ADS 2.11.6

Fixed bugs and introduced enhancements following our recent release, which included internal adjustments for future features and updates for the Jupyter Lab 3 upgrade.

ADS 2.11.5

Fixed bugs and introduced enhancements following our recent release, which included internal adjustments for future features and updates for the Jupyter Lab 3 upgrade.

ADS 2.11.4

Fixed bugs and introduced enhancements following our recent release, which included internal adjustments for future features and updates for the Jupyter Lab 3 upgrade.

ADS 2.11.3

Fixed bugs and introduced enhancements following our recent release, which included internal adjustments for future features and updates for the Jupyter Lab 3 upgrade.

ADS 2.11.2

Fixed bugs and introduced enhancements following our recent release, which included internal adjustments for future features and updates for the Jupyter Lab 3 upgrade.

ADS 2.11.1

Internal changes to support upcoming features and changes in Notebook related to Jupyter Lab 3 upgrade

ADS 2.11.0: Yanked

Reason this release was yanked: import errors in opctl.

ADS 2.10.1

• Releasing v1 of the Anomaly Detection Operator! The Anomaly Detection Operator is a no-code Anomaly or Outlier Detection solution through the OCI Data Science Platform. It uses dozens of models from Oracle’s own proprietary research and the best of open source. See the Anomaly Detection Section of the AI Operators tab for full details (link).
• Releasing a new version of the Forecast Operator. This release has faster explainability, improved support for reading from databases, upgrades to the automatic reporting, improved parallelization across all models, and an ability to save models for deferred inference. See the Forecast Section of the AI Operators tab for full details (link).
• Change to the default signer such that it now defaults to resource_prinicpal on any OCI Data Science resource (for example, jobs, notebooks, model deployments, dataflow).

ADS 2.10.0

• Improved the progress bar to use the percentage completed of workflow request instead of hardcoded steps.
• Used the service default for WEB_CONCURRENCY for model deployment.
• Fixed the bug with zipping the model artifacts directory when TMPRDIR is provided.
• Improved the watch() method for model deployment to keep streaming logs when the deployment is finished.
• Changed the default log type of watch to both access logs and predict logs.
• Changed the target directory to artifact_dir instead of temp directory when saving the model artifacts.
• Fixed the mount file system pre-check to check for duplicate dest.
• Fixed duplicate logs in the model deployment consolidated logs.
• Added support for the optional downloading of artifacts in GenericModel using a download_artifact() method.
• Set the Data Science service endpoint through the environment variable in OCIDataScienceMixin.
• Made reloading the model to environment as optional at the time of invoking GenericModel.from_id().
• Mandated the Python version in GenericModel.prepare() when it can't be resolved.
• Added a print out of the model deployment OCID in the notebook cell when deploy() is called.

ADS 2.9.1

• Added support for deploying LangChain application as OCI Model Deployment.
• Added support for using HuggingFace Evaluation as LLM guardrail.
• Added deployment support for RetrievalQA when using OpenSearchVectorSearch or FAISS vector DB as retriever.
• Added reload parameters in GenericModel.save() to provide option to not reload score.py.
• Fixed a bug in model deployment progress bar due to fixed number of steps.
• Fixed a bug in ads opctl build-image job-local command.