Mdhvince / Epochsviz

High level visualization module built on top of Bokeh in order to visualize train and val losses in real time (During the training process)

Epochsviz (Bokeh + PyTorch)

This is a simple high level visualization module built on top of Bokeh in order to visualize training and validation losses during the training (in real time). This module was tested using PyTorch.

Python 3 compatible only!

Installation

Clone the repository in your project location
git clone https://github.com/Mdhvince/Epochsviz.git

Go to the folder
cd Epochsviz

Install the necessary dependencies
pip install -r requirements.txt

Get Started

Import the module in your python script
from Epochsviz.epochsviz import Epochsviz

Create an Epochsviz object
eviz = Epochsviz() For a custom Epochsviz object, you can change these attribute

Epochviz(title='figure', plot_width=600, plot_height=600)

Since this module was tested using PyTorch, I'll show you how to use it within the train() function that we usually use for training a Neural Network.

The unique line to add in your training loop is

eviz.send_data(current_epoch=epoch,
			   current_train_loss=train_loss,
			   current_val_loss=valid_loss)

• current_epoch: the current epoch in the loop
• current_train_loss: the current loss on the train loader data
• current_val_loss: the current loss on the validation loader data

Find an example here

def train(n_epochs=n_epochs,
          train_loader=train_loader, valid_loader=valid_loader,
          save_location_path=save_location_path):

    for epoch in range(1, n_epochs+1):
        # TRACK LOSSES AS USUAL
        # ...
        for data in train_loader:
            # DO YOUR USUAL TRAINING STEPS
            # ...
        
        model.eval()
        for data in valid_loader:
        	# DO YOUR USUAL VALIDATION STEPS
        	# ...

        eviz.send_data(current_epoch=epoch,
                       current_train_loss=train_loss,
                       current_val_loss=valid_loss)

Finally we start the thread by adding this line after the function
eviz.start_thread(train_function=train)

• train_function: the name of your train function (without parameters), so make sure all params are declared before.

Run the script in the Terminal

If your script is named training.py, instead of launching the script with the python command, you have to launch it with the bokeh one bokeh serve --show training.py.
This command will automatically open your default browser and display the result. If you want to visualize the result in another browser remove the --show option and copy the link in the other browser.

See result in the browser

TO DO

• Add a method to Terminate the thread when the training is done
• Make it Python 2 compatible