Improve permission

docs/tutorials/autogluon-cloud.md

@@ -29,7 +29,8 @@ To help you to setup the necessary permissions, you can generate trust relations
 ```python
 from autogluon.cloud import TabularCloudPredictor  # Can be other CloudPredictor as well
 
-TabularCloudPredictor.generate_trust_relationship_and_iam_policy_file(
+TabularCloudPredictor.generate_default_permission(
+    backend="BACKNED_YOU_WANT"  # We currently support sagemaker and ray_aws
     account_id="YOUR_ACCOUNT_ID",  # The AWS account ID you plan to use for CloudPredictor.
     cloud_output_bucket="S3_BUCKET"  # S3 bucket name where intermediate artifacts will be uploaded and trained models should be saved. You need to create this bucket beforehand.
 )

src/autogluon/cloud/backend/backend_factory.py

@@ -1,6 +1,6 @@
 from .backend import Backend
 from .multimodal_sagemaker_backend import MultiModalSagemakerBackend
-from .ray_aws_backend import TabularRayAWSBackend
+from .ray_aws_backend import RayAWSBackend, TabularRayAWSBackend
 from .sagemaker_backend import SagemakerBackend
 from .tabular_sagemaker_backend import TabularSagemakerBackend
 from .timeseries_sagemaker_backend import TimeSeriesSagemakerBackend
@@ -12,10 +12,16 @@ class BackendFactory:
         TabularSagemakerBackend,
         MultiModalSagemakerBackend,
         TimeSeriesSagemakerBackend,
+        RayAWSBackend,
         TabularRayAWSBackend,
     ]
     __name_to_backend = {cls.name: cls for cls in __supported_backend}
 
+    @staticmethod
+    def get_backend_cls(backend: str) -> Backend.__class__:
+        assert backend in BackendFactory.__name_to_backend, f"{backend} not supported"
+        return BackendFactory.__name_to_backend[backend]
+
     @staticmethod
     def get_backend(backend: str, **init_args) -> Backend:
         """Return the corresponding backend"""

src/autogluon/cloud/backend/ray_aws_backend.py

@@ -60,7 +60,8 @@ def initialize(self, **kwargs) -> None:
             self.region is not None
         ), "Please setup a region via `export AWS_DEFAULT_REGION=YOUR_REGION` in the terminal"
 
-    def generate_default_permission(self, **kwargs) -> Dict[str, str]:
+    @staticmethod
+    def generate_default_permission(**kwargs) -> Dict[str, str]:
         """Generate default permission file user could use to setup the corresponding entity, i.e. IAM Role in AWS"""
         return RayAWSClusterManager.generate_default_permission(**kwargs)
 

src/autogluon/cloud/backend/ray_backend.py

@@ -53,7 +53,8 @@ def initialize(self, **kwargs) -> None:
         self._fit_job = RayFitJob()
         os.makedirs(os.path.join(self.local_output_path, "job"), exist_ok=True)
 
-    def generate_default_permission(self, **kwargs) -> Dict[str, str]:
+    @staticmethod
+    def generate_default_permission(**kwargs) -> Dict[str, str]:
         """Generate default permission file user could use to setup the corresponding entity, i.e. IAM Role in AWS"""
         return RayClusterManager.generate_default_permission(**kwargs)
 

src/autogluon/cloud/backend/sagemaker_backend.py

@@ -84,8 +84,9 @@ def initialize(self, **kwargs) -> None:
         self._fit_job: SageMakerFitJob = SageMakerFitJob(session=self.sagemaker_session)
         self._batch_transform_jobs = MostRecentInsertedOrderedDict()
 
+    @staticmethod
     def generate_default_permission(
-        self, account_id: str, cloud_output_bucket: str, output_path: Optional[str] = None
+        account_id: str, cloud_output_bucket: str, output_path: Optional[str] = None
     ) -> Dict[str, str]:
         """
         Generate required trust relationship and IAM policy file in json format for CloudPredictor with SageMaker backend.

src/autogluon/cloud/cluster/ray_aws_cluster_manager.py

@@ -1,7 +1,7 @@
 import json
 import logging
 import os
-from typing import Dict
+from typing import Dict, Optional
 
 from ..utils.iam import replace_iam_policy_place_holder, replace_trust_relationship_place_holder
 from ..utils.ray_aws_iam import (
@@ -21,7 +21,9 @@ def __init__(self, config: str, cloud_output_bucket: str, **kwargs) -> None:
         self.cloud_output_bucket = cloud_output_bucket
 
     @staticmethod
-    def generate_default_permission(account_id: str, cloud_output_bucket: str, output_path: str) -> Dict[str, str]:
+    def generate_default_permission(
+        account_id: str, cloud_output_bucket: str, output_path: Optional[str] = None
+    ) -> Dict[str, str]:
         """
         Generate trust relationship and iam policy required to manage cluster
         Users can use the generated files to create an IAM role for themselves.

src/autogluon/cloud/predictor/cloud_predictor.py

@@ -27,6 +27,7 @@
 
 class CloudPredictor(ABC):
     predictor_file_name = "CloudPredictor.pkl"
+    backend_map = {}
 
     def __init__(
         self,
@@ -83,14 +84,6 @@ def predictor_type(self) -> str:
         """
         raise NotImplementedError
 
-    @property
-    @abstractmethod
-    def backend_map(self) -> Dict:
-        """
-        Map between general backend to module specific backend
-        """
-        raise NotImplementedError
-
     @property
     def is_fit(self) -> bool:
         """
@@ -107,7 +100,8 @@ def endpoint_name(self) -> Optional[str]:
             return self.backend.endpoint.endpoint_name
         return None
 
-    def generate_default_permission(self, **kwargs) -> Dict[str, str]:
+    @staticmethod
+    def generate_default_permission(backend: str = SAGEMAKER, **kwargs) -> Dict[str, str]:
         """
         Generate required permission file in json format for CloudPredictor with your choice of backend.
         Users can use the generated files to create an entity for themselves.
@@ -122,7 +116,7 @@ def generate_default_permission(self, **kwargs) -> Dict[str, str]:
         ------
         A dict containing the trust relationship and IAM policy files paths
         """
-        return self.backend.generate_default_permission(**kwargs)
+        return BackendFactory.get_backend_cls(backend=backend).generate_default_permission(**kwargs)
 
     def info(self) -> Dict[str, Any]:
         """

src/autogluon/cloud/predictor/multimodal_cloud_predictor.py

@@ -1,5 +1,4 @@
 import logging
-from typing import Dict
 
 from ..backend.constant import MULTIMODL_SAGEMAKER, SAGEMAKER
 from .cloud_predictor import CloudPredictor
@@ -9,6 +8,7 @@
 
 class MultiModalCloudPredictor(CloudPredictor):
     predictor_file_name = "MultiModalCloudPredictor.pkl"
+    backend_map = {SAGEMAKER: MULTIMODL_SAGEMAKER}
 
     @property
     def predictor_type(self) -> str:
@@ -17,13 +17,6 @@ def predictor_type(self) -> str:
         """
         return "multimodal"
 
-    @property
-    def backend_map(self) -> Dict:
-        """
-        Map between general backend to module specific backend
-        """
-        return {SAGEMAKER: MULTIMODL_SAGEMAKER}
-
     def _get_local_predictor_cls(self):
         from autogluon.multimodal import MultiModalPredictor
 

src/autogluon/cloud/predictor/tabular_cloud_predictor.py

@@ -1,5 +1,4 @@
 import logging
-from typing import Dict
 
 from ..backend.constant import RAY_AWS, SAGEMAKER, TABULAR_RAY_AWS, TABULAR_SAGEMAKER
 from .cloud_predictor import CloudPredictor
@@ -9,6 +8,7 @@
 
 class TabularCloudPredictor(CloudPredictor):
     predictor_file_name = "TabularCloudPredictor.pkl"
+    backend_map = {SAGEMAKER: TABULAR_SAGEMAKER, RAY_AWS: TABULAR_RAY_AWS}
 
     @property
     def predictor_type(self):
@@ -17,13 +17,6 @@ def predictor_type(self):
         """
         return "tabular"
 
-    @property
-    def backend_map(self) -> Dict:
-        """
-        Map between general backend to module specific backend
-        """
-        return {SAGEMAKER: TABULAR_SAGEMAKER, RAY_AWS: TABULAR_RAY_AWS}
-
     def _get_local_predictor_cls(self):
         from autogluon.tabular import TabularPredictor
 

src/autogluon/cloud/predictor/timeseries_cloud_predictor.py

@@ -13,6 +13,7 @@
 
 class TimeSeriesCloudPredictor(CloudPredictor):
     predictor_file_name = "TimeSeriesCloudPredictor.pkl"
+    backend_map = {SAGEMAKER: TIMESERIES_SAGEMAKER}
 
     @property
     def predictor_type(self):
@@ -21,13 +22,6 @@ def predictor_type(self):
         """
         return "timeseries"
 
-    @property
-    def backend_map(self) -> Dict:
-        """
-        Map between general backend to module specific backend
-        """
-        return {SAGEMAKER: TIMESERIES_SAGEMAKER}
-
     def _get_local_predictor_cls(self):
         from autogluon.timeseries import TimeSeriesPredictor
 

tests/unittests/general/test_general.py

@@ -1,13 +1,18 @@
 import json
 import tempfile
 
-from autogluon.cloud.backend import SagemakerBackend
+import pytest
 
+from autogluon.cloud import TabularCloudPredictor
+from autogluon.cloud.backend.constant import RAY_AWS, SAGEMAKER
 
-def test_generate_trust_relationship_and_iam_policy():
+
+@pytest.mark.parametrize("backend", [RAY_AWS, SAGEMAKER])
+def test_generate_default_permission(backend):
     with tempfile.TemporaryDirectory() as root:
-        backend = SagemakerBackend(local_output_path="dummy", cloud_output_path="dummy", predictor_type="dummy")
-        paths = backend.generate_default_permission(account_id="foo", cloud_output_bucket="foo", output_path=root)
+        paths = TabularCloudPredictor.generate_default_permission(
+            backend=backend, account_id="foo", cloud_output_bucket="foo", output_path=root
+        )
         trust_relationship_path, iam_policy_path = paths["trust_relationship"], paths["iam_policy"]
         for path in [trust_relationship_path, iam_policy_path]:
             with open(path, "r") as file: