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Release Notes

7.0.0

User-facing capabilities

Data processing

  • Introduced a single API call for getting features and labels (get_features_and_labels())) for PyTorch modeling. This call uses user model training parameters to greatly simplify the process of retrieving and splitting data for training, validation, and testing purposes.
  • Custom data transformation operations can now accept parameters via the No Code client for increased flexibility and reuse of transformation operations.

No-code ML modeling

  • Train/create API call now supports deploying no-code models. Currently, there is support for two types of clustering: K-Means and Spectral.
  • Support users’ parameters from the No Code client for these two no-code clustering models, e.g., number of clusters, elbow detection, etc.
  • Train/create API also supports deploying an LSTM text classification model in a no-code manner while using distributed Horovod.
  • All configurable parameters related to LSTM text classification are supported via the No Code client.

DaskHub notebooks

  • Jupyter notebooks and directories are implemented as a virtual filesystem on top of Elasticsearch.
  • Users may utilize Jupyter notebooks and automatically store their model cells as boss model objects by attaching relevant metadata to the cell containing model code.
  • Ability to connect models from Jupyter notebooks to the GUI client as well as open relevant notebooks from the GUI.
  • Jupyter enabled models are notated in the GUI client.
  • OAuth integration for DaskHub.

Refactored BOSS AI python library

  • Package updated to use new CAS mechanism.
  • Package structure (modules, naming conventions, etc.) has been updated for increased convenience and reflects changes in the BOSS AI backend.

Expanded data visualization capabilities

  • Various regression analysis plots are added to the No Code client. New plots support regression models implemented in PyTorch, TensorFlow, XGBoost, and Scikit-learn.
  • Filtering on correlation matrix (e.g., by correlation coefficient and/or features) has been added to help data analytics.
  • 2D correlation matrix added, in addition to the current 3D matrix.
  • Histogram configuration has been added, enabling users to select the number of intervals/buckets to use in the chart.

Expanded federation view in No Code client

  • New view in client to see the whole federation across the globe.
  • Ability to view relative information like Virtual Datasets, Sources, and Trainings across federates.

User Experience Improvements in the No Code Client

  • New Main Top Menu for section and window flow
  • New Log Panel at bottom of the screen to see logs and open No Code Debug Console
  • Modelling section panels improved with more reliability.
  • Added Asset Training functionality to Modelling Section.
  • Data Pooling added to Data Tables and Modeling lists.
  • Added helping tooltips for Query Builder save and exit.
  • Improved information is shown to users on selected nodes in Data Transformation.
  • Added ability to copy the Model ID from the GUI.
  • Added ability to preview Model code from the GUI.

Back-end capabilities

Machine learning capabilities

  • Auto-activation of “eval mode” for PyTorch models, simplifying usage of trained models for inference purposes.
  • Horovod training failure on limited or insufficient resources are handled dynamically after each failed run where the next run adds 50% more number of Kubernetes pods for training.
  • Major updates to plotting functionality and introduction of new Plot & Plotter classes within the boss core libraries.
  • Major updates to confusion matrix capability and introduction of new ConfusionMatrix class within the boss core libraries.

Data loading performance improvement

  • Data loading and No Code client responsiveness improved due to the use of Elasticsearch for storage and querying.

Oauth2

  • User Credentials and Registration is now handled outside of the GUI.

ElasticSearch Integration

  • Support users’ authentication to ElasticSearch via Oauth2
  • Index-level security is provided by implementing security groups using Keycloak
  • Feature-level security is also supported via customized query DSL on top of ElasticSearch

Known Issues

  • Remote-federate plots not currently being collected
  • PyTorch PredictExplain testing not working properly
  • Search update not recording the update to queries
  • BOSS Client dynamic panels break on logout

Bug Fixes

  • Various No Code code cleanup
  • Various UI cleanup
  • Fixed missing tooltips in No Code client
  • Fixed missing error on Model upload
  • Fixed endpoint read formats for federates, virtual datasets, sources, plots, and training
  • Fixed parameters for Tabular, Image, and NLP operations
  • Fixed user flow for global application logout, close, and exit transitions
  • Fixed Model download
  • Minor format update to embeddings for better usage of ElasticSearch.
  • Corrected Multi VDS training from 3 to 2 VDS’s