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.

ADS 2.9.0

• Introducing AI Forecast Operator. Learn more about Operators in the "Operators" section of the docs.
• Introducing PII Operator which aims to detect and redact Personal Identifiable Information in data.
• Fixed a bug with the opctl conda create and opctl conda publish commands to ensure functionality on M1 and M2 local machines.
• Fixed a bug with failed model deployment return value.
• Fixed a bug when sorting logs for jobs and model deployment.

ADS 2.8.11

• Added support to mount file systems in Data Science notebook sessions and jobs.
• Added support to cancel all job runs in the ADS api and opctl commands.
• Updated ads.set_auth() to use both config and signer when provided.
• Fixed a bug when initializing distributed training artifacts with "Ray" framework.

ADS 2.9.0rc0

We are pleased to announce a release candidate for ADS 2.9.0. If all goes well, we'll release ADS 2.9.0 in few weeks.

The release will be available on PyPI and can be installed with --pre flag:

python -m pip install --pre oracle-ads==2.9.0rc0

Please report any issues with the release candidate on the ADS issue tracker.

ADS 2.8.10

• Improved the LargeArtifactUploader class to understand OCI paths to upload model artifacts to the model catalog by reference.
• Removed ADSDataset runtime dependency on geopandas.
• Fixed a bug in the progress bar during model registration.
• Fixed a bug where session variable could be referenced before assignment.
• Fixed a bug with model artifact save.
• Fixed a bug with pipelines step.

ADS 2.8.9

• Upgraded the scikit-learn dependency to >=1.0.
• Upgraded the pandas dependency to >1.2.1,<2.1 to allow you to use ADS with pandas 2.0.
• Implemented multi-part upload in the ArtifactUploader to upload model artifacts to the model catalog.
• Fixed the "Attribute not found" error, when deploy() called twice in GenericModel.
• Fixed the fetch of the security token, when the relative path for the security_token_file is provided (used in session token-bases authentication).

ADS 2.8.8

• Added PyTorchDistributed runtime option for Data Science jobs to add support for training large language models with PyTorch.
• Added options to configure flexible shape in opctl.
• Refactored deploy() in GenericModel to prioritize the parameters.
• Fixed the opctl commands delete/cancel/watch/activate/deactivate commands to add missing parameter options.
• Fixed the opctl commands to call run to start an ML job when no YAML is specified.
• Deprecated the DatasetFactory class, and refactored the code.

ADS 2.8.7

• Added support for leveraging pools in the Data Flow applications.
• Added support for token-based authentication.
• Revised help information for opctl commands.