[Retiarii] support experiment view and resume (#4985)

nni/experiment/config/experiment_config.py

@@ -12,6 +12,7 @@ import json
 import logging
 from pathlib import Path
 from typing import Any, List, Optional, Union
+from typing_extensions import Literal
 
 import yaml
 
@@ -61,6 +62,7 @@ class ExperimentConfig(ConfigBase):
     # In latter case hybrid training services can have different settings.
 
     experiment_name: Optional[str] = None
+    experiment_type: Literal['hpo'] = 'hpo'
     search_space_file: Optional[utils.PathLike] = None
     search_space: Any = None
     trial_command: Optional[str] = None  # training service field

nni/experiment/config/utils/internal.py

@@ -15,6 +15,7 @@ __all__ = [
     'fields', 'is_instance', 'validate_type', 'is_path_like',
     'guess_config_type', 'guess_list_config_type',
     'training_service_config_factory', 'load_training_service_config',
+    'load_experiment_config', 'get_experiment_cls_using_config',
     'get_ipv4_address'
 ]
 
@@ -25,7 +26,7 @@ import json
 import os.path
 from pathlib import Path
 import socket
-import typing
+from typing import Tuple, TYPE_CHECKING, get_type_hints
 
 import typeguard
 
@@ -33,8 +34,12 @@ import nni.runtime.config
 
 from .public import is_missing
 
-if typing.TYPE_CHECKING:
+if TYPE_CHECKING:
+    from nni.nas.experiment.pytorch import RetiariiExperiment
+    from nni.nas.experiment.config import RetiariiExeConfig
+    from ...experiment import Experiment
     from ..base import ConfigBase
+    from ..experiment_config import ExperimentConfig
     from ..training_service import TrainingServiceConfig
 
 ## handle relative path ##
@@ -78,7 +83,7 @@ def fields(config: ConfigBase) -> list[dataclasses.Field]:
     # Similar to `dataclasses.fields()`, but use `typing.get_types_hints()` to get `field.type`.
     # This is useful when postponed evaluation is enabled.
     ret = [copy.copy(field) for field in dataclasses.fields(config)]
-    types = typing.get_type_hints(type(config))
+    types = get_type_hints(type(config))
     for field in ret:
         field.type = types[field.name]
     return ret
@@ -198,3 +203,31 @@ def get_ipv4_address() -> str:
     addr = s.getsockname()[0]
     s.close()
     return addr
+
+def load_experiment_config(config_json: dict) -> ExperimentConfig | RetiariiExeConfig:
+    _, exp_conf_cls = get_experiment_cls_using_config(config_json)
+    return exp_conf_cls(**config_json)
+
+def get_experiment_cls_using_config(config_json: dict) -> Tuple[type[Experiment] | type[RetiariiExperiment],
+                                                                type[ExperimentConfig] | type[RetiariiExeConfig]]:
+    # avoid circular import and unnecessary dependency on pytorch
+    if 'experimentType' in config_json:
+        if config_json['experimentType'] == 'hpo':
+            from ...experiment import Experiment
+            from ..experiment_config import ExperimentConfig
+            return Experiment, ExperimentConfig
+        elif config_json['experimentType'] == 'nas':
+            from nni.nas.experiment.pytorch import RetiariiExperiment
+            from nni.nas.experiment.config import RetiariiExeConfig
+            return RetiariiExperiment, RetiariiExeConfig
+        else:
+            raise ValueError(f'Unknown experiment_type: {config_json["experimentType"]}')
+    else:
+        if 'executionEngine' in config_json:
+            from nni.nas.experiment.pytorch import RetiariiExperiment
+            from nni.nas.experiment.config import RetiariiExeConfig
+            return RetiariiExperiment, RetiariiExeConfig
+        else:
+            from ...experiment import Experiment
+            from ..experiment_config import ExperimentConfig
+            return Experiment, ExperimentConfig

nni/experiment/launcher.py

@@ -18,6 +18,7 @@ from typing import Any, TYPE_CHECKING, cast
 from typing_extensions import Literal
 
 from .config import ExperimentConfig
+from .config.utils import load_experiment_config
 from . import rest
 from ..tools.nnictl.config_utils import Experiments, Config
 from ..tools.nnictl.nnictl_utils import update_experiment
@@ -203,7 +204,7 @@ def _save_experiment_information(experiment_id: str, port: int, start_time: int,
 
 def get_stopped_experiment_config(exp_id, exp_dir=None):
     config_json = get_stopped_experiment_config_json(exp_id, exp_dir)  # type: ignore
-    config = ExperimentConfig(**config_json)  # type: ignore
+    config = load_experiment_config(config_json)  # type: ignore
     if exp_dir and not os.path.samefile(exp_dir, config.experiment_working_directory):
         msg = 'Experiment working directory provided in command line (%s) is different from experiment config (%s)'
         _logger.warning(msg, exp_dir, config.experiment_working_directory)

nni/nas/execution/api.py

@@ -3,8 +3,9 @@
 
 import time
 import warnings
-from typing import Iterable
+from typing import Iterable, cast
 
+from nni.experiment.config.training_services import RemoteConfig
 from nni.nas.execution.common import (
     Model, ModelStatus,
     AbstractExecutionEngine,
@@ -14,11 +15,44 @@ from nni.nas.execution.common import (
 _execution_engine = None
 _default_listener = None
 
-__all__ = ['get_execution_engine', 'get_and_register_default_listener',
+__all__ = ['init_execution_engine', 'get_execution_engine', 'get_and_register_default_listener',
            'list_models', 'submit_models', 'wait_models', 'query_available_resources',
            'set_execution_engine', 'is_stopped_exec', 'budget_exhausted']
 
 
+def init_execution_engine(config, port, url_prefix) -> AbstractExecutionEngine:
+    from ..experiment.config import (
+        BaseEngineConfig, PyEngineConfig,
+        CgoEngineConfig, BenchmarkEngineConfig
+    )
+    if isinstance(config.execution_engine, BaseEngineConfig):
+        from .pytorch.graph import BaseExecutionEngine
+        return BaseExecutionEngine(port, url_prefix)
+    elif isinstance(config.execution_engine, CgoEngineConfig):
+        from .pytorch.cgo.engine import CGOExecutionEngine
+
+        assert not isinstance(config.training_service, list) \
+            and config.training_service.platform == 'remote', \
+            "CGO execution engine currently only supports remote training service"
+        assert config.execution_engine.batch_waiting_time is not None \
+            and config.execution_engine.max_concurrency_cgo is not None
+        return CGOExecutionEngine(cast(RemoteConfig, config.training_service),
+                                    max_concurrency=config.execution_engine.max_concurrency_cgo,
+                                    batch_waiting_time=config.execution_engine.batch_waiting_time,
+                                    rest_port=port,
+                                    rest_url_prefix=url_prefix)
+    elif isinstance(config.execution_engine, PyEngineConfig):
+        from .pytorch.simplified import PurePythonExecutionEngine
+        return PurePythonExecutionEngine(port, url_prefix)
+    elif isinstance(config.execution_engine, BenchmarkEngineConfig):
+        from .pytorch.benchmark import BenchmarkExecutionEngine
+        assert config.execution_engine.benchmark is not None, \
+            '"benchmark" must be set when benchmark execution engine is used.'
+        return BenchmarkExecutionEngine(config.execution_engine.benchmark)
+    else:
+        raise ValueError(f'Unsupported engine type: {config.execution_engine}')
+
+
 def set_execution_engine(engine: AbstractExecutionEngine) -> None:
     global _execution_engine
     if _execution_engine is not None:

nni/nas/execution/common/graph.py

@@ -124,14 +124,22 @@ class Model:
     def _load(ir: Any) -> 'Model':
         model = Model(_internal=True)
         for graph_name, graph_data in ir.items():
-            if graph_name != '_evaluator':
+            if graph_name not in ['_evaluator', 'model_id', 'python_class', 'python_init_params']:
                 Graph._load(model, graph_name, graph_data)._register()
+        if 'model_id' in ir: # backward compatibility
+            model.model_id = ir['model_id']
+            model.python_class = ir['python_class']
+            model.python_init_params = ir['python_init_params']
         if '_evaluator' in ir:
             model.evaluator = Evaluator._load(ir['_evaluator'])
         return model
 
     def _dump(self) -> Any:
         ret = {name: graph._dump() for name, graph in self.graphs.items()}
+        # NOTE: only dump some necessary member variable, will be refactored
+        ret['model_id'] = self.model_id
+        ret['python_class'] = self.python_class
+        ret['python_init_params'] = self.python_init_params
         if self.evaluator is not None:
             ret['_evaluator'] = self.evaluator._dump()
         return ret

nni/nas/execution/common/integration.py

@@ -233,3 +233,7 @@ class RetiariiAdvisor(MsgDispatcherBase):
             else:
                 return value
         return value
+
+    def handle_import_data(self, data):
+        # FIXME: ignore imported data for now, as strategy has not supported resume
+        pass

nni/nas/experiment/config/experiment_config.py

@@ -5,6 +5,7 @@ import os
 import sys
 from dataclasses import dataclass
 from typing import Any, Dict, Union, Optional
+from typing_extensions import Literal
 
 from nni.experiment.config import utils, ExperimentConfig
 
@@ -12,12 +13,20 @@ from .engine_config import ExecutionEngineConfig
 
 __all__ = ['RetiariiExeConfig']
 
-def execution_engine_config_factory(engine_name):
-    # FIXME: may move this function to experiment utils in future
+# TODO: may move this function to experiment utils in future
+def init_execution_engine_config(engine_config: Union[str, dict]) -> ExecutionEngineConfig:
+    if isinstance(engine_config, str):
+        engine_name = engine_config
+    else:
+        engine_name = engine_config['name']
     cls = _get_ee_config_class(engine_name)
     if cls is None:
         raise ValueError(f'Invalid execution engine name: {engine_name}')
-    return cls()
+    engine = cls()
+    if isinstance(engine_config, dict):
+        for key, value in engine_config.items():
+            setattr(engine, key, value)
+    return engine
 
 def _get_ee_config_class(engine_name):
     for cls in ExecutionEngineConfig.__subclasses__():
@@ -28,6 +37,7 @@ def _get_ee_config_class(engine_name):
 @dataclass(init=False)
 class RetiariiExeConfig(ExperimentConfig):
     # FIXME: refactor this class to inherit from a new common base class with HPO config
+    experiment_type: Literal['nas'] = 'nas'
     search_space: Any = ''
     trial_code_directory: utils.PathLike = '.'
     trial_command: str = '_reserved'
@@ -42,34 +52,51 @@ class RetiariiExeConfig(ExperimentConfig):
                  execution_engine: Union[str, ExecutionEngineConfig] = 'py',
                  **kwargs):
         super().__init__(training_service_platform, **kwargs)
-        self.execution_engine = execution_engine
+
+        if not utils.is_missing(self.execution_engine):
+            # this branch means kwargs is not {} and self.execution_engine has been assigned in super(),
+            # reassign it because super() may instantiate ExecutionEngineConfig by mistake
+            self.execution_engine = init_execution_engine_config(kwargs['executionEngine'])
+            del kwargs['executionEngine']
+        elif isinstance(execution_engine, str):
+            self.execution_engine = init_execution_engine_config(execution_engine)
+        else:
+            self.execution_engine = execution_engine
+
+        self._is_complete_config = False
+        if self.search_space != '' and self.trial_code_directory != '.' and self.trial_command != '_reserved':
+            # only experiment view and resume have complete config in init, as the config is directly loaded
+            self._is_complete_config = True
 
     def _canonicalize(self, _parents):
-        msg = '{} is not supposed to be set in Retiarii experiment by users, your config is {}.'
-        if self.search_space != '':
-            raise ValueError(msg.format('search_space', self.search_space))
-        # TODO: maybe we should also allow users to specify trial_code_directory
-        if str(self.trial_code_directory) != '.' and not os.path.isabs(self.trial_code_directory):
-            raise ValueError(msg.format('trial_code_directory', self.trial_code_directory))
-
-        trial_command_tmpl = '{envs} {python} -m nni.retiarii.trial_entry {execution_engine}'
-        if self.trial_command != '_reserved' and '-m nni.retiarii.trial_entry' not in self.trial_command:
-            raise ValueError(msg.format('trial_command', self.trial_command))
-
-        if isinstance(self.execution_engine, str):
-            self.execution_engine = execution_engine_config_factory(self.execution_engine)
-
-        _trial_command_params = {
-            # Default variables
-            'envs': '',
-            # TODO: maybe use sys.executable rendered in trial side (e.g., trial_runner)
-            'python': sys.executable,
-            'execution_engine': self.execution_engine.name,
-
-            # This should override the parameters above.
-            **(self._trial_command_params or {})
-        }
-
-        self.trial_command = trial_command_tmpl.format(**_trial_command_params).strip()
+        if not self._is_complete_config:
+            msg = '{} is not supposed to be set in Retiarii experiment by users, your config is {}.'
+            if self.search_space != '':
+                raise ValueError(msg.format('search_space', self.search_space))
+            # TODO: maybe we should also allow users to specify trial_code_directory
+            if str(self.trial_code_directory) != '.' and not os.path.isabs(self.trial_code_directory):
+                raise ValueError(msg.format('trial_code_directory', self.trial_code_directory))
+
+            trial_command_tmpl = '{envs} {python} -m nni.retiarii.trial_entry {execution_engine}'
+            if self.trial_command != '_reserved' and '-m nni.retiarii.trial_entry' not in self.trial_command:
+                raise ValueError(msg.format('trial_command', self.trial_command))
+
+            # this canonicalize is necessary because users may assign new execution engine str
+            # after execution engine config is instantiated
+            if isinstance(self.execution_engine, str):
+                self.execution_engine = init_execution_engine_config(self.execution_engine)
+
+            _trial_command_params = {
+                # Default variables
+                'envs': '',
+                # TODO: maybe use sys.executable rendered in trial side (e.g., trial_runner)
+                'python': sys.executable,
+                'execution_engine': self.execution_engine.name,
+
+                # This should override the parameters above.
+                **(self._trial_command_params or {})
+            }
+
+            self.trial_command = trial_command_tmpl.format(**_trial_command_params).strip()
 
         super()._canonicalize([self])

nni/nas/experiment/pytorch.py

@@ -6,20 +6,22 @@ from __future__ import annotations
 __all__ = ['RetiariiExeConfig', 'RetiariiExperiment', 'preprocess_model', 'debug_mutated_model']
 
 import logging
-
+import os
+import time
 import warnings
 from threading import Thread
-from typing import Any, List, cast
+from typing import Any, List, cast, Tuple, TYPE_CHECKING
 
 import colorama
 
 import torch
 import torch.nn as nn
-from nni.experiment import Experiment, RunMode
-from nni.experiment.config.training_services import RemoteConfig
+from nni.common import dump, load
+from nni.experiment import Experiment, RunMode, launcher
 
 from nni.nas.execution import list_models, set_execution_engine
-from nni.nas.execution.common import RetiariiAdvisor, get_mutation_dict
+from nni.nas.execution.api import init_execution_engine
+from nni.nas.execution.common import RetiariiAdvisor, get_mutation_dict, Model
 from nni.nas.execution.pytorch.codegen import model_to_pytorch_script
 from nni.nas.execution.pytorch.converter import convert_to_graph
 from nni.nas.execution.pytorch.converter.graph_gen import GraphConverterWithShape
@@ -36,6 +38,9 @@ from .config import (
     PyEngineConfig, CgoEngineConfig, BenchmarkEngineConfig
 )
 
+if TYPE_CHECKING:
+    from nni.experiment.config.utils import PathLike
+
 _logger = logging.getLogger(__name__)
 
 
@@ -170,7 +175,7 @@ class RetiariiExperiment(Experiment):
     ...     final_model = Net()
     """
 
-    def __init__(self, base_model: nn.Module,
+    def __init__(self, base_model: nn.Module = cast(nn.Module, None),
                  evaluator: Evaluator = cast(Evaluator, None),
                  applied_mutators: List[Mutator] = cast(List[Mutator], None),
                  strategy: BaseStrategy = cast(BaseStrategy, None),
@@ -183,8 +188,16 @@ class RetiariiExperiment(Experiment):
                           'Please consider specifying it as a positional argument, or use `evaluator`.', DeprecationWarning)
             evaluator = trainer
 
-        if evaluator is None:
-            raise ValueError('Evaluator should not be none.')
+        # base_model is None means the experiment is in resume or view mode
+        if base_model is not None:
+            if evaluator is None:
+                raise ValueError('Evaluator should not be none.')
+            # check for sanity
+            if not is_model_wrapped(base_model):
+                warnings.warn(colorama.Style.BRIGHT + colorama.Fore.RED +
+                    '`@model_wrapper` is missing for the base model. The experiment might still be able to run, '
+                    'but it may cause inconsistent behavior compared to the time when you add it.' + colorama.Style.RESET_ALL,
+                    RuntimeWarning)
 
         self.base_model = base_model
         self.evaluator: Evaluator = evaluator
@@ -194,58 +207,39 @@ class RetiariiExperiment(Experiment):
         self._dispatcher = None
         self._dispatcher_thread = None
 
-        # check for sanity
-        if not is_model_wrapped(base_model):
-            warnings.warn(colorama.Style.BRIGHT + colorama.Fore.RED +
-                          '`@model_wrapper` is missing for the base model. The experiment might still be able to run, '
-                          'but it may cause inconsistent behavior compared to the time when you add it.' + colorama.Style.RESET_ALL,
-                          RuntimeWarning)
-
-    def _run_strategy(self, config: RetiariiExeConfig):
-        base_model_ir, self.applied_mutators = preprocess_model(
-            self.base_model, self.evaluator, self.applied_mutators,
-            full_ir=not isinstance(config.execution_engine, (PyEngineConfig, BenchmarkEngineConfig)),
-            dummy_input=config.execution_engine.dummy_input
-                if isinstance(config.execution_engine, (BaseEngineConfig, CgoEngineConfig)) else None
-        )
-
+    def _run_strategy(self, base_model_ir: Model, applied_mutators: List[Mutator]) -> None:
         _logger.info('Start strategy...')
-        search_space = dry_run_for_formatted_search_space(base_model_ir, self.applied_mutators)
+        search_space = dry_run_for_formatted_search_space(base_model_ir, applied_mutators)
         self.update_search_space(search_space)
-        self.strategy.run(base_model_ir, self.applied_mutators)
+        self.strategy.run(base_model_ir, applied_mutators)
         _logger.info('Strategy exit')
         # TODO: find out a proper way to show no more trial message on WebUI
 
     def _create_execution_engine(self, config: RetiariiExeConfig) -> None:
-        #TODO: we will probably need a execution engine factory to make this clean and elegant
-        if isinstance(config.execution_engine, BaseEngineConfig):
-            from nni.nas.execution.pytorch.graph import BaseExecutionEngine
-            engine = BaseExecutionEngine(self.port, self.url_prefix)
-        elif isinstance(config.execution_engine, CgoEngineConfig):
-            from nni.nas.execution.pytorch.cgo import CGOExecutionEngine
-
-            assert not isinstance(config.training_service, list) \
-                and config.training_service.platform == 'remote', \
-                "CGO execution engine currently only supports remote training service"
-            assert config.execution_engine.batch_waiting_time is not None \
-                and config.execution_engine.max_concurrency_cgo is not None
-            engine = CGOExecutionEngine(cast(RemoteConfig, config.training_service),
-                                        max_concurrency=config.execution_engine.max_concurrency_cgo,
-                                        batch_waiting_time=config.execution_engine.batch_waiting_time,
-                                        rest_port=self.port,
-                                        rest_url_prefix=self.url_prefix)
-        elif isinstance(config.execution_engine, PyEngineConfig):
-            from nni.nas.execution.pytorch.simplified import PurePythonExecutionEngine
-            engine = PurePythonExecutionEngine(self.port, self.url_prefix)
-        elif isinstance(config.execution_engine, BenchmarkEngineConfig):
-            from nni.nas.execution.pytorch.benchmark import BenchmarkExecutionEngine
-            assert config.execution_engine.benchmark is not None, \
-                '"benchmark" must be set when benchmark execution engine is used.'
-            engine = BenchmarkExecutionEngine(config.execution_engine.benchmark)
-        else:
-            raise ValueError(f'Unsupported engine type: {config.execution_engine}')
+        engine = init_execution_engine(config, self.port, self.url_prefix)
         set_execution_engine(engine)
 
+    def _save_experiment_checkpoint(self, base_model_ir: Model, applied_mutators: List[Mutator],
+                                    strategy: BaseStrategy, exp_work_dir: PathLike) -> None:
+        ckp_path = os.path.join(exp_work_dir, self.id, 'checkpoint')
+        with open(os.path.join(ckp_path, 'nas_model'), 'w') as fp:
+            dump(base_model_ir._dump(), fp, pickle_size_limit=int(os.getenv('PICKLE_SIZE_LIMIT', 64 * 1024)))
+        with open(os.path.join(ckp_path, 'applied_mutators'), 'w') as fp:
+            dump(applied_mutators, fp)
+        with open(os.path.join(ckp_path, 'strategy'), 'w') as fp:
+            dump(strategy, fp)
+
+    def _load_experiment_checkpoint(self, exp_work_dir: PathLike) -> Tuple[Model, List[Mutator], BaseStrategy]:
+        ckp_path = os.path.join(exp_work_dir, self.id, 'checkpoint')
+        with open(os.path.join(ckp_path, 'nas_model'), 'r') as fp:
+            base_model_ir = load(fp=fp)
+            base_model_ir = Model._load(base_model_ir)
+        with open(os.path.join(ckp_path, 'applied_mutators'), 'r') as fp:
+            applied_mutators = load(fp=fp)
+        with open(os.path.join(ckp_path, 'strategy'), 'r') as fp:
+            strategy = load(fp=fp)
+        return base_model_ir, applied_mutators, strategy
+
     def start(self, *args, **kwargs) -> None:
         """
         By design, the only different between `start` and `run` is that `start` is asynchronous,
@@ -262,7 +256,6 @@ class RetiariiExperiment(Experiment):
         Run the experiment.
         This function will block until experiment finish or error.
         """
-
         from nni.retiarii.oneshot.interface import BaseOneShotTrainer
         if isinstance(self.evaluator, BaseOneShotTrainer):
             warnings.warn('You are using the old implementation of one-shot algos based on One-shot trainer. '
@@ -287,15 +280,30 @@ class RetiariiExperiment(Experiment):
             self.strategy.run(base_model_ir, self.applied_mutators)
         else:
             ws_url = f'ws://localhost:{port}/tuner'
-            canonicalized_config = self._start_impl(port, debug, RunMode.Background, ws_url, ['retiarii'])
-            canonicalized_config = cast(RetiariiExeConfig, canonicalized_config)
+            canoni_conf = self._start_impl(port, debug, RunMode.Background, ws_url, ['retiarii'])
+            canoni_conf = cast(RetiariiExeConfig, canoni_conf)
             self._dispatcher = RetiariiAdvisor(ws_url)
             self._dispatcher_thread = Thread(target=self._dispatcher.run, daemon=True)
             self._dispatcher_thread.start()
             # FIXME: engine cannot be created twice
-            self._create_execution_engine(canonicalized_config)
+            self._create_execution_engine(canoni_conf)
             try:
-                self._run_strategy(canonicalized_config)
+                if self._action == 'create':
+                    base_model_ir, self.applied_mutators = preprocess_model(
+                        self.base_model, self.evaluator, self.applied_mutators,
+                        full_ir=not isinstance(canoni_conf.execution_engine, (PyEngineConfig, BenchmarkEngineConfig)),
+                        dummy_input=canoni_conf.execution_engine.dummy_input
+                            if isinstance(canoni_conf.execution_engine, (BaseEngineConfig, CgoEngineConfig)) else None
+                    )
+                    self._save_experiment_checkpoint(base_model_ir, self.applied_mutators, self.strategy,
+                                                     canoni_conf.experiment_working_directory)
+                elif self._action == 'resume':
+                    base_model_ir, self.applied_mutators, self.strategy = self._load_experiment_checkpoint(
+                        canoni_conf.experiment_working_directory)
+                else:
+                    raise RuntimeError(f'The experiment mode "{self._action}" is not supposed to invoke run() method.')
+
+                self._run_strategy(base_model_ir, self.applied_mutators)
                 # FIXME: move this logic to strategy with a new API provided by execution engine
                 self._wait_completion()
             except KeyboardInterrupt:
@@ -359,3 +367,67 @@ class RetiariiExperiment(Experiment):
                 return [model_to_pytorch_script(model) for model in all_models[:top_k]]
             elif formatter == 'dict':
                 return [get_mutation_dict(model) for model in all_models[:top_k]]
+
+    @staticmethod
+    def view(experiment_id: str, port: int = 8080, non_blocking: bool = False) -> RetiariiExperiment | None:
+        """
+        View a stopped experiment.
+
+        Parameters
+        ----------
+        experiment_id
+            The stopped experiment id.
+        port
+            The port of web UI.
+        non_blocking
+            If false, run in the foreground. If true, run in the background.
+        """
+        experiment = RetiariiExperiment._view(experiment_id)
+        # view is nothing specific about RetiariiExperiment, directly using the method in base experiment class
+        super(RetiariiExperiment, experiment).start(port=port, debug=False, run_mode=RunMode.Detach)
+        if non_blocking:
+            return experiment
+        else:
+            try:
+                while True:
+                    time.sleep(10)
+            except KeyboardInterrupt:
+                _logger.warning('KeyboardInterrupt detected')
+            finally:
+                experiment.stop()
+
+    @staticmethod
+    def resume(experiment_id: str, port: int = 8080, debug: bool = False) -> RetiariiExperiment:
+        """
+        Resume a stopped experiment.
+
+        Parameters
+        ----------
+        experiment_id
+            The stopped experiment id.
+        port
+            The port of web UI.
+        debug
+            Whether to start in debug mode.
+        """
+        experiment = RetiariiExperiment._resume(experiment_id)
+        experiment.run(experiment.config, port=port, debug=debug)
+        # always return experiment for user's follow-up operations on the experiment
+        # wait_completion is not necessary as nas experiment is always in foreground
+        return experiment
+
+    @staticmethod
+    def _resume(exp_id, exp_dir=None):
+        exp = RetiariiExperiment(cast(nn.Module, None))
+        exp.id = exp_id
+        exp._action = 'resume'
+        exp.config = cast(RetiariiExeConfig, launcher.get_stopped_experiment_config(exp_id, exp_dir))
+        return exp
+
+    @staticmethod
+    def _view(exp_id, exp_dir=None):
+        exp = RetiariiExperiment(cast(nn.Module, None))
+        exp.id = exp_id
+        exp._action = 'view'
+        exp.config = cast(RetiariiExeConfig, launcher.get_stopped_experiment_config(exp_id, exp_dir))
+        return exp

nni/tools/nnictl/launcher.py

@@ -104,9 +104,14 @@ def resume_experiment(args):
         legacy_launcher.resume_experiment(args)
         exit()
 
-    exp = Experiment._resume(exp_id, exp_dir)
-    run_mode = RunMode.Foreground if foreground else RunMode.Detach
-    exp.start(port, debug, run_mode)
+    exp_cls, _ = utils.get_experiment_cls_using_config(config_json)
+    if exp_cls is Experiment:
+        exp = exp_cls._resume(exp_id, exp_dir)
+        run_mode = RunMode.Foreground if foreground else RunMode.Detach
+        exp.start(port, debug, run_mode)
+    else:
+        # exp_cls is RetiariiExperiment
+        exp_cls.resume(exp_id, port, debug)
 
 def view_experiment(args):
     exp_id = args.id
@@ -118,5 +123,10 @@ def view_experiment(args):
         legacy_launcher.view_experiment(args)
         exit()
 
-    exp = Experiment._view(exp_id, exp_dir)
-    exp.start(port, run_mode=RunMode.Detach)
+    exp_cls, _ = utils.get_experiment_cls_using_config(config_json)
+    if exp_cls is Experiment:
+        exp = exp_cls._view(exp_id, exp_dir)
+        exp.start(port, run_mode=RunMode.Detach)
+    else:
+        # exp_cls is RetiariiExperiment
+        exp_cls.view(exp_id, port, non_blocking=True)

test/algo/nas/test_multitrial.py

 import multiprocessing
 import os
-import sys
+import subprocess
 import time
 
 import pytest
@@ -76,3 +76,44 @@ def test_exp_exit_without_stop(pytestconfig):
             return
     process.kill()
     raise RuntimeError(f'Experiment fails to stop in {timeout} seconds.')
+
+
+def test_multitrial_experiment_resume_view(pytestconfig):
+    # start a normal nas experiment
+    base_model, evaluator = _mnist_net('simple', {'max_epochs': 1})
+    search_strategy = strategy.Random()
+    exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
+    exp_id = exp.id
+    exp_config = RetiariiExeConfig('local')
+    exp_config.trial_concurrency = 1
+    exp_config.max_trial_number = 1
+    exp_config._trial_command_params = nas_experiment_trial_params(pytestconfig.rootpath)
+    exp.run(exp_config)
+    ensure_success(exp)
+    assert isinstance(exp.export_top_models()[0], dict)
+    exp.stop()
+
+    # resume the above nas experiment. only tested the resume logic in the python side,
+    # as no more trial is executed after resume, the above experiment is already finished
+    print('python api resume...')
+    exp = RetiariiExperiment.resume(exp_id)
+    ensure_success(exp)
+    # TODO: currently `export_top_models` does not work as strategy's states are not resumed
+    # assert isinstance(exp.export_top_models()[0], dict)
+    exp.stop()
+    # view the above experiment in non blocking mode then stop it
+    print('python api view...')
+    exp = RetiariiExperiment.view(exp_id, non_blocking=True)
+    exp.stop()
+
+    # the following is nnictl resume and view
+    print('nnictl resume...')
+    new_env = os.environ.copy()
+    new_env['PYTHONPATH'] = str(pytestconfig.rootpath)
+    proc = subprocess.run(f'nnictl resume {exp_id}', shell=True, env=new_env)
+    assert proc.returncode == 0, 'resume nas experiment failed with code %d' % proc.returncode
+    print('nnictl view...')
+    proc = subprocess.run(f'nnictl view {exp_id}', shell=True)
+    assert proc.returncode == 0, 'view nas experiment failed with code %d' % proc.returncode
+    proc = subprocess.run(f'nnictl stop {exp_id}', shell=True)
+    assert proc.returncode == 0, 'stop viewed nas experiment failed with code %d' % proc.returncode
\ No newline at end of file

test/ut/experiment/test_exp_config.py

@@ -28,6 +28,7 @@ minimal_class.trial_concurrency = 2
 minimal_class.tuner.name = 'random'
 
 minimal_canon = {
+    'experimentType': 'hpo',
     'searchSpace': {'a': 1},
     'trialCommand': 'python main.py',
     'trialCodeDirectory': os.path.realpath('.'),
@@ -54,6 +55,7 @@ minimal_canon_2['tuner']['classArgs'] = {}
 
 detailed_canon = {
     'experimentName': 'test case',
+    'experimentType': 'hpo',
     'searchSpaceFile': expand_path('assets/search_space.json'),
     'searchSpace': {'a': 1},
     'trialCommand': 'python main.py',

test/ut/experiment/test_ts_remote.py

@@ -43,6 +43,7 @@ minimal_class = ExperimentConfig(
 )
 
 minimal_canon = {
+    'experimentType': 'hpo',
     'searchSpace': {'a': 1},
     'trialCommand': 'python main.py',
     'trialCodeDirectory': os.path.realpath('.'),
@@ -106,6 +107,7 @@ detailed_json = {
 }
 
 detailed_canon = {
+    'experimentType': 'hpo',
     'searchSpace': {'a': 1},
     'trialCommand': 'python main.py',
     'trialCodeDirectory': os.path.realpath('.'),

test/ut/nas/test_graph.py

@@ -37,6 +37,10 @@ def _test_file(json_path):
     # skip comparison of _evaluator
     orig_ir.pop('_evaluator')
     dump_ir.pop('_evaluator')
+    # skip three experiment fields
+    dump_ir.pop('model_id')
+    dump_ir.pop('python_class')
+    dump_ir.pop('python_init_params')
 
     assert orig_ir == dump_ir