Add native support for v2 config

nni/__main__.py

@@ -78,26 +78,26 @@ def _run_advisor(exp_params):
 
 
 def _create_tuner(exp_params):
-    if exp_params.get('tuner').get('builtinTunerName'):
+    if exp_params['tuner'].get('name'):
         tuner = create_builtin_class_instance(
-            exp_params.get('tuner').get('builtinTunerName'),
-            exp_params.get('tuner').get('classArgs'),
+            exp_params['tuner']['name'],
+            exp_params['tuner'].get('classArgs'),
             'tuners')
     else:
-        tuner = create_customized_class_instance(exp_params.get('tuner'))
+        tuner = create_customized_class_instance(exp_params['tuner'])
     if tuner is None:
         raise AssertionError('Failed to create Tuner instance')
     return tuner
 
 
 def _create_assessor(exp_params):
-    if exp_params.get('assessor').get('builtinAssessorName'):
+    if exp_params['assessor'].get('name'):
         assessor = create_builtin_class_instance(
-            exp_params.get('assessor').get('builtinAssessorName'),
-            exp_params.get('assessor').get('classArgs'),
+            exp_params['assessor']['name'],
+            exp_params['assessor'].get('classArgs'),
             'assessors')
     else:
-        assessor = create_customized_class_instance(exp_params.get('assessor'))
+        assessor = create_customized_class_instance(exp_params['assessor'])
     if assessor is None:
         raise AssertionError('Failed to create Assessor instance')
     return assessor

nni/experiment/config/__init__.py

@@ -9,3 +9,4 @@
 from .kubeflow import *
 from .frameworkcontroller import *
 from .adl import *
+from .shared_storage import *

nni/experiment/config/common.py

@@ -5,6 +5,8 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
+from ruamel.yaml import YAML
+
 from .base import ConfigBase, PathLike
 from . import util
 
@@ -27,13 +29,11 @@ def validate(self):
         super().validate()
         _validate_algo(self)
 
-
 @dataclass(init=False)
 class AlgorithmConfig(_AlgorithmConfig):
     name: str
     class_args: Optional[Dict[str, Any]] = None
 
-
 @dataclass(init=False)
 class CustomAlgorithmConfig(_AlgorithmConfig):
     class_name: str
@@ -44,6 +44,12 @@ class CustomAlgorithmConfig(_AlgorithmConfig):
 class TrainingServiceConfig(ConfigBase):
     platform: str
 
+class SharedStorageConfig(ConfigBase):
+    storage_type: str
+    local_mount_point: str
+    remote_mount_point: str
+    local_mounted: str
+
 
 @dataclass(init=False)
 class ExperimentConfig(ConfigBase):
@@ -53,7 +59,7 @@ class ExperimentConfig(ConfigBase):
     trial_command: str
     trial_code_directory: PathLike = '.'
     trial_concurrency: int
-    trial_gpu_number: Optional[int] = None
+    trial_gpu_number: Optional[int] = None  # TODO: in openpai cannot be None
     max_experiment_duration: Optional[str] = None
     max_trial_number: Optional[int] = None
     nni_manager_ip: Optional[str] = None
@@ -66,6 +72,8 @@ class ExperimentConfig(ConfigBase):
     assessor: Optional[_AlgorithmConfig] = None
     advisor: Optional[_AlgorithmConfig] = None
     training_service: Union[TrainingServiceConfig, List[TrainingServiceConfig]]
+    shared_storage: Optional[SharedStorageConfig] = None
+    _deprecated: Optional[Dict[str, Any]] = None
 
     def __init__(self, training_service_platform: Optional[Union[str, List[str]]] = None, **kwargs):
         base_path = kwargs.pop('_base_path', None)
@@ -100,6 +108,12 @@ def validate(self, initialized_tuner: bool = False) -> None:
             if self.training_service.use_active_gpu is None:
                 raise ValueError('Please set "use_active_gpu"')
 
+    def json(self) -> Dict[str, Any]:
+        obj = super().json()
+        if obj.get('searchSpaceFile'):
+            obj['searchSpace'] = YAML().load(open(obj.pop('searchSpaceFile')))
+        return obj
+
 ## End of public API ##
 
     @property