diff --git a/src/autogluon/cloud/backend/sagemaker_backend.py b/src/autogluon/cloud/backend/sagemaker_backend.py index 0d16db2..fc11ddc 100644 --- a/src/autogluon/cloud/backend/sagemaker_backend.py +++ b/src/autogluon/cloud/backend/sagemaker_backend.py @@ -223,10 +223,10 @@ def fit( Use `get_fit_job_status` to get job status. autogluon_sagemaker_estimator_kwargs: dict, default = dict() Any extra arguments needed to initialize AutoGluonSagemakerEstimator - Please refer to https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Framework for all options + Please refer to https://sagemaker.readthedocs.io/en/v2/api/training/estimators.html#sagemaker.estimator.Estimator for all options fit_kwargs: Any extra arguments needed to pass to fit. - Please refer to https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Framework.fit for all options + Please refer to https://sagemaker.readthedocs.io/en/v2/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options extra_ag_args: Optional[Dict[str, Any]], default = None Additional entries to merge into ``ag_args.json``. Use this to ship caller-specific metadata to the train script (e.g. ``predict_after_fit``, ``save_predictor``, or ``id_column`` / @@ -400,7 +400,7 @@ def deploy( custom_image_uri: Optional[str], default = None, Custom image to use to deploy endpoint with. If not specified, with use official DLC image: - https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-inference-containers + https://aws.github.io/deep-learning-containers/reference/available_images/#autogluon volume_size: int, default = None The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported. @@ -409,10 +409,10 @@ def deploy( To be noticed, the function won't return immediately because there are some preparations needed prior deployment. model_kwargs: dict, default = dict() Any extra arguments needed to initialize Sagemaker Model - Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options + Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options deploy_kwargs: Any extra arguments needed to pass to deploy. - Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.model.Model.deploy for all options + Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#sagemaker.model.Model.deploy for all options fm_serve_config: Optional[Dict[str, Any]], default = None Configuration dict passed to the FM serve script via the AG_FM_SERVE_CONFIG env var. inference_mode: {"realtime", "serverless"}, default = "realtime" @@ -821,14 +821,14 @@ def predict( If `persist` is `False`, file would first be downloaded to this path and then removed. model_kwargs: dict, default = dict() Any extra arguments needed to initialize Sagemaker Model - Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options + Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options transformer_kwargs: dict Any extra arguments needed to pass to transformer. - Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer for all options. + Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer for all options. transform_kwargs: Any extra arguments needed to pass to transform. Please refer to - https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options. + https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options. Returns ------- @@ -926,14 +926,14 @@ def predict_proba( If `persist` is `False`, file would first be downloaded to this path and then removed. model_kwargs: dict, default = dict() Any extra arguments needed to initialize Sagemaker Model - Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options + Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options transformer_kwargs: dict Any extra arguments needed to pass to transformer. - Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer for all options. + Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer for all options. transform_kwargs: Any extra arguments needed to pass to transform. Please refer to - https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options. + https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options. Returns diff --git a/src/autogluon/cloud/predictor/multimodal_cloud_predictor.py b/src/autogluon/cloud/predictor/multimodal_cloud_predictor.py index b8d0b1c..48976bc 100644 --- a/src/autogluon/cloud/predictor/multimodal_cloud_predictor.py +++ b/src/autogluon/cloud/predictor/multimodal_cloud_predictor.py @@ -20,7 +20,7 @@ class MultiModalCloudPredictor(CloudPredictor): def __init__(self, *args, **kwargs) -> None: warnings.warn( "AutoGluon Multimodal is on a deprecation path. " - "MultiModalCloudPredictor will be removed in autogluon.cloud v0.7.0.", + "MultiModalCloudPredictor will be removed in a future release.", FutureWarning, stacklevel=2, ) diff --git a/src/autogluon/cloud/scripts/sagemaker_scripts/train.py b/src/autogluon/cloud/scripts/sagemaker_scripts/train.py index 52473dc..f4efaac 100644 --- a/src/autogluon/cloud/scripts/sagemaker_scripts/train.py +++ b/src/autogluon/cloud/scripts/sagemaker_scripts/train.py @@ -81,7 +81,7 @@ def prepare_data(data_file, predictor_type, ag_args, static_features_df=None): print(f"Args: {args}") - # See SageMaker-specific environment variables: https://sagemaker.readthedocs.io/en/stable/overview.html#prepare-a-training-script + # See SageMaker-specific environment variables: https://sagemaker.readthedocs.io/en/v2/overview.html#prepare-a-training-script os.makedirs(args.output_data_dir, mode=0o777, exist_ok=True) ag_args_file = get_input_path(args.ag_args) diff --git a/src/autogluon/cloud/utils/ag_sagemaker.py b/src/autogluon/cloud/utils/ag_sagemaker.py index f2a76e4..8a247b7 100644 --- a/src/autogluon/cloud/utils/ag_sagemaker.py +++ b/src/autogluon/cloud/utils/ag_sagemaker.py @@ -31,7 +31,7 @@ def transform(self, **kwargs): return self._session.transform(**kwargs) -# Estimator documentation: https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#estimators +# Estimator documentation: https://sagemaker.readthedocs.io/en/v2/api/training/estimators.html#estimators class AutoGluonSagemakerEstimator(Estimator): def __init__( self, @@ -132,7 +132,7 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na return init_params -# Documentation for Model: https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model +# Documentation for Model: https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model class AutoGluonSagemakerInferenceModel(Model): def __init__( self, @@ -253,7 +253,7 @@ def prepare_container_def( ) -# Predictor documentation: https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html +# Predictor documentation: https://sagemaker.readthedocs.io/en/v2/api/inference/predictors.html class AutoGluonRealtimePredictor(Predictor): def __init__(self, *args, **kwargs): super().__init__(*args, serializer=AutoGluonSerializer(), deserializer=PandasDeserializer(), **kwargs) @@ -264,7 +264,7 @@ def __init__(self, *args, **kwargs): super().__init__(*args, serializer=MultiModalSerializer(), deserializer=PandasDeserializer(), **kwargs) -# Predictor documentation: https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html +# Predictor documentation: https://sagemaker.readthedocs.io/en/v2/api/inference/predictors.html # SageMaker can only take in csv format for batch transformation because files need to be easily splitable to be batch processed. class AutoGluonBatchPredictor(Predictor): def __init__(self, *args, **kwargs):