Explicit Reporting Overview

Trains provides Logger class methods for explicit reporting, in addition to Trains automatic logging. These include:

  • 2D Plots Reporting - Reporting series as 2D plots in histogram, confusion matrix, and 2D scatter plot formats.
  • 3D Plots Reporting - Reporting series as a surface plot and as a 3D scatter plot.
  • Artifacts Reporting - Uploading objects (other than models) to storage as experiment artifacts.
  • Configuring Models - Configuring a model and defining class label enumeration.
  • HTML Reporting - Reporting local HTML files and HTML by URL.
  • Hyperparameters Reporting - The example hyper_parameters.py demonstrates automatic logging of command line options from argparse, TensorFlow DEFINEs, and parameter dictionaries which are explicitly connected to Tasks.
  • Images Reporting - Reporting (uploading) images in several formats, including NumPy arrays, uint8, uint8 RGB, PIL Image objects, and local files.
  • Media Reporting - Reporting images, audio, and video. Upload from a local path, provide a BytesIO stream, or provide the URL of media already uploaded to some storage.
  • Plotly Reporting - Report Plotly plots in Trains by calling the Logger.report_plotly method, and passing it a complex Plotly figure using the figure parameter.
  • Scalars Reporting - Reporting scalars.
  • Tables Reporting (Pandas and CSV Files) - Reporting tabular data from Pandas DataFrames and CSV files as tables.
  • Text Reporting - Explicitly reporting (as compared to automatic logging) text.