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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
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 = self.node_list[temp_output_id]
new_layers[-1].output = self.node_list[end_node_id]
self._redirect_edge(start_node_id, end_node_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 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.
"""
self.operation_history.append(("to_add_skip_model", start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
start_node_id = self.layer_id_to_output_node_ids[start_id][0]
pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
end_node_id = self.layer_id_to_output_node_ids[end_id][0]
skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
# Add the conv layer
new_conv_layer = get_conv_class(self.n_dim)(filters_start, filters_end, 1)
skip_output_id = self.add_layer(new_conv_layer, skip_output_id)
# Add the add layer.
add_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
add_layer = StubAdd()
self._redirect_edge(pre_end_node_id, end_node_id, add_input_node_id)
self._add_edge(add_layer, add_input_node_id, end_node_id)
self._add_edge(add_layer, skip_output_id, end_node_id)
add_layer.input = [
self.node_list[add_input_node_id],
self.node_list[skip_output_id],
]
add_layer.output = self.node_list[end_node_id]
self.node_list[end_node_id].shape = add_layer.output_shape
# Set weights to the additional conv layer.
if self.weighted:
filter_shape = (1,) * self.n_dim
weights = np.zeros((filters_end, filters_start) + filter_shape)
bias = np.zeros(filters_end)
new_conv_layer.set_weights(
(add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
) |
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):
"""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.
"""
self.operation_history.append(("to_concat_skip_model", start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
start_node_id = self.layer_id_to_output_node_ids[start_id][0]
pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
end_node_id = self.layer_id_to_output_node_ids[end_id][0]
skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
concat_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
self._redirect_edge(pre_end_node_id, end_node_id, concat_input_node_id)
concat_layer = StubConcatenate()
concat_layer.input = [
self.node_list[concat_input_node_id],
self.node_list[skip_output_id],
]
concat_output_node_id = self._add_node(Node(concat_layer.output_shape))
self._add_edge(concat_layer, concat_input_node_id, concat_output_node_id)
self._add_edge(concat_layer, skip_output_id, concat_output_node_id)
concat_layer.output = self.node_list[concat_output_node_id]
self.node_list[concat_output_node_id].shape = concat_layer.output_shape
# Add the concatenate layer.
new_conv_layer = get_conv_class(self.n_dim)(
filters_start + filters_end, filters_end, 1
)
self._add_edge(new_conv_layer, concat_output_node_id, end_node_id)
new_conv_layer.input = self.node_list[concat_output_node_id]
new_conv_layer.output = self.node_list[end_node_id]
self.node_list[end_node_id].shape = new_conv_layer.output_shape
if self.weighted:
filter_shape = (1,) * self.n_dim
weights = np.zeros((filters_end, filters_end) + filter_shape)
for i in range(filters_end):
filter_weight = np.zeros((filters_end,) + filter_shape)
center_index = (i,) + (0,) * self.n_dim
filter_weight[center_index] = 1
weights[i, ...] = filter_weight
weights = np.concatenate(
(weights, np.zeros((filters_end, filters_start) + filter_shape)), axis=1
)
bias = np.zeros(filters_end)
new_conv_layer.set_weights(
(add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
) |
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_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_main_chain:
continue
layer = self.layer_list[layer_id]
copied_layer = copy(layer)
copied_layer.weights = None
ret.add_layer(deepcopy(copied_layer))
for u in index_in_main_chain:
for v, layer_id in self.adj_list[u]:
if v not in index_in_main_chain:
temp_u = u
temp_v = v
temp_layer_id = layer_id
skip_type = None
while not (temp_v in index_in_main_chain and temp_u in index_in_main_chain):
if is_layer(self.layer_list[temp_layer_id], "Concatenate"):
skip_type = NetworkDescriptor.CONCAT_CONNECT
if is_layer(self.layer_list[temp_layer_id], "Add"):
skip_type = NetworkDescriptor.ADD_CONNECT
temp_u = temp_v
temp_v, temp_layer_id = self.adj_list[temp_v][0]
ret.add_skip_connection(
index_in_main_chain[u], index_in_main_chain[temp_u], skip_type
)
elif index_in_main_chain[v] - index_in_main_chain[u] != 1:
skip_type = None
if is_layer(self.layer_list[layer_id], "Concatenate"):
skip_type = NetworkDescriptor.CONCAT_CONNECT
if is_layer(self.layer_list[layer_id], "Add"):
skip_type = NetworkDescriptor.ADD_CONNECT
ret.add_skip_connection(
index_in_main_chain[u], index_in_main_chain[v], skip_type
)
return ret |
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 in main_chain:
ret.append(layer_id)
return ret |
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 range(self.n_nodes):
for v, _ in self.adj_list[u]:
if distance[u] + 1 > distance[v]:
distance[v] = distance[u] + 1
pre_node[v] = u
temp_id = 0
for i in range(self.n_nodes):
if distance[i] > distance[temp_id]:
temp_id = i
ret = []
for i in range(self.n_nodes + 5):
ret.append(temp_id)
if pre_node[temp_id] == temp_id:
break
temp_id = pre_node[temp_id]
assert temp_id == pre_node[temp_id]
ret.reverse()
return ret |
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 True:
command, data = receive()
if data:
data = json_tricks.loads(data)
if command is None or command is CommandType.Terminate:
break
if multi_thread_enabled():
result = self.pool.map_async(self.process_command_thread, [(command, data)])
self.thread_results.append(result)
if any([thread_result.ready() and not thread_result.successful() for thread_result in self.thread_results]):
_logger.debug('Caught thread exception')
break
else:
self.enqueue_command(command, data)
if self.worker_exceptions:
break
_logger.info('Dispatcher exiting...')
self.stopping = True
if multi_thread_enabled():
self.pool.close()
self.pool.join()
else:
self.default_worker.join()
self.assessor_worker.join()
_logger.info('Terminated by NNI manager') |
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)
try:
self.process_command(command, data)
except Exception as e:
_logger.exception(e)
self.worker_exceptions.append(e)
break
except Empty:
pass
if self.stopping and (_worker_fast_exit_on_terminate or command_queue.empty()):
break |
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))
else:
self.default_command_queue.put((command, data))
qsize = self.default_command_queue.qsize()
if qsize >= QUEUE_LEN_WARNING_MARK:
_logger.warning('default queue length: %d', qsize)
qsize = self.assessor_command_queue.qsize()
if qsize >= QUEUE_LEN_WARNING_MARK:
_logger.warning('assessor queue length: %d', qsize) |
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:
_logger.exception(str(e))
raise
else:
pass |
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 closest integer in the array, vals_bounds
vals_new.append(min(vals_bounds[i], key=lambda x: abs(x - vals[i])))
elif vals_types[i] == "range_int":
# Round down to the nearest integer
vals_new.append(math.floor(vals[i]))
elif vals_types[i] == "range_continuous":
# Don't do any processing for continous numbers
vals_new.append(vals[i])
else:
return None
return vals_new |
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)]
outputs.append(temp)
elif x_types[i] == "range_int":
temp = random.randint(x_bounds[i][0], x_bounds[i][1])
outputs.append(temp)
elif x_types[i] == "range_continuous":
temp = random.uniform(x_bounds[i][0], x_bounds[i][1])
outputs.append(temp)
else:
return None
return outputs |
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 = graph.layer_list[layer_id]
if is_layer(layer, "Conv"):
n_add = layer.filters
else:
n_add = layer.units
graph.to_wider_model(layer_id, n_add)
return graph |
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_CONNECT, NetworkDescriptor.CONCAT_CONNECT]):
for index_a in range(len(weighted_layer_ids)):
for index_b in range(len(weighted_layer_ids))[index_a + 1 :]:
valid_connection.append((index_a, index_b, skip_type))
if not valid_connection:
return graph
for index_a, index_b, skip_type in sample(valid_connection, 1):
a_id = weighted_layer_ids[index_a]
b_id = weighted_layer_ids[index_b]
if skip_type == NetworkDescriptor.ADD_CONNECT:
graph.to_add_skip_model(a_id, b_id)
else:
graph.to_concat_skip_model(a_id, b_id)
return graph |
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]
if is_layer(layer, "ReLU"):
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim)]
dense_deeper_classes = [StubDense, get_dropout_class(n_dim)]
elif is_layer(layer, "Dropout"):
dense_deeper_classes = [StubDense, StubReLU]
elif is_layer(layer, "BatchNormalization"):
conv_deeper_classes = [get_conv_class(n_dim), StubReLU]
layer_class = None
if len(input_shape) == 1:
# It is in the dense layer part.
layer_class = sample(dense_deeper_classes, 1)[0]
else:
# It is in the conv layer part.
layer_class = sample(conv_deeper_classes, 1)[0]
if layer_class == StubDense:
new_layer = StubDense(input_shape[0], input_shape[0])
elif layer_class == get_dropout_class(n_dim):
new_layer = layer_class(Constant.DENSE_DROPOUT_RATE)
elif layer_class == get_conv_class(n_dim):
new_layer = layer_class(
input_shape[-1], input_shape[-1], sample((1, 3, 5), 1)[0], stride=1
)
elif layer_class == get_batch_norm_class(n_dim):
new_layer = layer_class(input_shape[-1])
elif layer_class == get_pooling_class(n_dim):
new_layer = layer_class(sample((1, 3, 5), 1)[0])
else:
new_layer = layer_class()
return new_layer |
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]
new_layer = create_new_layer(layer, graph.n_dim)
graph.to_deeper_model(layer_id, new_layer)
return graph |
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))
elif random_num == 1:
temp_graph = to_wider_graph(deepcopy(graph))
elif random_num == 2:
temp_graph = to_skip_connection_graph(deepcopy(graph))
if temp_graph is not None and temp_graph.size() <= Constant.MAX_MODEL_SIZE:
graphs.append(temp_graph)
if len(graphs) >= Constant.N_NEIGHBOURS:
break
return graphs |
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 of numpy.random.RandomState
'''
assert high > low, 'Upper bound must be larger than lower bound'
return random_state.uniform(low, high) |
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
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(uniform(low, high, random_state) / q) * q |
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 object of numpy.random.RandomState
'''
assert low > 0, 'Lower bound must be positive'
return np.exp(uniform(np.log(low), np.log(high), 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
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 |
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 object of numpy.random.RandomState
'''
return np.round(normal(mu, sigma, random_state) / q) * q |
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
'''
return np.exp(normal(mu, sigma, 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
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 |
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 response
except Exception as e:
if rethrow_exception is True:
raise
print('Get exception {0} when sending http post to url {1}'.format(str(e), url))
return None |
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:
print('Get exception {0} when sending http put to url {1}'.format(str(e), url))
return None |
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 performance of completed trial job
Parameters
----------
trial_job_id: int
trial job id
success: bool
True if succssfully finish the experiment, False otherwise
"""
if success:
if self.set_best_performance:
self.completed_best_performance = max(self.completed_best_performance, self.trial_history[-1])
else:
self.set_best_performance = True
self.completed_best_performance = self.trial_history[-1]
logger.info('Updated complted best performance, trial job id:', trial_job_id)
else:
logger.info('No need to update, trial job id: ', trial_job_id) |
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.Good or AssessResult.Bad
Raises
------
Exception
unrecognize exception in curvefitting_assessor
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
Returns
-------
bool
AssessResult.Good or AssessResult.Bad
Raises
------
Exception
unrecognize exception in curvefitting_assessor
"""
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.last_judgment_num.keys() and curr_step - self.last_judgment_num[trial_job_id] < self.gap:
return AssessResult.Good
self.last_judgment_num[trial_job_id] = curr_step
try:
start_time = datetime.datetime.now()
# Predict the final result
curvemodel = CurveModel(self.target_pos)
predict_y = curvemodel.predict(trial_history)
logger.info('Prediction done. Trial job id = ', trial_job_id, '. Predict value = ', predict_y)
if predict_y is None:
logger.info('wait for more information to predict precisely')
return AssessResult.Good
standard_performance = self.completed_best_performance * self.threshold
end_time = datetime.datetime.now()
if (end_time - start_time).seconds > 60:
logger.warning('Curve Fitting Assessor Runtime Exceeds 60s, Trial Id = ', self.trial_job_id, 'Trial History = ', self.trial_history)
if self.higher_better:
if predict_y > standard_performance:
return AssessResult.Good
return AssessResult.Bad
else:
if predict_y < standard_performance:
return AssessResult.Good
return AssessResult.Bad
except Exception as exception:
logger.exception('unrecognize exception in curvefitting_assessor', exception) |
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
----------
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.training_queue.append((generated_graph, new_father_id, new_model_id))
self.descriptors.append(generated_graph.extract_descriptor())
graph, father_id, model_id = self.training_queue.pop(0)
# from graph to json
json_model_path = os.path.join(self.path, str(model_id) + ".json")
json_out = graph_to_json(graph, json_model_path)
self.total_data[parameter_id] = (json_out, father_id, model_id)
return json_out |
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 observation of the objective function.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
"""
reward = extract_scalar_reward(value)
if parameter_id not in self.total_data:
raise RuntimeError("Received parameter_id not in total_data.")
(_, father_id, model_id) = self.total_data[parameter_id]
graph = self.bo.searcher.load_model_by_id(model_id)
# to use the value and graph
self.add_model(reward, model_id)
self.update(father_id, graph, reward, model_id) |
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 = generator(self.n_classes, self.input_shape).generate(
self.default_model_len, self.default_model_width
)
model_id = self.model_count
self.model_count += 1
self.training_queue.append((graph, -1, model_id))
self.descriptors.append(graph.extract_descriptor())
if self.verbose:
logger.info("Initialization finished.") |
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 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_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)
return new_father_id, generated_graph |
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
The final evaluated metric value.
model_id: int
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
An instance of Graph. The trained neural architecture.
metric_value: float
The final evaluated metric value.
model_id: int
"""
father_id = other_info
self.bo.fit([graph.extract_descriptor()], [metric_value])
self.bo.add_child(father_id, model_id) |
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
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
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"), "w")
file.write("best model: " + str(model_id))
file.close()
return ret |
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"]
return min(self.history, key=lambda x: x["metric_value"])["model_id"] |
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
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
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 |
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 : 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:
key_type = search_space[key]['_type']
key_range = search_space[key]['_value']
idx = self.key_order.index(key)
if key_type == 'quniform':
if key_range[2] == 1:
self.x_bounds[idx] = [key_range[0], key_range[1]]
self.x_types[idx] = 'range_int'
else:
bounds = []
for value in np.arange(key_range[0], key_range[1], key_range[2]):
bounds.append(value)
self.x_bounds[idx] = bounds
self.x_types[idx] = 'discrete_int'
elif key_type == 'randint':
self.x_bounds[idx] = [0, key_range[0]]
self.x_types[idx] = 'range_int'
elif key_type == 'uniform':
self.x_bounds[idx] = [key_range[0], key_range[1]]
self.x_types[idx] = 'range_continuous'
elif key_type == 'choice':
self.x_bounds[idx] = key_range
for key_value in key_range:
if not isinstance(key_value, (int, float)):
raise RuntimeError("Metis Tuner only support numerical choice.")
self.x_types[idx] = 'discrete_int'
else:
logger.info("Metis Tuner doesn't support this kind of variable: " + str(key_type))
raise RuntimeError("Metis Tuner doesn't support this kind of variable: " + str(key_type))
else:
logger.info("The format of search space is not a dict.")
raise RuntimeError("The format of search space is not a dict.")
self.minimize_starting_points = _rand_init(self.x_bounds, self.x_types, \
self.selection_num_starting_points) |
Pack the output
Parameters
----------
init_parameter : dict
Returns
-------
output : dict
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 |
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
----------
parameter_id : int
Returns
-------
result : dict
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 random generate some parameters.
Otherwise, metis will choose the parameters by the Gussian Process Model and the Gussian Mixture Model.
Parameters
----------
parameter_id : int
Returns
-------
result : dict
"""
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, \
self.selection_num_starting_points)
results = self._selection(self.samples_x, self.samples_y_aggregation, self.samples_y,
self.x_bounds, self.x_types,
threshold_samplessize_resampling=(None if self.no_resampling is True else 50),
no_candidates=self.no_candidates,
minimize_starting_points=self.minimize_starting_points,
minimize_constraints_fun=self.minimize_constraints_fun)
logger.info("Generate paramageters:\n" + str(results))
return results |
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.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
"""
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
sample_x = [0 for i in range(len(self.key_order))]
for key in parameters:
idx = self.key_order.index(key)
sample_x[idx] = parameters[key]
# parse value to sample_y
temp_y = []
if sample_x in self.samples_x:
idx = self.samples_x.index(sample_x)
temp_y = self.samples_y[idx]
temp_y.append(value)
self.samples_y[idx] = temp_y
# calculate y aggregation
median = get_median(temp_y)
self.samples_y_aggregation[idx] = [median]
else:
self.samples_x.append(sample_x)
self.samples_y.append([value])
# calculate y aggregation
self.samples_y_aggregation.append([value]) |
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:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
logger.info("Importing data, current processing progress %s / %s" %(_completed_num, len(data)))
_completed_num += 1
assert "parameter" in trial_info
_params = trial_info["parameter"]
assert "value" in trial_info
_value = trial_info['value']
if not _value:
logger.info("Useless trial data, value is %s, skip this trial data." %_value)
continue
self.supplement_data_num += 1
_parameter_id = '_'.join(["ImportData", str(self.supplement_data_num)])
self.total_data.append(_params)
self.receive_trial_result(parameter_id=_parameter_id, parameters=_params, value=_value)
logger.info("Successfully import data to metis tuner.") |
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, 1e12)) * \
gp.kernels.Matern(nu=1.5)
if is_white_kernel is True:
kernel += gp.kernels.WhiteKernel(noise_level=1, noise_level_bounds=(1e-12, 1e12))
regressor = gp.GaussianProcessRegressor(kernel=kernel,
n_restarts_optimizer=n_restarts_optimizer,
normalize_y=True,
alpha=1e-10)
regressor.fit(numpy.array(samples_x), numpy.array(samples_y_aggregation))
model = {}
model['model'] = regressor
model['kernel_prior'] = str(kernel)
model['kernel_posterior'] = str(regressor.kernel_)
model['model_loglikelihood'] = regressor.log_marginal_likelihood(regressor.kernel_.theta)
return model |
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(ss_spec, dict):
if '_type' in ss_spec.keys():
_type = ss_spec['_type']
_value = ss_spec['_value']
chosen_params = list()
if _type == 'choice':
for value in _value:
choice = self.json2paramater(value)
if isinstance(choice, list):
chosen_params.extend(choice)
else:
chosen_params.append(choice)
else:
chosen_params = self.parse_qtype(_type, _value)
else:
chosen_params = dict()
for key in ss_spec.keys():
chosen_params[key] = self.json2paramater(ss_spec[key])
return self.expand_parameters(chosen_params)
elif isinstance(ss_spec, list):
chosen_params = list()
for subspec in ss_spec[1:]:
choice = self.json2paramater(subspec)
if isinstance(choice, list):
chosen_params.extend(choice)
else:
chosen_params.append(choice)
chosen_params = list(map(lambda v: {ss_spec[0]: v}, chosen_params))
else:
chosen_params = copy.deepcopy(ss_spec)
return chosen_params |
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], param_value[1], param_value[2]
interval = (high - low) / (count - 1)
return [float(low + interval * i) for i in range(count)] |
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])
return list(np.exp(self._parse_quniform(param_value)))
raise RuntimeError("Not supported type: %s" % param_type) |
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
para: {key1: [v11, v12, ...], key2: [v21, v22, ...], ...}
return: {{key1: v11, key2: v21, ...}, {key1: v11, key2: v22, ...}, ...}
'''
if len(para) == 1:
for key, values in para.items():
return list(map(lambda v: {key: v}, values))
key = list(para)[0]
values = para.pop(key)
rest_para = self.expand_parameters(para)
ret_para = list()
for val in values:
for config in rest_para:
config[key] = val
ret_para.append(copy.deepcopy(config))
return ret_para |
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:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
logger.info("Importing data, current processing progress %s / %s" %(_completed_num, len(data)))
_completed_num += 1
assert "parameter" in trial_info
_params = trial_info["parameter"]
assert "value" in trial_info
_value = trial_info['value']
if not _value:
logger.info("Useless trial data, value is %s, skip this trial data." %_value)
continue
_params_tuple = convert_dict2tuple(_params)
self.supplement_data[_params_tuple] = True
logger.info("Successfully import data to grid search tuner.") |
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(self.log_level, line.rstrip())
except Exception as e:
pass |
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.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):
continue
self.queue.put(line)
self.pipeReader.close() |
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 from trial result.
Raises
------
RuntimeError
Incorrect final result: the final result should be float/int,
or a dict which has a key named "default" whose value is float/int.
"""
if isinstance(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/int, or a dict which has a key named "default" whose value is float/int.')
return reward |
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.items()))
else:
return value |
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_path)
init_logger(logger_file_path, dispatcher_env_vars.NNI_LOG_LEVEL) |
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)/g(x).
Parameters:
-----------
info_dict: dict
record the information of this configuration
Returns
-------
dict:
new configuration named sample
dict:
info_dict, record the information of this configuration
def sample_from_largest_budget(self, info_dict):
"""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)/g(x).
Parameters:
-----------
info_dict: dict
record the information of this configuration
Returns
-------
dict:
new configuration named sample
dict:
info_dict, record the information of this configuration
"""
best = np.inf
best_vector = None
budget = max(self.kde_models.keys())
l = self.kde_models[budget]['good'].pdf
g = self.kde_models[budget]['bad'].pdf
minimize_me = lambda x: max(1e-32, g(x))/max(l(x), 1e-32)
kde_good = self.kde_models[budget]['good']
kde_bad = self.kde_models[budget]['bad']
for i in range(self.num_samples):
idx = np.random.randint(0, len(kde_good.data))
datum = kde_good.data[idx]
vector = []
for m, bw, t in zip(datum, kde_good.bw, self.vartypes):
bw = max(bw, self.min_bandwidth)
if t == 0:
bw = self.bw_factor*bw
vector.append(sps.truncnorm.rvs(-m/bw, (1-m)/bw, loc=m, scale=bw))
else:
if np.random.rand() < (1-bw):
vector.append(int(m))
else:
vector.append(np.random.randint(t))
val = minimize_me(vector)
if not np.isfinite(val):
logger.warning('sampled vector: %s has EI value %s'%(vector, val))
logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
logger.warning("l(x) = %s"%(l(vector)))
logger.warning("g(x) = %s"%(g(vector)))
# right now, this happens because a KDE does not contain all values for a categorical parameter
# this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
# if the good_kde has a finite value, i.e. there is no config with that value in the bad kde,
# so it shouldn't be terrible.
if np.isfinite(l(vector)):
best_vector = vector
break
if val < best:
best = val
best_vector = vector
if best_vector is None:
logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
sample = self.configspace.sample_configuration().get_dictionary()
info_dict['model_based_pick'] = False
else:
logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
for i, hp_value in enumerate(best_vector):
if isinstance(
self.configspace.get_hyperparameter(
self.configspace.get_hyperparameter_by_idx(i)
),
ConfigSpace.hyperparameters.CategoricalHyperparameter
):
best_vector[i] = int(np.rint(best_vector[i]))
sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
configuration_space=self.configspace,
configuration=sample)
info_dict['model_based_pick'] = True
return sample, info_dict |
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 configuration with parameters and budget
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
-------
config
return a valid configuration with parameters and budget
"""
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 = self.configspace.sample_configuration()
info_dict['model_based_pick'] = False
if sample is None:
sample, info_dict= self.sample_from_largest_budget(info_dict)
sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
configuration_space=self.configspace,
configuration=sample.get_dictionary()
).get_dictionary()
logger.debug('done sampling a new configuration.')
sample['TRIAL_BUDGET'] = budget
return sample |
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: dict
the parameters of this trial
update_model: bool
whether use this parameter to update BP model
Returns
-------
None
def new_result(self, loss, budget, parameters, update_model=True):
"""
Function to register finished runs. Every time a run has finished, this function should be called
to register it with the loss.
Parameters:
-----------
loss: float
the loss of the parameters
budget: float
the budget of the parameters
parameters: dict
the parameters of this trial
update_model: bool
whether use this parameter to update BP model
Returns
-------
None
"""
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 model building if we already have a bigger model
if max(list(self.kde_models.keys()) + [-np.inf]) > budget:
return
# We want to get a numerical representation of the configuration in the original space
conf = ConfigSpace.Configuration(self.configspace, parameters)
self.configs[budget].append(conf.get_array())
self.losses[budget].append(loss)
# skip model building:
# a) if not enough points are available
if len(self.configs[budget]) <= self.min_points_in_model - 1:
logger.debug("Only %i run(s) for budget %f available, need more than %s \
-> can't build model!"%(len(self.configs[budget]), budget, self.min_points_in_model+1))
return
# b) during warnm starting when we feed previous results in and only update once
if not update_model:
return
train_configs = np.array(self.configs[budget])
train_losses = np.array(self.losses[budget])
n_good = max(self.min_points_in_model, (self.top_n_percent * train_configs.shape[0])//100)
n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*train_configs.shape[0])//100)
# Refit KDE for the current budget
idx = np.argsort(train_losses)
train_data_good = self.impute_conditional_data(train_configs[idx[:n_good]])
train_data_bad = self.impute_conditional_data(train_configs[idx[n_good:n_good+n_bad]])
if train_data_good.shape[0] <= train_data_good.shape[1]:
return
if train_data_bad.shape[0] <= train_data_bad.shape[1]:
return
#more expensive crossvalidation method
#bw_estimation = 'cv_ls'
# quick rule of thumb
bw_estimation = 'normal_reference'
bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad, var_type=self.kde_vartypes, bw=bw_estimation)
good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type=self.kde_vartypes, bw=bw_estimation)
bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth, None)
good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth, None)
self.kde_models[budget] = {
'good': good_kde,
'bad' : bad_kde
}
# update probs for the categorical parameters for later sampling
logger.debug('done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n'
%(budget, n_good, n_bad, np.min(train_losses))) |
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 : dict
"""
if not len(search_space) == 1:
raise RuntimeError('BatchTuner only supprt one combined-paramreters key.')
for param in search_space:
param_type = search_space[param][TYPE]
if not param_type == CHOICE:
raise RuntimeError('BatchTuner only supprt one combined-paramreters type is choice.')
else:
if isinstance(search_space[param][VALUE], list):
return search_space[param][VALUE]
raise RuntimeError('The combined-paramreters value in BatchTuner is not a list.')
return None |
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_id : int
"""
self.count +=1
if self.count>len(self.values)-1:
raise nni.NoMoreTrialError('no more parameters now.')
return self.values[self.count] |
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 the weights of a previous layer
by the same name.
Returns:
A tensor with the same shape and data dtype as `inputs`.
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.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A tensor with the same shape and data dtype as `inputs`.
'''
with tf.variable_scope(scope):
inputs_shape = inputs.get_shape()
params_shape = inputs_shape[-1:]
mean, variance = tf.nn.moments(inputs, [-1], keep_dims=True)
beta = tf.Variable(tf.zeros(params_shape))
gamma = tf.Variable(tf.ones(params_shape))
normalized = (inputs - mean) / ((variance + epsilon) ** (.5))
outputs = gamma * normalized + beta
return outputs |
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: Boolean. If true, units that reference the future are masked.
num_heads: An int. Number of heads.
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 shape of (N, T_q, C)
def multihead_attention(queries,
keys,
scope="multihead_attention",
num_units=None,
num_heads=4,
dropout_rate=0,
is_training=True,
causality=False):
'''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: Boolean. If true, units that reference the future are masked.
num_heads: An int. Number of heads.
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 shape of (N, T_q, C)
'''
global look5
with tf.variable_scope(scope):
# Set the fall back option for num_units
if num_units is None:
num_units = queries.get_shape().as_list()[-1]
Q_ = []
K_ = []
V_ = []
for head_i in range(num_heads):
Q = tf.layers.dense(queries, num_units / num_heads,
activation=tf.nn.relu, name='Query' + str(head_i)) # (N, T_q, C)
K = tf.layers.dense(keys, num_units / num_heads,
activation=tf.nn.relu, name='Key' + str(head_i)) # (N, T_k, C)
V = tf.layers.dense(keys, num_units / num_heads,
activation=tf.nn.relu, name='Value' + str(head_i)) # (N, T_k, C)
Q_.append(Q)
K_.append(K)
V_.append(V)
# Split and concat
Q_ = tf.concat(Q_, axis=0) # (h*N, T_q, C/h)
K_ = tf.concat(K_, axis=0) # (h*N, T_k, C/h)
V_ = tf.concat(V_, axis=0) # (h*N, T_k, C/h)
# Multiplication
outputs = tf.matmul(Q_, tf.transpose(K_, [0, 2, 1])) # (h*N, T_q, T_k)
# Scale
outputs = outputs / (K_.get_shape().as_list()[-1] ** 0.5)
# Key Masking
key_masks = tf.sign(tf.abs(tf.reduce_sum(keys, axis=-1))) # (N, T_k)
key_masks = tf.tile(key_masks, [num_heads, 1]) # (h*N, T_k)
key_masks = tf.tile(tf.expand_dims(key_masks, 1),
[1, tf.shape(queries)[1], 1]) # (h*N, T_q, T_k)
paddings = tf.ones_like(outputs) * (-2 ** 32 + 1)
outputs = tf.where(tf.equal(key_masks, 0), paddings,
outputs) # (h*N, T_q, T_k)
# Causality = Future blinding
if causality:
diag_vals = tf.ones_like(outputs[0, :, :]) # (T_q, T_k)
tril = tf.contrib.linalg.LinearOperatorTriL(
diag_vals).to_dense() # (T_q, T_k)
masks = tf.tile(tf.expand_dims(tril, 0),
[tf.shape(outputs)[0], 1, 1]) # (h*N, T_q, T_k)
paddings = tf.ones_like(masks) * (-2 ** 32 + 1)
outputs = tf.where(tf.equal(masks, 0), paddings,
outputs) # (h*N, T_q, T_k)
# Activation
look5 = outputs
outputs = tf.nn.softmax(outputs) # (h*N, T_q, T_k)
# Query Masking
query_masks = tf.sign(
tf.abs(tf.reduce_sum(queries, axis=-1))) # (N, T_q)
query_masks = tf.tile(query_masks, [num_heads, 1]) # (h*N, T_q)
query_masks = tf.tile(tf.expand_dims(
query_masks, -1), [1, 1, tf.shape(keys)[1]]) # (h*N, T_q, T_k)
outputs *= query_masks # broadcasting. (N, T_q, C)
# Dropouts
outputs = dropout(outputs, dropout_rate, is_training)
# Weighted sum
outputs = tf.matmul(outputs, V_) # ( h*N, T_q, C/h)
# Restore shape
outputs = tf.concat(tf.split(outputs, num_heads,
axis=0), axis=2) # (N, T_q, C)
# Residual connection
if queries.get_shape().as_list()[-1] == num_units:
outputs += queries
# Normalize
outputs = normalize(outputs, scope=scope) # (N, T_q, C)
return outputs |
Return positinal embedding.
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 = Shape[0]
T = Shape[1]
num_units = Shape[2]
with tf.variable_scope(scope, reuse=reuse):
position_ind = tf.tile(tf.expand_dims(tf.range(T), 0), [N, 1])
# First part of the PE function: sin and cos argument
# Second part, apply the cosine to even columns and sin to odds.
X = tf.expand_dims(tf.cast(tf.range(T), tf.float32), axis=1)
Y = tf.expand_dims(
tf.cast(10000 ** -(2 * tf.range(num_units) / num_units), tf.float32), axis=0)
h1 = tf.cast((tf.range(num_units) + 1) % 2, tf.float32)
h2 = tf.cast((tf.range(num_units) % 2), tf.float32)
position_enc = tf.multiply(X, Y)
position_enc = tf.sin(position_enc) * tf.multiply(tf.ones_like(X), h1) + \
tf.cos(position_enc) * tf.multiply(tf.ones_like(X), h2)
# Convert to a tensor
lookup_table = position_enc
if zero_pad:
lookup_table = tf.concat((tf.zeros(shape=[1, num_units]),
lookup_table[1:, :]), 0)
outputs = tf.nn.embedding_lookup(lookup_table, position_ind)
if scale:
outputs = outputs * tf.sqrt(tf.cast(num_units, tf.float32))
return outputs |
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 same shape and dtype as inputs
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 reuse the weights of a previous layer
by the same name.
Returns:
A 3d tensor with the same shape and dtype as inputs
'''
with tf.variable_scope(scope):
# Inner layer
params = {"inputs": inputs, "filters": num_units[0], "kernel_size": 1,
"activation": tf.nn.relu, "use_bias": True}
outputs = tf.layers.conv1d(**params)
# Readout layer
params = {"inputs": outputs, "filters": num_units[1], "kernel_size": 1,
"activation": None, "use_bias": True}
outputs = tf.layers.conv1d(**params)
# Residual connection
outputs += inputs
# Normalize
outputs = normalize(outputs)
return outputs |
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)
"""
try:
ast_tree = ast.parse(code)
except Exception:
raise RuntimeError('Bad Python code')
visitor = SearchSpaceGenerator(module_name)
try:
visitor.visit(ast_tree)
except AssertionError as exc:
raise RuntimeError('%d: %s' % (visitor.last_line, exc.args[0]))
return visitor.search_space, astor.to_source(ast_tree) |
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
except Exception as exception:
if show_error:
print_error(exception)
return None |
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
except Exception as exception:
if show_error:
print_error(exception)
return None |
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:
return True, response
else:
return False, response
else:
time.sleep(3)
return False, response |
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
Returns
-------
float
np.exp(a+b/x+c*np.log(x))
"""
return np.exp(a+b/x+c*np.log(x)) |
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
"""
x = np.log(x)
return a*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: int
theta: float
eta: float
kappa: float
Returns
-------
float
(theta* x**eta) / (kappa**eta + x**eta)
"""
return (theta* x**eta) / (kappa**eta + x**eta) |
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
-------
float
a/(1.+(x/np.exp(b))**c)
"""
return a/(1.+(x/np.exp(b))**c) |
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
-------
float
c - (a*x+b)**-alpha
"""
return c - (a*x+b)**-alpha |
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, 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 - beta) / (1. + (kappa * x)**delta)
"""
return alpha - (alpha - beta) / (1. + (kappa * x)**delta) |
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
Returns
-------
float
c - np.exp(-a*(x**alpha)+b)
"""
return c - np.exp(-a*(x**alpha)+b) |
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, 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 - beta) * np.exp(-(kappa * x)**delta)
"""
return alpha - (alpha - beta) * np.exp(-(kappa * x)**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)
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 - beta) * np.exp(-k*x**delta) |
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')
parser.set_defaults(func=nni_info)
# create subparsers for args with sub values
subparsers = parser.add_subparsers()
# parse start command
parser_start = subparsers.add_parser('create', help='create a new experiment')
parser_start.add_argument('--config', '-c', required=True, dest='config', help='the path of yaml config file')
parser_start.add_argument('--port', '-p', default=DEFAULT_REST_PORT, dest='port', help='the port of restful server')
parser_start.add_argument('--debug', '-d', action='store_true', help=' set debug mode')
parser_start.set_defaults(func=create_experiment)
# parse resume command
parser_resume = subparsers.add_parser('resume', help='resume a new experiment')
parser_resume.add_argument('id', nargs='?', help='The id of the experiment you want to resume')
parser_resume.add_argument('--port', '-p', default=DEFAULT_REST_PORT, dest='port', help='the port of restful server')
parser_resume.add_argument('--debug', '-d', action='store_true', help=' set debug mode')
parser_resume.set_defaults(func=resume_experiment)
# parse update command
parser_updater = subparsers.add_parser('update', help='update the experiment')
#add subparsers for parser_updater
parser_updater_subparsers = parser_updater.add_subparsers()
parser_updater_searchspace = parser_updater_subparsers.add_parser('searchspace', help='update searchspace')
parser_updater_searchspace.add_argument('id', nargs='?', help='the id of experiment')
parser_updater_searchspace.add_argument('--filename', '-f', required=True)
parser_updater_searchspace.set_defaults(func=update_searchspace)
parser_updater_concurrency = parser_updater_subparsers.add_parser('concurrency', help='update concurrency')
parser_updater_concurrency.add_argument('id', nargs='?', help='the id of experiment')
parser_updater_concurrency.add_argument('--value', '-v', required=True)
parser_updater_concurrency.set_defaults(func=update_concurrency)
parser_updater_duration = parser_updater_subparsers.add_parser('duration', help='update duration')
parser_updater_duration.add_argument('id', nargs='?', help='the id of experiment')
parser_updater_duration.add_argument('--value', '-v', required=True, help='the unit of time should in {\'s\', \'m\', \'h\', \'d\'}')
parser_updater_duration.set_defaults(func=update_duration)
parser_updater_trialnum = parser_updater_subparsers.add_parser('trialnum', help='update maxtrialnum')
parser_updater_trialnum.add_argument('--id', '-i', dest='id', help='the id of experiment')
parser_updater_trialnum.add_argument('--value', '-v', required=True)
parser_updater_trialnum.set_defaults(func=update_trialnum)
#parse stop command
parser_stop = subparsers.add_parser('stop', help='stop the experiment')
parser_stop.add_argument('id', nargs='?', help='the id of experiment, use \'all\' to stop all running experiments')
parser_stop.set_defaults(func=stop_experiment)
#parse trial command
parser_trial = subparsers.add_parser('trial', help='get trial information')
#add subparsers for parser_trial
parser_trial_subparsers = parser_trial.add_subparsers()
parser_trial_ls = parser_trial_subparsers.add_parser('ls', help='list trial jobs')
parser_trial_ls.add_argument('id', nargs='?', help='the id of experiment')
parser_trial_ls.set_defaults(func=trial_ls)
parser_trial_kill = parser_trial_subparsers.add_parser('kill', help='kill trial jobs')
parser_trial_kill.add_argument('id', nargs='?', help='the id of experiment')
parser_trial_kill.add_argument('--trial_id', '-T', required=True, dest='trial_id', help='the id of trial to be killed')
parser_trial_kill.set_defaults(func=trial_kill)
#parse experiment command
parser_experiment = subparsers.add_parser('experiment', help='get experiment information')
#add subparsers for parser_experiment
parser_experiment_subparsers = parser_experiment.add_subparsers()
parser_experiment_show = parser_experiment_subparsers.add_parser('show', help='show the information of experiment')
parser_experiment_show.add_argument('id', nargs='?', help='the id of experiment')
parser_experiment_show.set_defaults(func=list_experiment)
parser_experiment_status = parser_experiment_subparsers.add_parser('status', help='show the status of experiment')
parser_experiment_status.add_argument('id', nargs='?', help='the id of experiment')
parser_experiment_status.set_defaults(func=experiment_status)
parser_experiment_list = parser_experiment_subparsers.add_parser('list', help='list all of running experiment ids')
parser_experiment_list.add_argument('all', nargs='?', help='list all of experiments')
parser_experiment_list.set_defaults(func=experiment_list)
#import tuning data
parser_import_data = parser_experiment_subparsers.add_parser('import', help='import additional data')
parser_import_data.add_argument('id', nargs='?', help='the id of experiment')
parser_import_data.add_argument('--filename', '-f', required=True)
parser_import_data.set_defaults(func=import_data)
#export trial data
parser_trial_export = parser_experiment_subparsers.add_parser('export', help='export trial job results to csv or json')
parser_trial_export.add_argument('id', nargs='?', help='the id of experiment')
parser_trial_export.add_argument('--type', '-t', choices=['json', 'csv'], required=True, dest='type', help='target file type')
parser_trial_export.add_argument('--filename', '-f', required=True, dest='path', help='target file path')
parser_trial_export.set_defaults(func=export_trials_data)
#TODO:finish webui function
#parse board command
parser_webui = subparsers.add_parser('webui', help='get web ui information')
#add subparsers for parser_board
parser_webui_subparsers = parser_webui.add_subparsers()
parser_webui_url = parser_webui_subparsers.add_parser('url', help='show the url of web ui')
parser_webui_url.add_argument('id', nargs='?', help='the id of experiment')
parser_webui_url.set_defaults(func=webui_url)
#parse config command
parser_config = subparsers.add_parser('config', help='get config information')
parser_config_subparsers = parser_config.add_subparsers()
parser_config_show = parser_config_subparsers.add_parser('show', help='show the information of config')
parser_config_show.add_argument('id', nargs='?', help='the id of experiment')
parser_config_show.set_defaults(func=get_config)
#parse log command
parser_log = subparsers.add_parser('log', help='get log information')
# add subparsers for parser_log
parser_log_subparsers = parser_log.add_subparsers()
parser_log_stdout = parser_log_subparsers.add_parser('stdout', help='get stdout information')
parser_log_stdout.add_argument('id', nargs='?', help='the id of experiment')
parser_log_stdout.add_argument('--tail', '-T', dest='tail', type=int, help='get tail -100 content of stdout')
parser_log_stdout.add_argument('--head', '-H', dest='head', type=int, help='get head -100 content of stdout')
parser_log_stdout.add_argument('--path', action='store_true', default=False, help='get the path of stdout file')
parser_log_stdout.set_defaults(func=log_stdout)
parser_log_stderr = parser_log_subparsers.add_parser('stderr', help='get stderr information')
parser_log_stderr.add_argument('id', nargs='?', help='the id of experiment')
parser_log_stderr.add_argument('--tail', '-T', dest='tail', type=int, help='get tail -100 content of stderr')
parser_log_stderr.add_argument('--head', '-H', dest='head', type=int, help='get head -100 content of stderr')
parser_log_stderr.add_argument('--path', action='store_true', default=False, help='get the path of stderr file')
parser_log_stderr.set_defaults(func=log_stderr)
parser_log_trial = parser_log_subparsers.add_parser('trial', help='get trial log path')
parser_log_trial.add_argument('id', nargs='?', help='the id of experiment')
parser_log_trial.add_argument('--trial_id', '-T', dest='trial_id', help='find trial log path by id')
parser_log_trial.set_defaults(func=log_trial)
#parse package command
parser_package = subparsers.add_parser('package', help='control nni tuner and assessor packages')
# add subparsers for parser_package
parser_package_subparsers = parser_package.add_subparsers()
parser_package_install = parser_package_subparsers.add_parser('install', help='install packages')
parser_package_install.add_argument('--name', '-n', dest='name', help='package name to be installed')
parser_package_install.set_defaults(func=package_install)
parser_package_show = parser_package_subparsers.add_parser('show', help='show the information of packages')
parser_package_show.set_defaults(func=package_show)
#parse tensorboard command
parser_tensorboard = subparsers.add_parser('tensorboard', help='manage tensorboard')
parser_tensorboard_subparsers = parser_tensorboard.add_subparsers()
parser_tensorboard_start = parser_tensorboard_subparsers.add_parser('start', help='start tensorboard')
parser_tensorboard_start.add_argument('id', nargs='?', help='the id of experiment')
parser_tensorboard_start.add_argument('--trial_id', '-T', dest='trial_id', help='the id of trial')
parser_tensorboard_start.add_argument('--port', dest='port', default=6006, help='the port to start tensorboard')
parser_tensorboard_start.set_defaults(func=start_tensorboard)
parser_tensorboard_start = parser_tensorboard_subparsers.add_parser('stop', help='stop tensorboard')
parser_tensorboard_start.add_argument('id', nargs='?', help='the id of experiment')
parser_tensorboard_start.set_defaults(func=stop_tensorboard)
#parse top command
parser_top = subparsers.add_parser('top', help='monitor the experiment')
parser_top.add_argument('--time', '-t', dest='time', type=int, default=3, help='the time interval to update the experiment status, ' \
'the unit is second')
parser_top.set_defaults(func=monitor_experiment)
args = parser.parse_args()
args.func(args) |
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_full_path |
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_command(stderr_full_path)) |
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 different installation path sequentially util nni is found.
Return None if nothing is found
'''
def _generate_installation_path(sitepackages_path):
python_dir = get_python_dir(sitepackages_path)
entry_file = os.path.join(python_dir, 'nni', 'main.js')
if os.path.isfile(entry_file):
return python_dir
return None
for sitepackage in sitepackages:
python_dir = _generate_installation_path(sitepackage)
if python_dir:
return python_dir
return None
if os.getenv('VIRTUAL_ENV'):
# if 'virtualenv' package is used, `site` has not attr getsitepackages, so we will instead use VIRTUAL_ENV
# Note that conda venv will not have VIRTUAL_ENV
python_dir = os.getenv('VIRTUAL_ENV')
else:
python_sitepackage = site.getsitepackages()[0]
# If system-wide python is used, we will give priority to using `local sitepackage`--"usersitepackages()" given that nni exists there
if python_sitepackage.startswith('/usr') or python_sitepackage.startswith('/Library'):
python_dir = try_installation_path_sequentially(site.getusersitepackages(), site.getsitepackages()[0])
else:
python_dir = try_installation_path_sequentially(site.getsitepackages()[0], site.getusersitepackages())
if python_dir:
entry_file = os.path.join(python_dir, 'nni', 'main.js')
if os.path.isfile(entry_file):
return os.path.join(python_dir, 'nni')
print_error('Fail to find nni under python library')
exit(1) |
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!\n' \
'You could use \'nnictl create --help\' to get help information' % port)
exit(1)
if (platform != 'local') and detect_port(int(port) + 1):
print_error('PAI mode need an additional adjacent port %d, and the port %d is used by another process!\n' \
'You could set another port to start experiment!\n' \
'You could use \'nnictl create --help\' to get help information' % ((int(port) + 1), (int(port) + 1)))
exit(1)
print_normal('Starting restful server...')
entry_dir = get_nni_installation_path()
entry_file = os.path.join(entry_dir, 'main.js')
node_command = 'node'
if sys.platform == 'win32':
node_command = os.path.join(entry_dir[:-3], 'Scripts', 'node.exe')
cmds = [node_command, entry_file, '--port', str(port), '--mode', platform, '--start_mode', mode]
if log_dir is not None:
cmds += ['--log_dir', log_dir]
if log_level is not None:
cmds += ['--log_level', log_level]
if mode == 'resume':
cmds += ['--experiment_id', experiment_id]
stdout_full_path, stderr_full_path = get_log_path(config_file_name)
stdout_file = open(stdout_full_path, 'a+')
stderr_file = open(stderr_full_path, 'a+')
time_now = time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))
#add time information in the header of log files
log_header = LOG_HEADER % str(time_now)
stdout_file.write(log_header)
stderr_file.write(log_header)
if sys.platform == 'win32':
from subprocess import CREATE_NEW_PROCESS_GROUP
process = Popen(cmds, cwd=entry_dir, stdout=stdout_file, stderr=stderr_file, creationflags=CREATE_NEW_PROCESS_GROUP)
else:
process = Popen(cmds, cwd=entry_dir, stdout=stdout_file, stderr=stderr_file)
return process, str(time_now) |
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_response(response):
return True
else:
print('Error message is {}'.format(response.text))
_, stderr_full_path = get_log_path(config_file_name)
if response:
with open(stderr_full_path, 'a+') as fout:
fout.write(json.dumps(json.loads(response.text), indent=4, sort_keys=True, separators=(',', ':')))
return False |
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_config'] and request_data['local_config'].get('gpuIndices') \
and isinstance(request_data['local_config'].get('gpuIndices'), int):
request_data['local_config']['gpuIndices'] = str(request_data['local_config'].get('gpuIndices'))
response = rest_put(cluster_metadata_url(port), json.dumps(request_data), REST_TIME_OUT)
err_message = ''
if not response or not check_response(response):
if response is not None:
err_message = response.text
_, stderr_full_path = get_log_path(config_file_name)
with open(stderr_full_path, 'a+') as fout:
fout.write(json.dumps(json.loads(err_message), indent=4, sort_keys=True, separators=(',', ':')))
return False, err_message
return set_trial_config(experiment_config, port, config_file_name) |
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 in range(len(request_data['machine_list'])):
if isinstance(request_data['machine_list'][i].get('gpuIndices'), int):
request_data['machine_list'][i]['gpuIndices'] = str(request_data['machine_list'][i].get('gpuIndices'))
response = rest_put(cluster_metadata_url(port), json.dumps(request_data), REST_TIME_OUT)
err_message = ''
if not response or not check_response(response):
if response is not None:
err_message = response.text
_, stderr_full_path = get_log_path(config_file_name)
with open(stderr_full_path, 'a+') as fout:
fout.write(json.dumps(json.loads(err_message), indent=4, sort_keys=True, separators=(',', ':')))
return False, err_message
result, message = setNNIManagerIp(experiment_config, port, config_file_name)
if not result:
return result, message
#set trial_config
return set_trial_config(experiment_config, port, config_file_name), err_message |
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'] }
response = rest_put(cluster_metadata_url(port), json.dumps(ip_config_dict), REST_TIME_OUT)
err_message = None
if not response or not response.status_code == 200:
if response is not None:
err_message = response.text
_, stderr_full_path = get_log_path(config_file_name)
with open(stderr_full_path, 'a+') as fout:
fout.write(json.dumps(json.loads(err_message), indent=4, sort_keys=True, separators=(',', ':')))
return False, err_message
return True, None |
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 = rest_put(cluster_metadata_url(port), json.dumps(frameworkcontroller_config_data), REST_TIME_OUT)
err_message = None
if not response or not response.status_code == 200:
if response is not None:
err_message = response.text
_, stderr_full_path = get_log_path(config_file_name)
with open(stderr_full_path, 'a+') as fout:
fout.write(json.dumps(json.loads(err_message), indent=4, sort_keys=True, separators=(',', ':')))
return False, err_message
result, message = setNNIManagerIp(experiment_config, port, config_file_name)
if not result:
return result, message
#set trial_config
return set_trial_config(experiment_config, port, config_file_name), err_message |
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_data['experimentName'] = experiment_config['experimentName']
request_data['trialConcurrency'] = experiment_config['trialConcurrency']
request_data['maxExecDuration'] = experiment_config['maxExecDuration']
request_data['maxTrialNum'] = experiment_config['maxTrialNum']
request_data['searchSpace'] = experiment_config.get('searchSpace')
request_data['trainingServicePlatform'] = experiment_config.get('trainingServicePlatform')
if experiment_config.get('description'):
request_data['description'] = experiment_config['description']
if experiment_config.get('multiPhase'):
request_data['multiPhase'] = experiment_config.get('multiPhase')
if experiment_config.get('multiThread'):
request_data['multiThread'] = experiment_config.get('multiThread')
if experiment_config.get('advisor'):
request_data['advisor'] = experiment_config['advisor']
else:
request_data['tuner'] = experiment_config['tuner']
if 'assessor' in experiment_config:
request_data['assessor'] = experiment_config['assessor']
#debug mode should disable version check
if experiment_config.get('debug') is not None:
request_data['versionCheck'] = not experiment_config.get('debug')
if experiment_config.get('logCollection'):
request_data['logCollection'] = experiment_config.get('logCollection')
request_data['clusterMetaData'] = []
if experiment_config['trainingServicePlatform'] == 'local':
request_data['clusterMetaData'].append(
{'key':'codeDir', 'value':experiment_config['trial']['codeDir']})
request_data['clusterMetaData'].append(
{'key': 'command', 'value': experiment_config['trial']['command']})
elif experiment_config['trainingServicePlatform'] == 'remote':
request_data['clusterMetaData'].append(
{'key': 'machine_list', 'value': experiment_config['machineList']})
request_data['clusterMetaData'].append(
{'key': 'trial_config', 'value': experiment_config['trial']})
elif experiment_config['trainingServicePlatform'] == 'pai':
request_data['clusterMetaData'].append(
{'key': 'pai_config', 'value': experiment_config['paiConfig']})
request_data['clusterMetaData'].append(
{'key': 'trial_config', 'value': experiment_config['trial']})
elif experiment_config['trainingServicePlatform'] == 'kubeflow':
request_data['clusterMetaData'].append(
{'key': 'kubeflow_config', 'value': experiment_config['kubeflowConfig']})
request_data['clusterMetaData'].append(
{'key': 'trial_config', 'value': experiment_config['trial']})
elif experiment_config['trainingServicePlatform'] == 'frameworkcontroller':
request_data['clusterMetaData'].append(
{'key': 'frameworkcontroller_config', 'value': experiment_config['frameworkcontrollerConfig']})
request_data['clusterMetaData'].append(
{'key': 'trial_config', 'value': experiment_config['trial']})
response = rest_post(experiment_url(port), json.dumps(request_data), REST_TIME_OUT, show_error=True)
if check_response(response):
return response
else:
_, stderr_full_path = get_log_path(config_file_name)
if response is not None:
with open(stderr_full_path, 'a+') as fout:
fout.write(json.dumps(json.loads(response.text), indent=4, sort_keys=True, separators=(',', ':')))
print_error('Setting experiment error, error message is {}'.format(response.text))
return None |
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('tuner') and experiment_config['tuner'].get('builtinTunerName'):
tuner_name = experiment_config['tuner']['builtinTunerName']
module_name = ModuleName[tuner_name]
try:
check_call([sys.executable, '-c', 'import %s'%(module_name)])
except ModuleNotFoundError as e:
print_error('The tuner %s should be installed through nnictl'%(tuner_name))
exit(1)
log_dir = experiment_config['logDir'] if experiment_config.get('logDir') else None
log_level = experiment_config['logLevel'] if experiment_config.get('logLevel') else None
if log_level not in ['trace', 'debug'] and args.debug:
log_level = 'debug'
# start rest server
rest_process, start_time = start_rest_server(args.port, experiment_config['trainingServicePlatform'], mode, config_file_name, experiment_id, log_dir, log_level)
nni_config.set_config('restServerPid', rest_process.pid)
# Deal with annotation
if experiment_config.get('useAnnotation'):
path = os.path.join(tempfile.gettempdir(), get_user(), 'nni', 'annotation')
if not os.path.isdir(path):
os.makedirs(path)
path = tempfile.mkdtemp(dir=path)
code_dir = expand_annotations(experiment_config['trial']['codeDir'], path)
experiment_config['trial']['codeDir'] = code_dir
search_space = generate_search_space(code_dir)
experiment_config['searchSpace'] = json.dumps(search_space)
assert search_space, ERROR_INFO % 'Generated search space is empty'
elif experiment_config.get('searchSpacePath'):
search_space = get_json_content(experiment_config.get('searchSpacePath'))
experiment_config['searchSpace'] = json.dumps(search_space)
else:
experiment_config['searchSpace'] = json.dumps('')
# check rest server
running, _ = check_rest_server(args.port)
if running:
print_normal('Successfully started Restful server!')
else:
print_error('Restful server start failed!')
print_log_content(config_file_name)
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Rest server stopped!')
exit(1)
# set remote config
if experiment_config['trainingServicePlatform'] == 'remote':
print_normal('Setting remote config...')
config_result, err_msg = set_remote_config(experiment_config, args.port, config_file_name)
if config_result:
print_normal('Successfully set remote config!')
else:
print_error('Failed! Error is: {}'.format(err_msg))
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Rest server stopped!')
exit(1)
# set local config
if experiment_config['trainingServicePlatform'] == 'local':
print_normal('Setting local config...')
if set_local_config(experiment_config, args.port, config_file_name):
print_normal('Successfully set local config!')
else:
print_error('Set local config failed!')
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Rest server stopped!')
exit(1)
#set pai config
if experiment_config['trainingServicePlatform'] == 'pai':
print_normal('Setting pai config...')
config_result, err_msg = set_pai_config(experiment_config, args.port, config_file_name)
if config_result:
print_normal('Successfully set pai config!')
else:
if err_msg:
print_error('Failed! Error is: {}'.format(err_msg))
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Restful server stopped!')
exit(1)
#set kubeflow config
if experiment_config['trainingServicePlatform'] == 'kubeflow':
print_normal('Setting kubeflow config...')
config_result, err_msg = set_kubeflow_config(experiment_config, args.port, config_file_name)
if config_result:
print_normal('Successfully set kubeflow config!')
else:
if err_msg:
print_error('Failed! Error is: {}'.format(err_msg))
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Restful server stopped!')
exit(1)
#set kubeflow config
if experiment_config['trainingServicePlatform'] == 'frameworkcontroller':
print_normal('Setting frameworkcontroller config...')
config_result, err_msg = set_frameworkcontroller_config(experiment_config, args.port, config_file_name)
if config_result:
print_normal('Successfully set frameworkcontroller config!')
else:
if err_msg:
print_error('Failed! Error is: {}'.format(err_msg))
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Restful server stopped!')
exit(1)
# start a new experiment
print_normal('Starting experiment...')
# set debug configuration
if experiment_config.get('debug') is None:
experiment_config['debug'] = args.debug
response = set_experiment(experiment_config, mode, args.port, config_file_name)
if response:
if experiment_id is None:
experiment_id = json.loads(response.text).get('experiment_id')
nni_config.set_config('experimentId', experiment_id)
else:
print_error('Start experiment failed!')
print_log_content(config_file_name)
try:
kill_command(rest_process.pid)
except Exception:
raise Exception(ERROR_INFO % 'Restful server stopped!')
exit(1)
if experiment_config.get('nniManagerIp'):
web_ui_url_list = ['{0}:{1}'.format(experiment_config['nniManagerIp'], str(args.port))]
else:
web_ui_url_list = get_local_urls(args.port)
nni_config.set_config('webuiUrl', web_ui_url_list)
#save experiment information
nnictl_experiment_config = Experiments()
nnictl_experiment_config.add_experiment(experiment_id, args.port, start_time, config_file_name, experiment_config['trainingServicePlatform'])
print_normal(EXPERIMENT_SUCCESS_INFO % (experiment_id, ' '.join(web_ui_url_list))) |
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('Please set experiment id! \nYou could use \'nnictl resume {id}\' to resume a stopped experiment!\n' \
'You could use \'nnictl experiment list all\' to show all of stopped experiments!')
exit(1)
else:
if experiment_dict.get(args.id) is None:
print_error('Id %s not exist!' % args.id)
exit(1)
if experiment_dict[args.id]['status'] != 'STOPPED':
print_error('Experiment %s is running!' % args.id)
exit(1)
experiment_id = args.id
print_normal('Resuming experiment %s...' % experiment_id)
nni_config = Config(experiment_dict[experiment_id]['fileName'])
experiment_config = nni_config.get_config('experimentConfig')
experiment_id = nni_config.get_config('experimentId')
new_config_file_name = ''.join(random.sample(string.ascii_letters + string.digits, 8))
new_nni_config = Config(new_config_file_name)
new_nni_config.set_config('experimentConfig', experiment_config)
launch_experiment(args, experiment_config, 'resume', new_config_file_name, experiment_id)
new_nni_config.set_config('restServerPort', args.port) |
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_error('Please set correct config path!')
exit(1)
experiment_config = get_yml_content(config_path)
validate_all_content(experiment_config, config_path)
nni_config.set_config('experimentConfig', experiment_config)
launch_experiment(args, experiment_config, 'new', config_file_name)
nni_config.set_config('restServerPort', args.port) |
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