change: Add Pipeline annotation in model base class and tensorflow estimator

Model annotate update

change: Add PipelineVariable annotation to composite argument of training

go with model base and tf

Author: Dewen Qi

src/sagemaker/amazon/amazon_estimator.py

@@ -16,6 +16,7 @@
 import json
 import logging
 import tempfile
+from typing import Union
 
 from six.moves.urllib.parse import urlparse
 
@@ -27,6 +28,7 @@
 from sagemaker.estimator import EstimatorBase, _TrainingJob
 from sagemaker.inputs import FileSystemInput, TrainingInput
 from sagemaker.utils import sagemaker_timestamp
+from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.workflow.pipeline_context import runnable_by_pipeline
 
 logger = logging.getLogger(__name__)
@@ -304,7 +306,12 @@ class RecordSet(object):
     """Placeholder docstring"""
 
     def __init__(
-        self, s3_data, num_records, feature_dim, s3_data_type="ManifestFile", channel="train"
+        self,
+        s3_data: Union[str, PipelineVariable],
+        num_records: int,
+        feature_dim: int,
+        s3_data_type: Union[str, PipelineVariable] = "ManifestFile",
+        channel: Union[str, PipelineVariable] = "train",
     ):
         """A collection of Amazon :class:~`Record` objects serialized and stored in S3.
 

src/sagemaker/debugger/debugger.py

@@ -24,12 +24,15 @@
 
 from abc import ABC
 
+from typing import Union, Optional, List, Dict
+
 import attr
 
 import smdebug_rulesconfig as rule_configs
 
 from sagemaker import image_uris
 from sagemaker.utils import build_dict
+from sagemaker.workflow.entities import PipelineVariable
 
 framework_name = "debugger"
 DEBUGGER_FLAG = "USE_SMDEBUG"
@@ -311,17 +314,17 @@ def sagemaker(
     @classmethod
     def custom(
         cls,
-        name,
-        image_uri,
-        instance_type,
-        volume_size_in_gb,
-        source=None,
-        rule_to_invoke=None,
-        container_local_output_path=None,
-        s3_output_path=None,
-        other_trials_s3_input_paths=None,
-        rule_parameters=None,
-        collections_to_save=None,
+        name: str,
+        image_uri: Union[str, PipelineVariable],
+        instance_type: Union[str, PipelineVariable],
+        volume_size_in_gb: Union[int, PipelineVariable],
+        source: Optional[str] = None,
+        rule_to_invoke: Optional[Union[str, PipelineVariable]] = None,
+        container_local_output_path: Optional[Union[str, PipelineVariable]] = None,
+        s3_output_path: Optional[Union[str, PipelineVariable]] = None,
+        other_trials_s3_input_paths: Optional[List[Union[str, PipelineVariable]]] = None,
+        rule_parameters: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        collections_to_save: Optional[List["CollectionConfig"]] = None,
         actions=None,
     ):
         """Initialize a ``Rule`` object for a *custom* debugging rule.
@@ -610,10 +613,10 @@ class DebuggerHookConfig(object):
 
     def __init__(
         self,
-        s3_output_path=None,
-        container_local_output_path=None,
-        hook_parameters=None,
-        collection_configs=None,
+        s3_output_path: Optional[Union[str, PipelineVariable]] = None,
+        container_local_output_path: Optional[Union[str, PipelineVariable]] = None,
+        hook_parameters: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        collection_configs: Optional[List["CollectionConfig"]] = None,
     ):
         """Initialize the DebuggerHookConfig instance.
 
@@ -679,7 +682,11 @@ def _to_request_dict(self):
 class TensorBoardOutputConfig(object):
     """Create a tensor ouput configuration object for debugging visualizations on TensorBoard."""
 
-    def __init__(self, s3_output_path, container_local_output_path=None):
+    def __init__(
+        self,
+        s3_output_path: Union[str, PipelineVariable],
+        container_local_output_path: Optional[Union[str, PipelineVariable]] = None,
+    ):
         """Initialize the TensorBoardOutputConfig instance.
 
         Args:
@@ -708,7 +715,11 @@ def _to_request_dict(self):
 class CollectionConfig(object):
     """Creates tensor collections for SageMaker Debugger."""
 
-    def __init__(self, name, parameters=None):
+    def __init__(
+        self,
+        name: Union[str, PipelineVariable],
+        parameters: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+    ):
         """Constructor for collection configuration.
 
         Args:

src/sagemaker/debugger/profiler_config.py

@@ -13,7 +13,10 @@
 """Configuration for collecting system and framework metrics in SageMaker training jobs."""
 from __future__ import absolute_import
 
+from typing import Optional, Union
+
 from sagemaker.debugger.framework_profile import FrameworkProfile
+from sagemaker.workflow.entities import PipelineVariable
 
 
 class ProfilerConfig(object):
@@ -26,9 +29,9 @@ class ProfilerConfig(object):
 
     def __init__(
         self,
-        s3_output_path=None,
-        system_monitor_interval_millis=None,
-        framework_profile_params=None,
+        s3_output_path: Optional[Union[str, PipelineVariable]] = None,
+        system_monitor_interval_millis: Optional[Union[int, PipelineVariable]] = None,
+        framework_profile_params: Optional[FrameworkProfile] = None,
     ):
         """Initialize a ``ProfilerConfig`` instance.
 

src/sagemaker/drift_check_baselines.py

@@ -13,21 +13,25 @@
 """This file contains code related to drift check baselines"""
 from __future__ import absolute_import
 
+from typing import Optional
+
+from sagemaker.model_metrics import MetricsSource, FileSource
+
 
 class DriftCheckBaselines(object):
     """Accepts drift check baselines parameters for conversion to request dict."""
 
     def __init__(
         self,
-        model_statistics=None,
-        model_constraints=None,
-        model_data_statistics=None,
-        model_data_constraints=None,
-        bias_config_file=None,
-        bias_pre_training_constraints=None,
-        bias_post_training_constraints=None,
-        explainability_constraints=None,
-        explainability_config_file=None,
+        model_statistics: Optional[MetricsSource] = None,
+        model_constraints: Optional[MetricsSource] = None,
+        model_data_statistics: Optional[MetricsSource] = None,
+        model_data_constraints: Optional[MetricsSource] = None,
+        bias_config_file: Optional[FileSource] = None,
+        bias_pre_training_constraints: Optional[MetricsSource] = None,
+        bias_post_training_constraints: Optional[MetricsSource] = None,
+        explainability_constraints: Optional[MetricsSource] = None,
+        explainability_config_file: Optional[FileSource] = None,
     ):
         """Initialize a ``DriftCheckBaselines`` instance and turn parameters into dict.
 

src/sagemaker/estimator.py

@@ -50,6 +50,7 @@
     validate_source_code_input_against_pipeline_variables,
 )
 from sagemaker.inputs import TrainingInput, FileSystemInput
+from sagemaker.instance_group import InstanceGroup
 from sagemaker.job import _Job
 from sagemaker.jumpstart.utils import (
     add_jumpstart_tags,
@@ -149,7 +150,7 @@ def __init__(
         code_location: Optional[str] = None,
         entry_point: Optional[Union[str, PipelineVariable]] = None,
         dependencies: Optional[List[Union[str]]] = None,
-        instance_groups: Optional[Dict[str, Union[str, int]]] = None,
+        instance_groups: Optional[List[InstanceGroup]] = None,
         **kwargs,
     ):
         """Initialize an ``EstimatorBase`` instance.
@@ -1580,6 +1581,8 @@ def _get_instance_type(self):
 
         for instance_group in self.instance_groups:
             instance_type = instance_group.instance_type
+            if is_pipeline_variable(instance_type):
+                continue
             match = re.match(r"^ml[\._]([a-z\d]+)\.?\w*$", instance_type)
 
             if match:
@@ -2179,7 +2182,7 @@ def __init__(
         code_location: Optional[str] = None,
         entry_point: Optional[Union[str, PipelineVariable]] = None,
         dependencies: Optional[List[str]] = None,
-        instance_groups: Optional[Dict[str, Union[str, int]]] = None,
+        instance_groups: Optional[List[InstanceGroup]] = None,
         **kwargs,
     ):
         """Initialize an ``Estimator`` instance.
@@ -2874,7 +2877,15 @@ def _validate_and_set_debugger_configs(self):
         # Disable debugger if checkpointing is enabled by the customer
         if self.checkpoint_s3_uri and self.checkpoint_local_path and self.debugger_hook_config:
             if self._framework_name in {"mxnet", "pytorch", "tensorflow"}:
-                if self.instance_count > 1 or (
+                if is_pipeline_variable(self.instance_count):
+                    logger.warning(
+                        "SMDebug does not currently support distributed training jobs "
+                        "with checkpointing enabled. Therefore, to allow parameterized "
+                        "instance_count and allow to change it to any values in execution time, "
+                        "the debugger_hook_config is disabled."
+                    )
+                    self.debugger_hook_config = False
+                elif self.instance_count > 1 or (
                     hasattr(self, "distribution")
                     and self.distribution is not None  # pylint: disable=no-member
                 ):

src/sagemaker/fw_utils.py

@@ -871,6 +871,14 @@ def validate_distribution_instance(sagemaker_session, distribution, instance_typ
         # Strategy modelparallel is not enabled
         return
 
+    if is_pipeline_variable(instance_type):
+        logger.warning(
+            "instance_type is a pipeline variable, which is only interpreted in "
+            "pipeline execution time. As modelparallel only runs on GPU-enabled "
+            "instances, in execution time, the specified instance type has to support GPU."
+        )
+        return
+
     instance_desc = sagemaker_session.boto_session.client("ec2").describe_instance_types(
         InstanceTypes=[f"{instance_type}"]
     )

src/sagemaker/huggingface/training_compiler/config.py

@@ -13,8 +13,10 @@
 """Configuration for the SageMaker Training Compiler."""
 from __future__ import absolute_import
 import logging
+from typing import Union
 
 from sagemaker.training_compiler.config import TrainingCompilerConfig as BaseConfig
+from sagemaker.workflow.entities import PipelineVariable
 
 logger = logging.getLogger(__name__)
 
@@ -26,8 +28,8 @@ class TrainingCompilerConfig(BaseConfig):
 
     def __init__(
         self,
-        enabled=True,
-        debug=False,
+        enabled: Union[bool, PipelineVariable] = True,
+        debug: Union[bool, PipelineVariable] = False,
     ):
         """This class initializes a ``TrainingCompilerConfig`` instance.
 

src/sagemaker/inputs.py

@@ -13,8 +13,11 @@
 """Amazon SageMaker channel configurations for S3 data sources and file system data sources"""
 from __future__ import absolute_import, print_function
 
+from typing import Union, Optional, List
 import attr
 
+from sagemaker.workflow.entities import PipelineVariable
+
 FILE_SYSTEM_TYPES = ["FSxLustre", "EFS"]
 FILE_SYSTEM_ACCESS_MODES = ["ro", "rw"]
 
@@ -29,17 +32,17 @@ class TrainingInput(object):
 
     def __init__(
         self,
-        s3_data,
-        distribution=None,
-        compression=None,
-        content_type=None,
-        record_wrapping=None,
-        s3_data_type="S3Prefix",
-        instance_groups=None,
-        input_mode=None,
-        attribute_names=None,
-        target_attribute_name=None,
-        shuffle_config=None,
+        s3_data: Union[str, PipelineVariable],
+        distribution: Optional[Union[str, PipelineVariable]] = None,
+        compression: Optional[Union[str, PipelineVariable]] = None,
+        content_type: Optional[Union[str, PipelineVariable]] = None,
+        record_wrapping: Optional[Union[str, PipelineVariable]] = None,
+        s3_data_type: Union[str, PipelineVariable] = "S3Prefix",
+        instance_groups: Optional[List[Union[str, PipelineVariable]]] = None,
+        input_mode: Optional[Union[str, PipelineVariable]] = None,
+        attribute_names: Optional[List[Union[str, PipelineVariable]]] = None,
+        target_attribute_name: Optional[Union[str, PipelineVariable]] = None,
+        shuffle_config: Optional["ShuffleConfig"] = None,
     ):
         r"""Create a definition for input data used by an SageMaker training job.
 

src/sagemaker/metadata_properties.py

@@ -13,16 +13,20 @@
 """This file contains code related to metadata properties."""
 from __future__ import absolute_import
 
+from typing import Optional, Union
+
+from sagemaker.workflow.entities import PipelineVariable
+
 
 class MetadataProperties(object):
     """Accepts metadata properties parameters for conversion to request dict."""
 
     def __init__(
         self,
-        commit_id=None,
-        repository=None,
-        generated_by=None,
-        project_id=None,
+        commit_id: Optional[Union[str, PipelineVariable]] = None,
+        repository: Optional[Union[str, PipelineVariable]] = None,
+        generated_by: Optional[Union[str, PipelineVariable]] = None,
+        project_id: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Initialize a ``MetadataProperties`` instance and turn parameters into dict.