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.

ADS 2.8.6

• Resolved an issue in ads opctl build-image job-local when the build of job-local would get stuck. Updated the Python version to 3.8 in the base environment of the job-local image.
• Fixed a bug that prevented the support of defined tags for Data Science job runs.
• Fixed a bug in the entryscript.sh of ads opctl that attempted to create a temporary folder in the /var/folders directory.
• Added support for defined tags in the Data Flow application and application run.
• Deprecated the old ModelDeploymentProperties and ModelDeployer classes, and their corresponding APIs.
• Enabled the uploading of large size model artifacts for the ModelDeployment class.
• Implemented validation for shape name and shape configuration details in Data Science jobs and Data Flow applications.
• Added the capability to create ADSDataset using the Pandas accessor.
• Provided a prebuilt watch command for monitoring Data Science jobs with ads opctl.
• Eliminated the legacy ads.dataflow package from ADS.