Fix SageMaker SDK v2 documentation links

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

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,
         )

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)

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):