2.8.2

ADS

• Remove support for Python 3.7.
• Improved the DataScienceMode.create() to support timeout argument and auto extract region from the signer and signer config.
• Support Jupyter Notebook as entrypoint when defining Data Science jobs with PythonRuntime and GitPythonRuntime.
• Support environment variable substitution in Data Science job names and output URI.
• Support JSON serialization of list/dictionary when assigning them as Data Science jobs environment variables.
• Support saving the notebook to output URI even if the job run failed when running a Data Science job using NotebookRuntime.
• Added job.build() method to Data Science job to load default values from environment.
• Added DataScienceJob.fast_launch_shapes() method to list fast launch shapes available for Data Science job.
• Added :doc:`HuggingFacePipelineModel class to support prepare, save, deploy and predict for HuggingFace pipelines.
• Updated Data Science job run YAML representation to include configurations inherited from the job.
• Fixed custom conda environment not showing in Data Science Job YAML specification.
• Fixed an issue where model saving was failing in notebook session without ipywidgets installed.
• Fixed "Unknown archive format" error in ads.jobs.PythonRuntime, when the source code folder name ends with "zip". List of supported archive files are: "zip", "tar.gz", "tar" and "tgz".

2.8.1

ADS

• Fixed a bug for ads opctl run when --auth flag is passed and image is built by ADS.
• Fixed a bug in GenericModel.save() when the work requests are not successfully populated.
• Fixed a bug in DataScienceModel.create() to when the provenance metadata is not provided.

2.8.0

ADS

• Added support for the machine learning pipelines feature.
• Fixed a bug in fetch_training_code_details(). When git commit is empty string, set it as None to avoid service error.
• Fixed a bug in fetch_training_code_details(). Use the folder of training_script_path as the artifact directory, instead of ..

2.7.3

ADS

• Added support for the model version set feature.
• Added --job-info option to ads opctl run CLI to save job run information to a YAML file.
• Added the AuthContext class. It supports API key configuration, resource principal, and instance principal authentication. In addition, predefined signers, callable signers, or API keys configurations from specified locations.
• Added restart_deployment() method to the framework-specific classes. Update model deployment associated with the model.
• Added activate() and deactivate() method to the model deployment classes.
• Fixed a bug in to_sql(). The string length for the column created in Oracle Database table was counting characters, not bytes.
• Fixed a bug where any exception that occurred in a notebook cell printed "ADS Exception" even if the ADS code was not responsible for the error.

2.7.2

2.7.2

• Fixed a bug in ADS jobs. The job_run.watch() method sometimes threw an exception due to an unexpected logging parameter.

2.7.1

Description

• Fixed a bug with ads.set_auth('resource_principal') - #38

2.7.0

ADS

• Fixed a bug in GenericModel.prepare. The .model-ignore file was not included in the Manifest.in.

2.6.9

ADS

• Added compatibility with Python 3.10.
• Added update_deployment() method to the framework-specificclasses. Update model deployment associated with the model.
• Added from_id() method to the framework-specific classes. Load existing model by OCID directly from the model catalog and model deployment.
• Added upload_artifact() to the framework-specific classes. Upload model artifacts to Object Storage.
• Added update() method to the framework-specific classes. Update the model metadata for the registered model.
• Added config, singer, signer_callable attributes to the ads.set_auth() to support additional signers.
• Added support for Instance Principals authentication for the ads opctl conda publish and ads opctl conda install commands.
• Added an option for PyTorchModel framework allowing to serialize model in a TorchScript format.
• Added an option to import :doc:framework-specific <framework_specific_instruction> classes directly from the ads.model package. Example: from ads.model import LightGBMModel, AutoMLModel, GenericModel.
• Fixed a bug in ADSDataset get_recommendations when imbalanced correction depends on classes alpha order.
• Fixed a bug in ADS jobs. The shape configuration details were incorrectly extracted from a notebook session.
• Fixed a bug to replace the use of a deprecated API with latest API in the Model Evaluation module.

Following modules are marked as deprecated:

• ads.catalog.model.py.
• ads.catalog.notebook.py
• ads.catalog.project.py
• ads.catalog.summary.py

2.6.8

ADS

• Fixed a bug in ads.dataset.helper to support Python 3.8 and Python 3.9.

2.6.7

ADS

• Fixed a bug in PyTorchModel. The score.py failed when torch.Tensor was used as input data.
• Added support for flexible shapes for Data Flow Jobs.
• Loading a model from Model Catalog (GenericModel.from_model_catalog()) and Model Deployment (GenericModel.from_model_deployment()) no longer requires a model file name.
• Switched from using cx_Oracle interface to the oracledb driver to connect to Oracle Databases.
• Added support for image attribute for the PyTorchModel.predict() and TensorFlowModel.predict() methods. Images can now be directly passed to the model Deployment predict.

The following APIs are deprecated:

• OracleAutoMLProvider