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Version 0.11 |
:::important 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
orCUDA_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, useLogger.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.