TensorFlow Progress
Introduction
The easy-to-use library for logging training progress of TensorFlow.
Now you can visualize the progress of machine learning jobs with tf_progress.
Installation
pip install tf_progress
Quick Start
Run with the simple APIs and checkout more examples.
from tf_progress.tf_progress import TFProgress
progress = TFProgress(total_epoch_number=10, enable_print_progress_thread=True)
for i in range(10):
progress.increase_current_epoch_number()
Advanced Usage
Initialize the TFProgress object.
progress = TFProgress(total_epoch_number=10)
Choose the display type.
progress = TFProgress(total_epoch_number=10, display_type=TFProgress.DISPLAY_TYPE_STDOUT_TEXT)
progress = TFProgress(total_epoch_number=10, display_type=TFProgress.DISPLAY_TYPE_STDOUT_BAR)
progress = TFProgress(total_epoch_number=10, display_type=TFProgress.DISPLAY_TYPE_LOCAL_FILE)
progress = TFProgress(total_epoch_number=10, display_type=TFProgress.DISPLAY_TYPE_HTTP_REQUEST)
Set the total epoch number.
progress.set_total_epoch_number(10)
Update the current epoch number.
progress.increase_current_epoch_number()
progress.set_current_epoch_number(10)
progress.clear_current_epoch_number()
Print the progress status.
print(progress.get_current_progress())
progress.print_progress()
progress.start_print_progress_thread()
Display Progress
Now it supports 4 types of displaying progress.
The default display type is DISPLAY_TYPE_STDOUT_TEXT
.
The better one with progress bar is DISPLAY_TYPE_STDOUT_BAR
.
The method to store progress in file is DISPLAY_TYPE_LOCAL_FILE
.
The way to send progress to server is DISPLAY_TYPE_HTTP_REQUEST
.