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Insert the new_layers after the node with start_node_id.
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
... |
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.
def to_add_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-connection.
def to_concat_skip_model(self, start_id, end_id)... |
Extract the the description of the Graph as an instance of NetworkDescriptor.
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_chai... |
clear weights of the graph
def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None |
Return a list of layer IDs in the main chain.
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 i... |
Returns the main chain node ID list.
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 rang... |
Run the tuner.
This function will never return unless raise.
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... |
Process commands in command queues.
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)
... |
Enqueue command into command queues
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))... |
Worker thread to process a command.
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:
... |
Update values in the array, to match their corresponding 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... |
Random generate variable value within their bounds
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)]
... |
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:
... |
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(... |
create new layer for the graph
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]
... |
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... |
judge if a graph is legal or not.
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 |
core transform function for graph.
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))
... |
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
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... |
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
def quniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
... |
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
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 obj... |
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
def qloguniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
... |
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
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 o... |
mu: float or array_like of floats
sigma: float or array_like of floats
random_state: an object of numpy.random.RandomState
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
'... |
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
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: a... |
Predict by Gaussian Process Model
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... |
Call rest get method
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 |
Call rest post method
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 respo... |
Call rest put method
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:... |
Call rest delete method
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 |
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
def trial_end(self, trial_job_id, success):
"""update the best perfor... |
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
Returns
-------
bool
AssessResult.Goo... |
data is search space
def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True |
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
def generate_parameters(self, parameter_id):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
p... |
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.
def receive_trial_result(self, parameter_id, parameters, value):
""" Record an ... |
Call the generators to generate the initial architectures for the 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 =... |
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.
def generate(self):
"""Generate the next neural architec... |
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
An instance of Graph. The trained neural architecture.
metric_value: float... |
Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
... |
Get the best model_id from history using the metric value
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"]
ret... |
Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
def load_model_by_id(self, model_id):
"""Get the model by model_id
Parameters
... |
Random sample some init seed within bounds.
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)] |
Return 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 |
Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : dict
def update_search_space(self, search_space):
"""Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : ... |
Pack the output
Parameters
----------
init_parameter : dict
Returns
-------
output : dict
def _pack_output(self, init_parameter):
"""Pack the output
Parameters
----------
init_parameter : dict
Returns
-------
ou... |
Generate next parameter for trial
If the number of trial result is lower than cold start number,
metis will first random generate some parameters.
Otherwise, metis will choose the parameters by the Gussian Process Model and the Gussian Mixture Model.
Parameters
----------
... |
Tuner receive result from trial.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
def receive_trial_result(self, parameter_id, parameters, value):
"""Tuner receive result from trial... |
Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
... |
Trains GP regression 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, 1... |
generate all possible configs for hyperparameters from hyperparameter space.
ss_spec: hyperparameter space
def json2paramater(self, ss_spec):
'''
generate all possible configs for hyperparameters from hyperparameter space.
ss_spec: hyperparameter space
'''
if isinstance(... |
parse type of quniform parameter and return a list
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], p... |
parse type of quniform or qloguniform
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... |
Enumerate all possible combinations of all parameters
para: {key1: [v11, v12, ...], key2: [v21, v22, ...], ...}
return: {{key1: v11, key2: v21, ...}, {key1: v11, key2: v22, ...}, ...}
def expand_parameters(self, para):
'''
Enumerate all possible combinations of all parameters
pa... |
Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
... |
Log message into stdout
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)) |
Write buffer data into logger/stdout
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(s... |
Run the thread, logging everything.
If the log_collection is 'none', the log content will not be enqueued
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.readli... |
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.
def extract_scalar_reward(value, scalar_key='default'):
"""
Extract scalar reward fr... |
convert dict type to tuple to solve unhashable problem.
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.it... |
Initialize dispatcher logging configuration
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_p... |
We opted for a single multidimensional KDE compared to the
hierarchy of one-dimensional KDEs used in TPE. The dimensional is
seperated by budget. This function sample a configuration from
largest budget. Firstly we sample "num_samples" configurations,
then prefer one with the largest l(x... |
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
-------
config
return a valid confi... |
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 parameters
budget: float
the budget of the parameters
parameters: d... |
Check the search space is valid: only contains 'choice' type
Parameters
----------
search_space : dict
def is_valid(self, search_space):
"""
Check the search space is valid: only contains 'choice' type
Parameters
----------
search_space ... |
Returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_id : int
def generate_parameters(self, parameter_id):
"""Returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_... |
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.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse ... |
Applies multihead attention.
Args:
queries: A 3d tensor with shape of [N, T_q, C_q].
keys: A 3d tensor with shape of [N, T_k, C_k].
num_units: A cdscalar. Attention size.
dropout_rate: A floating point number.
is_training: Boolean. Controller of mechanism for dropout.
causality:... |
Return positinal embedding.
def positional_encoding(inputs,
num_units=None,
zero_pad=True,
scale=True,
scope="positional_encoding",
reuse=None):
'''
Return positinal embedding.
'''
Sh... |
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 reuse the weights of a previous layer
by the same name.
Returns:
A 3d tensor with the ... |
Generate search space.
Return a serializable search space object.
module_name: name of the module (str)
code: user code (str)
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)
... |
Call rest put method
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
exce... |
Call rest post method
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
... |
Call rest get method
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 |
Call rest delete method
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 |
Check if restful server is ready
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:
ret... |
Check if restful server is ready, only check once
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 |
Vapor pressure model
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
float
np.exp(a+b/x+c*np.log(x))
def vap(x, a, b, c):
"""Vapor pressure model
Parameters
----------
x: int
a: float
b: float
c: float
Retur... |
logx linear
Parameters
----------
x: int
a: float
b: float
Returns
-------
float
a * np.log(x) + b
def logx_linear(x, a, b):
"""logx linear
Parameters
----------
x: int
a: float
b: float
Returns
-------
float
a * np.log(x) + b
... |
dr hill zero background
Parameters
----------
x: int
theta: float
eta: float
kappa: float
Returns
-------
float
(theta* x**eta) / (kappa**eta + x**eta)
def dr_hill_zero_background(x, theta, eta, kappa):
"""dr hill zero background
Parameters
----------
x:... |
logistic power
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
float
a/(1.+(x/np.exp(b))**c)
def log_power(x, a, b, c):
""""logistic power
Parameters
----------
x: int
a: float
b: float
c: float
Returns
-------
... |
pow4
Parameters
----------
x: int
alpha: float
a: float
b: float
c: float
Returns
-------
float
c - (a*x+b)**-alpha
def pow4(x, alpha, a, b, c):
"""pow4
Parameters
----------
x: int
alpha: float
a: float
b: float
c: float
Returns
... |
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 - beta) / (1. + (kappa * x)**delta)
def mmf(x... |
exp4
Parameters
----------
x: int
c: float
a: float
b: float
alpha: float
Returns
-------
float
c - np.exp(-a*(x**alpha)+b)
def exp4(x, c, a, b, alpha):
"""exp4
Parameters
----------
x: int
c: float
a: float
b: float
alpha: float
R... |
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 - beta) * np.exp(-(kappa * x)**delta)
def weibull(x,... |
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)
def janoschek(x, a, beta, k, delta):
"""http://www.pisces-conservation... |
Definite the arguments users need to follow and input
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')
pa... |
generate stdout and stderr 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_ful... |
print log information
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_com... |
Find nni lib from the following locations in order
Return nni root directory if it exists
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 differen... |
Run nni manager process
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!\... |
set trial configuration
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_re... |
set local configuration
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_... |
Call setClusterMetadata to pass trial
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 ... |
set nniManagerIp
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'] }
respo... |
set kubeflow configuration
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 = ... |
Call startExperiment (rest POST /experiment) with yaml file content
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_d... |
follow steps to start rest server and start 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('tune... |
resume an 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('Pl... |
start a new 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_erro... |
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