OneCycle LR set in Pytorch lightning

 

Add configure_optimizer member function in pytorch lightning model class.

refer to code:

..

def configure_optimizers(self):

optimizer = getattr(torch.optim, self.cfg.optimizer)

self.optimizer = optimizer(self.parameters(), lr=float(self.cfg.lr))

total_bs = int(self.cfg.train_dataloader_len / self.cfg.gpus)

epochs = self.cfg.epochs

self.scheduler = torch.optim.lr_scheduler.OneCycleLR(

self.optimizer, max_lr=self.cfg.lr,

anneal_strategy='linear', div_factor=100,

steps_per_epoch=total_bs, pct_start=(1/self.cfg.epochs),

epochs=epochs)

sched = {

'scheduler': self.scheduler,

'interval': 'step',

}

return [self.optimizer], [sched]

..

Thank you. 🙇🏻‍♂️

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python list reverse

refer to code: 

..

Test_list = [1, 2, 3, 4, 5]
Test_list.reverse()
print(Test_list)  # Output:  [5,4,3,2,1]

..

Thank you.

re-install nvidia drive (cuda) in ubuntu

command it on terminal

 

..

sudo apt clean
sudo apt update
sudo apt purge nvidia-* 
sudo apt autoremove
sudo apt install -y cuda

..

Thank you.

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notion embedding iframe html script

 

refer to this page:

https://www.notion.so/Create-a-widget-in-5-minutes-865211938cfb4ef2aad53d67da476736

Thank you.

pytorch lightning set validation interval

 

refer to ex:

..

# default used by the Trainer

trainer = Trainer(val_check_interval=1.0)

# check validation set 4 times during a training epoch

trainer = Trainer(val_check_interval=0.25)

# check validation set every 1000 training batches in the current epoch

trainer = Trainer(val_check_interval=1000)

..

detail is here: https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html

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python load json file

 

..

# importing the module

import json

# Opening JSON file

with open(FileA) as json_file:

data = json.load(json_file)

print("Type:", type(data))

print('length: ', len(data))

print('first item type: ', type(data[0]))

..

Thank you.

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python, pandas, to create empty data frame with same header from other df.

 

refer to code.

..

import pandas as pd

data_df = pd.read_csv('train_data.tsv', delimiter='\t')

col = list(data_df.columns)

#make empty pandas with same header

empty_df = pd.DataFrame(columns=col)

print(empty_df)

..

Thank you.

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python os mkdir example code

..

if not os.path.exists(output_dir):

    os.mkdir(output_dir)

..

Thank you.

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