Add native support for v2 config (#3466)

nni/__main__.py

@@ -7,7 +7,7 @@
 import json
 import base64
 
-from .runtime.common import enable_multi_thread, enable_multi_phase
+from .runtime.common import enable_multi_thread
 from .runtime.msg_dispatcher import MsgDispatcher
 from .tools.package_utils import create_builtin_class_instance, create_customized_class_instance
 
@@ -29,10 +29,8 @@ def main():
     exp_params = json.loads(exp_params_decode)
     logger.debug('exp_params json obj: [%s]', json.dumps(exp_params, indent=4))
 
-    if exp_params.get('multiThread'):
+    if exp_params.get('deprecated', {}).get('multiThread'):
         enable_multi_thread()
-    if exp_params.get('multiPhase'):
-        enable_multi_phase()
 
     if exp_params.get('advisor') is not None:
         # advisor is enabled and starts to run
@@ -61,10 +59,10 @@ def main():
 
 
 def _run_advisor(exp_params):
-    if exp_params.get('advisor').get('builtinAdvisorName'):
+    if exp_params.get('advisor').get('name'):
         dispatcher = create_builtin_class_instance(
-            exp_params.get('advisor').get('builtinAdvisorName'),
-            exp_params.get('advisor').get('classArgs'),
+            exp_params['advisor']['name'],
+            exp_params['advisor'].get('classArgs'),
             'advisors')
     else:
         dispatcher = create_customized_class_instance(exp_params.get('advisor'))
@@ -78,26 +76,26 @@ 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/base.py

@@ -101,6 +101,8 @@ def canonical(self: T) -> T:
             elif isinstance(value, ConfigBase):
                 setattr(ret, key, value.canonical())
                 # value will be copied twice, should not be a performance issue anyway
+            elif isinstance(value, Path):
+                setattr(ret, key, str(value))
         return ret
 
     def validate(self) -> None:

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
 
@@ -27,23 +29,27 @@ 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
-    class_directory: Optional[PathLike] = None
+    class_directory: Optional[PathLike] = '.'
     class_args: Optional[Dict[str, Any]] = None
 
 
 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):
@@ -53,19 +59,21 @@ 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
     use_annotation: bool = False
     debug: bool = False
     log_level: Optional[str] = None
-    experiment_working_directory: Optional[PathLike] = None
+    experiment_working_directory: PathLike = '~/nni-experiments'
     tuner_gpu_indices: Optional[Union[List[int], str]] = None
     tuner: Optional[_AlgorithmConfig] = None
     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
@@ -117,9 +131,9 @@ def _validation_rules(self):
     'max_experiment_duration': lambda value: f'{util.parse_time(value)}s' if value is not None else None,
     'experiment_working_directory': util.canonical_path,
     'tuner_gpu_indices': lambda value: [int(idx) for idx in value.split(',')] if isinstance(value, str) else value,
-    'tuner': lambda config: None if config is None or config.name == '_none_' else config,
-    'assessor': lambda config: None if config is None or config.name == '_none_' else config,
-    'advisor': lambda config: None if config is None or config.name == '_none_' else config,
+    'tuner': lambda config: None if config is None or config.name == '_none_' else config.canonical(),
+    'assessor': lambda config: None if config is None or config.name == '_none_' else config.canonical(),
+    'advisor': lambda config: None if config is None or config.name == '_none_' else config.canonical(),
 }
 
 _validation_rules = {