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.