def parse_events(event):
actions = ['start', 'kill']
try:
if event['Action'] in actions:
item = {
'name': event['Actor']['Attributes']['com.docker.swarm.service.name'],
'action': event['Action'] }
logging.info("Service {name} has been {action}ed".format(**item))
utils.generate_config(options)
except: pass
def __init__(self, config, section):
self.config = os.path.abspath(config)
self.config_parser = ConfigParser.ConfigParser()
self.section = section
try:
with open(self.config) as conf_file:
self.config_parser.readfp(conf_file)
except IOError:
print "Unable to load configuration file: %s" % self.config
utils.generate_config()
sys.exit(1)
def __init__(self, config, section):
self.config = os.path.abspath(config)
self.config_parser = ConfigParser.ConfigParser()
self.section = section
try:
with open(self.config) as conf_file:
self.config_parser.readfp(conf_file)
except IOError:
print "Unable to load configuration file: %s" % self.config
utils.generate_config()
sys.exit(1)
def train_model(config):
#加载数据
X_drug, X_target, y = dataset.load_process(config.input_file)
#分割训练集、验证集和测试集
train, val, test = utils.data_process(X_drug, X_target, y,
config.drug_encoding, config.target_encoding,
split_method = 'random', frac = [0.7,0.1,0.2])
#模型配置生成
model_config = utils.generate_config(drug_encoding = config.drug_encoding,
target_encoding = config.target_encoding,
result_folder = config.result_folder,
input_dim_drug = config.input_dim_drug,
input_dim_protein = config.input_dim_protein,
hidden_dim_drug = config.hidden_dim_drug,
hidden_dim_protein = config.hidden_dim_protein,
cls_hidden_dims = config.cls_hidden_dims,
mlp_hidden_dims_drug = config.mlp_hidden_dims_drug,
mlp_hidden_dims_target = config.mlp_hidden_dims_target,
batch_size = config.batch_size,
train_epoch = config.train_epoch,
test_every_X_epoch = config.test_every_X_epoch,
LR = config.LR,
decay = config.decay,
transformer_emb_size_drug = config.transformer_emb_size_drug,
transformer_intermediate_size_drug = config.transformer_intermediate_size_drug,
transformer_num_attention_heads_drug = config.transformer_num_attention_heads_drug,
transformer_n_layer_drug = config.transformer_n_layer_drug,
transformer_emb_size_target = config.transformer_emb_size_target,
transformer_intermediate_size_target = config.transformer_intermediate_size_target,
transformer_num_attention_heads_target = config.transformer_num_attention_heads_target,
transformer_n_layer_target = config.transformer_n_layer_target,
transformer_dropout_rate = config.transformer_dropout_rate,
transformer_attention_probs_dropout = config.transformer_attention_probs_dropout,
transformer_hidden_dropout_rate = config.transformer_hidden_dropout_rate,
mpnn_hidden_size = config.mpnn_hidden_size,
mpnn_depth = config.mpnn_depth,
cnn_drug_filters = config.cnn_drug_filters,
cnn_drug_kernels = config.cnn_drug_kernels,
cnn_target_filters = config.cnn_target_filters,
cnn_target_kernels = config.cnn_target_kernels,
rnn_Use_GRU_LSTM_drug = config.rnn_Use_GRU_LSTM_drug,
rnn_drug_hid_dim = config.rnn_drug_hid_dim,
rnn_drug_n_layers = config.rnn_drug_n_layers,
rnn_drug_bidirectional = config.rnn_drug_bidirectional,
rnn_Use_GRU_LSTM_target = config.rnn_Use_GRU_LSTM_target,
rnn_target_hid_dim = config.rnn_target_hid_dim,
rnn_target_n_layers = config.rnn_target_n_layers,
rnn_target_bidirectional = config.rnn_target_bidirectional,
num_workers = config.num_workers)
#模型初始化
model = DTI.model_initialize(**model_config)
#训练模型
model.train(train, val, test)
#保存模型
model.save_model(config.output_dir)
def get_model_config(config):
model_config = generate_config(drug_encoding = config["drug_encoding"],
result_folder = config["result_folder"],
input_dim_drug = config["input_dim_drug"],
input_dim_protein = config["input_dim_protein"],
hidden_dim_drug = config["hidden_dim_drug"],
hidden_dim_protein = config["hidden_dim_protein"],
cls_hidden_dims = config["cls_hidden_dims"],
batch_size = config["batch_size"],
train_epoch = config["train_epoch"],
test_every_X_epoch = config["test_every_X_epoch"],
LR = config["LR"],
decay = config["decay"],
num_workers = config["num_workers"],
transformer_emb_size_drug = config["transformer_emb_size_drug"],
transformer_intermediate_size_drug =
config["transformer_intermediate_size_drug"],
transformer_num_attention_heads_drug =
config["transformer_num_attention_heads_drug"],
transformer_n_layer_drug = config["transformer_n_layer_drug"],
transformer_emb_size_target = config["transformer_emb_size_target"],
transformer_intermediate_size_target =
config["transformer_intermediate_size_target"],
transformer_num_attention_heads_target =
config["transformer_num_attention_heads_target"],
transformer_n_layer_target = config["transformer_n_layer_target"],
transformer_dropout_rate = config["transformer_dropout_rate"],
transformer_attention_probs_dropout =
config["transformer_attention_probs_dropout"],
transformer_hidden_dropout_rate =
config["transformer_hidden_dropout_rate"])
return model_config
def get_model_config(config):
model_config = generate_config(
drug_encoding=config.drug_encoding,
result_folder=config.result_folder,
input_dim_drug=config.input_dim_drug,
input_dim_protein=config.input_dim_protein,
hidden_dim_drug=config.hidden_dim_drug,
hidden_dim_protein=config.hidden_dim_protein,
cls_hidden_dims=config.cls_hidden_dims,
batch_size=config.batch_size,
train_epoch=config.train_epoch,
test_every_X_epoch=config.test_every_X_epoch,
LR=config.LR,
decay=config.decay,
num_workers=config.num_workers,
cnn_drug_filters=config.cnn_drug_filters,
cnn_drug_kernels=config.cnn_drug_kernels,
cnn_target_filters=config.cnn_target_filters,
cnn_target_kernels=config.cnn_target_kernels,
rnn_Use_GRU_LSTM_drug=config.rnn_Use_GRU_LSTM_drug,
rnn_drug_hid_dim=config.rnn_drug_hid_dim,
rnn_drug_n_layers=config.rnn_drug_n_layers,
rnn_drug_bidirectional=config.rnn_drug_bidirectional,
rnn_Use_GRU_LSTM_target=config.rnn_Use_GRU_LSTM_target,
rnn_target_hid_dim=config.rnn_target_hid_dim,
rnn_target_n_layers=config.rnn_target_n_layers,
rnn_target_bidirectional=config.rnn_target_bidirectional)
return model_config
def get_model_config(config):
model_config = generate_config(drug_encoding = config.drug_encoding,
result_folder = config.result_folder,
input_dim_drug = config.input_dim_drug,
hidden_dim_drug = config.hidden_dim_drug,
cls_hidden_dims = config.cls_hidden_dims,
batch_size = config.batch_size,
train_epoch = config.train_epoch,
test_every_X_epoch = config.test_every_X_epoch,
LR = config.LR,
decay = config.decay,
num_workers = config.num_workers,
mlp_hidden_dims_drug = config.mlp_hidden_dims_drug)
return model_config
def get_model_config(config):
model_config = generate_config(
drug_encoding=config.drug_encoding,
result_folder=config.result_folder,
input_dim_drug=config.input_dim_drug,
input_dim_protein=config.input_dim_protein,
hidden_dim_drug=config.hidden_dim_drug,
hidden_dim_protein=config.hidden_dim_protein,
cls_hidden_dims=config.cls_hidden_dims,
batch_size=config.batch_size,
train_epoch=config.train_epoch,
test_every_X_epoch=config.test_every_X_epoch,
LR=config.LR,
decay=config.decay,
num_workers=config.num_workers,
cnn_drug_filters=config.cnn_drug_filters,
cnn_drug_kernels=config.cnn_drug_kernels,
cnn_target_filters=config.cnn_target_filters,
cnn_target_kernels=config.cnn_target_kernels)
return model_config
def update_config(): results = utils.generate_config(options) return flask.jsonify(**results)
#!/usr/bin/env python
import os
import flask
import utils
import options
from flask import request, Response
server = flask.Flask(__name__)
options = options.get_options()
@server.route("/get_endpoints", methods=['GET'])
def get_endpoints():
results = { "endpoints": utils.get_services(options) }
return flask.jsonify(**results)
@server.route("/update_config", methods=['POST'])
def update_config():
results = utils.generate_config(options)
return flask.jsonify(**results)
@server.route("/reload_nginx", methods=['POST'])
def reload_nginx():
results = utils.reload_nginx()
return flask.jsonify(**results)
if __name__ == "__main__":
utils.generate_config(options)
server.run(host='0.0.0.0', port=options['proxy_port'])
#!/usr/bin/env python from utils import generate_config generate_config()
