Explicit Reporting Overview

Trains is now ClearML

This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.

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

  • Explicit Reporting - Jupyter Notebook - Several explicit reporting examples running in a Jupyter Notebook, including scalars, plots, media (audio, HTML, images, and video), and text.
  • 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.
  • Manual Matplotlib Reporting - Reporting using Matplotlib and Seaborn in Trains.
  • 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.