Releases: oracle/accelerated-data-science
Releases · oracle/accelerated-data-science
2.6.5
2.6.4
ADS
- Added support for large models with artifact size between 2 and 6 GB. The large models can be saved to the Model Catalog, downloaded from the Model Catalog, and deployed as a Model Deployment resource.
- Added
delete()
method to theGenericModel
class. Deletes models and associated model deployments. - The Model Input Schema is improved to return features sorted by the
order
attribute. - Added user-friendly default names for created Jobs, Model Deployments, and Models.
2.6.3
ADS
- Deprecated the
ads.dataflow.DataFlow
class. It has been superseded by theads.jobs.DataFlow
class. - Added
prepare_save_deploy()
method to theGenericModel
class. Prepare model artifacts and deploy the model with one command. - Added support for binary payloads in model deployment.
- Updated
AutoMLModel
,GenericModel
,LightgbmModel
,PyTorchModel
,SklearnModel
,TensorflowModel
, andXgboostModel
classes to support binary payloads in model deployment. - The maximum runtime for a Job can be limited with the
with_maximum_runtime_in_minutes()
method in theCondaRuntime
,DataFlowNotebookRuntime
,DataFlowRuntime
,GitPythonRuntime
,NotebookRuntime
, andScriptRuntime
classes. - The
ads.jobs.DataFlow
class supports Published conda environments.
2.6.2
ADS
-
Added
from_model_deployment()
method to theGenericModel
class. Now you can load a model directly from an existing model deployment. -
Moved dependencies from being default into optional installation groups:
all-optional
bds
boosted
data
geo
notebook
onnx
opctl
optuna
tensorflow
text
torch
viz
Use
python3 -m pip install oracle-ads[XXX]
where XXX are the group names.
2.6.1
ADS
- Added support for running a container as jobs using
ads.jobs.ContainerRuntime
. - The
ModelArtifact
class is deprecated. Use the model serialization classes (GenericModel
,PyTorchModel
,SklearnModel
, etc.).
2.5.10
ADS
- Added
BDSSecretKeeper
to store and save configuration parameters to connect to Big Data service to the vault. - Added the
krbcontext
andrefresh_ticket
functions to configure Kerberos authentication for the Big Data service. - Added authentication options to logging APIs to allow you to pass in the OCI API key configuration or signer.
- Added the configuration file path option to the
set_auth
method to allow to change the path of the OCI configuration. - Fixed a bug in AutoML for Ttext datasets.
- Fixed bug in
import ads.jobs
to notify users installing ADS optional dependencies. - Fixed a bug in the generated
score.py
file, where Pandas dataframe's dtypes changed when deserializing. Now you can recover it from the input schema. - Updated requirements to oci>=2.59.0.
2.5.9
ADS
-
Added framework specific model serialization to add more inputs to the generated
score.py
file. -
Added the following framework-specific model classes:
AutoMLModel
SKlearnModel
XGBoostModel
LightGBMModel
PyTorchModel
TensorFlowModel
-
For any framework not included in the preceding list, added another class:
GenericModel
-
These model classes include methods specific to the frameworks that improve deployment speed. Some example methods are:
- Prepare (the artifacts)
- Save (metadata and model to model catalog)
- Deploy (the models quickly with this method)
- Predict (perform inference operations)
-
Added support to create jobs with managed egress.
-
Shortened the time for streaming large number of logs for job run logging.
2.5.8
ADS
- Fixed bug in automatic extraction of taxonomy metadata for
Sklearn
models. - Fixed bug in jobs
NotebookRuntime
when using non-ASCII encoding. - Added compatibility with Python
3.8
and3.9
. - Added an enhanced string class, called
ADSString
. It adds functionality such as regular expression (RegEx) matching, and natural language processing (NLP) parsing. The class can be expanded by registering custom plugins to perform custom string processing actions.
2.5.7
ADS
- Fixed bug in DataFlow Job creation.
- Fixed bug in ADSDataset get_recommendations raising HTML is not defined exception.
- Fixed bug in jobs ScriptRuntime causing the parent artifact folder to be zipped and uploaded instead of the specified folder.
- Fixed bug in ModelDeployment raising TypeError exception when updating an existing model deployment.