Tensorboard

  • Supports PyTorch
  • Shows scalars (accuracy, loss etc)
  • Shows the computational graph
  • Shows the histogram of weights
  1. How to Use
    • Create directory to save log
      import os
      log_dir = 'log_file'
      os.makedirs(log_dir)
      
    • Import tensorboard log writer
      from torch.utils.tensorboard import SummaryWriter
      import numpy as np
      
    • create different experiments
      experiment_dir = log_dir + '/exp1'
      writer = SummaryWriter(experiment_dir)
      
    • Log content
      for iter_nth in range(100):
       writer.add_scalar('loss/train' value, iter_nth)
      writer.flush() # save log on the drive
      
    • Activate Tensorboard on Jupyter Notebook
      %load_ext tensorboard
      %tensorboard --logdir {log_dir}
      
    • Use Tensorboard
      tensorboard --logdir PATH --host ADDR --port PORT