Merge pull request #4668 from microsoft/doc-refactor

nni/retiarii/nn/pytorch/nn.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from packaging.version import Version
 import torch
 import torch.nn as nn
 
 from ...serializer import basic_unit
-from ...utils import version_larger_equal
 
 # NOTE: support pytorch version >= 1.5.0
 
@@ -31,10 +31,10 @@ __all__ = [
     'Flatten', 'Hardsigmoid'
 ]
 
-if version_larger_equal(torch.__version__, '1.6.0'):
+if Version(torch.__version__) >= Version('1.6.0'):
     __all__.append('Hardswish')
 
-if version_larger_equal(torch.__version__, '1.7.0'):
+if Version(torch.__version__) >= Version('1.7.0'):
     __all__.extend(['Unflatten', 'SiLU', 'TripletMarginWithDistanceLoss'])
 
 
@@ -149,10 +149,10 @@ Transformer = basic_unit(nn.Transformer)
 Flatten = basic_unit(nn.Flatten)
 Hardsigmoid = basic_unit(nn.Hardsigmoid)
 
-if version_larger_equal(torch.__version__, '1.6.0'):
+if Version(torch.__version__) >= Version('1.6.0'):
     Hardswish = basic_unit(nn.Hardswish)
 
-if version_larger_equal(torch.__version__, '1.7.0'):
+if Version(torch.__version__) >= Version('1.7.0'):
     SiLU = basic_unit(nn.SiLU)
     Unflatten = basic_unit(nn.Unflatten)
     TripletMarginWithDistanceLoss = basic_unit(nn.TripletMarginWithDistanceLoss)

nni/retiarii/serializer.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT license.
 
 import inspect
+import os
 import warnings
 from typing import Any, TypeVar, Union
 
@@ -64,6 +65,12 @@ def basic_unit(cls: T, basic_unit_tag: bool = True) -> Union[T, Traceable]:
         class PrimitiveOp(nn.Module):
             ...
     """
+
+    # Internal flag. See nni.trace
+    nni_trace_flag = os.environ.get('NNI_TRACE_FLAG', '')
+    if nni_trace_flag.lower() == 'disable':
+        return cls
+
     if _check_wrapped(cls, 'basic_unit'):
         return cls
 
@@ -90,12 +97,18 @@ def model_wrapper(cls: T) -> Union[T, Traceable]:
 
     The wrapper serves two purposes:
 
-        1. Capture the init parameters of python class so that it can be re-instantiated in another process.
-        2. Reset uid in namespace so that the auto label counting in each model stably starts from zero.
+    1. Capture the init parameters of python class so that it can be re-instantiated in another process.
+    2. Reset uid in namespace so that the auto label counting in each model stably starts from zero.
 
     Currently, NNI might not complain in simple cases where ``@model_wrapper`` is actually not needed.
     But in future, we might enforce ``@model_wrapper`` to be required for base model.
     """
+
+    # Internal flag. See nni.trace
+    nni_trace_flag = os.environ.get('NNI_TRACE_FLAG', '')
+    if nni_trace_flag.lower() == 'disable':
+        return cls
+
     if _check_wrapped(cls, 'model_wrapper'):
         return cls
 

nni/retiarii/strategy/__init__.py

@@ -4,6 +4,6 @@
 from .base import BaseStrategy
 from .bruteforce import Random, GridSearch
 from .evolution import RegularizedEvolution
-from .tpe_strategy import TPEStrategy
+from .tpe_strategy import TPEStrategy, TPE
 from .local_debug_strategy import _LocalDebugStrategy
 from .rl import PolicyBasedRL

nni/retiarii/strategy/rl.py

@@ -23,7 +23,7 @@ class PolicyBasedRL(BaseStrategy):
     """
     Algorithm for policy-based reinforcement learning.
     This is a wrapper of algorithms provided in tianshou (PPO by default),
-    and can be easily customized with other algorithms that inherit ``BasePolicy`` (e.g., REINFORCE [1]_).
+    and can be easily customized with other algorithms that inherit ``BasePolicy`` (e.g., REINFORCE :footcite:p:`zoph2017neural`).
 
     Parameters
     ----------
@@ -34,12 +34,6 @@ class PolicyBasedRL(BaseStrategy):
         After each collect, trainer will sample batch from replay buffer and do the update. Default: 20.
     policy_fn : function
         Takes ``ModelEvaluationEnv`` as input and return a policy. See ``_default_policy_fn`` for an example.
-
-    References
-    ----------
-
-    .. [1] Barret Zoph and Quoc V. Le, "Neural Architecture Search with Reinforcement Learning".
-        https://arxiv.org/abs/1611.01578
     """
 
     def __init__(self, max_collect: int = 100, trial_per_collect = 20,

nni/retiarii/strategy/tpe_strategy.py

@@ -39,17 +39,14 @@ class TPESampler(Sampler):
         return chosen
 
 
-class TPEStrategy(BaseStrategy):
+class TPE(BaseStrategy):
     """
-    The Tree-structured Parzen Estimator (TPE) [bergstrahpo]_ is a sequential model-based optimization (SMBO) approach.
-    SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
-    and then subsequently choose new hyperparameters to test based on this model.
+    The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach.
 
-    References
-    ----------
+    Refer to :footcite:t:`bergstra2011algorithms` for details.
 
-    .. [bergstrahpo] Bergstra et al., "Algorithms for Hyper-Parameter Optimization".
-        https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
+    SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
+    and then subsequently choose new hyperparameters to test based on this model.
     """
 
     def __init__(self):
@@ -92,3 +89,7 @@ class TPEStrategy(BaseStrategy):
                     to_be_deleted.append(_id)
             for _id in to_be_deleted:
                 del self.running_models[_id]
+
+
+# alias for backward compatibility
+TPEStrategy = TPE

nni/retiarii/utils.py

@@ -9,6 +9,8 @@ from contextlib import contextmanager
 from typing import Any, List, Dict
 from pathlib import Path
 
+__all__ = ['NoContextError', 'ContextStack', 'ModelNamespace']
+
 
 def import_(target: str, allow_none: bool = False) -> Any:
     if target is None:
@@ -18,13 +20,6 @@ def import_(target: str, allow_none: bool = False) -> Any:
     return getattr(module, identifier)
 
 
-def version_larger_equal(a: str, b: str) -> bool:
-    # TODO: refactor later
-    a = a.split('+')[0]
-    b = b.split('+')[0]
-    return tuple(map(int, a.split('.'))) >= tuple(map(int, b.split('.')))
-
-
 _last_uid = defaultdict(int)
 
 _DEFAULT_MODEL_NAMESPACE = 'model'
@@ -72,12 +67,13 @@ def get_importable_name(cls, relocate_module=False):
 
 
 class NoContextError(Exception):
+    """Exception raised when context is missing."""
     pass
 
 
 class ContextStack:
     """
-    This is to maintain a globally-accessible context envinronment that is visible to everywhere.
+    This is to maintain a globally-accessible context environment that is visible to everywhere.
 
     Use ``with ContextStack(namespace, value):`` to initiate, and use ``get_current_context(namespace)`` to
     get the corresponding value in the namespace.

nni/runtime/log.py

@@ -61,6 +61,8 @@ def init_logger_for_command_line() -> None:
         _cli_log_initialized = True
         colorful_formatter = Formatter(log_format, time_format)
         colorful_formatter.format = _colorful_format
+        if '_default_' not in handlers:  # this happens when building sphinx gallery
+            _register_handler(StreamHandler(sys.stdout), logging.INFO)
         handlers['_default_'].setFormatter(colorful_formatter)
 
 def start_experiment_log(experiment_id: str, log_directory: Path, debug: bool) -> None:

nni/runtime/platform/standalone.py

@@ -23,10 +23,10 @@ def get_next_parameter():
     warning_message = ''.join([
         colorama.Style.BRIGHT,
         colorama.Fore.RED,
-        'Running NNI code without runtime. ',
-        'Check the following tutorial if you are new to NNI: ',
+        'Running trial code without runtime. ',
+        'Please check the tutorial if you are new to NNI: ',
         colorama.Fore.YELLOW,
-        'https://nni.readthedocs.io/en/stable/Tutorial/QuickStart.html#id1',
+        'https://nni.readthedocs.io/en/stable/tutorials/hpo_quickstart_pytorch/main.html',
         colorama.Style.RESET_ALL
     ])
     warnings.warn(warning_message, RuntimeWarning)

nni/tools/nnictl/hello.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+Provide ``nnictl hello`` command to generate quickstart example.
+"""
+
+from pathlib import Path
+import shutil
+
+from colorama import Fore
+
+import nni_assets
+
+def create_example(_args):
+    example_path = Path(nni_assets.__path__[0], 'hello_hpo')
+    try:
+        shutil.copytree(example_path, 'nni_hello_hpo')
+    except PermissionError:
+        print(Fore.RED + 'Permission denied. Please run the command in a writable directory.' + Fore.RESET)
+        exit(1)
+    except FileExistsError:
+        print('File exists. Please run "python nni_hello_hpo/main.py" to start the example.')
+        exit(1)
+    print('A hyperparameter optimization example has been created at "nni_hello_hpo" directory.')
+    print('Please run "python nni_hello_hpo/main.py" to try it out.')

nni/tools/nnictl/nnictl.py

@@ -16,7 +16,8 @@ from .nnictl_utils import stop_experiment, trial_ls, trial_kill, list_experiment
     save_experiment, load_experiment
 from .algo_management import algo_reg, algo_unreg, algo_show, algo_list
 from .constants import DEFAULT_REST_PORT
-from .import ts_management
+from . import hello
+from . import ts_management
 
 init(autoreset=True)
 
@@ -483,6 +484,10 @@ def get_parser():
     jupyter_uninstall_parser = jupyter_subparsers.add_parser('uninstall', description='Uninstall JupyterLab extension.')
     jupyter_uninstall_parser.set_defaults(func=_jupyter_uninstall)
 
+    # hello command
+    parser_hello = subparsers.add_parser('hello', description='Create "hello nni" example in current directory.')
+    parser_hello.set_defaults(func=hello.create_example)
+
     return parser
 
 

nni/tools/nnictl/nnictl_utils.py

@@ -105,7 +105,7 @@ def parse_ids(args):
     3.If there is an id specified, return the corresponding id
     4.If there is no id specified, and there is an experiment running, return the id, or return Error
     5.If the id matches an experiment, nnictl will return the id.
-    6.If the id ends with *, nnictl will match all ids matchs the regular
+    6.If the id ends with ``*``, nnictl will match all ids matchs the regular
     7.If the id does not exist but match the prefix of an experiment id, nnictl will return the matched id
     8.If the id does not exist but match multiple prefix of the experiment ids, nnictl will give id information
     '''

nni/trial.py

@@ -25,16 +25,35 @@ _sequence_id = platform.get_sequence_id()
 
 def get_next_parameter():
     """
-    Get the hyper paremeters generated by tuner. For a multiphase experiment, it returns a new group of hyper
-    parameters at each call of get_next_parameter. For a non-multiphase (multiPhase is not configured or set to False)
-    experiment, it returns hyper parameters only on the first call for each trial job, it returns None since second call.
-    This API should be called only once in each trial job of an experiment which is not specified as multiphase.
+    Get the hyperparameters generated by tuner.
+
+    Each trial should and should only invoke this function once.
+    Otherwise the behavior is undefined.
+
+    Examples
+    --------
+    Assuming the search space is:
+
+    .. code-block::
+
+        {
+            'activation': {'_type': 'choice', '_value': ['relu', 'tanh', 'sigmoid']},
+            'learning_rate': {'_type': 'loguniform', '_value': [0.0001, 0.1]}
+        }
+
+    Then this function might return:
+
+    .. code-block::
+
+        {
+            'activation': 'relu',
+            'learning_rate': 0.02
+        }
 
     Returns
     -------
     dict
-        A dict object contains the hyper parameters generated by tuner, the keys of the dict are defined in
-        search space. Returns None if no more hyper parameters can be generated by tuner.
+        A hyperparameter set sampled from search space.
     """
     global _params
     _params = platform.get_next_parameter()
@@ -43,15 +62,6 @@ def get_next_parameter():
     return _params['parameters']
 
 def get_current_parameter(tag=None):
-    """
-    Get current hyper parameters generated by tuner. It returns the same group of hyper parameters as the last
-    call of get_next_parameter returns.
-
-    Parameters
-    ----------
-    tag: str
-        hyper parameter key
-    """
     global _params
     if _params is None:
         return None
@@ -59,39 +69,25 @@ def get_current_parameter(tag=None):
         return _params['parameters']
     return _params['parameters'][tag]
 
-def get_experiment_id():
+def get_experiment_id() -> str:
     """
-    Get experiment ID.
-
-    Returns
-    -------
-    str
-        Identifier of current experiment
+    Return experiment ID.
     """
     return _experiment_id
 
-def get_trial_id():
+def get_trial_id() -> str:
     """
-    Get trial job ID which is string identifier of a trial job, for example 'MoXrp'. In one experiment, each trial
-    job has an unique string ID.
+    Return unique ID of the trial that is current running.
 
-    Returns
-    -------
-    str
-        Identifier of current trial job which is calling this API.
+    This is shown as "ID" in the web portal's trial table.
     """
     return _trial_id
 
-def get_sequence_id():
+def get_sequence_id() -> int:
     """
-    Get trial job sequence nubmer. A sequence number is an integer value assigned to each trial job base on the
-    order they are submitted, incremental starting from 0. In one experiment, both trial job ID and sequence number
-    are unique for each trial job, they are of different data types.
+    Return sequence nubmer of the trial that is currently running.
 
-    Returns
-    -------
-    int
-        Sequence number of current trial job which is calling this API.
+    This is shown as "Trial No." in the web portal's trial table.
     """
     return _sequence_id
 
@@ -99,14 +95,6 @@ _intermediate_seq = 0
 
 
 def overwrite_intermediate_seq(value):
-    """
-    Overwrite intermediate sequence value.
-
-    Parameters
-    ----------
-    value:
-        int
-    """
     assert isinstance(value, int)
     global _intermediate_seq
     _intermediate_seq = value
@@ -116,10 +104,12 @@ def report_intermediate_result(metric):
     """
     Reports intermediate result to NNI.
 
-    Parameters
-    ----------
-    metric:
-        serializable object.
+    ``metric`` should either be a float, or a dict that ``metric['default']`` is a float.
+
+    If ``metric`` is a dict, ``metric['default']`` will be used by tuner,
+    and other items can be visualized with web portal.
+
+    Typically ``metric`` is per-epoch accuracy or loss.
     """
     global _intermediate_seq
     assert _params or trial_env_vars.NNI_PLATFORM is None, \
@@ -138,11 +128,12 @@ def report_final_result(metric):
     """
     Reports final result to NNI.
 
-    Parameters
-    ----------
-    metric: serializable object
-        Usually (for built-in tuners to work), it should be a number, or
-        a dict with key "default" (a number), and any other extra keys.
+    ``metric`` should either be a float, or a dict that ``metric['default']`` is a float.
+
+    If ``metric`` is a dict, ``metric['default']`` will be used by tuner,
+    and other items can be visualized with web portal.
+
+    Typically ``metric`` is the final accuracy or loss.
     """
     assert _params or trial_env_vars.NNI_PLATFORM is None, \
         'nni.get_next_parameter() needs to be called before report_final_result'

nni/typehint.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import sys
+import typing
+
+if typing.TYPE_CHECKING or sys.version_info >= (3, 8):
+    Literal = typing.Literal
+else:
+    Literal = typing.Any

nni_assets/hello_hpo/main.py

+"""
+NNI hyperparameter optimization example.
+
+Check the online tutorial for details:
+https://nni.readthedocs.io/en/stable/tutorials/hpo_quickstart_pytorch/main.html
+"""
+
+from pathlib import Path
+import signal
+
+from nni.experiment import Experiment
+
+# Define search space
+search_space = {
+    'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},
+    'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+    'momentum': {'_type': 'uniform', '_value': [0, 1]},
+}
+
+# Configure experiment
+experiment = Experiment('local')
+experiment.config.trial_command = 'python model.py'
+experiment.config.trial_code_directory = Path(__file__).parent
+experiment.config.search_space = search_space
+experiment.config.tuner.name = 'Random'
+experiment.config.max_trial_number = 10
+experiment.config.trial_concurrency = 2
+
+# Run it!
+experiment.run(port=8080, wait_completion=False)
+
+print('Experiment is running. Press Ctrl-C to quit.')
+signal.pause()

nni_assets/hello_hpo/model.py

+"""
+Run main.py to start.
+
+This script is modified from PyTorch quickstart:
+https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+import nni
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# Get optimized hyperparameters
+params = {'features': 512, 'lr': 0.001, 'momentum': 0}
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+
+# Load dataset
+training_data = datasets.FashionMNIST(root='data', train=True, download=True, transform=ToTensor())
+test_data = datasets.FashionMNIST(root='data', train=False, download=True, transform=ToTensor())
+train_dataloader = DataLoader(training_data, batch_size=64)
+test_dataloader = DataLoader(test_data, batch_size=64)
+
+# Build model
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = nn.Sequential(
+    nn.Flatten(),
+    nn.Linear(28*28, params['features']),
+    nn.ReLU(),
+    nn.Linear(params['features'], params['features']),
+    nn.ReLU(),
+    nn.Linear(params['features'], 10)
+).to(device)
+
+# Training functions
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+def train(dataloader, model, loss_fn, optimizer):
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+def test(dataloader, model, loss_fn):
+    model.eval()
+    correct = 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    return correct / len(dataloader.dataset)
+
+# Train the model
+epochs = 5
+for t in range(epochs):
+    train(train_dataloader, model, loss_fn, optimizer)
+    accuracy = test(test_dataloader, model, loss_fn)
+    nni.report_intermediate_result(accuracy)
+nni.report_final_result(accuracy)

setup.py

@@ -27,7 +27,7 @@ Uninstall:
 
   $ pip uninstall nni
 
-Remove generated files: (use "--all" to remove toolchain and built wheel)
+Remove generated files: (use "--all" to remove built wheel)
 
   $ python setup.py clean [--all]
 
@@ -112,6 +112,7 @@ def _setup():
         packages = _find_python_packages(),
         package_data = {
             'nni': _find_requirements_txt() + _find_default_config(),  # setuptools issue #1806
+            'nni_assets': _find_asset_files(),
             'nni_node': _find_node_files()  # note: this does not work before building
         },
 
@@ -124,6 +125,7 @@ def _setup():
             'BOHB': _read_requirements_txt('dependencies/required_extra.txt', 'BOHB'),
             'PPOTuner': _read_requirements_txt('dependencies/required_extra.txt', 'PPOTuner'),
             'DNGO': _read_requirements_txt('dependencies/required_extra.txt', 'DNGO'),
+            'all': _read_requirements_txt('dependencies/required_extra.txt'),
         },
         setup_requires = ['requests'],
 
@@ -165,6 +167,14 @@ def _find_requirements_txt():
 def _find_default_config():
     return ['runtime/default_config/' + name for name in os.listdir('nni/runtime/default_config')]
 
+def _find_asset_files():
+    files = []
+    for dirpath, dirnames, filenames in os.walk('nni_assets'):
+        for filename in filenames:
+            if os.path.splitext(filename)[1] == '.py':
+                files.append(os.path.join(dirpath[len('nni_assets/'):], filename))
+    return sorted(files)
+
 def _find_node_files():
     if not os.path.exists('nni_node'):
         if release and 'build_ts' not in sys.argv and 'clean' not in sys.argv:
@@ -221,7 +231,7 @@ class Build(build):
         check_jupyter_lab_version()
 
         if os.path.islink('nni_node/main.js'):
-            sys.exit('A development build already exists. Please uninstall NNI and run "python3 setup.py clean --all".')
+            sys.exit('A development build already exists. Please uninstall NNI and run "python3 setup.py clean".')
         open('nni/version.py', 'w').write(f"__version__ = '{release}'")
         super().run()
 
@@ -259,7 +269,7 @@ class Clean(clean):
 
     def run(self):
         super().run()
-        setup_ts.clean(self._all)
+        setup_ts.clean()
         _clean_temp_files()
         shutil.rmtree('nni.egg-info', ignore_errors=True)
         if self._all:
@@ -279,7 +289,10 @@ _temp_files = [
     'test/model_path/',
     'test/temp.json',
     'test/ut/sdk/*.pth',
-    'test/ut/tools/annotation/_generated/'
+    'test/ut/tools/annotation/_generated/',
+
+    # example
+    'nni_assets/**/data/',
 ]
 
 

setup_ts.py

@@ -15,6 +15,7 @@ from io import BytesIO
 import json
 import os
 from pathlib import Path
+import platform
 import shutil
 import subprocess
 import sys
@@ -56,12 +57,13 @@ def build(release):
         symlink_nni_node()
     restore_package()
 
-def clean(clean_all=False):
+def clean():
     """
     Remove TypeScript-related intermediate files.
     Python intermediate files are not touched here.
     """
     shutil.rmtree('nni_node', ignore_errors=True)
+    shutil.rmtree('toolchain', ignore_errors=True)
 
     for file_or_dir in generated_files:
         path = Path(file_or_dir)
@@ -70,13 +72,11 @@ def clean(clean_all=False):
         elif path.is_dir():
             shutil.rmtree(path)
 
-    if clean_all:
-        shutil.rmtree('toolchain', ignore_errors=True)
-
 
 if sys.platform == 'linux' or sys.platform == 'darwin':
     node_executable = 'node'
-    node_spec = f'node-{node_version}-{sys.platform}-x64'
+    _arch = 'x64' if platform.machine() == 'x86_64' else platform.machine()
+    node_spec = f'node-{node_version}-{sys.platform}-' + _arch
     node_download_url = f'https://nodejs.org/dist/{node_version}/{node_spec}.tar.xz'
     node_extractor = lambda data: tarfile.open(fileobj=BytesIO(data), mode='r:xz')
     node_executable_in_tarball = 'bin/node'
@@ -183,16 +183,14 @@ def compile_ts(release):
         _yarn('ts/webui', 'build')
 
     _print('Building JupyterLab extension')
-    if release:
+    try:
         _yarn('ts/jupyter_extension')
         _yarn('ts/jupyter_extension', 'build')
-    else:
-        try:
-            _yarn('ts/jupyter_extension')
-            _yarn('ts/jupyter_extension', 'build')
-        except Exception:
-            _print('Failed to build JupyterLab extension, skip for develop mode', color='yellow')
-            _print(traceback.format_exc(), color='yellow')
+    except Exception:
+        if release:
+            raise
+        _print('Failed to build JupyterLab extension, skip for develop mode', color='yellow')
+        _print(traceback.format_exc(), color='yellow')
 
 
 def symlink_nni_node():
@@ -225,12 +223,9 @@ def copy_nni_node(version):
     """
     _print('Copying files')
 
-    # copytree(..., dirs_exist_ok=True) is not supported by Python 3.6
-    for path in Path('ts/nni_manager/dist').iterdir():
-        if path.is_dir():
-            shutil.copytree(path, Path('nni_node', path.name))
-        elif path.name != 'nni_manager.tsbuildinfo':
-            shutil.copyfile(path, Path('nni_node', path.name))
+    shutil.copytree('ts/nni_manager/dist', 'nni_node', dirs_exist_ok=True)
+    shutil.copyfile('ts/nni_manager/yarn.lock', 'nni_node/yarn.lock')
+    Path('nni_node/nni_manager.tsbuildinfo').unlink()
 
     package_json = json.load(open('ts/nni_manager/package.json'))
     if version: