Explicit Reporting Overview
Trains is now ClearML
This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.
- 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_plotlymethod, 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.