Useful tools to make Colab more handy.
!pip install calabar
Mount your drive:
from calabar import MyDrive
drive = MyDrive('/drive')
drive.mount()Copy file/folder from mounted Drive, for example, some data, model checkpoint from the previous training, etc:
file2copy = '/drive/My Drive/data.tar'
dest = '/content'
drive.cp(file2copy, dest)NOTE: During mounting internal colab tool will create 'My Drive' folder inside /drive,
so don't forget to add 'My Drive' to the file path.
Export file from Colab to Drive, for example, training logs, model checkpoints, etc:
from calabar import SaveToDrive
export = SaveToDrive()
export.to_drive('file_on_colab')
export.to_drive('another_file')NOTE: Simply copying to mounted Drive would lead to nothing.
Currently tar/tar.gz and zip archives extracting are supported.
from calabar import untar
untar('data.tar', 'to_destination')from calabar import unzip
unzip('data.zip', 'to_destination')NOTE: To use this option, you need to allow “less secure apps” on your Gmail account:
SECURITY WARNING: Continuous sending of emails (for example, sending email after each epoch) requires storing your Gmail account password in memory, allowing you not to relogin to SMTP server each time, when you want to send an email. BUT, Calabar uses secure TLS connection. Anyway, if you feel unsafe because of "less secure appps" and using SMTP server, or you just don't like it, you may not use it or use another "less secured" account (with "less secure apps” option on) and send emails to your primary account, as shown below:
from calabar import Email
from_addr = '[email protected]' # email with "allow less secure", this account's SMTP server will be used as a sender
to_addrs = ['[email protected]',] # a list of receivers
email = Email(from_addr, to_addrs)
msg_subject = 'Subject of the email'
msg_body = 'Email body.'
email.send(msg_subject, msg_body)from calabar.utils import *
# Print system info, gpu, and installed pytorch version
print_sysinfo()
# Get current GPU RAM usage:
print(get_gpu_usage())
# Get disk usage:
print(get_disk_usage())
# Get current time as formatted string in %Y-%m-%d-%H-%M:
print(current_time())
# Get distro description:
print(get_distro_descr())The following example shows up how to integrate Calabar with your training pipeline.
from calabar import MyDrive, SaveToDrive, untar, unzip, Email
from calabar.utils import *
# Authorize to Drive to allow mounting
drive = MyDrive('/drive')
drive.mount()
# Authorize to Drive to to allow exporting | NOTE: you can use here another Drive
export = SaveToDrive()
from_addr = '[email protected]'
to_addrs = ['[email protected]']
email = Email(from_addr, to_addrs)
# Prepare your data
# Copy your data:
drive.cp('/drive/My Drive/data.tar', '/content')
# Untar/unzip it:
untar('/content/data.tar', '/content')
# ...
# Model definition, training, validation functions etc...
# ...
# Main loop:
# Notification about beginning of the model training process
start_sub = 'Starting training...'
start_msg = f'Training (your model descr) has started at {current_time()}\
\nCurrent GPU usage: {get_gpu_usage()}\
\nCurrent disk usage: {get_disk_usage()}'
email.send(start_sub, start_msg)
for epoch in n_epochs:
train_loss, train_acc = train(...)
val_loss, val_ acc = validate(...)
# Here you're saving your model checkpoint
checkpoint_name = f'{epoch}-{val_loss:.3f}-{acc:.3f}-mymodel.checkpoint' # this is just example
save_checkpoint(f'/content/checkpoints/{checkpoint_name}')
# Here you're loading it to your Drive:
export.to_drive(f'/content/checkpoints/{checkpoint_name}')
# Epoch is over, model checkpoint is exported to your drive, let's notify you about current success:
epoch_sub = f'{epoch}/{n_epoch} has finished!'
epoch_msg = f'{epoch}/{n_epoch} has finished successfully at {current_time()}\
\ntrain_loss: {train_loss:.3f}|val_loss: {val_loss:.3f}|train_acc: {train_acc:.3f}|val_acc: {val_acc:.3f}\
\nModel checkpoint {checkpoint_name} successfully loaded to Drive\
\nCurrent GPU usage: {get_gpu_usage()}\
\nCurrent disk usage: {get_disk_usage()}'
email.send(epoch_sub, epoch_msg)
# Notification email about model training completion.
end_sub = 'Training has finished!'
end_msg = f'Training (your model descr) has finished at {current_time()}\
\nCurrent GPU usage: {get_gpu_usage()}\
\nCurrent disk usage: {get_disk_usage()}'
email.send(end_sub, end_msg)complete usage example | misc | usage | contents
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