Version 0.11

info

Trains is now ClearML.

Version 0.11.3

Trains

Features and Bug Fixes

• Resource-Monitor will only monitor active GPU devices based on environment variables: NVIDIA_VISIBLE_DEVICES or CUDA_VISIBLE_DEVICES.
• Fix issue (GitHub Issue #48).

Version 0.11.2

Trains

Features and Bug Fixes

• Fix Python 2.7 support.
• Improve sample code Windows support.

Version 0.11.1

Trains

Features and Bug Fixes

• Embed GPU Monitoring into Trains (removed gpustat dependency).
• Add initial support for TensorFlow v2.0 (tested with v2.0.0rc1).
• Add artifact upload retry on network errors (default: 3).
• Suppress urllib3 retry warnings.
• Fix Matplotlib support with Agg backend (multiple plot windows caused repeated graphs to be sent).
• Fix support for tuples in hyperparameters.
• Fix multi processing issues with different task types.

Version 0.11.0

Trains

Features and Bug Fixes

• Full artifacts support (supported by trains-server >= 0.11.0).
• Artifacts include, Pandas.DataFrame, Numpy, PIL Image, local files, and local folder / wildcard (example).
• Artifacts support for folder / wildcard, selected files will be zipped and uploaded.
• Resource monitoring, remove sensor reading failure warnings.

Breaking Changes

• Logger info/error/warning/console functions were removed, use Logger.report_text (or Python logging or print instead).
• TensorBoard scalars are not grouped into one graph, but are stored on individual graphs (to match TensorBoard behavior). To restore previous behavior call: Logger.tensorboard_auto_group_scalars(group_scalars=True).

Trains Server

• Add scalar graphs smoothing (matched TensorBoard behavior).
• Add scalar graphs X-axis selection: Iteration / Relative / Wall Time.
• Add min / max / last value for all scalars (available in experiment table column selection / sorting).
• Add hyperparameter to experiment table custom columns.
• Add artifacts support (Models tab moved into artifacts as specific artifact type).
• Add full Azure storage support.
• Fix column sorting N/A values.