Calabar

Useful tools to make Colab more handy.

Contents

• Installation
• Usage
  • Working with Google Drive
  • Working with Archives
  • Sending Notifications over Gmail
  • Misc
• Complete Usage Example

Installation

!pip install calabar

Usage

Working with Google Drive

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.

Working with Archives

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')

Sending Notifications over Gmail

NOTE: To use this option, you need to allow “less secure apps” on your Gmail account:

https://www.google.com/settings/security/lesssecureapps

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 = 'johndoe@gmail.com'  # email with "allow less secure", this account's SMTP server will be used as a sender
to_addrs = ['another@gmail.com',]  # 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)

usage | contents

Misc

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())

misc | usage | contents

Complete Usage Example

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 = 'johndoe@gmail.com'
to_addrs = ['another@gmail.com']

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

TODOs:

• tar/zip archiving
• More docs
• More examples