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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner.json2paramater | def json2paramater(self, ss_spec):
'''
generate all possible configs for hyperparameters from hyperparameter space.
ss_spec: hyperparameter space
'''
if isinstance(ss_spec, dict):
if '_type' in ss_spec.keys():
_type = ss_spec['_type']
_... | python | def json2paramater(self, ss_spec):
'''
generate all possible configs for hyperparameters from hyperparameter space.
ss_spec: hyperparameter space
'''
if isinstance(ss_spec, dict):
if '_type' in ss_spec.keys():
_type = ss_spec['_type']
_... | [
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner._parse_quniform | def _parse_quniform(self, param_value):
'''parse type of quniform parameter and return a list'''
if param_value[2] < 2:
raise RuntimeError("The number of values sampled (q) should be at least 2")
low, high, count = param_value[0], param_value[1], param_value[2]
interval = (hi... | python | def _parse_quniform(self, param_value):
'''parse type of quniform parameter and return a list'''
if param_value[2] < 2:
raise RuntimeError("The number of values sampled (q) should be at least 2")
low, high, count = param_value[0], param_value[1], param_value[2]
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner.parse_qtype | def parse_qtype(self, param_type, param_value):
'''parse type of quniform or qloguniform'''
if param_type == 'quniform':
return self._parse_quniform(param_value)
if param_type == 'qloguniform':
param_value[:2] = np.log(param_value[:2])
return list(np.exp(self.... | python | def parse_qtype(self, param_type, param_value):
'''parse type of quniform or qloguniform'''
if param_type == 'quniform':
return self._parse_quniform(param_value)
if param_type == 'qloguniform':
param_value[:2] = np.log(param_value[:2])
return list(np.exp(self.... | [
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner.expand_parameters | def expand_parameters(self, para):
'''
Enumerate all possible combinations of all parameters
para: {key1: [v11, v12, ...], key2: [v21, v22, ...], ...}
return: {{key1: v11, key2: v21, ...}, {key1: v11, key2: v22, ...}, ...}
'''
if len(para) == 1:
for key, value... | python | def expand_parameters(self, para):
'''
Enumerate all possible combinations of all parameters
para: {key1: [v11, v12, ...], key2: [v21, v22, ...], ...}
return: {{key1: v11, key2: v21, ...}, {key1: v11, key2: v22, ...}, ...}
'''
if len(para) == 1:
for key, value... | [
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner.import_data | def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
logger.info("I... | python | def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
logger.info("I... | [
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Microsoft/nni | tools/nni_trial_tool/log_utils.py | nni_log | def nni_log(log_type, log_message):
'''Log message into stdout'''
dt = datetime.now()
print('[{0}] {1} {2}'.format(dt, log_type.value, log_message)) | python | def nni_log(log_type, log_message):
'''Log message into stdout'''
dt = datetime.now()
print('[{0}] {1} {2}'.format(dt, log_type.value, log_message)) | [
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Microsoft/nni | tools/nni_trial_tool/log_utils.py | RemoteLogger.write | def write(self, buf):
'''
Write buffer data into logger/stdout
'''
for line in buf.rstrip().splitlines():
self.orig_stdout.write(line.rstrip() + '\n')
self.orig_stdout.flush()
try:
self.logger.log(self.log_level, line.rstrip())
... | python | def write(self, buf):
'''
Write buffer data into logger/stdout
'''
for line in buf.rstrip().splitlines():
self.orig_stdout.write(line.rstrip() + '\n')
self.orig_stdout.flush()
try:
self.logger.log(self.log_level, line.rstrip())
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Microsoft/nni | tools/nni_trial_tool/log_utils.py | PipeLogReader.run | def run(self):
"""Run the thread, logging everything.
If the log_collection is 'none', the log content will not be enqueued
"""
for line in iter(self.pipeReader.readline, ''):
self.orig_stdout.write(line.rstrip() + '\n')
self.orig_stdout.flush()
if ... | python | def run(self):
"""Run the thread, logging everything.
If the log_collection is 'none', the log content will not be enqueued
"""
for line in iter(self.pipeReader.readline, ''):
self.orig_stdout.write(line.rstrip() + '\n')
self.orig_stdout.flush()
if ... | [
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Microsoft/nni | src/sdk/pynni/nni/utils.py | extract_scalar_reward | def extract_scalar_reward(value, scalar_key='default'):
"""
Extract scalar reward from trial result.
Raises
------
RuntimeError
Incorrect final result: the final result should be float/int,
or a dict which has a key named "default" whose value is float/int.
"""
if isinstance... | python | def extract_scalar_reward(value, scalar_key='default'):
"""
Extract scalar reward from trial result.
Raises
------
RuntimeError
Incorrect final result: the final result should be float/int,
or a dict which has a key named "default" whose value is float/int.
"""
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Microsoft/nni | src/sdk/pynni/nni/utils.py | convert_dict2tuple | def convert_dict2tuple(value):
"""
convert dict type to tuple to solve unhashable problem.
"""
if isinstance(value, dict):
for _keys in value:
value[_keys] = convert_dict2tuple(value[_keys])
return tuple(sorted(value.items()))
else:
return value | python | def convert_dict2tuple(value):
"""
convert dict type to tuple to solve unhashable problem.
"""
if isinstance(value, dict):
for _keys in value:
value[_keys] = convert_dict2tuple(value[_keys])
return tuple(sorted(value.items()))
else:
return value | [
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Microsoft/nni | src/sdk/pynni/nni/utils.py | init_dispatcher_logger | def init_dispatcher_logger():
""" Initialize dispatcher logging configuration"""
logger_file_path = 'dispatcher.log'
if dispatcher_env_vars.NNI_LOG_DIRECTORY is not None:
logger_file_path = os.path.join(dispatcher_env_vars.NNI_LOG_DIRECTORY, logger_file_path)
init_logger(logger_file_path, dispat... | python | def init_dispatcher_logger():
""" Initialize dispatcher logging configuration"""
logger_file_path = 'dispatcher.log'
if dispatcher_env_vars.NNI_LOG_DIRECTORY is not None:
logger_file_path = os.path.join(dispatcher_env_vars.NNI_LOG_DIRECTORY, logger_file_path)
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/config_generator.py | CG_BOHB.sample_from_largest_budget | def sample_from_largest_budget(self, info_dict):
"""We opted for a single multidimensional KDE compared to the
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"""We opted for a single multidimensional KDE compared to the
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/config_generator.py | CG_BOHB.get_config | def get_config(self, budget):
"""Function to sample a new configuration
This function is called inside BOHB to query a new configuration
Parameters:
-----------
budget: float
the budget for which this configuration is scheduled
Returns
-------
... | python | def get_config(self, budget):
"""Function to sample a new configuration
This function is called inside BOHB to query a new configuration
Parameters:
-----------
budget: float
the budget for which this configuration is scheduled
Returns
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/config_generator.py | CG_BOHB.new_result | def new_result(self, loss, budget, parameters, update_model=True):
"""
Function to register finished runs. Every time a run has finished, this function should be called
to register it with the loss.
Parameters:
-----------
loss: float
the loss of the paramete... | python | def new_result(self, loss, budget, parameters, update_model=True):
"""
Function to register finished runs. Every time a run has finished, this function should be called
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Microsoft/nni | src/sdk/pynni/nni/batch_tuner/batch_tuner.py | BatchTuner.is_valid | def is_valid(self, search_space):
"""
Check the search space is valid: only contains 'choice' type
Parameters
----------
search_space : dict
"""
if not len(search_space) == 1:
raise RuntimeError('BatchTuner only supprt one combined-paramreters... | python | def is_valid(self, search_space):
"""
Check the search space is valid: only contains 'choice' type
Parameters
----------
search_space : dict
"""
if not len(search_space) == 1:
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Microsoft/nni | src/sdk/pynni/nni/batch_tuner/batch_tuner.py | BatchTuner.generate_parameters | def generate_parameters(self, parameter_id):
"""Returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_id : int
"""
self.count +=1
if self.count>len(self.values)-1:
raise nni.NoMoreTrialError('no more par... | python | def generate_parameters(self, parameter_id):
"""Returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_id : int
"""
self.count +=1
if self.count>len(self.values)-1:
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/graph_to_tf.py | normalize | def normalize(inputs,
epsilon=1e-8,
scope="ln"):
'''Applies layer normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
... | python | def normalize(inputs,
epsilon=1e-8,
scope="ln"):
'''Applies layer normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/graph_to_tf.py | multihead_attention | def multihead_attention(queries,
keys,
scope="multihead_attention",
num_units=None,
num_heads=4,
dropout_rate=0,
is_training=True,
causality=False):
... | python | def multihead_attention(queries,
keys,
scope="multihead_attention",
num_units=None,
num_heads=4,
dropout_rate=0,
is_training=True,
causality=False):
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/graph_to_tf.py | positional_encoding | def positional_encoding(inputs,
num_units=None,
zero_pad=True,
scale=True,
scope="positional_encoding",
reuse=None):
'''
Return positinal embedding.
'''
Shape = tf.shape(inputs)
N ... | python | def positional_encoding(inputs,
num_units=None,
zero_pad=True,
scale=True,
scope="positional_encoding",
reuse=None):
'''
Return positinal embedding.
'''
Shape = tf.shape(inputs)
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/graph_to_tf.py | feedforward | def feedforward(inputs,
num_units,
scope="multihead_attention"):
'''Point-wise feed forward net.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to r... | python | def feedforward(inputs,
num_units,
scope="multihead_attention"):
'''Point-wise feed forward net.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
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Microsoft/nni | tools/nni_annotation/search_space_generator.py | generate | def generate(module_name, code):
"""Generate search space.
Return a serializable search space object.
module_name: name of the module (str)
code: user code (str)
"""
try:
ast_tree = ast.parse(code)
except Exception:
raise RuntimeError('Bad Python code')
visitor = SearchS... | python | def generate(module_name, code):
"""Generate search space.
Return a serializable search space object.
module_name: name of the module (str)
code: user code (str)
"""
try:
ast_tree = ast.parse(code)
except Exception:
raise RuntimeError('Bad Python code')
visitor = SearchS... | [
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Microsoft/nni | tools/nni_cmd/rest_utils.py | rest_put | def rest_put(url, data, timeout, show_error=False):
'''Call rest put method'''
try:
response = requests.put(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exception as except... | python | def rest_put(url, data, timeout, show_error=False):
'''Call rest put method'''
try:
response = requests.put(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exception as except... | [
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Microsoft/nni | tools/nni_cmd/rest_utils.py | rest_post | def rest_post(url, data, timeout, show_error=False):
'''Call rest post method'''
try:
response = requests.post(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exception as ex... | python | def rest_post(url, data, timeout, show_error=False):
'''Call rest post method'''
try:
response = requests.post(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exception as ex... | [
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Microsoft/nni | tools/nni_cmd/rest_utils.py | rest_get | def rest_get(url, timeout, show_error=False):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as exception:
if show_error:
print_error(exception)
return None | python | def rest_get(url, timeout, show_error=False):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as exception:
if show_error:
print_error(exception)
return None | [
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Microsoft/nni | tools/nni_cmd/rest_utils.py | rest_delete | def rest_delete(url, timeout, show_error=False):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as exception:
if show_error:
print_error(exception)
return None | python | def rest_delete(url, timeout, show_error=False):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as exception:
if show_error:
print_error(exception)
return None | [
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Microsoft/nni | tools/nni_cmd/rest_utils.py | check_rest_server | def check_rest_server(rest_port):
'''Check if restful server is ready'''
retry_count = 5
for _ in range(retry_count):
response = rest_get(check_status_url(rest_port), REST_TIME_OUT)
if response:
if response.status_code == 200:
return True, response
els... | python | def check_rest_server(rest_port):
'''Check if restful server is ready'''
retry_count = 5
for _ in range(retry_count):
response = rest_get(check_status_url(rest_port), REST_TIME_OUT)
if response:
if response.status_code == 200:
return True, response
els... | [
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Microsoft/nni | tools/nni_cmd/rest_utils.py | check_rest_server_quick | def check_rest_server_quick(rest_port):
'''Check if restful server is ready, only check once'''
response = rest_get(check_status_url(rest_port), 5)
if response and response.status_code == 200:
return True, response
return False, None | python | def check_rest_server_quick(rest_port):
'''Check if restful server is ready, only check once'''
response = rest_get(check_status_url(rest_port), 5)
if response and response.status_code == 200:
return True, response
return False, None | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | vap | def vap(x, a, b, c):
"""Vapor pressure model
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
float
np.exp(a+b/x+c*np.log(x))
"""
return np.exp(a+b/x+c*np.log(x)) | python | def vap(x, a, b, c):
"""Vapor pressure model
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
float
np.exp(a+b/x+c*np.log(x))
"""
return np.exp(a+b/x+c*np.log(x)) | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | logx_linear | def logx_linear(x, a, b):
"""logx linear
Parameters
----------
x: int
a: float
b: float
Returns
-------
float
a * np.log(x) + b
"""
x = np.log(x)
return a*x + b | python | def logx_linear(x, a, b):
"""logx linear
Parameters
----------
x: int
a: float
b: float
Returns
-------
float
a * np.log(x) + b
"""
x = np.log(x)
return a*x + b | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | dr_hill_zero_background | def dr_hill_zero_background(x, theta, eta, kappa):
"""dr hill zero background
Parameters
----------
x: int
theta: float
eta: float
kappa: float
Returns
-------
float
(theta* x**eta) / (kappa**eta + x**eta)
"""
return (theta* x**eta) / (kappa**eta + x**eta) | python | def dr_hill_zero_background(x, theta, eta, kappa):
"""dr hill zero background
Parameters
----------
x: int
theta: float
eta: float
kappa: float
Returns
-------
float
(theta* x**eta) / (kappa**eta + x**eta)
"""
return (theta* x**eta) / (kappa**eta + x**eta) | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | log_power | def log_power(x, a, b, c):
""""logistic power
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
float
a/(1.+(x/np.exp(b))**c)
"""
return a/(1.+(x/np.exp(b))**c) | python | def log_power(x, a, b, c):
""""logistic power
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
float
a/(1.+(x/np.exp(b))**c)
"""
return a/(1.+(x/np.exp(b))**c) | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | pow4 | def pow4(x, alpha, a, b, c):
"""pow4
Parameters
----------
x: int
alpha: float
a: float
b: float
c: float
Returns
-------
float
c - (a*x+b)**-alpha
"""
return c - (a*x+b)**-alpha | python | def pow4(x, alpha, a, b, c):
"""pow4
Parameters
----------
x: int
alpha: float
a: float
b: float
c: float
Returns
-------
float
c - (a*x+b)**-alpha
"""
return c - (a*x+b)**-alpha | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | mmf | def mmf(x, alpha, beta, kappa, delta):
"""Morgan-Mercer-Flodin
http://www.pisces-conservation.com/growthhelp/index.html?morgan_mercer_floden.htm
Parameters
----------
x: int
alpha: float
beta: float
kappa: float
delta: float
Returns
-------
float
alpha - (alpha ... | python | def mmf(x, alpha, beta, kappa, delta):
"""Morgan-Mercer-Flodin
http://www.pisces-conservation.com/growthhelp/index.html?morgan_mercer_floden.htm
Parameters
----------
x: int
alpha: float
beta: float
kappa: float
delta: float
Returns
-------
float
alpha - (alpha ... | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | weibull | def weibull(x, alpha, beta, kappa, delta):
"""Weibull model
http://www.pisces-conservation.com/growthhelp/index.html?morgan_mercer_floden.htm
Parameters
----------
x: int
alpha: float
beta: float
kappa: float
delta: float
Returns
-------
float
alpha - (alpha - b... | python | def weibull(x, alpha, beta, kappa, delta):
"""Weibull model
http://www.pisces-conservation.com/growthhelp/index.html?morgan_mercer_floden.htm
Parameters
----------
x: int
alpha: float
beta: float
kappa: float
delta: float
Returns
-------
float
alpha - (alpha - b... | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py | janoschek | def janoschek(x, a, beta, k, delta):
"""http://www.pisces-conservation.com/growthhelp/janoschek.htm
Parameters
----------
x: int
a: float
beta: float
k: float
delta: float
Returns
-------
float
a - (a - beta) * np.exp(-k*x**delta)
"""
return a - (a - bet... | python | def janoschek(x, a, beta, k, delta):
"""http://www.pisces-conservation.com/growthhelp/janoschek.htm
Parameters
----------
x: int
a: float
beta: float
k: float
delta: float
Returns
-------
float
a - (a - beta) * np.exp(-k*x**delta)
"""
return a - (a - bet... | [
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Microsoft/nni | tools/nni_cmd/nnictl.py | parse_args | def parse_args():
'''Definite the arguments users need to follow and input'''
parser = argparse.ArgumentParser(prog='nnictl', description='use nnictl command to control nni experiments')
parser.add_argument('--version', '-v', action='store_true')
parser.set_defaults(func=nni_info)
# create subparse... | python | def parse_args():
'''Definite the arguments users need to follow and input'''
parser = argparse.ArgumentParser(prog='nnictl', description='use nnictl command to control nni experiments')
parser.add_argument('--version', '-v', action='store_true')
parser.set_defaults(func=nni_info)
# create subparse... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | get_log_path | def get_log_path(config_file_name):
'''generate stdout and stderr log path'''
stdout_full_path = os.path.join(NNICTL_HOME_DIR, config_file_name, 'stdout')
stderr_full_path = os.path.join(NNICTL_HOME_DIR, config_file_name, 'stderr')
return stdout_full_path, stderr_full_path | python | def get_log_path(config_file_name):
'''generate stdout and stderr log path'''
stdout_full_path = os.path.join(NNICTL_HOME_DIR, config_file_name, 'stdout')
stderr_full_path = os.path.join(NNICTL_HOME_DIR, config_file_name, 'stderr')
return stdout_full_path, stderr_full_path | [
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Microsoft/nni | tools/nni_cmd/launcher.py | print_log_content | def print_log_content(config_file_name):
'''print log information'''
stdout_full_path, stderr_full_path = get_log_path(config_file_name)
print_normal(' Stdout:')
print(check_output_command(stdout_full_path))
print('\n\n')
print_normal(' Stderr:')
print(check_output_command(stderr_full_path)) | python | def print_log_content(config_file_name):
'''print log information'''
stdout_full_path, stderr_full_path = get_log_path(config_file_name)
print_normal(' Stdout:')
print(check_output_command(stdout_full_path))
print('\n\n')
print_normal(' Stderr:')
print(check_output_command(stderr_full_path)) | [
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Microsoft/nni | tools/nni_cmd/launcher.py | start_rest_server | def start_rest_server(port, platform, mode, config_file_name, experiment_id=None, log_dir=None, log_level=None):
'''Run nni manager process'''
nni_config = Config(config_file_name)
if detect_port(port):
print_error('Port %s is used by another process, please reset the port!\n' \
'You could u... | python | def start_rest_server(port, platform, mode, config_file_name, experiment_id=None, log_dir=None, log_level=None):
'''Run nni manager process'''
nni_config = Config(config_file_name)
if detect_port(port):
print_error('Port %s is used by another process, please reset the port!\n' \
'You could u... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | set_trial_config | def set_trial_config(experiment_config, port, config_file_name):
'''set trial configuration'''
request_data = dict()
request_data['trial_config'] = experiment_config['trial']
response = rest_put(cluster_metadata_url(port), json.dumps(request_data), REST_TIME_OUT)
if check_response(response):
... | python | def set_trial_config(experiment_config, port, config_file_name):
'''set trial configuration'''
request_data = dict()
request_data['trial_config'] = experiment_config['trial']
response = rest_put(cluster_metadata_url(port), json.dumps(request_data), REST_TIME_OUT)
if check_response(response):
... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | set_local_config | def set_local_config(experiment_config, port, config_file_name):
'''set local configuration'''
#set machine_list
request_data = dict()
if experiment_config.get('localConfig'):
request_data['local_config'] = experiment_config['localConfig']
if request_data['local_config'] and request_data... | python | def set_local_config(experiment_config, port, config_file_name):
'''set local configuration'''
#set machine_list
request_data = dict()
if experiment_config.get('localConfig'):
request_data['local_config'] = experiment_config['localConfig']
if request_data['local_config'] and request_data... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | set_remote_config | def set_remote_config(experiment_config, port, config_file_name):
'''Call setClusterMetadata to pass trial'''
#set machine_list
request_data = dict()
request_data['machine_list'] = experiment_config['machineList']
if request_data['machine_list']:
for i in range(len(request_data['machine_list... | python | def set_remote_config(experiment_config, port, config_file_name):
'''Call setClusterMetadata to pass trial'''
#set machine_list
request_data = dict()
request_data['machine_list'] = experiment_config['machineList']
if request_data['machine_list']:
for i in range(len(request_data['machine_list... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | setNNIManagerIp | def setNNIManagerIp(experiment_config, port, config_file_name):
'''set nniManagerIp'''
if experiment_config.get('nniManagerIp') is None:
return True, None
ip_config_dict = dict()
ip_config_dict['nni_manager_ip'] = { 'nniManagerIp' : experiment_config['nniManagerIp'] }
response = rest_put(clu... | python | def setNNIManagerIp(experiment_config, port, config_file_name):
'''set nniManagerIp'''
if experiment_config.get('nniManagerIp') is None:
return True, None
ip_config_dict = dict()
ip_config_dict['nni_manager_ip'] = { 'nniManagerIp' : experiment_config['nniManagerIp'] }
response = rest_put(clu... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | set_frameworkcontroller_config | def set_frameworkcontroller_config(experiment_config, port, config_file_name):
'''set kubeflow configuration'''
frameworkcontroller_config_data = dict()
frameworkcontroller_config_data['frameworkcontroller_config'] = experiment_config['frameworkcontrollerConfig']
response = rest_put(cluster_metadata_ur... | python | def set_frameworkcontroller_config(experiment_config, port, config_file_name):
'''set kubeflow configuration'''
frameworkcontroller_config_data = dict()
frameworkcontroller_config_data['frameworkcontroller_config'] = experiment_config['frameworkcontrollerConfig']
response = rest_put(cluster_metadata_ur... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | set_experiment | def set_experiment(experiment_config, mode, port, config_file_name):
'''Call startExperiment (rest POST /experiment) with yaml file content'''
request_data = dict()
request_data['authorName'] = experiment_config['authorName']
request_data['experimentName'] = experiment_config['experimentName']
reque... | python | def set_experiment(experiment_config, mode, port, config_file_name):
'''Call startExperiment (rest POST /experiment) with yaml file content'''
request_data = dict()
request_data['authorName'] = experiment_config['authorName']
request_data['experimentName'] = experiment_config['experimentName']
reque... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | launch_experiment | def launch_experiment(args, experiment_config, mode, config_file_name, experiment_id=None):
'''follow steps to start rest server and start experiment'''
nni_config = Config(config_file_name)
# check packages for tuner
if experiment_config.get('tuner') and experiment_config['tuner'].get('builtinTunerName... | python | def launch_experiment(args, experiment_config, mode, config_file_name, experiment_id=None):
'''follow steps to start rest server and start experiment'''
nni_config = Config(config_file_name)
# check packages for tuner
if experiment_config.get('tuner') and experiment_config['tuner'].get('builtinTunerName... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | resume_experiment | def resume_experiment(args):
'''resume an experiment'''
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
experiment_id = None
experiment_endTime = None
#find the latest stopped experiment
if not args.id:
print_error('Please set experiment id... | python | def resume_experiment(args):
'''resume an experiment'''
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
experiment_id = None
experiment_endTime = None
#find the latest stopped experiment
if not args.id:
print_error('Please set experiment id... | [
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Microsoft/nni | tools/nni_cmd/launcher.py | create_experiment | def create_experiment(args):
'''start a new experiment'''
config_file_name = ''.join(random.sample(string.ascii_letters + string.digits, 8))
nni_config = Config(config_file_name)
config_path = os.path.abspath(args.config)
if not os.path.exists(config_path):
print_error('Please set correct co... | python | def create_experiment(args):
'''start a new experiment'''
config_file_name = ''.join(random.sample(string.ascii_letters + string.digits, 8))
nni_config = Config(config_file_name)
config_path = os.path.abspath(args.config)
if not os.path.exists(config_path):
print_error('Please set correct co... | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.fit_theta | def fit_theta(self):
"""use least squares to fit all default curves parameter seperately
Returns
-------
None
"""
x = range(1, self.point_num + 1)
y = self.trial_history
for i in range(NUM_OF_FUNCTIONS):
model = curve_combination_model... | python | def fit_theta(self):
"""use least squares to fit all default curves parameter seperately
Returns
-------
None
"""
x = range(1, self.point_num + 1)
y = self.trial_history
for i in range(NUM_OF_FUNCTIONS):
model = curve_combination_model... | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.filter_curve | def filter_curve(self):
"""filter the poor performing curve
Returns
-------
None
"""
avg = np.sum(self.trial_history) / self.point_num
standard = avg * avg * self.point_num
predict_data = []
tmp_model = []
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"""filter the poor performing curve
Returns
-------
None
"""
avg = np.sum(self.trial_history) / self.point_num
standard = avg * avg * self.point_num
predict_data = []
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.predict_y | def predict_y(self, model, pos):
"""return the predict y of 'model' when epoch = pos
Parameters
----------
model: string
name of the curve function model
pos: int
the epoch number of the position you want to predict
Returns
------... | python | def predict_y(self, model, pos):
"""return the predict y of 'model' when epoch = pos
Parameters
----------
model: string
name of the curve function model
pos: int
the epoch number of the position you want to predict
Returns
------... | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.f_comb | def f_comb(self, pos, sample):
"""return the value of the f_comb when epoch = pos
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pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
... | python | def f_comb(self, pos, sample):
"""return the value of the f_comb when epoch = pos
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pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.normalize_weight | def normalize_weight(self, samples):
"""normalize weight
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a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
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... | python | def normalize_weight(self, samples):
"""normalize weight
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a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.sigma_sq | def sigma_sq(self, sample):
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"""returns the value of sigma square, given the weight's sample
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.normal_distribution | def normal_distribution(self, pos, sample):
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"""returns the value of normal distribution, given the weight's sample and target position
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pos: int
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.likelihood | def likelihood(self, samples):
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sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
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float
likelihood
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ret = np.ones(NUM_OF_INST... | python | def likelihood(self, samples):
"""likelihood
Parameters
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sample: list
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likelihood
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.prior | def prior(self, samples):
"""priori distribution
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a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
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Retu... | python | def prior(self, samples):
"""priori distribution
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samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.target_distribution | def target_distribution(self, samples):
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a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.mcmc_sampling | def mcmc_sampling(self):
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(1)Definition of sample:
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(2)Definitio... | python | def mcmc_sampling(self):
"""Adjust the weight of each function using mcmc sampling.
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.predict | def predict(self, trial_history):
"""predict the value of target position
Parameters
----------
trial_history: list
The history performance matrix of each trial.
Returns
-------
float
expected final result performance of this hype... | python | def predict(self, trial_history):
"""predict the value of target position
Parameters
----------
trial_history: list
The history performance matrix of each trial.
Returns
-------
float
expected final result performance of this hype... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py | _outlierDetection_threaded | def _outlierDetection_threaded(inputs):
'''
Detect the outlier
'''
[samples_idx, samples_x, samples_y_aggregation] = inputs
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
% (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
outlier = None
... | python | def _outlierDetection_threaded(inputs):
'''
Detect the outlier
'''
[samples_idx, samples_x, samples_y_aggregation] = inputs
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
% (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
outlier = None
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py | outlierDetection_threaded | def outlierDetection_threaded(samples_x, samples_y_aggregation):
'''
Use Multi-thread to detect the outlier
'''
outliers = []
threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
for samples_idx in range(0, len(samples_x))]
threads_pool = ThreadPool(min... | python | def outlierDetection_threaded(samples_x, samples_y_aggregation):
'''
Use Multi-thread to detect the outlier
'''
outliers = []
threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
for samples_idx in range(0, len(samples_x))]
threads_pool = ThreadPool(min... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | deeper_conv_block | def deeper_conv_block(conv_layer, kernel_size, weighted=True):
'''deeper conv layer.
'''
n_dim = get_n_dim(conv_layer)
filter_shape = (kernel_size,) * 2
n_filters = conv_layer.filters
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filt... | python | def deeper_conv_block(conv_layer, kernel_size, weighted=True):
'''deeper conv layer.
'''
n_dim = get_n_dim(conv_layer)
filter_shape = (kernel_size,) * 2
n_filters = conv_layer.filters
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filt... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | dense_to_deeper_block | def dense_to_deeper_block(dense_layer, weighted=True):
'''deeper dense layer.
'''
units = dense_layer.units
weight = np.eye(units)
bias = np.zeros(units)
new_dense_layer = StubDense(units, units)
if weighted:
new_dense_layer.set_weights(
(add_noise(weight, np.array([0, 1]... | python | def dense_to_deeper_block(dense_layer, weighted=True):
'''deeper dense layer.
'''
units = dense_layer.units
weight = np.eye(units)
bias = np.zeros(units)
new_dense_layer = StubDense(units, units)
if weighted:
new_dense_layer.set_weights(
(add_noise(weight, np.array([0, 1]... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_pre_dense | def wider_pre_dense(layer, n_add, weighted=True):
'''wider previous dense layer.
'''
if not weighted:
return StubDense(layer.input_units, layer.units + n_add)
n_units2 = layer.units
teacher_w, teacher_b = layer.get_weights()
rand = np.random.randint(n_units2, size=n_add)
student_w ... | python | def wider_pre_dense(layer, n_add, weighted=True):
'''wider previous dense layer.
'''
if not weighted:
return StubDense(layer.input_units, layer.units + n_add)
n_units2 = layer.units
teacher_w, teacher_b = layer.get_weights()
rand = np.random.randint(n_units2, size=n_add)
student_w ... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_pre_conv | def wider_pre_conv(layer, n_add_filters, weighted=True):
'''wider previous conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(
layer.input_channel,
layer.filters + n_add_filters,
kernel_size=layer.kernel_size,
)
... | python | def wider_pre_conv(layer, n_add_filters, weighted=True):
'''wider previous conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(
layer.input_channel,
layer.filters + n_add_filters,
kernel_size=layer.kernel_size,
)
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_next_conv | def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(layer.input_channel + n_add,
layer.filters,
kerne... | python | def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(layer.input_channel + n_add,
layer.filters,
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_bn | def wider_bn(layer, start_dim, total_dim, n_add, weighted=True):
'''wider batch norm layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_batch_norm_class(n_dim)(layer.num_features + n_add)
weights = layer.get_weights()
new_weights = [
add_noise(np.ones(n_add, dtype=... | python | def wider_bn(layer, start_dim, total_dim, n_add, weighted=True):
'''wider batch norm layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_batch_norm_class(n_dim)(layer.num_features + n_add)
weights = layer.get_weights()
new_weights = [
add_noise(np.ones(n_add, dtype=... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_next_dense | def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next dense layer.
'''
if not weighted:
return StubDense(layer.input_units + n_add, layer.units)
teacher_w, teacher_b = layer.get_weights()
student_w = teacher_w.copy()
n_units_each_channel = int(teacher_w.s... | python | def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next dense layer.
'''
if not weighted:
return StubDense(layer.input_units + n_add, layer.units)
teacher_w, teacher_b = layer.get_weights()
student_w = teacher_w.copy()
n_units_each_channel = int(teacher_w.s... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | add_noise | def add_noise(weights, other_weights):
'''add noise to the layer.
'''
w_range = np.ptp(other_weights.flatten())
noise_range = NOISE_RATIO * w_range
noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape)
return np.add(noise, weights) | python | def add_noise(weights, other_weights):
'''add noise to the layer.
'''
w_range = np.ptp(other_weights.flatten())
noise_range = NOISE_RATIO * w_range
noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape)
return np.add(noise, weights) | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | init_dense_weight | def init_dense_weight(layer):
'''initilize dense layer weight.
'''
units = layer.units
weight = np.eye(units)
bias = np.zeros(units)
layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
) | python | def init_dense_weight(layer):
'''initilize dense layer weight.
'''
units = layer.units
weight = np.eye(units)
bias = np.zeros(units)
layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | init_conv_weight | def init_conv_weight(layer):
'''initilize conv layer weight.
'''
n_filters = layer.filters
filter_shape = (layer.kernel_size,) * get_n_dim(layer)
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
for i in range(n_filters):... | python | def init_conv_weight(layer):
'''initilize conv layer weight.
'''
n_filters = layer.filters
filter_shape = (layer.kernel_size,) * get_n_dim(layer)
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
for i in range(n_filters):... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | init_bn_weight | def init_bn_weight(layer):
'''initilize batch norm layer weight.
'''
n_filters = layer.num_features
new_weights = [
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters,... | python | def init_bn_weight(layer):
'''initilize batch norm layer weight.
'''
n_filters = layer.num_features
new_weights = [
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters,... | [
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Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | parse_log_path | def parse_log_path(args, trial_content):
'''parse log path'''
path_list = []
host_list = []
for trial in trial_content:
if args.trial_id and args.trial_id != 'all' and trial.get('id') != args.trial_id:
continue
pattern = r'(?P<head>.+)://(?P<host>.+):(?P<path>.*)'
mat... | python | def parse_log_path(args, trial_content):
'''parse log path'''
path_list = []
host_list = []
for trial in trial_content:
if args.trial_id and args.trial_id != 'all' and trial.get('id') != args.trial_id:
continue
pattern = r'(?P<head>.+)://(?P<host>.+):(?P<path>.*)'
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Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | copy_data_from_remote | def copy_data_from_remote(args, nni_config, trial_content, path_list, host_list, temp_nni_path):
'''use ssh client to copy data from remote machine to local machien'''
machine_list = nni_config.get_config('experimentConfig').get('machineList')
machine_dict = {}
local_path_list = []
for machine in ma... | python | def copy_data_from_remote(args, nni_config, trial_content, path_list, host_list, temp_nni_path):
'''use ssh client to copy data from remote machine to local machien'''
machine_list = nni_config.get_config('experimentConfig').get('machineList')
machine_dict = {}
local_path_list = []
for machine in ma... | [
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Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | get_path_list | def get_path_list(args, nni_config, trial_content, temp_nni_path):
'''get path list according to different platform'''
path_list, host_list = parse_log_path(args, trial_content)
platform = nni_config.get_config('experimentConfig').get('trainingServicePlatform')
if platform == 'local':
print_norm... | python | def get_path_list(args, nni_config, trial_content, temp_nni_path):
'''get path list according to different platform'''
path_list, host_list = parse_log_path(args, trial_content)
platform = nni_config.get_config('experimentConfig').get('trainingServicePlatform')
if platform == 'local':
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Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | start_tensorboard_process | def start_tensorboard_process(args, nni_config, path_list, temp_nni_path):
'''call cmds to start tensorboard process in local machine'''
if detect_port(args.port):
print_error('Port %s is used by another process, please reset port!' % str(args.port))
exit(1)
stdout_file = open(os.path.j... | python | def start_tensorboard_process(args, nni_config, path_list, temp_nni_path):
'''call cmds to start tensorboard process in local machine'''
if detect_port(args.port):
print_error('Port %s is used by another process, please reset port!' % str(args.port))
exit(1)
stdout_file = open(os.path.j... | [
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Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | stop_tensorboard | def stop_tensorboard(args):
'''stop tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
tensorb... | python | def stop_tensorboard(args):
'''stop tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
tensorb... | [
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Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | start_tensorboard | def start_tensorboard(args):
'''start tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
rest_... | python | def start_tensorboard(args):
'''start tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | _ratio_scores | def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma | python | def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | selection_r | def selection_r(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Call selection
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_type... | python | def selection_r(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Call selection
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_type... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | selection | def selection(x_bounds,
x_types,
clusteringmodel_gmm_good,
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minimize_starting_points,
minimize_constraints_fun=None):
'''
Select the lowest mu value
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x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
minimize_starting_points,
minimize_constraints_fun=None):
'''
Select the lowest mu value
'''
results = lib_acquisition_function.next_hyperparameter_lo... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | _minimize_constraints_fun_summation | def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
'''
summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND | python | def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
'''
summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND | [
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Microsoft/nni | examples/trials/sklearn/classification/main.py | load_data | def load_data():
'''Load dataset, use 20newsgroups dataset'''
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
retu... | python | def load_data():
'''Load dataset, use 20newsgroups dataset'''
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
retu... | [
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Microsoft/nni | examples/trials/sklearn/classification/main.py | get_model | def get_model(PARAMS):
'''Get model according to parameters'''
model = SVC()
model.C = PARAMS.get('C')
model.keral = PARAMS.get('keral')
model.degree = PARAMS.get('degree')
model.gamma = PARAMS.get('gamma')
model.coef0 = PARAMS.get('coef0')
return model | python | def get_model(PARAMS):
'''Get model according to parameters'''
model = SVC()
model.C = PARAMS.get('C')
model.keral = PARAMS.get('keral')
model.degree = PARAMS.get('degree')
model.gamma = PARAMS.get('gamma')
model.coef0 = PARAMS.get('coef0')
return model | [
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | Bracket.get_hyperparameter_configurations | def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
... | python | def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
... | [
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_initialize | def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
Value... | python | def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.generate_new_bracket | def generate_new_bracket(self):
"""generate a new bracket"""
logger.debug(
'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s)
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
se... | python | def generate_new_bracket(self):
"""generate a new bracket"""
logger.debug(
'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s)
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_request_trial_jobs | def handle_request_trial_jobs(self, data):
"""recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate
"""
# Receive new request
self.credit += data
for _ in rang... | python | def handle_request_trial_jobs(self, data):
"""recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate
"""
# Receive new request
self.credit += data
for _ in rang... | [
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB._request_one_trial_job | def _request_one_trial_job(self):
"""get one trial job, i.e., one hyperparameter configuration.
If this function is called, Command will be sent by BOHB:
a. If there is a parameter need to run, will return "NewTrialJob" with a dict:
{
'parameter_id': id of new hyperparamete... | python | def _request_one_trial_job(self):
"""get one trial job, i.e., one hyperparameter configuration.
If this function is called, Command will be sent by BOHB:
a. If there is a parameter need to run, will return "NewTrialJob" with a dict:
{
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_update_search_space | def handle_update_search_space(self, data):
"""change json format to ConfigSpace format dict<dict> -> configspace
Parameters
----------
data: JSON object
search space of this experiment
"""
search_space = data
cs = CS.ConfigurationSpace()
for ... | python | def handle_update_search_space(self, data):
"""change json format to ConfigSpace format dict<dict> -> configspace
Parameters
----------
data: JSON object
search space of this experiment
"""
search_space = data
cs = CS.ConfigurationSpace()
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_trial_end | def handle_trial_end(self, data):
"""receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
even... | python | def handle_trial_end(self, data):
"""receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_report_metric_data | def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
... | python | def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_import_data | def handle_import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
Raises
------
AssertionError
data doesn't have requir... | python | def handle_import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
Raises
------
AssertionError
data doesn't have requir... | [
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Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | data_transforms_cifar10 | def data_transforms_cifar10(args):
""" data_transforms for cifar10 dataset
"""
cifar_mean = [0.49139968, 0.48215827, 0.44653124]
cifar_std = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms... | python | def data_transforms_cifar10(args):
""" data_transforms for cifar10 dataset
"""
cifar_mean = [0.49139968, 0.48215827, 0.44653124]
cifar_std = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms... | [
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Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | data_transforms_mnist | def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
""" data_transforms for mnist dataset
"""
if mnist_mean is None:
mnist_mean = [0.5]
if mnist_std is None:
mnist_std = [0.5]
train_transform = transforms.Compose(
[
transforms.RandomCrop(28, paddin... | python | def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
""" data_transforms for mnist dataset
"""
if mnist_mean is None:
mnist_mean = [0.5]
if mnist_std is None:
mnist_std = [0.5]
train_transform = transforms.Compose(
[
transforms.RandomCrop(28, paddin... | [
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Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | get_mean_and_std | def get_mean_and_std(dataset):
"""Compute the mean and std value of dataset."""
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True, num_workers=2
)
mean = torch.zeros(3)
std = torch.zeros(3)
print("==> Computing mean and std..")
for inputs, _ in dataloader:... | python | def get_mean_and_std(dataset):
"""Compute the mean and std value of dataset."""
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True, num_workers=2
)
mean = torch.zeros(3)
std = torch.zeros(3)
print("==> Computing mean and std..")
for inputs, _ in dataloader:... | [
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Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | init_params | def init_params(net):
"""Init layer parameters."""
for module in net.modules():
if isinstance(module, nn.Conv2d):
init.kaiming_normal(module.weight, mode="fan_out")
if module.bias:
init.constant(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
... | python | def init_params(net):
"""Init layer parameters."""
for module in net.modules():
if isinstance(module, nn.Conv2d):
init.kaiming_normal(module.weight, mode="fan_out")
if module.bias:
init.constant(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
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Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | EarlyStopping.step | def step(self, metrics):
""" EarlyStopping step on each epoch
Arguments:
metrics {float} -- metric value
"""
if self.best is None:
self.best = metrics
return False
if np.isnan(metrics):
return True
if self.is_better(metri... | python | def step(self, metrics):
""" EarlyStopping step on each epoch
Arguments:
metrics {float} -- metric value
"""
if self.best is None:
self.best = metrics
return False
if np.isnan(metrics):
return True
if self.is_better(metri... | [
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] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L43-L65 | train | EarlyStopping step on each epoch. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py | check_feasibility | def check_feasibility(x_bounds, lowerbound, upperbound):
'''
This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
'''
# x_bounds should be sorted, so even for "discrete_int" type,
# the smallest and the largest number should... | python | def check_feasibility(x_bounds, lowerbound, upperbound):
'''
This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
'''
# x_bounds should be sorted, so even for "discrete_int" type,
# the smallest and the largest number should... | [
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For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7. | [
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] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py#L27-L40 | train | Check feasibility of a given parameter range. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
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