PyPI v0.11.1

Features and Bug Fixes

• GPU Monitoring is now embedded 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 hyper-parameters
• Fix multi processing issues with different task types

PyPI v0.11.0

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)

PyPI v0.10.7

Features and Bug Fixes

• Artifacts support
• Removed apache-libcloud from requirements
• trains-init now verifies credentials against the trains-server installation

PyPI v0.10.6

Features and Bug Fixes

• Fix broken (v0.10.5) Keras Binding support

PyPI v0.10.5

Features and Bug Fixes

• Add GPU monitoring support (add gpustat package to extras_require)
  • Install with GPU monitoring support: pip install trains[gpu]
• Move all cloud storage package requirements to extras_require
  Install with specific cloud provider support:
  • Microsoft Azure support: pip install trains[azure]
  • Google Storage support: pip install trains[gs]
  • Amazon S3 support: pip install trains[s3]
• Combine Cloud support with GPU monitoring:
  For example S3 and GPU: pip install trains[s3,gpu]
• Improve stability with intermittent network connection
• Support upgrading trains-server while running training jobs without losing log data

PyPI v0.10.4

Features and Bug Fixes

• Replace opencv-python with the more standard Pillow package
• Improve matplotlib support (custom axis ticks)
• Improve python package detection

PyPI v0.10.3

Full feature overview here

Features and Bug Fixes

• Add scikit-learn support (load/store using joblib) #20
• Add xgboost support #10
• Add loguru support #29
• Add sub-domain support trains.conf #27
• Fix sub-process support
• Fix multiple Tensorboard writers #26

PyPI v0.10.2

Features and Bug Fixes

• Add Matplotlib SVG support
• Add Seaborn support
• Add TRAINS_LOG_ENVIRONMENT environment logging feature
• Add Microsoft Azure notebook support
• Add Google Colab support
• Fix Tensorboard RGB channel order

PyPI v0.10.1

Features and Bug Fixes

• Fix Jenkins CI/CD support

PyPI v0.10.0

• Experiment code execution detection

  • Automatically create package requirements section (including used versions)
  • Automatically detect and store source code uncommitted changes
  • Jupyter notebook support, automatically convert notebook to python script (stored under uncommitted changes)
  • Jupyter notebook support, automatically update used packages in notebook (including used versions)

• Add resource monitoring to experiment metrics

  • Sampled every 500ms, averaged over 30 seconds
  • CPU / Network / IO / Memory etc.
  • For GPU support please install gpustat
    (currently not part of the requirements due to gpustat compatibility issues with Windows)
    $ pip install gpustat

• Automatically stop inactive experiments (default: 2 hours)

• Improved visibility

  • Finer status definitions: Identify successful completion vs. user aborted
  • Experiment plot comparison: Ensure different colors for different experiments
  • Parse newline character in experiment description
  • Show experiment start time in table display
  • Add vertical guide in scalar plots
  • Move Hyper-parameters to designated tab
  • "Admin" section now named "Profile"