Explicit Reporting

ClearML provides Logger class methods for explicit reporting, in addition to ClearML 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 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 Definitions, 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 ClearML 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.