Training stuck and never stop on colab.

[hugit6]

I made a very small dataset based on official one, make it only 10 images.
Use free version colab, and tried all those free CPU types.
All stuck there and seems working on something but no result.
Here is config and console output:

number: '0123456789'
symbol: "!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ €"
lang_char: 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
experiment_name: 'en_filtered'
train_data: 'all_data/en_train_filtered'
valid_data: 'all_data/en_train_filtered'
manualSeed: 1111
workers: 6
batch_size: 32 #32
num_iter: 300000
valInterval: 20000
saved_model: '' #'saved_models/en_filtered/iter_300000.pth'
FT: False
optim: False # default is Adadelta
lr: 1.
beta1: 0.9
rho: 0.95
eps: 0.00000001
grad_clip: 5
#Data processing
select_data: 'en_train_filtered' # this is dataset folder in train_data
batch_ratio: '1'
total_data_usage_ratio: 1.0
batch_max_length: 34
imgH: 64
imgW: 600
rgb: False
contrast_adjust: False
sensitive: True
PAD: True
contrast_adjust: 0.0
data_filtering_off: False

Model Architecture

Transformation: 'None'
FeatureExtraction: 'VGG'
SequenceModeling: 'BiLSTM'
Prediction: 'CTC'
num_fiducial: 20
input_channel: 1
output_channel: 256
hidden_size: 256
decode: 'greedy'
new_prediction: False
freeze_FeatureFxtraction: False
freeze_SequenceModeling: False

Filtering the images containing characters which are not in opt.character
Filtering the images whose label is longer than opt.batch_max_length

dataset_root: all_data/en_train_filtered
opt.select_data: ['en_train_filtered']
opt.batch_ratio: ['1']

dataset_root: all_data/en_train_filtered dataset: en_train_filtered
all_data/en_train_filtered/
sub-directory: /. num samples: 10
#####:dataset_list [<dataset.OCRDataset object at 0x79baa9c83d30>]
num total samples of en_train_filtered: 10 x 1.0 (total_data_usage_ratio) = 10
num samples of en_train_filtered per batch: 32 x 1.0 (batch_ratio) = 32
/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 6 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(_create_warning_msg(

Total_batch_size: 32 = 32

dataset_root: all_data/en_train_filtered dataset: /
all_data/en_train_filtered/
sub-directory: /. num samples: 10
#####:dataset_list [<dataset.OCRDataset object at 0x79baa9c82c50>]

No Transformation module specified
/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 6 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(create_warning_msg(
model input parameters 64 600 20 1 256 256 97 34 None VGG BiLSTM CTC
Model:
DataParallel(
(module): Model(
(FeatureExtraction): VGG_FeatureExtractor(
(ConvNet): Sequential(
(0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)
(11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace=True)
(17): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)
(18): Conv2d(256, 256, kernel_size=(2, 2), stride=(1, 1))
(19): ReLU(inplace=True)
)
)
(AdaptiveAvgPool): AdaptiveAvgPool2d(output_size=(None, 1))
(SequenceModeling): Sequential(
(0): BidirectionalLSTM(
(rnn): LSTM(256, 256, batch_first=True, bidirectional=True)
(linear): Linear(in_features=512, out_features=256, bias=True)
)
(1): BidirectionalLSTM(
(rnn): LSTM(256, 256, batch_first=True, bidirectional=True)
(linear): Linear(in_features=512, out_features=256, bias=True)
)
)
(Prediction): Linear(in_features=256, out_features=97, bias=True)
)
)
Modules, Parameters
module.FeatureExtraction.ConvNet.0.weight 288
module.FeatureExtraction.ConvNet.0.bias 32
module.FeatureExtraction.ConvNet.3.weight 18432
module.FeatureExtraction.ConvNet.3.bias 64
module.FeatureExtraction.ConvNet.6.weight 73728
module.FeatureExtraction.ConvNet.6.bias 128
module.FeatureExtraction.ConvNet.8.weight 147456
module.FeatureExtraction.ConvNet.8.bias 128
module.FeatureExtraction.ConvNet.11.weight 294912
module.FeatureExtraction.ConvNet.12.weight 256
module.FeatureExtraction.ConvNet.12.bias 256
module.FeatureExtraction.ConvNet.14.weight 589824
module.FeatureExtraction.ConvNet.15.weight 256
module.FeatureExtraction.ConvNet.15.bias 256
module.FeatureExtraction.ConvNet.18.weight 262144
module.FeatureExtraction.ConvNet.18.bias 256
module.SequenceModeling.0.rnn.weight_ih_l0 262144
module.SequenceModeling.0.rnn.weight_hh_l0 262144
module.SequenceModeling.0.rnn.bias_ih_l0 1024
module.SequenceModeling.0.rnn.bias_hh_l0 1024
module.SequenceModeling.0.rnn.weight_ih_l0_reverse 262144
module.SequenceModeling.0.rnn.weight_hh_l0_reverse 262144
module.SequenceModeling.0.rnn.bias_ih_l0_reverse 1024
module.SequenceModeling.0.rnn.bias_hh_l0_reverse 1024
module.SequenceModeling.0.linear.weight 131072
module.SequenceModeling.0.linear.bias 256
module.SequenceModeling.1.rnn.weight_ih_l0 262144
module.SequenceModeling.1.rnn.weight_hh_l0 262144
module.SequenceModeling.1.rnn.bias_ih_l0 1024
module.SequenceModeling.1.rnn.bias_hh_l0 1024
module.SequenceModeling.1.rnn.weight_ih_l0_reverse 262144
module.SequenceModeling.1.rnn.weight_hh_l0_reverse 262144
module.SequenceModeling.1.rnn.bias_ih_l0_reverse 1024
module.SequenceModeling.1.rnn.bias_hh_l0_reverse 1024
module.SequenceModeling.1.linear.weight 131072
module.SequenceModeling.1.linear.bias 256
module.Prediction.weight 24832
module.Prediction.bias 97
Total Trainable Params: 3781345
Trainable params num : 3781345
Optimizer:
Adadelta (
Parameter Group 0
eps: 1e-08
foreach: None
lr: 1.0
maximize: False
rho: 0.95
weight_decay: 0
)
------------ Options -------------
number: 0123456789
symbol: !"#$%&'()*+,-./:;<=>?@[]^{|}~ € lang_char: ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz experiment_name: en_filtered train_data: all_data/en_train_filtered valid_data: all_data/en_train_filtered manualSeed: 1111 workers: 6 batch_size: 32 num_iter: 300000 valInterval: 20000 saved_model: FT: False optim: False lr: 1.0 beta1: 0.9 rho: 0.95 eps: 1e-08 grad_clip: 5 select_data: ['en_train_filtered'] batch_ratio: ['1'] total_data_usage_ratio: 1.0 batch_max_length: 34 imgH: 64 imgW: 600 rgb: False contrast_adjust: 0.0 sensitive: True PAD: True data_filtering_off: False Transformation: None FeatureExtraction: VGG SequenceModeling: BiLSTM Prediction: CTC num_fiducial: 20 input_channel: 1 output_channel: 256 hidden_size: 256 decode: greedy new_prediction: False freeze_FeatureFxtraction: False freeze_SequenceModeling: False character: 0123456789!"#$%&'()*+,-./:;<=>?@[\]^_{|}~ €ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
num_class: 97

/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")

[hugit6]

changed the number seems can continue:
workers: 1
num_iter: 500
valInterval: 20

[hugit6]

changed the number seems can continue:
workers: 1
num_iter: 500
valInterval: 20