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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._insert_new_layers | def _insert_new_layers(self, new_layers, start_node_id, end_node_id):
"""Insert the new_layers after the node with start_node_id."""
new_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
temp_output_id = new_node_id
for layer in new_layers[:-1]:
temp_output_id =... | python | def _insert_new_layers(self, new_layers, start_node_id, end_node_id):
"""Insert the new_layers after the node with start_node_id."""
new_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
temp_output_id = new_node_id
for layer in new_layers[:-1]:
temp_output_id =... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.to_add_skip_model | def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-connection... | python | def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
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start_id: The convolutional layer ID, after which to start the skip-connection.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.to_concat_skip_model | def to_concat_skip_model(self, start_id, end_id):
"""Add a weighted add concatenate connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-... | python | def to_concat_skip_model(self, start_id, end_id):
"""Add a weighted add concatenate connection from after start node to end node.
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start_id: The convolutional layer ID, after which to start the skip-connection.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.extract_descriptor | def extract_descriptor(self):
"""Extract the the description of the Graph as an instance of NetworkDescriptor."""
main_chain = self.get_main_chain()
index_in_main_chain = {}
for index, u in enumerate(main_chain):
index_in_main_chain[u] = index
ret = NetworkDescriptor... | python | def extract_descriptor(self):
"""Extract the the description of the Graph as an instance of NetworkDescriptor."""
main_chain = self.get_main_chain()
index_in_main_chain = {}
for index, u in enumerate(main_chain):
index_in_main_chain[u] = index
ret = NetworkDescriptor... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.clear_weights | def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None | python | def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.get_main_chain_layers | def get_main_chain_layers(self):
"""Return a list of layer IDs in the main chain."""
main_chain = self.get_main_chain()
ret = []
for u in main_chain:
for v, layer_id in self.adj_list[u]:
if v in main_chain and u in main_chain:
ret.append(la... | python | def get_main_chain_layers(self):
"""Return a list of layer IDs in the main chain."""
main_chain = self.get_main_chain()
ret = []
for u in main_chain:
for v, layer_id in self.adj_list[u]:
if v in main_chain and u in main_chain:
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.get_main_chain | def get_main_chain(self):
"""Returns the main chain node ID list."""
pre_node = {}
distance = {}
for i in range(self.n_nodes):
distance[i] = 0
pre_node[i] = i
for i in range(self.n_nodes - 1):
for u in range(self.n_nodes):
for v... | python | def get_main_chain(self):
"""Returns the main chain node ID list."""
pre_node = {}
distance = {}
for i in range(self.n_nodes):
distance[i] = 0
pre_node[i] = i
for i in range(self.n_nodes - 1):
for u in range(self.n_nodes):
for v... | [
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.run | def run(self):
"""Run the tuner.
This function will never return unless raise.
"""
_logger.info('Start dispatcher')
if dispatcher_env_vars.NNI_MODE == 'resume':
self.load_checkpoint()
while True:
command, data = receive()
if data:
... | python | def run(self):
"""Run the tuner.
This function will never return unless raise.
"""
_logger.info('Start dispatcher')
if dispatcher_env_vars.NNI_MODE == 'resume':
self.load_checkpoint()
while True:
command, data = receive()
if data:
... | [
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.command_queue_worker | def command_queue_worker(self, command_queue):
"""Process commands in command queues.
"""
while True:
try:
# set timeout to ensure self.stopping is checked periodically
command, data = command_queue.get(timeout=3)
try:
... | python | def command_queue_worker(self, command_queue):
"""Process commands in command queues.
"""
while True:
try:
# set timeout to ensure self.stopping is checked periodically
command, data = command_queue.get(timeout=3)
try:
... | [
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.enqueue_command | def enqueue_command(self, command, data):
"""Enqueue command into command queues
"""
if command == CommandType.TrialEnd or (command == CommandType.ReportMetricData and data['type'] == 'PERIODICAL'):
self.assessor_command_queue.put((command, data))
else:
self.defau... | python | def enqueue_command(self, command, data):
"""Enqueue command into command queues
"""
if command == CommandType.TrialEnd or (command == CommandType.ReportMetricData and data['type'] == 'PERIODICAL'):
self.assessor_command_queue.put((command, data))
else:
self.defau... | [
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.process_command_thread | def process_command_thread(self, request):
"""Worker thread to process a command.
"""
command, data = request
if multi_thread_enabled():
try:
self.process_command(command, data)
except Exception as e:
_logger.exception(str(e))
... | python | def process_command_thread(self, request):
"""Worker thread to process a command.
"""
command, data = request
if multi_thread_enabled():
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except Exception as e:
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_data.py | match_val_type | def match_val_type(vals, vals_bounds, vals_types):
'''
Update values in the array, to match their corresponding type
'''
vals_new = []
for i, _ in enumerate(vals_types):
if vals_types[i] == "discrete_int":
# Find the closest integer in the array, vals_bounds
vals_new... | python | def match_val_type(vals, vals_bounds, vals_types):
'''
Update values in the array, to match their corresponding type
'''
vals_new = []
for i, _ in enumerate(vals_types):
if vals_types[i] == "discrete_int":
# Find the closest integer in the array, vals_bounds
vals_new... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_data.py | rand | def rand(x_bounds, x_types):
'''
Random generate variable value within their bounds
'''
outputs = []
for i, _ in enumerate(x_bounds):
if x_types[i] == "discrete_int":
temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
outputs.append(temp)
elif x_type... | python | def rand(x_bounds, x_types):
'''
Random generate variable value within their bounds
'''
outputs = []
for i, _ in enumerate(x_bounds):
if x_types[i] == "discrete_int":
temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
outputs.append(temp)
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | to_wider_graph | def to_wider_graph(graph):
''' wider graph
'''
weighted_layer_ids = graph.wide_layer_ids()
weighted_layer_ids = list(
filter(lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
)
wider_layers = sample(weighted_layer_ids, 1)
for layer_id in wider_layers:
layer... | python | def to_wider_graph(graph):
''' wider graph
'''
weighted_layer_ids = graph.wide_layer_ids()
weighted_layer_ids = list(
filter(lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
)
wider_layers = sample(weighted_layer_ids, 1)
for layer_id in wider_layers:
layer... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | to_skip_connection_graph | def to_skip_connection_graph(graph):
''' skip connection graph
'''
# The last conv layer cannot be widen since wider operator cannot be done over the two sides of flatten.
weighted_layer_ids = graph.skip_connection_layer_ids()
valid_connection = []
for skip_type in sorted([NetworkDescriptor.ADD_... | python | def to_skip_connection_graph(graph):
''' skip connection graph
'''
# The last conv layer cannot be widen since wider operator cannot be done over the two sides of flatten.
weighted_layer_ids = graph.skip_connection_layer_ids()
valid_connection = []
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | create_new_layer | def create_new_layer(layer, n_dim):
''' create new layer for the graph
'''
input_shape = layer.output.shape
dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim), StubReLU]
if is_layer(layer, "ReLU"):
... | python | def create_new_layer(layer, n_dim):
''' create new layer for the graph
'''
input_shape = layer.output.shape
dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim), StubReLU]
if is_layer(layer, "ReLU"):
... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | to_deeper_graph | def to_deeper_graph(graph):
''' deeper graph
'''
weighted_layer_ids = graph.deep_layer_ids()
if len(weighted_layer_ids) >= Constant.MAX_LAYERS:
return None
deeper_layer_ids = sample(weighted_layer_ids, 1)
for layer_id in deeper_layer_ids:
layer = graph.layer_list[layer_id]
... | python | def to_deeper_graph(graph):
''' deeper graph
'''
weighted_layer_ids = graph.deep_layer_ids()
if len(weighted_layer_ids) >= Constant.MAX_LAYERS:
return None
deeper_layer_ids = sample(weighted_layer_ids, 1)
for layer_id in deeper_layer_ids:
layer = graph.layer_list[layer_id]
... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | legal_graph | def legal_graph(graph):
'''judge if a graph is legal or not.
'''
descriptor = graph.extract_descriptor()
skips = descriptor.skip_connections
if len(skips) != len(set(skips)):
return False
return True | python | def legal_graph(graph):
'''judge if a graph is legal or not.
'''
descriptor = graph.extract_descriptor()
skips = descriptor.skip_connections
if len(skips) != len(set(skips)):
return False
return True | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | transform | def transform(graph):
'''core transform function for graph.
'''
graphs = []
for _ in range(Constant.N_NEIGHBOURS * 2):
random_num = randrange(3)
temp_graph = None
if random_num == 0:
temp_graph = to_deeper_graph(deepcopy(graph))
elif random_num == 1:
... | python | def transform(graph):
'''core transform function for graph.
'''
graphs = []
for _ in range(Constant.N_NEIGHBOURS * 2):
random_num = randrange(3)
temp_graph = None
if random_num == 0:
temp_graph = to_deeper_graph(deepcopy(graph))
elif random_num == 1:
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | uniform | def uniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert high > low, 'Upper bound must be larger than lower bound'
return random_state.uniform(low, high) | python | def uniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert high > low, 'Upper bound must be larger than lower bound'
return random_state.uniform(low, high) | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | quniform | def quniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(uniform(low, high, random_state) / q) * q | python | def quniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(uniform(low, high, random_state) / q) * q | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | loguniform | def loguniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert low > 0, 'Lower bound must be positive'
return np.exp(uniform(np.log(low), np.log(high), random... | python | def loguniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert low > 0, 'Lower bound must be positive'
return np.exp(uniform(np.log(low), np.log(high), random... | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | qloguniform | def qloguniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(loguniform(low, high, random_state) / q) * q | python | def qloguniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(loguniform(low, high, random_state) / q) * q | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | qnormal | def qnormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(normal(mu, sigma, random_state) / q) * q | python | def qnormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(normal(mu, sigma, random_state) / q) * q | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | lognormal | def lognormal(mu, sigma, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
random_state: an object of numpy.random.RandomState
'''
return np.exp(normal(mu, sigma, random_state)) | python | def lognormal(mu, sigma, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
random_state: an object of numpy.random.RandomState
'''
return np.exp(normal(mu, sigma, random_state)) | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | qlognormal | def qlognormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(lognormal(mu, sigma, random_state) / q) * q | python | def qlognormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(lognormal(mu, sigma, random_state) / q) * q | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/Prediction.py | predict | def predict(parameters_value, regressor_gp):
'''
Predict by Gaussian Process Model
'''
parameters_value = numpy.array(parameters_value).reshape(-1, len(parameters_value))
mu, sigma = regressor_gp.predict(parameters_value, return_std=True)
return mu[0], sigma[0] | python | def predict(parameters_value, regressor_gp):
'''
Predict by Gaussian Process Model
'''
parameters_value = numpy.array(parameters_value).reshape(-1, len(parameters_value))
mu, sigma = regressor_gp.predict(parameters_value, return_std=True)
return mu[0], sigma[0] | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_get | def rest_get(url, timeout):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http get to url {1}'.format(str(e), url))
return None | python | def rest_get(url, timeout):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http get to url {1}'.format(str(e), url))
return None | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_post | def rest_post(url, data, timeout, rethrow_exception=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 Exceptio... | python | def rest_post(url, data, timeout, rethrow_exception=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 Exceptio... | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_put | def rest_put(url, data, timeout):
'''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 e:
print('Get ex... | python | def rest_put(url, data, timeout):
'''Call rest put method'''
try:
response = requests.put(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
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return response
except Exception as e:
print('Get ex... | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_delete | def rest_delete(url, timeout):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http delete to url {1}'.format(str(e), url))
return None | python | def rest_delete(url, timeout):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http delete to url {1}'.format(str(e), url))
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py | CurvefittingAssessor.trial_end | def trial_end(self, trial_job_id, success):
"""update the best performance of completed trial job
Parameters
----------
trial_job_id: int
trial job id
success: bool
True if succssfully finish the experiment, False otherwise
"""
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"""update the best performance of completed trial job
Parameters
----------
trial_job_id: int
trial job id
success: bool
True if succssfully finish the experiment, False otherwise
"""
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py | CurvefittingAssessor.assess_trial | def assess_trial(self, trial_job_id, trial_history):
"""assess whether a trial should be early stop by curve fitting algorithm
Parameters
----------
trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
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"""assess whether a trial should be early stop by curve fitting algorithm
Parameters
----------
trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
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Microsoft/nni | src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py | MultiPhaseMsgDispatcher.handle_initialize | def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True | python | def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.generate_parameters | def generate_parameters(self, parameter_id):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
"""
if not self.history:
self.init_search()
new_father_id = None
generated... | python | def generate_parameters(self, parameter_id):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
"""
if not self.history:
self.init_search()
new_father_id = None
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.receive_trial_result | def receive_trial_result(self, parameter_id, parameters, value):
""" Record an observation of the objective function.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
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""" Record an observation of the objective function.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.init_search | def init_search(self):
"""Call the generators to generate the initial architectures for the search."""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
self... | python | def init_search(self):
"""Call the generators to generate the initial architectures for the search."""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.generate | def generate(self):
"""Generate the next neural architecture.
Returns
-------
other_info: any object
Anything to be saved in the training queue together with the architecture.
generated_graph: Graph
An instance of Graph.
"""
generated_grap... | python | def generate(self):
"""Generate the next neural architecture.
Returns
-------
other_info: any object
Anything to be saved in the training queue together with the architecture.
generated_graph: Graph
An instance of Graph.
"""
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.update | def update(self, other_info, graph, metric_value, model_id):
""" Update the controller with evaluation result of a neural architecture.
Parameters
----------
other_info: any object
In our case it is the father ID in the search tree.
graph: Graph
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""" Update the controller with evaluation result of a neural architecture.
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In our case it is the father ID in the search tree.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.add_model | def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
if self.verbose:
lo... | python | def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.get_best_model_id | def get_best_model_id(self):
""" Get the best model_id from history using the metric value
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history, key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["mod... | python | def get_best_model_id(self):
""" Get the best model_id from history using the metric value
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history, key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["mod... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.load_model_by_id | def load_model_by_id(self, model_id):
"""Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
with open(os.path.join(self... | python | def load_model_by_id(self, model_id):
"""Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | _rand_init | def _rand_init(x_bounds, x_types, selection_num_starting_points):
'''
Random sample some init seed within bounds.
'''
return [lib_data.rand(x_bounds, x_types) for i \
in range(0, selection_num_starting_points)] | python | def _rand_init(x_bounds, x_types, selection_num_starting_points):
'''
Random sample some init seed within bounds.
'''
return [lib_data.rand(x_bounds, x_types) for i \
in range(0, selection_num_starting_points)] | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | get_median | def get_median(temp_list):
"""Return median
"""
num = len(temp_list)
temp_list.sort()
print(temp_list)
if num % 2 == 0:
median = (temp_list[int(num/2)] + temp_list[int(num/2) - 1]) / 2
else:
median = temp_list[int(num/2)]
return median | python | def get_median(temp_list):
"""Return median
"""
num = len(temp_list)
temp_list.sort()
print(temp_list)
if num % 2 == 0:
median = (temp_list[int(num/2)] + temp_list[int(num/2) - 1]) / 2
else:
median = temp_list[int(num/2)]
return median | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.update_search_space | def update_search_space(self, search_space):
"""Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : dict
"""
self.x_bounds = [[] for i in range(len(search_space))]
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search_space : dict
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner._pack_output | def _pack_output(self, init_parameter):
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init_parameter : dict
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output : dict
"""
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"""Pack the output
Parameters
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init_parameter : dict
Returns
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output : dict
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.generate_parameters | def generate_parameters(self, parameter_id):
"""Generate next parameter for trial
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"""Generate next parameter for trial
If the number of trial result is lower than cold start number,
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.receive_trial_result | def receive_trial_result(self, parameter_id, parameters, value):
"""Tuner receive result from trial.
Parameters
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parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
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value = extr... | python | def receive_trial_result(self, parameter_id, parameters, value):
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Parameters
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parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.import_data | def import_data(self, data):
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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:
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"""Import additional data for tuning
Parameters
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data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/CreateModel.py | create_model | def create_model(samples_x, samples_y_aggregation,
n_restarts_optimizer=250, is_white_kernel=False):
'''
Trains GP regression model
'''
kernel = gp.kernels.ConstantKernel(constant_value=1,
constant_value_bounds=(1e-12, 1e12)) * \
... | python | def create_model(samples_x, samples_y_aggregation,
n_restarts_optimizer=250, is_white_kernel=False):
'''
Trains GP regression model
'''
kernel = gp.kernels.ConstantKernel(constant_value=1,
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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
'''
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner._parse_quniform | def _parse_quniform(self, param_value):
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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'''
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raise RuntimeError("The number of values sampled (q) should be at least 2")
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner.expand_parameters | def expand_parameters(self, para):
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Enumerate all possible combinations of all parameters
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'''
Enumerate all possible combinations of all parameters
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Microsoft/nni | src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py | GridSearchTuner.import_data | def import_data(self, data):
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data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
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a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
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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'''
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Microsoft/nni | tools/nni_trial_tool/log_utils.py | RemoteLogger.write | def write(self, buf):
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Microsoft/nni | tools/nni_trial_tool/log_utils.py | PipeLogReader.run | def run(self):
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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
------
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Incorrect final result: the final result should be float/int,
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"""
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Extract scalar reward from trial result.
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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()))
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Microsoft/nni | src/sdk/pynni/nni/utils.py | init_dispatcher_logger | def init_dispatcher_logger():
""" Initialize dispatcher logging configuration"""
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""" Initialize dispatcher logging configuration"""
logger_file_path = 'dispatcher.log'
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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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Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/config_generator.py | CG_BOHB.get_config | def get_config(self, budget):
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This function is called inside BOHB to query a new configuration
Parameters:
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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):
"""
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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:
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"""Returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_id : int
"""
self.count +=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"):
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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,
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num_heads=4,
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is_training=True,
causality=False):
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num_heads=4,
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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,
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scale=True,
scope="positional_encoding",
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'''
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,
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'''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.
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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:
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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'},\
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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'},\
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Microsoft/nni | tools/nni_cmd/rest_utils.py | rest_post | def rest_post(url, data, timeout, show_error=False):
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try:
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'''Call rest post method'''
try:
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Microsoft/nni | tools/nni_cmd/rest_utils.py | rest_get | def rest_get(url, timeout, show_error=False):
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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
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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:
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return response
except Exception as exception:
if show_error:
print_error(exception)
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'''Call rest delete method'''
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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:
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Microsoft/nni | tools/nni_cmd/rest_utils.py | check_rest_server_quick | def check_rest_server_quick(rest_port):
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response = rest_get(check_status_url(rest_port), 5)
if response and response.status_code == 200:
return True, response
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'''Check if restful server is ready, only check once'''
response = rest_get(check_status_url(rest_port), 5)
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return True, response
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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))
"""
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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)
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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)
"""
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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 | exp4 | def exp4(x, c, a, b, alpha):
"""exp4
Parameters
----------
x: int
c: float
a: float
b: float
alpha: float
Returns
-------
float
c - np.exp(-a*(x**alpha)+b)
"""
return c - np.exp(-a*(x**alpha)+b) | python | def exp4(x, c, a, b, alpha):
"""exp4
Parameters
----------
x: int
c: float
a: float
b: float
alpha: float
Returns
-------
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c - np.exp(-a*(x**alpha)+b)
"""
return c - np.exp(-a*(x**alpha)+b) | [
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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 | get_nni_installation_path | def get_nni_installation_path():
''' Find nni lib from the following locations in order
Return nni root directory if it exists
'''
def try_installation_path_sequentially(*sitepackages):
'''Try different installation path sequentially util nni is found.
Return None if nothing is found
... | python | def get_nni_installation_path():
''' Find nni lib from the following locations in order
Return nni root directory if it exists
'''
def try_installation_path_sequentially(*sitepackages):
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Return None if nothing is found
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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' \
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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']
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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):
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