text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
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| def get_buckets(min_length, max_length, bucket_count):
'''
Get bucket by length.
'''
if bucket_count <= 0:
return [max_length]
unit_length = int((max_length - min_length) // (bucket_count))
buckets = [min_length + unit_length *
(i + 1) for i in range(0, bucket_count)]
... |
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| def tokenize(self, text):
'''
tokenize function in Tokenizer.
'''
start = -1
tokens = []
for i, character in enumerate(text):
if character == ' ' or character == '\t':
if start >= 0:
word = text[start:i]
... |
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def generate_new_id(self):
""" generate new id and event hook for new Individual """ |
self.events.append(Event())
indiv_id = self.indiv_counter
self.indiv_counter += 1
return indiv_id |
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def init_population(self, population_size, graph_max_layer, graph_min_layer):
""" initialize populations for evolution tuner """ |
population = []
graph = Graph(max_layer_num=graph_max_layer, min_layer_num=graph_min_layer,
inputs=[Layer(LayerType.input.value, output=[4, 5], size='x'), Layer(LayerType.input.value, output=[4, 5], size='y')],
output=[Layer(LayerType.output.value, inputs=[4]... |
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| def copyHdfsDirectoryToLocal(hdfsDirectory, localDirectory, hdfsClient):
'''Copy directory from HDFS to local'''
if not os.path.exists(localDirectory):
os.makedirs(localDirectory)
try:
listing = hdfsClient.list_status(hdfsDirectory)
except Exception as exception:
nni_log(LogType.... |
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| def copyHdfsFileToLocal(hdfsFilePath, localFilePath, hdfsClient, override=True):
'''Copy file from HDFS to local'''
if not hdfsClient.exists(hdfsFilePath):
raise Exception('HDFS file {} does not exist!'.format(hdfsFilePath))
try:
file_status = hdfsClient.get_file_status(hdfsFilePath)
... |
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| def copyDirectoryToHdfs(localDirectory, hdfsDirectory, hdfsClient):
'''Copy directory from local to HDFS'''
if not os.path.exists(localDirectory):
raise Exception('Local Directory does not exist!')
hdfsClient.mkdirs(hdfsDirectory)
result = True
for file in os.listdir(localDirectory):
... |
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| def copyFileToHdfs(localFilePath, hdfsFilePath, hdfsClient, override=True):
'''Copy a local file to HDFS directory'''
if not os.path.exists(localFilePath):
raise Exception('Local file Path does not exist!')
if os.path.isdir(localFilePath):
raise Exception('localFile should not a directory!')... |
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| def load_data():
'''Load dataset, use boston dataset'''
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=99, test_size=0.25)
#normalize data
ss_X = StandardScaler()
ss_y = StandardScaler()
X_train = ss_X.fit_transform(X_trai... |
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| def run(X_train, X_test, y_train, y_test, PARAMS):
'''Train model and predict result'''
model.fit(X_train, y_train)
predict_y = model.predict(X_test)
score = r2_score(y_test, predict_y)
LOG.debug('r2 score: %s' % score)
nni.report_final_result(score) |
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| def to_json(self):
''' NetworkDescriptor to json representation
'''
skip_list = []
for u, v, connection_type in self.skip_connections:
skip_list.append({"from": u, "to": v, "type": connection_type})
return {"node_list": self.layers, "skip_list": skip_list} |
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def _add_edge(self, layer, input_id, output_id):
"""Add a new layer to the graph. The nodes should be created in advance.""" |
if layer in self.layer_to_id:
layer_id = self.layer_to_id[layer]
if input_id not in self.layer_id_to_input_node_ids[layer_id]:
self.layer_id_to_input_node_ids[layer_id].append(input_id)
if output_id not in self.layer_id_to_output_node_ids[layer_id]:
... |
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def _redirect_edge(self, u_id, v_id, new_v_id):
"""Redirect the layer to a new node. Change the edge originally from `u_id` to `v_id` into an edge from `u_id` to... |
layer_id = None
for index, edge_tuple in enumerate(self.adj_list[u_id]):
if edge_tuple[0] == v_id:
layer_id = edge_tuple[1]
self.adj_list[u_id][index] = (new_v_id, layer_id)
self.layer_list[layer_id].output = self.node_list[new_v_id]
... |
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def _replace_layer(self, layer_id, new_layer):
"""Replace the layer with a new layer.""" |
old_layer = self.layer_list[layer_id]
new_layer.input = old_layer.input
new_layer.output = old_layer.output
new_layer.output.shape = new_layer.output_shape
self.layer_list[layer_id] = new_layer
self.layer_to_id[new_layer] = layer_id
self.layer_to_id.pop(old_layer... |
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def topological_order(self):
"""Return the topological order of the node IDs from the input node to the output node.""" |
q = Queue()
in_degree = {}
for i in range(self.n_nodes):
in_degree[i] = 0
for u in range(self.n_nodes):
for v, _ in self.adj_list[u]:
in_degree[v] += 1
for i in range(self.n_nodes):
if in_degree[i] == 0:
q.put(i... |
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def _get_pooling_layers(self, start_node_id, end_node_id):
"""Given two node IDs, return all the pooling layers between them.""" |
layer_list = []
node_list = [start_node_id]
assert self._depth_first_search(end_node_id, layer_list, node_list)
ret = []
for layer_id in layer_list:
layer = self.layer_list[layer_id]
if is_layer(layer, "Pooling"):
ret.append(layer)
... |
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def _depth_first_search(self, target_id, layer_id_list, node_list):
"""Search for all the layers and nodes down the path. A recursive function to search all the ... |
assert len(node_list) <= self.n_nodes
u = node_list[-1]
if u == target_id:
return True
for v, layer_id in self.adj_list[u]:
layer_id_list.append(layer_id)
node_list.append(v)
if self._depth_first_search(target_id, layer_id_list, node_list... |
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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 = self.add_layer(layer, temp_output_id)
self._add_edge(new_layers[-1], temp_output_id, end_node_id)
new_layers[-1].input = s... |
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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()
for u in main_chain:
for v, layer_id in self.adj_list[u]:
if v not in index_in_mai... |
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| 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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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(layer_id)
return ret |
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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, _ in self.adj_list[u]:
if distance[u] + 1 > distance[v... |
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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:
data = json_tricks.loads(data)
if command is None or command is CommandType.Termi... |
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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:
self.process_command(command, data)
except Exception as e:
_logger... |
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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.default_command_queue.put((command, data))
qsize = self.default_command_queue.qsize()
if... |
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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))
raise
else:
pass |
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| 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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| 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... |
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| 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_... |
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| 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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| 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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| 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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| 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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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... |
self.trial_job_id = trial_job_id
self.trial_history = trial_history
if not self.set_best_performance:
return AssessResult.Good
curr_step = len(trial_history)
if curr_step < self.start_step:
return AssessResult.Good
if trial_job_id in self... |
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| def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True |
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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_graph = None
if not self.training_queue:
new_father_id, generated_graph = self.generate()
new_model_id = self.model_count
self.model_count += 1
self.traini... |
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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.default_model_len, self.default_model_width
)
model_id = self.model_count
... |
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def generate(self):
"""Generate the next neural architecture. Returns ------- other_info: any object Anything to be saved in the training queue together with the... |
generated_graph, new_father_id = self.bo.generate(self.descriptors)
if new_father_id is None:
new_father_id = 0
generated_graph = self.generators[0](
self.n_classes, self.input_shape
).generate(self.default_model_len, self.default_model_width)
... |
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def update(self, other_info, graph, metric_value, model_id):
""" Update the controller with evaluation result of a neural architecture. Parameters other_info: an... |
father_id = other_info
self.bo.fit([graph.extract_descriptor()], [metric_value])
self.bo.add_child(father_id, model_id) |
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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 Return... |
if self.verbose:
logger.info("Saving model.")
# Update best_model text file
ret = {"model_id": model_id, "metric_value": metric_value}
self.history.append(ret)
if model_id == self.get_best_model_id():
file = open(os.path.join(self.path, "best_model.txt")... |
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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"])["model_id"] |
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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 repre... |
with open(os.path.join(self.path, str(model_id) + ".json")) as fin:
json_str = fin.read().replace("\n", "")
load_model = json_to_graph(json_str)
return load_model |
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| 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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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))]
self.x_types = [NONE_TYPE for i in range(len(search_space))]
for key in search_space:
self.key_order.append(key)
key_type = {}
if isinstance(search_space, dict):
for key in search_space:
... |
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def _pack_output(self, init_parameter):
"""Pack the output Parameters init_parameter : dict Returns ------- output : dict """ |
output = {}
for i, param in enumerate(init_parameter):
output[self.key_order[i]] = param
return output |
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def generate_parameters(self, parameter_id):
"""Generate next parameter for trial If the number of trial result is lower than cold start number, metis will first... |
if len(self.samples_x) < self.cold_start_num:
init_parameter = _rand_init(self.x_bounds, self.x_types, 1)[0]
results = self._pack_output(init_parameter)
else:
self.minimize_starting_points = _rand_init(self.x_bounds, self.x_types, \
... |
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def receive_trial_result(self, parameter_id, parameters, value):
"""Tuner receive result from trial. Parameters parameter_id : int parameters : dict value : dict... |
value = extract_scalar_reward(value)
if self.optimize_mode == OptimizeMode.Maximize:
value = -value
logger.info("Received trial result.")
logger.info("value is :" + str(value))
logger.info("parameter is : " + str(parameters))
# parse parameter to sample_x
... |
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| 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)) * \
... |
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| 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... |
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| 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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| 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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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 self.log_collection == 'none':
# If not match metrics, do not put the line into queue
if not self.log_pattern.match(line):... |
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def extract_scalar_reward(value, scalar_key='default'):
""" Extract scalar reward from trial result. Raises ------ RuntimeError Incorrect final result: the final... |
if isinstance(value, float) or isinstance(value, int):
reward = value
elif isinstance(value, dict) and scalar_key in value and isinstance(value[scalar_key], (float, int)):
reward = value[scalar_key]
else:
raise RuntimeError('Incorrect final result: the final result should be float/i... |
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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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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, dispatcher_env_vars.NNI_LOG_LEVEL) |
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def get_config(self, budget):
"""Function to sample a new configuration This function is called inside BOHB to query a new configuration Parameters: budget: floa... |
logger.debug('start sampling a new configuration.')
sample = None
info_dict = {}
# If no model is available, sample from prior
# also mix in a fraction of random configs
if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
sample =... |
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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... |
if loss is None:
# One could skip crashed results, but we decided
# assign a +inf loss and count them as bad configurations
loss = np.inf
if budget not in self.configs.keys():
self.configs[budget] = []
self.losses[budget] = []
# skip... |
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| 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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| 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 ... |
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| 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... |
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| 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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| 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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| 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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| 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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| 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
... |
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| 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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| 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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| 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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| 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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| 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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| 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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| 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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def f_comb(self, pos, sample):
"""return the value of the f_comb when epoch = pos Parameters pos: int the epoch number of the position you want to predict sample... |
ret = 0
for i in range(self.effective_model_num):
model = self.effective_model[i]
y = self.predict_y(model, pos)
ret += sample[i] * y
return ret |
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| 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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| 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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| 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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| 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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| 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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| 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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| 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... |
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| 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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| 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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| 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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| 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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| 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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| 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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| 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... |
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| 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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| 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... |
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| 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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| 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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| def selection(x_bounds,
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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| 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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| 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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def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimizatio... |
global _KEY
assert self.i == 0
hyperparameter_configs = dict()
for _ in range(num):
params_id = create_bracket_parameter_id(self.s, self.i)
params = config_generator.get_config(r)
params[_KEY] = r
hyperparameter_configs[params_id] = params... |
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def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models and search space formations Parameters d... |
logger.info('start to handle_initialize')
# convert search space jason to ConfigSpace
self.handle_update_search_space(data)
# generate BOHB config_generator using Bayesian optimization
if self.search_space:
self.cg = CG_BOHB(configspace=self.search_space,
... |
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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")
self.curr_s = self.s_max
self.brackets[self.curr_s] = Bracket(... |
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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 t... |
# Receive new request
self.credit += data
for _ in range(self.credit):
self._request_one_trial_job() |
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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_... |
logger.debug('Tuner handle trial end, result is %s', data)
hyper_params = json_tricks.loads(data['hyper_params'])
s, i, _ = hyper_params['parameter_id'].split('_')
hyper_configs = self.brackets[int(s)].inform_trial_end(int(i))
if hyper_configs is not None:
logger.d... |
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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 h... |
logger.debug('handle report metric data = %s', data)
assert 'value' in data
value = extract_scalar_reward(data['value'])
if self.optimize_mode is OptimizeMode.Maximize:
reward = -value
else:
reward = value
assert 'parameter_id' in data
s,... |
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