteration: 36
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-01-34
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 37
mean_accuracy: 68.30379746835443
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 564.2835803031921
time_this_iter_s: 15.221211194992065
time_total_s: 564.2835803031921
timestamp: 1603594894
timesteps_since_restore: 0
train_accuracy: 15.193181818181818
train_loss: 2.313546090640805
training_iteration: 37
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-01-49
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 38
mean_accuracy: 67.86075949367088
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 579.5890865325928
time_this_iter_s: 15.305506229400635
time_total_s: 579.5890865325928
timestamp: 1603594909
timesteps_since_restore: 0
train_accuracy: 15.079545454545455
train_loss: 2.3132692900570957
training_iteration: 38
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-02-04
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 39
mean_accuracy: 68.48101265822785
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 594.8700513839722
time_this_iter_s: 15.280964851379395
time_total_s: 594.8700513839722
timestamp: 1603594924
timesteps_since_restore: 0
train_accuracy: 15.125
train_loss: 2.3137931884689764
training_iteration: 39
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-02-20
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 40
mean_accuracy: 67.73417721518987
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 610.1479284763336
time_this_iter_s: 15.27787709236145
time_total_s: 610.1479284763336
timestamp: 1603594940
timesteps_since_restore: 0
train_accuracy: 15.244318181818182
train_loss: 2.313844871791926
training_iteration: 40
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-02-35
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 41
mean_accuracy: 68.46835443037975
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 625.4203469753265
time_this_iter_s: 15.27241849899292
time_total_s: 625.4203469753265
timestamp: 1603594955
timesteps_since_restore: 0
train_accuracy: 15.017045454545455
train_loss: 2.3128070418130267
training_iteration: 41
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-02-50
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 42
mean_accuracy: 68.65822784810126
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 640.7628116607666
time_this_iter_s: 15.342464685440063
time_total_s: 640.7628116607666
timestamp: 1603594970
timesteps_since_restore: 0
train_accuracy: 15.122159090909092
train_loss: 2.3127410493113776
training_iteration: 42
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=22 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00024:
date: 2020-10-25_11-03-02
done: true
experiment_id: 7939512f11f14009a94a7ccf2de5b025
experiment_tag: 24_activation=SELU(inplace=True),drop_rate=0.020281,hidden_units=32,learning_rate=0.077258,momentum=0.76878
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 55.20253164556962
node_ip: 192.168.86.61
pid: 25522
time_since_restore: 457.9051032066345
time_this_iter_s: 457.9051032066345
time_total_s: 457.9051032066345
timestamp: 1603594982
timesteps_since_restore: 0
train_accuracy: 12.170454545454545
train_loss: 2.3483738018707796
training_iteration: 1
trial_id: e5a1b_00024
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (35 PENDING, 2 RUNNING, 23 TERMINATED)
2020-10-25 11:03:02,557 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=29242)[0m cpu
[2m[36m(pid=29242)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
0it [00:00, ?it/s]2)[0m
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Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-03-06
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 43
mean_accuracy: 68.55696202531645
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 655.9600610733032
time_this_iter_s: 15.197249412536621
time_total_s: 655.9600610733032
timestamp: 1603594986
timesteps_since_restore: 0
train_accuracy: 14.946022727272727
train_loss: 2.3137258006767794
training_iteration: 43
trial_id: e5a1b_00023
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Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-03-21
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 44
mean_accuracy: 66.81012658227849
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 671.0430960655212
time_this_iter_s: 15.083034992218018
time_total_s: 671.0430960655212
timestamp: 1603595001
timesteps_since_restore: 0
train_accuracy: 15.045454545454545
train_loss: 2.3138832287354902
training_iteration: 44
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
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[2m[36m(pid=29242)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=29242)[0m Files already downloaded and verified
[2m[36m(pid=29242)[0m 2020-10-25 11:03:34,585 INFO trainable.py:255 -- Trainable.setup took 31.162 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-03-36
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 45
mean_accuracy: 67.69620253164557
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 686.1444952487946
time_this_iter_s: 15.101399183273315
time_total_s: 686.1444952487946
timestamp: 1603595016
timesteps_since_restore: 0
train_accuracy: 15.238636363636363
train_loss: 2.312991354953159
training_iteration: 45
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-03-51
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 46
mean_accuracy: 68.21518987341773
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 701.345210313797
time_this_iter_s: 15.200715065002441
time_total_s: 701.345210313797
timestamp: 1603595031
timesteps_since_restore: 0
train_accuracy: 15.056818181818182
train_loss: 2.31382156773047
training_iteration: 46
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-04-06
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 47
mean_accuracy: 67.9493670886076
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 716.5923118591309
time_this_iter_s: 15.247101545333862
time_total_s: 716.5923118591309
timestamp: 1603595046
timesteps_since_restore: 0
train_accuracy: 15.247159090909092
train_loss: 2.31376605684107
training_iteration: 47
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-04-22
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 48
mean_accuracy: 67.82278481012658
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 731.9373376369476
time_this_iter_s: 15.345025777816772
time_total_s: 731.9373376369476
timestamp: 1603595062
timesteps_since_restore: 0
train_accuracy: 15.107954545454545
train_loss: 2.3132306682792576
training_iteration: 48
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-04-37
done: false
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 49
mean_accuracy: 68.0379746835443
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 747.2386381626129
time_this_iter_s: 15.301300525665283
time_total_s: 747.2386381626129
timestamp: 1603595077
timesteps_since_restore: 0
train_accuracy: 15.352272727272727
train_loss: 2.313250422477722
training_iteration: 49
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00023:
date: 2020-10-25_11-04-53
done: true
experiment_id: 2c353eb5734040f9babc0142c4b23066
experiment_tag: 23_activation=ELU(alpha=True),drop_rate=0.21667,hidden_units=256,learning_rate=0.0020745,momentum=0.16277
hostname: ironman
iterations_since_restore: 50
mean_accuracy: 67.51898734177215
node_ip: 192.168.86.61
pid: 24101
time_since_restore: 762.5316379070282
time_this_iter_s: 15.292999744415283
time_total_s: 762.5316379070282
timestamp: 1603595093
timesteps_since_restore: 0
train_accuracy: 15.147727272727273
train_loss: 2.3134404949166556
training_iteration: 50
trial_id: e5a1b_00023
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=23 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.62341772151899Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (34 PENDING, 2 RUNNING, 24 TERMINATED)
2020-10-25 11:04:53,270 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
[2m[36m(pid=30114)[0m cuda:0
[2m[36m(pid=30114)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=30114)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=30114)[0m Files already downloaded and verified
[2m[36m(pid=30114)[0m 2020-10-25 11:05:30,911 INFO trainable.py:255 -- Trainable.setup took 36.763 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00026:
date: 2020-10-25_11-05-46
done: true
experiment_id: 42d38a893e8d40b1a52d5c7eb1552f58
experiment_tag: 26_activation=ReLU(inplace=True),drop_rate=0.12515,hidden_units=128,learning_rate=0.00056329,momentum=0.53938
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 58.88607594936709
node_ip: 192.168.86.61
pid: 30114
time_since_restore: 15.20226263999939
time_this_iter_s: 15.20226263999939
time_total_s: 15.20226263999939
timestamp: 1603595146
timesteps_since_restore: 0
train_accuracy: 13.198863636363637
train_loss: 2.3144646747545763
training_iteration: 1
trial_id: e5a1b_00026
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=24 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 60.06962025316456Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (33 PENDING, 2 RUNNING, 25 TERMINATED)
2020-10-25 11:05:46,282 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s]1)[0m
[2m[36m(pid=30301)[0m cuda:0
[2m[36m(pid=30301)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=30301)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=30301)[0m Files already downloaded and verified
[2m[36m(pid=30301)[0m 2020-10-25 11:06:30,583 INFO trainable.py:255 -- Trainable.setup took 43.412 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00027:
date: 2020-10-25_11-06-45
done: true
experiment_id: b21f42d5feeb42afa67fc53f2c9375ed
experiment_tag: 27_activation=SELU(inplace=True),drop_rate=0.57168,hidden_units=256,learning_rate=0.009563,momentum=0.32395
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.949367088607595
node_ip: 192.168.86.61
pid: 30301
time_since_restore: 15.23840069770813
time_this_iter_s: 15.23840069770813
time_total_s: 15.23840069770813
timestamp: 1603595205
timesteps_since_restore: 0
train_accuracy: 12.789772727272727
train_loss: 2.353125711056319
training_iteration: 1
trial_id: e5a1b_00027
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=25 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 59.47784810126582Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (32 PENDING, 2 RUNNING, 26 TERMINATED)
2020-10-25 11:06:45,985 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
0it [00:00, ?it/s]6)[0m
[2m[36m(pid=30496)[0m cuda:0
[2m[36m(pid=30496)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=30496)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=30496)[0m Files already downloaded and verified
[2m[36m(pid=30496)[0m 2020-10-25 11:07:16,986 INFO trainable.py:255 -- Trainable.setup took 30.129 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00028:
date: 2020-10-25_11-07-32
done: false
experiment_id: 79cc050262b3485abe68f90f4a8d9aee
experiment_tag: 28_activation=SELU(inplace=True),drop_rate=0.76389,hidden_units=32,learning_rate=0.016357,momentum=0.54348
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 59.54430379746835
node_ip: 192.168.86.61
pid: 30496
time_since_restore: 15.295686960220337
time_this_iter_s: 15.295686960220337
time_total_s: 15.295686960220337
timestamp: 1603595252
timesteps_since_restore: 0
train_accuracy: 13.394886363636363
train_loss: 2.3545372885736553
training_iteration: 1
trial_id: e5a1b_00028
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=25 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 59.54430379746835Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (31 PENDING, 2 RUNNING, 27 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00028:
date: 2020-10-25_11-07-47
done: false
experiment_id: 79cc050262b3485abe68f90f4a8d9aee
experiment_tag: 28_activation=SELU(inplace=True),drop_rate=0.76389,hidden_units=32,learning_rate=0.016357,momentum=0.54348
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 59.30379746835443
node_ip: 192.168.86.61
pid: 30496
time_since_restore: 30.423644542694092
time_this_iter_s: 15.127957582473755
time_total_s: 30.423644542694092
timestamp: 1603595267
timesteps_since_restore: 0
train_accuracy: 13.605113636363637
train_loss: 2.3519909754395485
training_iteration: 2
trial_id: e5a1b_00028
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=25 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 59.54430379746835Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (31 PENDING, 2 RUNNING, 27 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00028:
date: 2020-10-25_11-08-02
done: false
experiment_id: 79cc050262b3485abe68f90f4a8d9aee
experiment_tag: 28_activation=SELU(inplace=True),drop_rate=0.76389,hidden_units=32,learning_rate=0.016357,momentum=0.54348
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 59.0
node_ip: 192.168.86.61
pid: 30496
time_since_restore: 45.529709339141846
time_this_iter_s: 15.106064796447754
time_total_s: 45.529709339141846
timestamp: 1603595282
timesteps_since_restore: 0
train_accuracy: 13.528409090909092
train_loss: 2.352176396684213
training_iteration: 3
trial_id: e5a1b_00028
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=25 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.29113924050634 | Iter 1.000: 59.54430379746835Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (31 PENDING, 2 RUNNING, 27 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00028:
date: 2020-10-25_11-08-17
done: true
experiment_id: 79cc050262b3485abe68f90f4a8d9aee
experiment_tag: 28_activation=SELU(inplace=True),drop_rate=0.76389,hidden_units=32,learning_rate=0.016357,momentum=0.54348
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 59.620253164556964
node_ip: 192.168.86.61
pid: 30496
time_since_restore: 60.66109848022461
time_this_iter_s: 15.131389141082764
time_total_s: 60.66109848022461
timestamp: 1603595297
timesteps_since_restore: 0
train_accuracy: 13.323863636363637
train_loss: 2.354520870203322
training_iteration: 4
trial_id: e5a1b_00028
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=26 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.20253164556962 | Iter 1.000: 59.54430379746835Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (31 PENDING, 2 RUNNING, 27 TERMINATED)
2020-10-25 11:08:17,980 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=31061)[0m cuda:0
[2m[36m(pid=31061)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=31061)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=31061)[0m Files already downloaded and verified
[2m[36m(pid=31061)[0m 2020-10-25 11:08:57,458 INFO trainable.py:255 -- Trainable.setup took 38.588 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00029:
date: 2020-10-25_11-09-12
done: false
experiment_id: 77a80421b24243f9bd0d9eb9a7b4c761
experiment_tag: 29_activation=ReLU(inplace=True),drop_rate=0.48938,hidden_units=64,learning_rate=0.0018147,momentum=0.29818
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 60.29113924050633
node_ip: 192.168.86.61
pid: 31061
time_since_restore: 15.32080364227295
time_this_iter_s: 15.32080364227295
time_total_s: 15.32080364227295
timestamp: 1603595352
timesteps_since_restore: 0
train_accuracy: 13.428977272727273
train_loss: 2.3223621167919855
training_iteration: 1
trial_id: e5a1b_00029
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=26 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.20253164556962 | Iter 1.000: 60.10443037974684Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (30 PENDING, 2 RUNNING, 28 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00029:
date: 2020-10-25_11-09-27
done: false
experiment_id: 77a80421b24243f9bd0d9eb9a7b4c761
experiment_tag: 29_activation=ReLU(inplace=True),drop_rate=0.48938,hidden_units=64,learning_rate=0.0018147,momentum=0.29818
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 60.30379746835443
node_ip: 192.168.86.61
pid: 31061
time_since_restore: 30.496139764785767
time_this_iter_s: 15.175336122512817
time_total_s: 30.496139764785767
timestamp: 1603595367
timesteps_since_restore: 0
train_accuracy: 13.397727272727273
train_loss: 2.3222684982148083
training_iteration: 2
trial_id: e5a1b_00029
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=26 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.20253164556962 | Iter 1.000: 60.10443037974684Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (30 PENDING, 2 RUNNING, 28 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00029:
date: 2020-10-25_11-09-43
done: false
experiment_id: 77a80421b24243f9bd0d9eb9a7b4c761
experiment_tag: 29_activation=ReLU(inplace=True),drop_rate=0.48938,hidden_units=64,learning_rate=0.0018147,momentum=0.29818
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 60.063291139240505
node_ip: 192.168.86.61
pid: 31061
time_since_restore: 45.66974425315857
time_this_iter_s: 15.173604488372803
time_total_s: 45.66974425315857
timestamp: 1603595383
timesteps_since_restore: 0
train_accuracy: 13.505681818181818
train_loss: 2.3219957175579937
training_iteration: 3
trial_id: e5a1b_00029
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=26 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.20253164556962 | Iter 1.000: 60.10443037974684Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (30 PENDING, 2 RUNNING, 28 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00029:
date: 2020-10-25_11-09-58
done: true
experiment_id: 77a80421b24243f9bd0d9eb9a7b4c761
experiment_tag: 29_activation=ReLU(inplace=True),drop_rate=0.48938,hidden_units=64,learning_rate=0.0018147,momentum=0.29818
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 60.12658227848101
node_ip: 192.168.86.61
pid: 31061
time_since_restore: 60.83627438545227
time_this_iter_s: 15.166530132293701
time_total_s: 60.83627438545227
timestamp: 1603595398
timesteps_since_restore: 0
train_accuracy: 13.514204545454545
train_loss: 2.321725669909607
training_iteration: 4
trial_id: e5a1b_00029
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=27 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 60.10443037974684Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (30 PENDING, 2 RUNNING, 28 TERMINATED)
2020-10-25 11:09:58,629 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
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[2m[36m(pid=31591)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=31591)[0m Files already downloaded and verified
[2m[36m(pid=31591)[0m 2020-10-25 11:10:21,784 INFO trainable.py:255 -- Trainable.setup took 22.266 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00030:
date: 2020-10-25_11-10-37
done: true
experiment_id: a1c8a7e50593463db77cba860f341fdd
experiment_tag: 30_activation=ELU(alpha=True),drop_rate=0.28478,hidden_units=128,learning_rate=0.018773,momentum=0.11151
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 53.936708860759495
node_ip: 192.168.86.61
pid: 31591
time_since_restore: 15.357723951339722
time_this_iter_s: 15.357723951339722
time_total_s: 15.357723951339722
timestamp: 1603595437
timesteps_since_restore: 0
train_accuracy: 12.071022727272727
train_loss: 2.3331582586873663
training_iteration: 1
trial_id: e5a1b_00030
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=28 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.91772151898734Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (29 PENDING, 2 RUNNING, 29 TERMINATED)
2020-10-25 11:10:37,307 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
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Result for PyTorchCIFAR10Trainable_e5a1b_00025:
date: 2020-10-25_11-11-08
done: true
experiment_id: c2382c17c16a4868933f10e77e5b8fc2
experiment_tag: 25_activation=ELU(alpha=True),drop_rate=0.55678,hidden_units=64,learning_rate=0.001686,momentum=0.23864
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.51898734177215
node_ip: 192.168.86.61
pid: 29242
time_since_restore: 454.2418324947357
time_this_iter_s: 454.2418324947357
time_total_s: 454.2418324947357
timestamp: 1603595468
timesteps_since_restore: 0
train_accuracy: 12.380681818181818
train_loss: 2.31973968107592
training_iteration: 1
trial_id: e5a1b_00025
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== Status ==Memory usage on this node: 4.0/125.8 GiBUsing AsyncHyperBand: num_stopped=29 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.73101265822785Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (28 PENDING, 2 RUNNING, 30 TERMINATED)
2020-10-25 11:11:08,936 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
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0it [00:00, ?it/s]4)[0m
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[2m[36m(pid=31824)[0m cpu
[2m[36m(pid=31824)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=31761)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
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[2m[36m(pid=31761)[0m 2020-10-25 11:11:24,543 INFO trainable.py:255 -- Trainable.setup took 46.360 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
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[2m[36m(pid=31824)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=31824)[0m Files already downloaded and verified
[2m[36m(pid=31824)[0m 2020-10-25 11:11:37,877 INFO trainable.py:255 -- Trainable.setup took 28.101 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00031:
date: 2020-10-25_11-11-39
done: true
experiment_id: 6d471cf01625402f98ffb06f26807ee4
experiment_tag: 31_activation=SELU(inplace=True),drop_rate=0.092858,hidden_units=256,learning_rate=0.00013741,momentum=0.13258
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 48.55696202531646
node_ip: 192.168.86.61
pid: 31761
time_since_restore: 15.146332502365112
time_this_iter_s: 15.146332502365112
time_total_s: 15.146332502365112
timestamp: 1603595499
timesteps_since_restore: 0
train_accuracy: 11.113636363636363
train_loss: 2.3635160557248374
training_iteration: 1
trial_id: e5a1b_00031
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=30 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.54430379746835Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (27 PENDING, 2 RUNNING, 31 TERMINATED)
2020-10-25 11:11:39,873 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
0it [00:00, ?it/s]1)[0m
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[2m[36m(pid=32031)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=32031)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=32031)[0m Files already downloaded and verified
[2m[36m(pid=32031)[0m 2020-10-25 11:12:08,869 INFO trainable.py:255 -- Trainable.setup took 28.121 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00033:
date: 2020-10-25_11-12-24
done: true
experiment_id: 8f0d00a59b3c4a828a097cea1263163e
experiment_tag: 33_activation=ReLU(inplace=True),drop_rate=0.078267,hidden_units=64,learning_rate=0.0029843,momentum=0.47878
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 53.177215189873415
node_ip: 192.168.86.61
pid: 32031
time_since_restore: 15.237906694412231
time_this_iter_s: 15.237906694412231
time_total_s: 15.237906694412231
timestamp: 1603595544
timesteps_since_restore: 0
train_accuracy: 11.889204545454545
train_loss: 2.3304925100369887
training_iteration: 1
trial_id: e5a1b_00033
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=31 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.379746835443036Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (26 PENDING, 2 RUNNING, 32 TERMINATED)
2020-10-25 11:12:24,287 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=32207)[0m cuda:0
[2m[36m(pid=32207)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=32207)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=32207)[0m Files already downloaded and verified
[2m[36m(pid=32207)[0m 2020-10-25 11:12:59,421 INFO trainable.py:255 -- Trainable.setup took 34.258 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00034:
date: 2020-10-25_11-13-14
done: false
experiment_id: 215d4130dec54a63af7b499d69e025cf
experiment_tag: 34_activation=ELU(alpha=True),drop_rate=0.13856,hidden_units=128,learning_rate=0.0020024,momentum=0.4188
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 64.9873417721519
node_ip: 192.168.86.61
pid: 32207
time_since_restore: 15.435506820678711
time_this_iter_s: 15.435506820678711
time_total_s: 15.435506820678711
timestamp: 1603595594
timesteps_since_restore: 0
train_accuracy: 14.443181818181818
train_loss: 2.31555621732365
training_iteration: 1
trial_id: e5a1b_00034
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=31 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.91772151898734Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (25 PENDING, 2 RUNNING, 33 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00034:
date: 2020-10-25_11-13-30
done: false
experiment_id: 215d4130dec54a63af7b499d69e025cf
experiment_tag: 34_activation=ELU(alpha=True),drop_rate=0.13856,hidden_units=128,learning_rate=0.0020024,momentum=0.4188
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 64.86075949367088
node_ip: 192.168.86.61
pid: 32207
time_since_restore: 30.613484859466553
time_this_iter_s: 15.177978038787842
time_total_s: 30.613484859466553
timestamp: 1603595610
timesteps_since_restore: 0
train_accuracy: 14.397727272727273
train_loss: 2.315260448239066
training_iteration: 2
trial_id: e5a1b_00034
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=31 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.91772151898734Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (25 PENDING, 2 RUNNING, 33 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00034:
date: 2020-10-25_11-13-45
done: false
experiment_id: 215d4130dec54a63af7b499d69e025cf
experiment_tag: 34_activation=ELU(alpha=True),drop_rate=0.13856,hidden_units=128,learning_rate=0.0020024,momentum=0.4188
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 65.58227848101266
node_ip: 192.168.86.61
pid: 32207
time_since_restore: 45.83326315879822
time_this_iter_s: 15.219778299331665
time_total_s: 45.83326315879822
timestamp: 1603595625
timesteps_since_restore: 0
train_accuracy: 14.607954545454545
train_loss: 2.3142960491505535
training_iteration: 3
trial_id: e5a1b_00034
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=31 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.1139240506329 | Iter 1.000: 59.91772151898734Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (25 PENDING, 2 RUNNING, 33 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00034:
date: 2020-10-25_11-14-00
done: true
experiment_id: 215d4130dec54a63af7b499d69e025cf
experiment_tag: 34_activation=ELU(alpha=True),drop_rate=0.13856,hidden_units=128,learning_rate=0.0020024,momentum=0.4188
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 64.67088607594937
node_ip: 192.168.86.61
pid: 32207
time_since_restore: 61.09494662284851
time_this_iter_s: 15.261683464050293
time_total_s: 61.09494662284851
timestamp: 1603595640
timesteps_since_restore: 0
train_accuracy: 14.389204545454545
train_loss: 2.3157897767695514
training_iteration: 4
trial_id: e5a1b_00034
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=32 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.0253164556962 | Iter 1.000: 59.91772151898734Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (25 PENDING, 2 RUNNING, 33 TERMINATED)
2020-10-25 11:14:00,847 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s]9)[0m
[2m[36m(pid=32729)[0m cuda:0
[2m[36m(pid=32729)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=32729)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=32729)[0m Files already downloaded and verified
[2m[36m(pid=32729)[0m 2020-10-25 11:14:30,769 INFO trainable.py:255 -- Trainable.setup took 29.068 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00035:
date: 2020-10-25_11-14-46
done: false
experiment_id: 4ed95324b2384a92b5ce4660a0b5b324
experiment_tag: 35_activation=SELU(inplace=True),drop_rate=0.49268,hidden_units=256,learning_rate=0.0080405,momentum=0.13624
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 64.78481012658227
node_ip: 192.168.86.61
pid: 32729
time_since_restore: 15.378620386123657
time_this_iter_s: 15.378620386123657
time_total_s: 15.378620386123657
timestamp: 1603595686
timesteps_since_restore: 0
train_accuracy: 14.735795454545455
train_loss: 2.323083731938492
training_iteration: 1
trial_id: e5a1b_00035
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=32 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.0253164556962 | Iter 1.000: 60.53164556962025Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (24 PENDING, 2 RUNNING, 34 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00035:
date: 2020-10-25_11-15-01
done: false
experiment_id: 4ed95324b2384a92b5ce4660a0b5b324
experiment_tag: 35_activation=SELU(inplace=True),drop_rate=0.49268,hidden_units=256,learning_rate=0.0080405,momentum=0.13624
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 65.49367088607595
node_ip: 192.168.86.61
pid: 32729
time_since_restore: 30.467445373535156
time_this_iter_s: 15.088824987411499
time_total_s: 30.467445373535156
timestamp: 1603595701
timesteps_since_restore: 0
train_accuracy: 14.869318181818182
train_loss: 2.3235799351876434
training_iteration: 2
trial_id: e5a1b_00035
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=32 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.0253164556962 | Iter 1.000: 60.53164556962025Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (24 PENDING, 2 RUNNING, 34 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00035:
date: 2020-10-25_11-15-16
done: false
experiment_id: 4ed95324b2384a92b5ce4660a0b5b324
experiment_tag: 35_activation=SELU(inplace=True),drop_rate=0.49268,hidden_units=256,learning_rate=0.0080405,momentum=0.13624
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 63.936708860759495
node_ip: 192.168.86.61
pid: 32729
time_since_restore: 45.53360319137573
time_this_iter_s: 15.066157817840576
time_total_s: 45.53360319137573
timestamp: 1603595716
timesteps_since_restore: 0
train_accuracy: 14.676136363636363
train_loss: 2.3224819018082186
training_iteration: 3
trial_id: e5a1b_00035
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=32 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 67.0253164556962 | Iter 1.000: 60.53164556962025Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (24 PENDING, 2 RUNNING, 34 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00035:
date: 2020-10-25_11-15-31
done: true
experiment_id: 4ed95324b2384a92b5ce4660a0b5b324
experiment_tag: 35_activation=SELU(inplace=True),drop_rate=0.49268,hidden_units=256,learning_rate=0.0080405,momentum=0.13624
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 64.89873417721519
node_ip: 192.168.86.61
pid: 32729
time_since_restore: 60.751296520233154
time_this_iter_s: 15.217693328857422
time_total_s: 60.751296520233154
timestamp: 1603595731
timesteps_since_restore: 0
train_accuracy: 14.792613636363637
train_loss: 2.3230981792915952
training_iteration: 4
trial_id: e5a1b_00035
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=33 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 66.49367088607595 | Iter 1.000: 60.53164556962025Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (24 PENDING, 2 RUNNING, 34 TERMINATED)
2020-10-25 11:15:31,856 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=827)[0m cuda:0
[2m[36m(pid=827)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=827)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=827)[0m Files already downloaded and verified
[2m[36m(pid=827)[0m 2020-10-25 11:16:01,838 INFO trainable.py:255 -- Trainable.setup took 29.139 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00036:
date: 2020-10-25_11-16-17
done: false
experiment_id: a9fe146fa21048f0a692005e6c4855fe
experiment_tag: 36_activation=ELU(alpha=True),drop_rate=0.29969,hidden_units=32,learning_rate=0.0075436,momentum=0.50251
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 64.24050632911393
node_ip: 192.168.86.61
pid: 827
time_since_restore: 15.364395380020142
time_this_iter_s: 15.364395380020142
time_total_s: 15.364395380020142
timestamp: 1603595777
timesteps_since_restore: 0
train_accuracy: 14.042613636363637
train_loss: 2.3286042619835245
training_iteration: 1
trial_id: e5a1b_00036
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=33 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 66.49367088607595 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (23 PENDING, 2 RUNNING, 35 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00036:
date: 2020-10-25_11-16-32
done: false
experiment_id: a9fe146fa21048f0a692005e6c4855fe
experiment_tag: 36_activation=ELU(alpha=True),drop_rate=0.29969,hidden_units=32,learning_rate=0.0075436,momentum=0.50251
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 63.822784810126585
node_ip: 192.168.86.61
pid: 827
time_since_restore: 30.578943014144897
time_this_iter_s: 15.214547634124756
time_total_s: 30.578943014144897
timestamp: 1603595792
timesteps_since_restore: 0
train_accuracy: 14.008522727272727
train_loss: 2.328404860740358
training_iteration: 2
trial_id: e5a1b_00036
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=33 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 66.49367088607595 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (23 PENDING, 2 RUNNING, 35 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00036:
date: 2020-10-25_11-16-47
done: false
experiment_id: a9fe146fa21048f0a692005e6c4855fe
experiment_tag: 36_activation=ELU(alpha=True),drop_rate=0.29969,hidden_units=32,learning_rate=0.0075436,momentum=0.50251
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 64.69620253164557
node_ip: 192.168.86.61
pid: 827
time_since_restore: 45.897640228271484
time_this_iter_s: 15.318697214126587
time_total_s: 45.897640228271484
timestamp: 1603595807
timesteps_since_restore: 0
train_accuracy: 13.974431818181818
train_loss: 2.3285267759453165
training_iteration: 3
trial_id: e5a1b_00036
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=33 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 66.49367088607595 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (23 PENDING, 2 RUNNING, 35 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00036:
date: 2020-10-25_11-17-03
done: true
experiment_id: a9fe146fa21048f0a692005e6c4855fe
experiment_tag: 36_activation=ELU(alpha=True),drop_rate=0.29969,hidden_units=32,learning_rate=0.0075436,momentum=0.50251
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 63.56962025316456
node_ip: 192.168.86.61
pid: 827
time_since_restore: 61.35237526893616
time_this_iter_s: 15.454735040664673
time_total_s: 61.35237526893616
timestamp: 1603595823
timesteps_since_restore: 0
train_accuracy: 14.221590909090908
train_loss: 2.3292572261257605
training_iteration: 4
trial_id: e5a1b_00036
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=34 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (23 PENDING, 2 RUNNING, 35 TERMINATED)
2020-10-25 11:17:03,524 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
[2m[36m(pid=1521)[0m cuda:0
[2m[36m(pid=1521)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=1521)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=1521)[0m Files already downloaded and verified
[2m[36m(pid=1521)[0m 2020-10-25 11:17:36,416 INFO trainable.py:255 -- Trainable.setup took 32.009 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00037:
date: 2020-10-25_11-17-51
done: true
experiment_id: 92ddf674de2a4661a2149524dc5c253b
experiment_tag: 37_activation=SELU(inplace=True),drop_rate=0.68519,hidden_units=64,learning_rate=0.0094641,momentum=0.23035
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 59.63291139240506
node_ip: 192.168.86.61
pid: 1521
time_since_restore: 15.339030504226685
time_this_iter_s: 15.339030504226685
time_total_s: 15.339030504226685
timestamp: 1603595871
timesteps_since_restore: 0
train_accuracy: 13.306818181818182
train_loss: 2.336575789207762
training_iteration: 1
trial_id: e5a1b_00037
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=35 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.01265822784811Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (22 PENDING, 2 RUNNING, 36 TERMINATED)
2020-10-25 11:17:51,922 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=1751)[0m cuda:0
[2m[36m(pid=1751)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=1751)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=1751)[0m Files already downloaded and verified
[2m[36m(pid=1751)[0m 2020-10-25 11:18:25,853 INFO trainable.py:255 -- Trainable.setup took 33.087 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00038:
date: 2020-10-25_11-18-41
done: true
experiment_id: e357ffcc18cc4cefa2326c94f648b071
experiment_tag: 38_activation=ReLU(inplace=True),drop_rate=0.056455,hidden_units=128,learning_rate=0.0084578,momentum=0.12121
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 55.278481012658226
node_ip: 192.168.86.61
pid: 1751
time_since_restore: 15.264434576034546
time_this_iter_s: 15.264434576034546
time_total_s: 15.264434576034546
timestamp: 1603595921
timesteps_since_restore: 0
train_accuracy: 12.207386363636363
train_loss: 2.321347370066426
training_iteration: 1
trial_id: e5a1b_00038
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=36 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 60.77215189873418Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (21 PENDING, 2 RUNNING, 37 TERMINATED)
2020-10-25 11:18:41,283 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s])[0m
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[2m[36m(pid=1950)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Result for PyTorchCIFAR10Trainable_e5a1b_00032:
date: 2020-10-25_11-19-07
done: false
experiment_id: 3d136453508644ff85ae181e3d6dc16f
experiment_tag: 32_activation=ReLU(inplace=True),drop_rate=0.68437,hidden_units=32,learning_rate=0.012911,momentum=0.47934
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 64.16455696202532
node_ip: 192.168.86.61
pid: 31824
time_since_restore: 449.1656758785248
time_this_iter_s: 449.1656758785248
time_total_s: 449.1656758785248
timestamp: 1603595947
timesteps_since_restore: 0
train_accuracy: 14.485795454545455
train_loss: 2.3142691654237835
training_iteration: 1
trial_id: e5a1b_00032
== Status ==Memory usage on this node: 4.1/125.8 GiBUsing AsyncHyperBand: num_stopped=36 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.61075949367089Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (20 PENDING, 2 RUNNING, 38 TERMINATED)
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170500096it [00:28, 6087788.77it/s]
[2m[36m(pid=1950)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=1950)[0m Files already downloaded and verified
[2m[36m(pid=1950)[0m 2020-10-25 11:19:14,672 INFO trainable.py:255 -- Trainable.setup took 32.495 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00039:
date: 2020-10-25_11-19-30
done: true
experiment_id: 55dadccb7f934be898b64d026fdf6c10
experiment_tag: 39_activation=SELU(inplace=True),drop_rate=0.46862,hidden_units=256,learning_rate=0.066175,momentum=0.56038
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 54.10126582278481
node_ip: 192.168.86.61
pid: 1950
time_since_restore: 15.398482084274292
time_this_iter_s: 15.398482084274292
time_total_s: 15.398482084274292
timestamp: 1603595970
timesteps_since_restore: 0
train_accuracy: 12.224431818181818
train_loss: 2.3473578576337206
training_iteration: 1
trial_id: e5a1b_00039
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=37 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (20 PENDING, 2 RUNNING, 38 TERMINATED)
2020-10-25 11:19:30,240 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=2324)[0m cuda:0
[2m[36m(pid=2324)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=2324)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=2324)[0m Files already downloaded and verified
[2m[36m(pid=2324)[0m 2020-10-25 11:20:06,362 INFO trainable.py:255 -- Trainable.setup took 35.236 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00040:
date: 2020-10-25_11-20-21
done: false
experiment_id: d7d469f5f4a04481b7fc57577ee95556
experiment_tag: 40_activation=ELU(alpha=True),drop_rate=0.31054,hidden_units=32,learning_rate=0.0085087,momentum=0.4666
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 62.40506329113924
node_ip: 192.168.86.61
pid: 2324
time_since_restore: 15.320078611373901
time_this_iter_s: 15.320078611373901
time_total_s: 15.320078611373901
timestamp: 1603596021
timesteps_since_restore: 0
train_accuracy: 14.056818181818182
train_loss: 2.3494861742312256
training_iteration: 1
trial_id: e5a1b_00040
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=37 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 62.11708860759494Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (19 PENDING, 2 RUNNING, 39 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00040:
date: 2020-10-25_11-20-36
done: false
experiment_id: d7d469f5f4a04481b7fc57577ee95556
experiment_tag: 40_activation=ELU(alpha=True),drop_rate=0.31054,hidden_units=32,learning_rate=0.0085087,momentum=0.4666
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 63.050632911392405
node_ip: 192.168.86.61
pid: 2324
time_since_restore: 30.48308777809143
time_this_iter_s: 15.16300916671753
time_total_s: 30.48308777809143
timestamp: 1603596036
timesteps_since_restore: 0
train_accuracy: 14.258522727272727
train_loss: 2.3485737050121482
training_iteration: 2
trial_id: e5a1b_00040
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=37 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 62.11708860759494Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (19 PENDING, 2 RUNNING, 39 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00040:
date: 2020-10-25_11-20-52
done: false
experiment_id: d7d469f5f4a04481b7fc57577ee95556
experiment_tag: 40_activation=ELU(alpha=True),drop_rate=0.31054,hidden_units=32,learning_rate=0.0085087,momentum=0.4666
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 63.12658227848101
node_ip: 192.168.86.61
pid: 2324
time_since_restore: 45.70472288131714
time_this_iter_s: 15.221635103225708
time_total_s: 45.70472288131714
timestamp: 1603596052
timesteps_since_restore: 0
train_accuracy: 14.173295454545455
train_loss: 2.3492661409757356
training_iteration: 3
trial_id: e5a1b_00040
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=37 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 62.11708860759494Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (19 PENDING, 2 RUNNING, 39 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00040:
date: 2020-10-25_11-21-07
done: true
experiment_id: d7d469f5f4a04481b7fc57577ee95556
experiment_tag: 40_activation=ELU(alpha=True),drop_rate=0.31054,hidden_units=32,learning_rate=0.0085087,momentum=0.4666
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 62.835443037974684
node_ip: 192.168.86.61
pid: 2324
time_since_restore: 60.96567940711975
time_this_iter_s: 15.260956525802612
time_total_s: 60.96567940711975
timestamp: 1603596067
timesteps_since_restore: 0
train_accuracy: 14.042613636363637
train_loss: 2.3500918116081846
training_iteration: 4
trial_id: e5a1b_00040
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=38 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 62.11708860759494Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (19 PENDING, 2 RUNNING, 39 TERMINATED)
2020-10-25 11:21:07,677 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
[2m[36m(pid=2865)[0m cuda:0
[2m[36m(pid=2865)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=2865)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=2865)[0m Files already downloaded and verified
[2m[36m(pid=2865)[0m 2020-10-25 11:21:34,275 INFO trainable.py:255 -- Trainable.setup took 25.727 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00041:
date: 2020-10-25_11-21-49
done: true
experiment_id: aa33ecd3a8e745148474fca281edd365
experiment_tag: 41_activation=ELU(alpha=True),drop_rate=0.43649,hidden_units=64,learning_rate=0.066741,momentum=0.40888
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 54.49367088607595
node_ip: 192.168.86.61
pid: 2865
time_since_restore: 15.353536367416382
time_this_iter_s: 15.353536367416382
time_total_s: 15.353536367416382
timestamp: 1603596109
timesteps_since_restore: 0
train_accuracy: 12.488636363636363
train_loss: 2.3337941908023576
training_iteration: 1
trial_id: e5a1b_00041
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=39 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.82911392405063Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (18 PENDING, 2 RUNNING, 40 TERMINATED)
2020-10-25 11:21:49,791 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=3058)[0m cuda:0
[2m[36m(pid=3058)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=3058)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=3058)[0m Files already downloaded and verified
[2m[36m(pid=3058)[0m 2020-10-25 11:22:19,984 INFO trainable.py:255 -- Trainable.setup took 29.312 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00042:
date: 2020-10-25_11-22-35
done: true
experiment_id: c67d4d0072a54c4483af55b8c395ccbe
experiment_tag: 42_activation=ReLU(inplace=True),drop_rate=0.76895,hidden_units=128,learning_rate=0.052006,momentum=0.25663
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.75949367088607
node_ip: 192.168.86.61
pid: 3058
time_since_restore: 15.402255535125732
time_this_iter_s: 15.402255535125732
time_total_s: 15.402255535125732
timestamp: 1603596155
timesteps_since_restore: 0
train_accuracy: 12.625
train_loss: 2.3198237622326072
training_iteration: 1
trial_id: e5a1b_00042
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=40 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.54113924050633Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (17 PENDING, 2 RUNNING, 41 TERMINATED)
2020-10-25 11:22:35,551 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=3335)[0m cuda:0
[2m[36m(pid=3335)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=3335)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=3335)[0m Files already downloaded and verified
[2m[36m(pid=3335)[0m 2020-10-25 11:23:08,118 INFO trainable.py:255 -- Trainable.setup took 31.717 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00043:
date: 2020-10-25_11-23-23
done: true
experiment_id: 235a76238437413cb9a46eeb595e4351
experiment_tag: 43_activation=ReLU(inplace=True),drop_rate=0.055489,hidden_units=256,learning_rate=0.0002006,momentum=0.11458
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 52.89873417721519
node_ip: 192.168.86.61
pid: 3335
time_since_restore: 15.372928142547607
time_this_iter_s: 15.372928142547607
time_total_s: 15.372928142547607
timestamp: 1603596203
timesteps_since_restore: 0
train_accuracy: 12.164772727272727
train_loss: 2.319461860439994
training_iteration: 1
trial_id: e5a1b_00043
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=41 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (16 PENDING, 2 RUNNING, 42 TERMINATED)
2020-10-25 11:23:23,659 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=3513)[0m cuda:0
[2m[36m(pid=3513)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=3513)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=3513)[0m Files already downloaded and verified
[2m[36m(pid=3513)[0m 2020-10-25 11:23:56,588 INFO trainable.py:255 -- Trainable.setup took 32.037 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00044:
date: 2020-10-25_11-24-11
done: true
experiment_id: 47e59082a11c40ad8ad60f56438812d6
experiment_tag: 44_activation=ReLU(inplace=True),drop_rate=0.075554,hidden_units=32,learning_rate=0.011195,momentum=0.15695
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 59.49367088607595
node_ip: 192.168.86.61
pid: 3513
time_since_restore: 15.235009670257568
time_this_iter_s: 15.235009670257568
time_total_s: 15.235009670257568
timestamp: 1603596251
timesteps_since_restore: 0
train_accuracy: 13.451704545454545
train_loss: 2.333432478660887
training_iteration: 1
trial_id: e5a1b_00044
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=42 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.01265822784811Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (15 PENDING, 2 RUNNING, 43 TERMINATED)
2020-10-25 11:24:11,992 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=3742)[0m cuda:0
[2m[36m(pid=3742)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=3742)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=3742)[0m Files already downloaded and verified
[2m[36m(pid=3742)[0m 2020-10-25 11:24:41,615 INFO trainable.py:255 -- Trainable.setup took 28.738 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00045:
date: 2020-10-25_11-24-56
done: true
experiment_id: 6d12fe1f30d340a585fde23c8459514a
experiment_tag: 45_activation=SELU(inplace=True),drop_rate=0.25518,hidden_units=64,learning_rate=0.034247,momentum=0.11862
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 49.151898734177216
node_ip: 192.168.86.61
pid: 3742
time_since_restore: 15.288264751434326
time_this_iter_s: 15.288264751434326
time_total_s: 15.288264751434326
timestamp: 1603596296
timesteps_since_restore: 0
train_accuracy: 11.255681818181818
train_loss: 2.354644461111589
training_iteration: 1
trial_id: e5a1b_00045
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=43 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 60.77215189873418Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (14 PENDING, 2 RUNNING, 44 TERMINATED)
2020-10-25 11:24:57,070 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=3922)[0m cuda:0
[2m[36m(pid=3922)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=3922)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=3922)[0m Files already downloaded and verified
[2m[36m(pid=3922)[0m 2020-10-25 11:25:28,927 INFO trainable.py:255 -- Trainable.setup took 30.949 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00046:
date: 2020-10-25_11-25-44
done: true
experiment_id: f5c67072c5ee46e0a1e11c679df6a652
experiment_tag: 46_activation=SELU(inplace=True),drop_rate=0.65157,hidden_units=128,learning_rate=0.00070075,momentum=0.19453
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 52.177215189873415
node_ip: 192.168.86.61
pid: 3922
time_since_restore: 15.470829725265503
time_this_iter_s: 15.470829725265503
time_total_s: 15.470829725265503
timestamp: 1603596344
timesteps_since_restore: 0
train_accuracy: 11.417613636363637
train_loss: 2.3873349157246677
training_iteration: 1
trial_id: e5a1b_00046
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=44 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 60.53164556962025Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (13 PENDING, 2 RUNNING, 45 TERMINATED)
2020-10-25 11:25:44,575 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=4101)[0m cuda:0
[2m[36m(pid=4101)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=4101)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=4101)[0m Files already downloaded and verified
[2m[36m(pid=4101)[0m 2020-10-25 11:26:14,141 INFO trainable.py:255 -- Trainable.setup took 28.709 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00047:
date: 2020-10-25_11-26-29
done: true
experiment_id: 2d63512806fa41f985fa090e774a3a99
experiment_tag: 47_activation=ELU(alpha=True),drop_rate=0.55739,hidden_units=256,learning_rate=0.007706,momentum=0.80198
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.936708860759495
node_ip: 192.168.86.61
pid: 4101
time_since_restore: 15.38187313079834
time_this_iter_s: 15.38187313079834
time_total_s: 15.38187313079834
timestamp: 1603596389
timesteps_since_restore: 0
train_accuracy: 12.784090909090908
train_loss: 2.3188470148227434
training_iteration: 1
trial_id: e5a1b_00047
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=45 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 60.29113924050633Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (12 PENDING, 2 RUNNING, 46 TERMINATED)
2020-10-25 11:26:29,694 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
[2m[36m(pid=4277)[0m cuda:0
[2m[36m(pid=4277)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Result for PyTorchCIFAR10Trainable_e5a1b_00032:
date: 2020-10-25_11-26-40
done: false
experiment_id: 3d136453508644ff85ae181e3d6dc16f
experiment_tag: 32_activation=ReLU(inplace=True),drop_rate=0.68437,hidden_units=32,learning_rate=0.012911,momentum=0.47934
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 63.58227848101266
node_ip: 192.168.86.61
pid: 31824
time_since_restore: 902.1572785377502
time_this_iter_s: 452.99160265922546
time_total_s: 902.1572785377502
timestamp: 1603596400
timesteps_since_restore: 0
train_accuracy: 14.264204545454545
train_loss: 2.3141327649354935
training_iteration: 2
trial_id: e5a1b_00032
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== Status ==Memory usage on this node: 4.2/125.8 GiBUsing AsyncHyperBand: num_stopped=45 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 60.29113924050633Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (11 PENDING, 2 RUNNING, 47 TERMINATED)
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170500096it [00:25, 6696556.58it/s]
[2m[36m(pid=4277)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=4277)[0m Files already downloaded and verified
[2m[36m(pid=4277)[0m 2020-10-25 11:27:00,490 INFO trainable.py:255 -- Trainable.setup took 29.917 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00048:
date: 2020-10-25_11-27-15
done: false
experiment_id: cfe4d1ea110e4b68a7d8affb1e8d0fde
experiment_tag: 48_activation=SELU(inplace=True),drop_rate=0.58806,hidden_units=32,learning_rate=0.02573,momentum=0.32563
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 62.20253164556962
node_ip: 192.168.86.61
pid: 4277
time_since_restore: 15.275273084640503
time_this_iter_s: 15.275273084640503
time_total_s: 15.275273084640503
timestamp: 1603596435
timesteps_since_restore: 0
train_accuracy: 13.681818181818182
train_loss: 2.3367935595187275
training_iteration: 1
trial_id: e5a1b_00048
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=45 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.01265822784811Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (11 PENDING, 2 RUNNING, 47 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00048:
date: 2020-10-25_11-27-30
done: false
experiment_id: cfe4d1ea110e4b68a7d8affb1e8d0fde
experiment_tag: 48_activation=SELU(inplace=True),drop_rate=0.58806,hidden_units=32,learning_rate=0.02573,momentum=0.32563
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 60.835443037974684
node_ip: 192.168.86.61
pid: 4277
time_since_restore: 30.344457626342773
time_this_iter_s: 15.06918454170227
time_total_s: 30.344457626342773
timestamp: 1603596450
timesteps_since_restore: 0
train_accuracy: 13.622159090909092
train_loss: 2.336757781830701
training_iteration: 2
trial_id: e5a1b_00048
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=45 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.01265822784811Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (11 PENDING, 2 RUNNING, 47 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00048:
date: 2020-10-25_11-27-45
done: false
experiment_id: cfe4d1ea110e4b68a7d8affb1e8d0fde
experiment_tag: 48_activation=SELU(inplace=True),drop_rate=0.58806,hidden_units=32,learning_rate=0.02573,momentum=0.32563
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 60.31645569620253
node_ip: 192.168.86.61
pid: 4277
time_since_restore: 45.48258829116821
time_this_iter_s: 15.13813066482544
time_total_s: 45.48258829116821
timestamp: 1603596465
timesteps_since_restore: 0
train_accuracy: 13.769886363636363
train_loss: 2.336479604244232
training_iteration: 3
trial_id: e5a1b_00048
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=45 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.01265822784811Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (11 PENDING, 2 RUNNING, 47 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00048:
date: 2020-10-25_11-28-01
done: true
experiment_id: cfe4d1ea110e4b68a7d8affb1e8d0fde
experiment_tag: 48_activation=SELU(inplace=True),drop_rate=0.58806,hidden_units=32,learning_rate=0.02573,momentum=0.32563
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 60.9746835443038
node_ip: 192.168.86.61
pid: 4277
time_since_restore: 60.610564947128296
time_this_iter_s: 15.127976655960083
time_total_s: 60.610564947128296
timestamp: 1603596481
timesteps_since_restore: 0
train_accuracy: 13.505681818181818
train_loss: 2.3363785032521593
training_iteration: 4
trial_id: e5a1b_00048
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=46 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.89873417721519 | Iter 1.000: 61.01265822784811Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (11 PENDING, 2 RUNNING, 47 TERMINATED)
2020-10-25 11:28:01,434 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=4846)[0m cuda:0
[2m[36m(pid=4846)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=4846)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=4846)[0m Files already downloaded and verified
[2m[36m(pid=4846)[0m 2020-10-25 11:28:33,750 INFO trainable.py:255 -- Trainable.setup took 31.464 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00049:
date: 2020-10-25_11-28-49
done: true
experiment_id: 994ba9a7266845a9a634dd2cb68bcae7
experiment_tag: 49_activation=ELU(alpha=True),drop_rate=0.14195,hidden_units=64,learning_rate=0.017858,momentum=0.74547
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 53.177215189873415
node_ip: 192.168.86.61
pid: 4846
time_since_restore: 15.366276741027832
time_this_iter_s: 15.366276741027832
time_total_s: 15.366276741027832
timestamp: 1603596529
timesteps_since_restore: 0
train_accuracy: 11.980113636363637
train_loss: 2.329791778867895
training_iteration: 1
trial_id: e5a1b_00049
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=47 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.89873417721519 | Iter 1.000: 60.77215189873418Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (10 PENDING, 2 RUNNING, 48 TERMINATED)
2020-10-25 11:28:49,285 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s])[0m
[2m[36m(pid=5024)[0m cuda:0
[2m[36m(pid=5024)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=5024)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=5024)[0m Files already downloaded and verified
[2m[36m(pid=5024)[0m 2020-10-25 11:29:22,678 INFO trainable.py:255 -- Trainable.setup took 32.529 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00050:
date: 2020-10-25_11-29-37
done: false
experiment_id: 8f3f21a85cec4e528f10450d7c1c88e6
experiment_tag: 50_activation=ELU(alpha=True),drop_rate=0.7924,hidden_units=128,learning_rate=0.0017292,momentum=0.39761
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 62.607594936708864
node_ip: 192.168.86.61
pid: 5024
time_since_restore: 15.289008140563965
time_this_iter_s: 15.289008140563965
time_total_s: 15.289008140563965
timestamp: 1603596577
timesteps_since_restore: 0
train_accuracy: 14.036931818181818
train_loss: 2.339764364741065
training_iteration: 1
trial_id: e5a1b_00050
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=47 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.89873417721519 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (9 PENDING, 2 RUNNING, 49 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00050:
date: 2020-10-25_11-29-53
done: false
experiment_id: 8f3f21a85cec4e528f10450d7c1c88e6
experiment_tag: 50_activation=ELU(alpha=True),drop_rate=0.7924,hidden_units=128,learning_rate=0.0017292,momentum=0.39761
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 62.139240506329116
node_ip: 192.168.86.61
pid: 5024
time_since_restore: 30.384215116500854
time_this_iter_s: 15.09520697593689
time_total_s: 30.384215116500854
timestamp: 1603596593
timesteps_since_restore: 0
train_accuracy: 13.860795454545455
train_loss: 2.3399290530519052
training_iteration: 2
trial_id: e5a1b_00050
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=47 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.89873417721519 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (9 PENDING, 2 RUNNING, 49 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00050:
date: 2020-10-25_11-30-08
done: false
experiment_id: 8f3f21a85cec4e528f10450d7c1c88e6
experiment_tag: 50_activation=ELU(alpha=True),drop_rate=0.7924,hidden_units=128,learning_rate=0.0017292,momentum=0.39761
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 61.75949367088607
node_ip: 192.168.86.61
pid: 5024
time_since_restore: 45.4997661113739
time_this_iter_s: 15.115550994873047
time_total_s: 45.4997661113739
timestamp: 1603596608
timesteps_since_restore: 0
train_accuracy: 13.784090909090908
train_loss: 2.339650559154424
training_iteration: 3
trial_id: e5a1b_00050
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=47 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.89873417721519 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (9 PENDING, 2 RUNNING, 49 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00050:
date: 2020-10-25_11-30-23
done: true
experiment_id: 8f3f21a85cec4e528f10450d7c1c88e6
experiment_tag: 50_activation=ELU(alpha=True),drop_rate=0.7924,hidden_units=128,learning_rate=0.0017292,momentum=0.39761
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 61.89873417721519
node_ip: 192.168.86.61
pid: 5024
time_since_restore: 60.733569622039795
time_this_iter_s: 15.233803510665894
time_total_s: 60.733569622039795
timestamp: 1603596623
timesteps_since_restore: 0
train_accuracy: 13.846590909090908
train_loss: 2.339522474868731
training_iteration: 4
trial_id: e5a1b_00050
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=48 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (9 PENDING, 2 RUNNING, 49 TERMINATED)
2020-10-25 11:30:23,729 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s])[0m
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[2m[36m(pid=5548)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=5548)[0m Files already downloaded and verified
[2m[36m(pid=5548)[0m 2020-10-25 11:30:57,063 INFO trainable.py:255 -- Trainable.setup took 32.464 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00051:
date: 2020-10-25_11-31-12
done: true
experiment_id: 63f19552896048fab59ba8af854d4556
experiment_tag: 51_activation=ReLU(inplace=True),drop_rate=0.62113,hidden_units=256,learning_rate=0.001053,momentum=0.84461
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 61.075949367088604
node_ip: 192.168.86.61
pid: 5548
time_since_restore: 15.326761960983276
time_this_iter_s: 15.326761960983276
time_total_s: 15.326761960983276
timestamp: 1603596672
timesteps_since_restore: 0
train_accuracy: 13.568181818181818
train_loss: 2.3254926313053477
training_iteration: 1
trial_id: e5a1b_00051
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=49 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (8 PENDING, 2 RUNNING, 50 TERMINATED)
2020-10-25 11:31:12,553 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=5776)[0m cuda:0
[2m[36m(pid=5776)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=5776)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=5776)[0m Files already downloaded and verified
[2m[36m(pid=5776)[0m 2020-10-25 11:31:41,559 INFO trainable.py:255 -- Trainable.setup took 28.119 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00052:
date: 2020-10-25_11-31-56
done: true
experiment_id: 19c4501ddbe246e991307838b2d1808b
experiment_tag: 52_activation=SELU(inplace=True),drop_rate=0.68673,hidden_units=32,learning_rate=0.0019363,momentum=0.7007
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.40506329113924
node_ip: 192.168.86.61
pid: 5776
time_since_restore: 15.328942775726318
time_this_iter_s: 15.328942775726318
time_total_s: 15.328942775726318
timestamp: 1603596716
timesteps_since_restore: 0
train_accuracy: 12.670454545454545
train_loss: 2.3795744519342077
training_iteration: 1
trial_id: e5a1b_00052
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=50 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.208860759493675Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (7 PENDING, 2 RUNNING, 51 TERMINATED)
2020-10-25 11:31:57,053 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': SELU(inplace=True)}
[2m[36m(pid=5952)[0m cuda:0
[2m[36m(pid=5952)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=5952)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=5952)[0m Files already downloaded and verified
[2m[36m(pid=5952)[0m 2020-10-25 11:32:28,131 INFO trainable.py:255 -- Trainable.setup took 30.225 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00053:
date: 2020-10-25_11-32-43
done: true
experiment_id: 8601865fa2504ddbbf0fb37985df9bc6
experiment_tag: 53_activation=ELU(alpha=True),drop_rate=0.60363,hidden_units=64,learning_rate=0.00020388,momentum=0.82204
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 58.0126582278481
node_ip: 192.168.86.61
pid: 5952
time_since_restore: 15.249919414520264
time_this_iter_s: 15.249919414520264
time_total_s: 15.249919414520264
timestamp: 1603596763
timesteps_since_restore: 0
train_accuracy: 13.122159090909092
train_loss: 2.3222454793073912
training_iteration: 1
trial_id: e5a1b_00053
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.164556962025316Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (6 PENDING, 2 RUNNING, 52 TERMINATED)
2020-10-25 11:32:43,544 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
[2m[36m(pid=6141)[0m cuda:0
[2m[36m(pid=6141)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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170500096it [00:23, 7236653.96it/s]
[2m[36m(pid=6141)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=6141)[0m Files already downloaded and verified
[2m[36m(pid=6141)[0m 2020-10-25 11:33:12,441 INFO trainable.py:255 -- Trainable.setup took 28.015 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-33-27
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 70.12658227848101
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 15.454138994216919
time_this_iter_s: 15.454138994216919
time_total_s: 15.454138994216919
timestamp: 1603596807
timesteps_since_restore: 0
train_accuracy: 15.786931818181818
train_loss: 2.348995240574533
training_iteration: 1
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-33-43
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 2
mean_accuracy: 71.32911392405063
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 30.644089221954346
time_this_iter_s: 15.189950227737427
time_total_s: 30.644089221954346
timestamp: 1603596823
timesteps_since_restore: 0
train_accuracy: 15.732954545454545
train_loss: 2.3483460559086367
training_iteration: 2
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-33-58
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 71.0126582278481
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 45.98223614692688
time_this_iter_s: 15.338146924972534
time_total_s: 45.98223614692688
timestamp: 1603596838
timesteps_since_restore: 0
train_accuracy: 15.784090909090908
train_loss: 2.349428227679296
training_iteration: 3
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00032:
date: 2020-10-25_11-34-11
done: false
experiment_id: 3d136453508644ff85ae181e3d6dc16f
experiment_tag: 32_activation=ReLU(inplace=True),drop_rate=0.68437,hidden_units=32,learning_rate=0.012911,momentum=0.47934
hostname: ironman
iterations_since_restore: 3
mean_accuracy: 64.16455696202532
node_ip: 192.168.86.61
pid: 31824
time_since_restore: 1353.9875626564026
time_this_iter_s: 451.83028411865234
time_total_s: 1353.9875626564026
timestamp: 1603596851
timesteps_since_restore: 0
train_accuracy: 14.178977272727273
train_loss: 2.3142609142444353
training_iteration: 3
trial_id: e5a1b_00032
== Status ==Memory usage on this node: 5.7/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 64.84177215189874 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-34-13
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 70.18987341772151
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 61.25513195991516
time_this_iter_s: 15.272895812988281
time_total_s: 61.25513195991516
timestamp: 1603596853
timesteps_since_restore: 0
train_accuracy: 15.877840909090908
train_loss: 2.3480719741095197
training_iteration: 4
trial_id: e5a1b_00054
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-34-29
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 5
mean_accuracy: 69.89873417721519
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 76.58313202857971
time_this_iter_s: 15.32800006866455
time_total_s: 76.58313202857971
timestamp: 1603596869
timesteps_since_restore: 0
train_accuracy: 15.678977272727273
train_loss: 2.3491730723868716
training_iteration: 5
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-34-44
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 6
mean_accuracy: 70.0126582278481
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 91.89777994155884
time_this_iter_s: 15.314647912979126
time_total_s: 91.89777994155884
timestamp: 1603596884
timesteps_since_restore: 0
train_accuracy: 15.857954545454545
train_loss: 2.348559120161967
training_iteration: 6
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-34-59
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 7
mean_accuracy: 70.40506329113924
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 107.1687216758728
time_this_iter_s: 15.270941734313965
time_total_s: 107.1687216758728
timestamp: 1603596899
timesteps_since_restore: 0
train_accuracy: 15.681818181818182
train_loss: 2.3493026664311234
training_iteration: 7
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-35-15
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 8
mean_accuracy: 70.24050632911393
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 122.50920104980469
time_this_iter_s: 15.340479373931885
time_total_s: 122.50920104980469
timestamp: 1603596915
timesteps_since_restore: 0
train_accuracy: 15.852272727272727
train_loss: 2.3481288904493507
training_iteration: 8
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-35-30
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 9
mean_accuracy: 71.62025316455696
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 137.92647075653076
time_this_iter_s: 15.417269706726074
time_total_s: 137.92647075653076
timestamp: 1603596930
timesteps_since_restore: 0
train_accuracy: 15.75
train_loss: 2.3482577644965867
training_iteration: 9
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-35-46
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 10
mean_accuracy: 70.59493670886076
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 153.26227355003357
time_this_iter_s: 15.335802793502808
time_total_s: 153.26227355003357
timestamp: 1603596946
timesteps_since_restore: 0
train_accuracy: 15.920454545454545
train_loss: 2.3480559892275115
training_iteration: 10
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-36-01
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 11
mean_accuracy: 70.74683544303798
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 168.48023676872253
time_this_iter_s: 15.217963218688965
time_total_s: 168.48023676872253
timestamp: 1603596961
timesteps_since_restore: 0
train_accuracy: 15.650568181818182
train_loss: 2.3479021821509707
training_iteration: 11
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-36-16
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 12
mean_accuracy: 69.9113924050633
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 183.77422642707825
time_this_iter_s: 15.293989658355713
time_total_s: 183.77422642707825
timestamp: 1603596976
timesteps_since_restore: 0
train_accuracy: 15.826704545454545
train_loss: 2.348308779299259
training_iteration: 12
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-36-32
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 13
mean_accuracy: 70.44303797468355
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 199.15598607063293
time_this_iter_s: 15.381759643554688
time_total_s: 199.15598607063293
timestamp: 1603596992
timesteps_since_restore: 0
train_accuracy: 15.826704545454545
train_loss: 2.3487615930763157
training_iteration: 13
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-36-47
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 14
mean_accuracy: 69.73417721518987
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 214.51218819618225
time_this_iter_s: 15.356202125549316
time_total_s: 214.51218819618225
timestamp: 1603597007
timesteps_since_restore: 0
train_accuracy: 15.860795454545455
train_loss: 2.3492246974598276
training_iteration: 14
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-37-03
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 15
mean_accuracy: 70.50632911392405
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 229.92751622200012
time_this_iter_s: 15.415328025817871
time_total_s: 229.92751622200012
timestamp: 1603597023
timesteps_since_restore: 0
train_accuracy: 15.786931818181818
train_loss: 2.3481628495183857
training_iteration: 15
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 67.96518987341773 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-37-18
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 16
mean_accuracy: 70.63291139240506
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 245.18981957435608
time_this_iter_s: 15.262303352355957
time_total_s: 245.18981957435608
timestamp: 1603597038
timesteps_since_restore: 0
train_accuracy: 15.863636363636363
train_loss: 2.3491013835776937
training_iteration: 16
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-37-33
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 17
mean_accuracy: 70.82278481012658
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 260.487988948822
time_this_iter_s: 15.298169374465942
time_total_s: 260.487988948822
timestamp: 1603597053
timesteps_since_restore: 0
train_accuracy: 15.761363636363637
train_loss: 2.347957956519994
training_iteration: 17
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-37-49
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 18
mean_accuracy: 70.63291139240506
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 275.7731761932373
time_this_iter_s: 15.285187244415283
time_total_s: 275.7731761932373
timestamp: 1603597069
timesteps_since_restore: 0
train_accuracy: 15.741477272727273
train_loss: 2.3486475836146963
training_iteration: 18
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-38-04
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 19
mean_accuracy: 70.43037974683544
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 291.06753516197205
time_this_iter_s: 15.294358968734741
time_total_s: 291.06753516197205
timestamp: 1603597084
timesteps_since_restore: 0
train_accuracy: 15.738636363636363
train_loss: 2.3489335308020767
training_iteration: 19
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-38-19
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 20
mean_accuracy: 70.15189873417721
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 306.30996584892273
time_this_iter_s: 15.242430686950684
time_total_s: 306.30996584892273
timestamp: 1603597099
timesteps_since_restore: 0
train_accuracy: 15.744318181818182
train_loss: 2.348619170486927
training_iteration: 20
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-38-35
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 21
mean_accuracy: 70.41772151898734
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 321.5515727996826
time_this_iter_s: 15.241606950759888
time_total_s: 321.5515727996826
timestamp: 1603597115
timesteps_since_restore: 0
train_accuracy: 15.769886363636363
train_loss: 2.347588947550817
training_iteration: 21
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-38-50
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 22
mean_accuracy: 70.17721518987342
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 337.0038492679596
time_this_iter_s: 15.452276468276978
time_total_s: 337.0038492679596
timestamp: 1603597130
timesteps_since_restore: 0
train_accuracy: 15.661931818181818
train_loss: 2.348400607027791
training_iteration: 22
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-39-05
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 23
mean_accuracy: 70.54430379746836
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 352.3274209499359
time_this_iter_s: 15.323571681976318
time_total_s: 352.3274209499359
timestamp: 1603597145
timesteps_since_restore: 0
train_accuracy: 15.877840909090908
train_loss: 2.348119370639324
training_iteration: 23
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-39-21
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 24
mean_accuracy: 70.65822784810126
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 367.73127484321594
time_this_iter_s: 15.40385389328003
time_total_s: 367.73127484321594
timestamp: 1603597161
timesteps_since_restore: 0
train_accuracy: 15.948863636363637
train_loss: 2.3480663401159374
training_iteration: 24
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-39-36
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 25
mean_accuracy: 70.11392405063292
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 383.00594425201416
time_this_iter_s: 15.274669408798218
time_total_s: 383.00594425201416
timestamp: 1603597176
timesteps_since_restore: 0
train_accuracy: 15.758522727272727
train_loss: 2.3491804518482904
training_iteration: 25
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-39-52
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 26
mean_accuracy: 70.86075949367088
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 398.3255150318146
time_this_iter_s: 15.319570779800415
time_total_s: 398.3255150318146
timestamp: 1603597192
timesteps_since_restore: 0
train_accuracy: 15.6875
train_loss: 2.348491658541289
training_iteration: 26
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-40-07
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 27
mean_accuracy: 70.63291139240506
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 413.68149185180664
time_this_iter_s: 15.355976819992065
time_total_s: 413.68149185180664
timestamp: 1603597207
timesteps_since_restore: 0
train_accuracy: 15.727272727272727
train_loss: 2.348385225642811
training_iteration: 27
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-40-23
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 28
mean_accuracy: 71.0
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 429.02853441238403
time_this_iter_s: 15.347042560577393
time_total_s: 429.02853441238403
timestamp: 1603597223
timesteps_since_restore: 0
train_accuracy: 15.639204545454545
train_loss: 2.347783440216021
training_iteration: 28
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-40-38
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 29
mean_accuracy: 70.37974683544304
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 444.3010022640228
time_this_iter_s: 15.272467851638794
time_total_s: 444.3010022640228
timestamp: 1603597238
timesteps_since_restore: 0
train_accuracy: 15.704545454545455
train_loss: 2.3477688167582857
training_iteration: 29
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-40-53
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 30
mean_accuracy: 71.0126582278481
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 459.6955289840698
time_this_iter_s: 15.394526720046997
time_total_s: 459.6955289840698
timestamp: 1603597253
timesteps_since_restore: 0
train_accuracy: 15.806818181818182
train_loss: 2.3486067727208138
training_iteration: 30
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-41-09
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 31
mean_accuracy: 69.59493670886076
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 474.99991941452026
time_this_iter_s: 15.30439043045044
time_total_s: 474.99991941452026
timestamp: 1603597269
timesteps_since_restore: 0
train_accuracy: 15.798295454545455
train_loss: 2.348618857562542
training_iteration: 31
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-41-24
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 32
mean_accuracy: 71.79746835443038
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 490.34077882766724
time_this_iter_s: 15.340859413146973
time_total_s: 490.34077882766724
timestamp: 1603597284
timesteps_since_restore: 0
train_accuracy: 15.75
train_loss: 2.3488333279436286
training_iteration: 32
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-41-39
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 33
mean_accuracy: 69.50632911392405
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 505.76656889915466
time_this_iter_s: 15.425790071487427
time_total_s: 505.76656889915466
timestamp: 1603597299
timesteps_since_restore: 0
train_accuracy: 15.772727272727273
train_loss: 2.3479002219709484
training_iteration: 33
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=51 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.9620253164557 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00032:
date: 2020-10-25_11-41-45
done: true
experiment_id: 3d136453508644ff85ae181e3d6dc16f
experiment_tag: 32_activation=ReLU(inplace=True),drop_rate=0.68437,hidden_units=32,learning_rate=0.012911,momentum=0.47934
hostname: ironman
iterations_since_restore: 4
mean_accuracy: 64.58227848101266
node_ip: 192.168.86.61
pid: 31824
time_since_restore: 1807.938559293747
time_this_iter_s: 453.95099663734436
time_total_s: 1807.938559293747
timestamp: 1603597305
timesteps_since_restore: 0
train_accuracy: 14.286931818181818
train_loss: 2.314218519763513
training_iteration: 4
trial_id: e5a1b_00032
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (5 PENDING, 2 RUNNING, 53 TERMINATED)
2020-10-25 11:41:46,055 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=10269)[0m cpu
[2m[36m(pid=10269)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
0it [00:00, ?it/s]9)[0m
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Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-41-55
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 34
mean_accuracy: 68.9113924050633
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 520.9520137310028
time_this_iter_s: 15.185444831848145
time_total_s: 520.9520137310028
timestamp: 1603597315
timesteps_since_restore: 0
train_accuracy: 15.713068181818182
train_loss: 2.3483769182454455
training_iteration: 34
trial_id: e5a1b_00054
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== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
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Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-42-10
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 35
mean_accuracy: 70.51898734177215
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 536.1477522850037
time_this_iter_s: 15.195738554000854
time_total_s: 536.1477522850037
timestamp: 1603597330
timesteps_since_restore: 0
train_accuracy: 15.849431818181818
train_loss: 2.3479370820251377
training_iteration: 35
trial_id: e5a1b_00054
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92%|█████████▏| 156295168/170498071 [00:23<00:01, 8712857.05it/s]
== Status ==Memory usage on this node: 5.2/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
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170500096it [00:24, 6825594.11it/s]
[2m[36m(pid=10269)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=10269)[0m Files already downloaded and verified
[2m[36m(pid=10269)[0m 2020-10-25 11:42:15,130 INFO trainable.py:255 -- Trainable.setup took 28.203 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-42-25
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 36
mean_accuracy: 71.37974683544304
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 551.4610443115234
time_this_iter_s: 15.313292026519775
time_total_s: 551.4610443115234
timestamp: 1603597345
timesteps_since_restore: 0
train_accuracy: 15.767045454545455
train_loss: 2.3478988151658666
training_iteration: 36
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-42-41
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 37
mean_accuracy: 70.82278481012658
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 566.7552094459534
time_this_iter_s: 15.294165134429932
time_total_s: 566.7552094459534
timestamp: 1603597361
timesteps_since_restore: 0
train_accuracy: 15.670454545454545
train_loss: 2.348832195455378
training_iteration: 37
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-42-56
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 38
mean_accuracy: 69.48101265822785
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 582.0310823917389
time_this_iter_s: 15.275872945785522
time_total_s: 582.0310823917389
timestamp: 1603597376
timesteps_since_restore: 0
train_accuracy: 15.747159090909092
train_loss: 2.3476994701407174
training_iteration: 38
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-43-11
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 39
mean_accuracy: 70.31645569620254
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 597.3626809120178
time_this_iter_s: 15.33159852027893
time_total_s: 597.3626809120178
timestamp: 1603597391
timesteps_since_restore: 0
train_accuracy: 15.514204545454545
train_loss: 2.348801386627284
training_iteration: 39
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-43-27
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 40
mean_accuracy: 71.18987341772151
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 612.6900839805603
time_this_iter_s: 15.32740306854248
time_total_s: 612.6900839805603
timestamp: 1603597407
timesteps_since_restore: 0
train_accuracy: 15.792613636363637
train_loss: 2.3479449423876675
training_iteration: 40
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-43-42
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 41
mean_accuracy: 70.77215189873418
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 628.0270471572876
time_this_iter_s: 15.336963176727295
time_total_s: 628.0270471572876
timestamp: 1603597422
timesteps_since_restore: 0
train_accuracy: 15.798295454545455
train_loss: 2.3488558063452896
training_iteration: 41
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-43-58
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 42
mean_accuracy: 69.49367088607595
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 643.3567879199982
time_this_iter_s: 15.329740762710571
time_total_s: 643.3567879199982
timestamp: 1603597438
timesteps_since_restore: 0
train_accuracy: 15.710227272727273
train_loss: 2.3486876785755157
training_iteration: 42
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-44-13
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 43
mean_accuracy: 69.74683544303798
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 658.6750028133392
time_this_iter_s: 15.318214893341064
time_total_s: 658.6750028133392
timestamp: 1603597453
timesteps_since_restore: 0
train_accuracy: 15.772727272727273
train_loss: 2.348830020563169
training_iteration: 43
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-44-28
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 44
mean_accuracy: 70.11392405063292
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 673.9126441478729
time_this_iter_s: 15.237641334533691
time_total_s: 673.9126441478729
timestamp: 1603597468
timesteps_since_restore: 0
train_accuracy: 15.798295454545455
train_loss: 2.347692383961244
training_iteration: 44
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-44-44
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 45
mean_accuracy: 70.45569620253164
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 689.2064654827118
time_this_iter_s: 15.293821334838867
time_total_s: 689.2064654827118
timestamp: 1603597484
timesteps_since_restore: 0
train_accuracy: 15.707386363636363
train_loss: 2.3494563184001227
training_iteration: 45
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-44-59
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 46
mean_accuracy: 70.62025316455696
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 704.5084397792816
time_this_iter_s: 15.301974296569824
time_total_s: 704.5084397792816
timestamp: 1603597499
timesteps_since_restore: 0
train_accuracy: 15.71875
train_loss: 2.3479415232484993
training_iteration: 46
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-45-14
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 47
mean_accuracy: 70.65822784810126
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 719.8761560916901
time_this_iter_s: 15.367716312408447
time_total_s: 719.8761560916901
timestamp: 1603597514
timesteps_since_restore: 0
train_accuracy: 15.818181818181818
train_loss: 2.3488499319011513
training_iteration: 47
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.9/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00054:
date: 2020-10-25_11-45-30
done: false
experiment_id: dce45b17e9e344069a325ef1b71234d5
experiment_tag: 54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604
hostname: ironman
iterations_since_restore: 48
mean_accuracy: 71.26582278481013
node_ip: 192.168.86.61
pid: 6141
time_since_restore: 735.2718136310577
time_this_iter_s: 15.395657539367676
time_total_s: 735.2718136310577
timestamp: 1603597530
timesteps_since_restore: 0
train_accuracy: 15.752840909090908
train_loss: 2.34839684583924
training_iteration: 48
trial_id: e5a1b_00054
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=52 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.49050632911393Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (4 PENDING, 2 RUNNING, 54 TERMINATED)
Result for PyTorchCIFAR10Trainable_e5a1b_00056:
date: 2020-10-25_11-46-58
done: true
experiment_id: aed266c20f9a4b6ba8a5b1982c3215c2
experiment_tag: 56_activation=ELU(alpha=True),drop_rate=0.72431,hidden_units=32,learning_rate=0.00018787,momentum=0.35545
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 56.40506329113924
node_ip: 192.168.86.61
pid: 12251
time_since_restore: 15.290166854858398
time_this_iter_s: 15.290166854858398
time_total_s: 15.290166854858398
timestamp: 1603597618
timesteps_since_restore: 0
train_accuracy: 12.684659090909092
train_loss: 2.325223444537683
training_iteration: 1
trial_id: e5a1b_00056
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=53 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.25316455696203Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (3 PENDING, 2 RUNNING, 55 TERMINATED)
2020-10-25 11:46:58,734 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
0it [00:00, ?it/s]7)[0m
[2m[36m(pid=12487)[0m cuda:0
[2m[36m(pid=12487)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=12487)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=12487)[0m Files already downloaded and verified
[2m[36m(pid=12487)[0m 2020-10-25 11:47:35,600 INFO trainable.py:255 -- Trainable.setup took 35.995 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00057:
date: 2020-10-25_11-47-50
done: true
experiment_id: 3a8bc9ba42174615bb831269d4115f67
experiment_tag: 57_activation=ReLU(inplace=True),drop_rate=0.76005,hidden_units=64,learning_rate=0.071092,momentum=0.55875
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 52.9873417721519
node_ip: 192.168.86.61
pid: 12487
time_since_restore: 15.306184768676758
time_this_iter_s: 15.306184768676758
time_total_s: 15.306184768676758
timestamp: 1603597670
timesteps_since_restore: 0
train_accuracy: 12.230113636363637
train_loss: 2.3225850503553045
training_iteration: 1
trial_id: e5a1b_00057
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=54 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.208860759493675Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (2 PENDING, 2 RUNNING, 56 TERMINATED)
2020-10-25 11:47:51,071 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ReLU(inplace=True)}
[2m[36m(pid=12672)[0m cuda:0
[2m[36m(pid=12672)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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[2m[36m(pid=12672)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=12672)[0m Files already downloaded and verified
[2m[36m(pid=12672)[0m 2020-10-25 11:48:28,241 INFO trainable.py:255 -- Trainable.setup took 36.279 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00058:
date: 2020-10-25_11-48-43
done: true
experiment_id: 15ad6731089149c5a420fdfc30a6d738
experiment_tag: 58_activation=ELU(alpha=True),drop_rate=0.50547,hidden_units=128,learning_rate=0.0022148,momentum=0.33457
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 55.949367088607595
node_ip: 192.168.86.61
pid: 12672
time_since_restore: 15.2929368019104
time_this_iter_s: 15.2929368019104
time_total_s: 15.2929368019104
timestamp: 1603597723
timesteps_since_restore: 0
train_accuracy: 12.78125
train_loss: 2.330710305408998
training_iteration: 1
trial_id: e5a1b_00058
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=55 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.164556962025316Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (1 PENDING, 2 RUNNING, 57 TERMINATED)
2020-10-25 11:48:43,695 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
[2m[36m(pid=12860)[0m cuda:0
[2m[36m(pid=12860)[0m Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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170500096it [00:29, 5830624.00it/s]
[2m[36m(pid=12860)[0m Extracting ./data/cifar-10-python.tar.gz to ./data
[2m[36m(pid=12860)[0m Files already downloaded and verified
[2m[36m(pid=12860)[0m 2020-10-25 11:49:18,364 INFO trainable.py:255 -- Trainable.setup took 33.796 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
Result for PyTorchCIFAR10Trainable_e5a1b_00059:
date: 2020-10-25_11-49-33
done: true
experiment_id: f8a44a1825ca40028f4753a5551235d0
experiment_tag: 59_activation=ELU(alpha=True),drop_rate=0.26293,hidden_units=256,learning_rate=0.010413,momentum=0.7019
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 53.08860759493671
node_ip: 192.168.86.61
pid: 12860
time_since_restore: 15.239164590835571
time_this_iter_s: 15.239164590835571
time_total_s: 15.239164590835571
timestamp: 1603597773
timesteps_since_restore: 0
train_accuracy: 12.136363636363637
train_loss: 2.3280689892443744
training_iteration: 1
trial_id: e5a1b_00059
== Status ==Memory usage on this node: 5.8/125.8 GiBUsing AsyncHyperBand: num_stopped=56 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.12025316455696Resources requested: 4/12 CPUs, 2/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (2 RUNNING, 58 TERMINATED)
2020-10-25 11:49:33,769 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
Result for PyTorchCIFAR10Trainable_e5a1b_00055:
date: 2020-10-25_11-49-49
done: true
experiment_id: 8b9d7e752a254c659396c674f4d03320
experiment_tag: 55_activation=ELU(alpha=True),drop_rate=0.71642,hidden_units=256,learning_rate=0.001471,momentum=0.10867
hostname: ironman
iterations_since_restore: 1
mean_accuracy: 43.18987341772152
node_ip: 192.168.86.61
pid: 10269
time_since_restore: 454.3322856426239
time_this_iter_s: 454.3322856426239
time_total_s: 454.3322856426239
timestamp: 1603597789
timesteps_since_restore: 0
train_accuracy: 10.014204545454545
train_loss: 2.3449794860048727
training_iteration: 1
trial_id: e5a1b_00055
== Status ==Memory usage on this node: 4.0/125.8 GiBUsing AsyncHyperBand: num_stopped=57 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.075949367088604Resources requested: 2/12 CPUs, 1/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (1 RUNNING, 59 TERMINATED)
2020-10-25 11:49:49,589 INFO logger.py:285 -- Removed the following hyperparameter values when logging to tensorboard: {'activation': ELU(alpha=True)}
== Status ==Memory usage on this node: 4.0/125.8 GiBUsing AsyncHyperBand: num_stopped=57 Bracket: Iter 64.000: None | Iter 16.000: 69.46835443037975 | Iter 4.000: 65.43037974683544 | Iter 1.000: 61.075949367088604Resources requested: 0/12 CPUs, 0/2 GPUs, 0.0/76.9 GiB heap, 0.0/25.49 GiB objects (0/1.0 accelerator_type:GTX)Result logdir: /home/ashish/ray_results/PyTorchCIFAR10TrainableNumber of trials: 60 (60 TERMINATED)
took = stop - start
print(f"Total time: {took//60 : .0f}m {took%60:.0f}s")
Total time: 87m 35s
5.2 Best Config
We can now use analysis to get the best configuration or best trial
print("Best config is:",
analysis.get_best_config(metric="mean_accuracy", mode="max"))
Best config is: {'hidden_units': 128, 'drop_rate': 0.40420189795828576, 'activation': SELU(inplace=True), 'learning_rate': 0.030155907210626897, 'momentum': 0.35603968082448945}
best_trial = analysis.get_best_trial(metric="mean_accuracy", mode="max")
best_trial.last_result["mean_accuracy"]
71.0
best_trial.last_result
{'train_loss': 2.347894461317496,
'train_accuracy': 15.917613636363637,
'mean_accuracy': 71.0,
'done': True,
'timesteps_total': None,
'episodes_total': None,
'training_iteration': 50,
'experiment_id': 'dce45b17e9e344069a325ef1b71234d5',
'date': '2020-10-25_11-46-01',
'timestamp': 1603597561,
'time_this_iter_s': 15.248047590255737,
'time_total_s': 765.8731515407562,
'pid': 6141,
'hostname': 'ironman',
'node_ip': '192.168.86.61',
'config': {'hidden_units': 128,
'drop_rate': 0.40420189795828576,
'activation': SELU(inplace=True),
'learning_rate': 0.030155907210626897,
'momentum': 0.35603968082448945},
'time_since_restore': 765.8731515407562,
'timesteps_since_restore': 0,
'iterations_since_restore': 50,
'trial_id': 'e5a1b_00054',
'experiment_tag': '54_activation=SELU(inplace=True),drop_rate=0.4042,hidden_units=128,learning_rate=0.030156,momentum=0.35604'}
6. Next Steps
And, that’s it.
Try designing the search space according to what you think might be a better configuration.
See also
- Exploration Log of Contrastive Language-Image Pre-training
- 📙 CIFAR-10 Classifiers: Part 6 - Speed up TensorFlow hyperparameter search using Optuna
- 📙 CIFAR-10 Classifiers: Part 5 - Speed up TensorFlow hyperparameter search using Ray Tune
- 🍻 💊 Number of People Searching Hangover Cure
- 📙 CIFAR-10 Classifiers: Part 4 - Build a Simple Image Classifier using PyTorch Lightning