def main():
args = parser.parse_args()
pipeline_config_path = args.config_path
if not Path(pipeline_config_path).is_file():
configs = [c for c in Path(__file__).parent.glob(f'configs/**/{pipeline_config_path}.json')
if str(c.with_suffix('')).endswith(pipeline_config_path)] # a simple way to not allow * and ?
if configs:
log.info(f"Interpriting '{pipeline_config_path}' as '{configs[0]}'")
pipeline_config_path = str(configs[0])
token = args.token or os.getenv('TELEGRAM_TOKEN')
if args.download or args.mode == 'download':
deep_download(['-c', pipeline_config_path])
if args.mode == 'train':
train_model_from_config(pipeline_config_path)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
log.error('Token required: initiate -t param or TELEGRAM_BOT env var with Telegram bot token')
else:
interact_model_by_telegram(pipeline_config_path, token)
elif args.mode == 'riseapi':
start_model_server(pipeline_config_path)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size, args.file_path)
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
https = args.https
ssl_key = args.key
ssl_cert = args.cert
if args.download or args.mode == 'download':
deep_download(pipeline_config_path)
multi_instance = args.multi_instance
stateful = args.stateful
start_epoch_num = args.start_epoch_num
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path, recursive=args.recursive,
start_epoch_num=start_epoch_num)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path, to_train=False, to_validate=False,
start_epoch_num=start_epoch_num)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
token = args.token
interact_model_by_telegram(pipeline_config_path, token)
elif args.mode == 'interactmsbot':
ms_id = args.ms_id
ms_secret = args.ms_secret
run_ms_bf_default_agent(model_config=pipeline_config_path,
app_id=ms_id,
app_secret=ms_secret,
multi_instance=multi_instance,
stateful=stateful,
port=args.port)
elif args.mode == 'alexa':
run_alexa_default_agent(model_config=pipeline_config_path,
multi_instance=multi_instance,
stateful=stateful,
port=args.port,
https=https,
ssl_key=ssl_key,
ssl_cert=ssl_cert)
elif args.mode == 'riseapi':
alice = args.api_mode == 'alice'
if alice:
start_alice_server(pipeline_config_path, https, ssl_key, ssl_cert, port=args.port)
else:
start_model_server(pipeline_config_path, https, ssl_key, ssl_cert, port=args.port)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size, args.file_path)
elif args.mode == 'install':
install_from_config(pipeline_config_path)
elif args.mode == 'crossval':
if args.folds < 2:
log.error('Minimum number of Folds is 2')
else:
n_folds = args.folds
calc_cv_score(pipeline_config_path, n_folds=n_folds, is_loo=False)
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
if args.download or args.mode == 'download':
deep_download(['-c', pipeline_config_path])
token = args.token or os.getenv('TELEGRAM_TOKEN')
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path,
to_train=False,
to_validate=False)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
log.error(
'Token required: initiate -t param or TELEGRAM_BOT env var with Telegram bot token'
)
else:
interact_model_by_telegram(pipeline_config_path, token)
elif args.mode == 'riseapi':
start_model_server(pipeline_config_path)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size,
args.file_path)
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
log.info("use gpu id:" + args.gpu_id)
if args.download or args.mode == 'download':
deep_download(pipeline_config_path)
multi_instance = args.multi_instance
stateful = args.stateful
start_epoch_num = args.start_epoch_num
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path,
recursive=args.recursive,
start_epoch_num=start_epoch_num)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path,
to_train=False,
to_validate=False,
start_epoch_num=start_epoch_num)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
token = args.token
interact_model_by_telegram(pipeline_config_path, token)
elif args.mode == 'interactmsbot':
ms_id = args.ms_id
ms_secret = args.ms_secret
run_ms_bf_default_agent(model_config=pipeline_config_path,
app_id=ms_id,
app_secret=ms_secret,
multi_instance=multi_instance,
stateful=stateful)
elif args.mode == 'riseapi':
alice = args.api_mode == 'alice'
https = args.https
ssl_key = args.key
ssl_cert = args.cert
if alice:
start_alice_server(pipeline_config_path, https, ssl_key, ssl_cert)
else:
start_model_server(pipeline_config_path, https, ssl_key, ssl_cert)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size,
args.file_path)
elif args.mode == 'install':
install_from_config(pipeline_config_path)
elif args.mode == 'crossval':
if args.folds < 2:
log.error('Minimum number of Folds is 2')
else:
n_folds = args.folds
calc_cv_score(pipeline_config_path, n_folds=n_folds, is_loo=False)
def main():
args = parser.parse_args()
pipeline_config_path = args.config_path
token = args.token or os.getenv('TELEGRAM_TOKEN')
if args.mode == 'train':
train_model_from_config(pipeline_config_path)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
log.error(
'Token required: initiate -t parm or TELEGRAM_BOT env var with Telegram bot token'
)
else:
interact_model_by_telegram(pipeline_config_path, token)
def main():
args = parser.parse_args()
pipeline_config_path = args.config_path
set_usr_dir(pipeline_config_path)
token = args.token or os.getenv('TELEGRAM_TOKEN')
try:
if args.mode == 'train':
train_model_from_config(pipeline_config_path)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
print(
'Token required: initiate -t parm or TELEGRAM_BOT env var with Telegram bot token')
else:
interact_model_by_telegram(pipeline_config_path, token)
finally:
usr_dir = get_usr_dir()
if not list(usr_dir.iterdir()):
usr_dir.rmdir()
def main():
args = parser.parse_args()
pipeline_config_path = args.config_path
set_usr_dir(pipeline_config_path)
token = args.token or os.getenv('TELEGRAM_TOKEN')
try:
if args.mode == 'train':
train_model_from_config(pipeline_config_path)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
print(
'Token required: initiate -t parm or TELEGRAM_BOT env var with Telegram bot token'
)
else:
interact_model_by_telegram(pipeline_config_path, token)
finally:
usr_dir = get_usr_dir()
if not list(usr_dir.iterdir()):
usr_dir.rmdir()
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
if args.download or args.mode == 'download':
deep_download(['-c', pipeline_config_path])
token = args.token or os.getenv('TELEGRAM_TOKEN')
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path, to_train=False, to_validate=False)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
log.error('Token required: initiate -t param or TELEGRAM_BOT env var with Telegram bot token')
else:
interact_model_by_telegram(pipeline_config_path, token)
elif args.mode == 'riseapi':
start_model_server(pipeline_config_path)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size, args.file_path)
elif args.mode == 'install':
install_from_config(pipeline_config_path)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from deeppavlov.core.commands.train import train_model_from_config
from deeppavlov.core.commands.infer import interact_model
from deeppavlov.core.commands.utils import set_deeppavlov_root
# PIPELINE_CONFIG_PATH = 'configs/intents/config_dstc2_train.json'
# PIPELINE_CONFIG_PATH = 'configs/intents/config_snips.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/ner_conll2003.json'
# PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json'
PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_wikitypos_en.json'
# PIPELINE_CONFIG_PATH = 'configs/error_model/brillmoore_kartaslov_ru.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_minimal.json'
# PIPELINE_CONFIG_PATH = 'configs/go_bot/config_all.json'
train_model_from_config(PIPELINE_CONFIG_PATH)
interact_model(PIPELINE_CONFIG_PATH)
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
https = args.https
ssl_key = args.key
ssl_cert = args.cert
if args.download or args.mode == 'download':
deep_download(pipeline_config_path)
multi_instance = args.multi_instance
stateful = args.stateful
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path,
recursive=args.recursive,
start_epoch_num=args.start_epoch_num)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path,
to_train=False,
start_epoch_num=args.start_epoch_num)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
token = args.token
interact_model_by_telegram(
model_config=pipeline_config_path,
token=token,
default_skill_wrap=not args.no_default_skill)
elif args.mode == 'interactmsbot':
ms_id = args.ms_id
ms_secret = args.ms_secret
run_ms_bf_default_agent(model_config=pipeline_config_path,
app_id=ms_id,
app_secret=ms_secret,
multi_instance=multi_instance,
stateful=stateful,
port=args.port,
https=https,
ssl_key=ssl_key,
ssl_cert=ssl_cert,
default_skill_wrap=not args.no_default_skill)
elif args.mode == 'alexa':
run_alexa_default_agent(model_config=pipeline_config_path,
multi_instance=multi_instance,
stateful=stateful,
port=args.port,
https=https,
ssl_key=ssl_key,
ssl_cert=ssl_cert,
default_skill_wrap=not args.no_default_skill)
elif args.mode == 'riseapi':
alice = args.api_mode == 'alice'
if alice:
start_alice_server(pipeline_config_path,
https,
ssl_key,
ssl_cert,
port=args.port)
else:
start_model_server(pipeline_config_path,
https,
ssl_key,
ssl_cert,
port=args.port)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size,
args.file_path)
elif args.mode == 'install':
install_from_config(pipeline_config_path)
elif args.mode == 'crossval':
if args.folds < 2:
log.error('Minimum number of Folds is 2')
else:
n_folds = args.folds
calc_cv_score(pipeline_config_path, n_folds=n_folds, is_loo=False)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 14 10:44:26 2018
@author: lsm
"""
from deeppavlov.core.commands.train import train_evaluate_model_from_config
from deeppavlov.core.commands.infer import interact_model
from text_normalizer import *
from embedder import *
from augmenter import *
from CNN_model import *
from label_transformer import *
interact_model('tns_config.json')
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
if args.download or args.mode == 'download':
deep_download(['-c', pipeline_config_path])
token = args.token or os.getenv('TELEGRAM_TOKEN')
ms_id = args.ms_id or os.getenv('MS_APP_ID')
ms_secret = args.ms_secret or os.getenv('MS_APP_SECRET')
multi_instance = args.multi_instance
stateful = args.stateful
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path,
to_train=False,
to_validate=False)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'interactbot':
if not token:
log.error(
'Token required: initiate -t param or TELEGRAM_BOT env var with Telegram bot token'
)
else:
interact_model_by_telegram(pipeline_config_path, token)
elif args.mode == 'interactmsbot':
if not ms_id:
log.error(
'Microsoft Bot Framework app id required: initiate -i param '
'or MS_APP_ID env var with Microsoft app id')
elif not ms_secret:
log.error(
'Microsoft Bot Framework app secret required: initiate -s param '
'or MS_APP_SECRET env var with Microsoft app secret')
else:
run_ms_bf_default_agent(model_config_path=pipeline_config_path,
app_id=ms_id,
app_secret=ms_secret,
multi_instance=multi_instance,
stateful=stateful)
elif args.mode == 'riseapi':
alice = args.api_mode == 'alice'
https = args.https
ssl_key = args.key
ssl_cert = args.cert
start_model_server(pipeline_config_path, alice, https, ssl_key,
ssl_cert)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size,
args.file_path)
elif args.mode == 'install':
install_from_config(pipeline_config_path)
elif args.mode == 'crossval':
if args.folds < 2:
log.error('Minimum number of Folds is 2')
else:
n_folds = args.folds
calc_cv_score(pipeline_config_path=pipeline_config_path,
n_folds=n_folds,
is_loo=False)
def main():
args = parser.parse_args()
pipeline_config_path = find_config(args.config_path)
if args.download or args.mode == 'download':
deep_download(pipeline_config_path)
if args.mode == 'train':
train_evaluate_model_from_config(pipeline_config_path,
recursive=args.recursive,
start_epoch_num=args.start_epoch_num)
elif args.mode == 'evaluate':
train_evaluate_model_from_config(pipeline_config_path,
to_train=False,
start_epoch_num=args.start_epoch_num)
elif args.mode == 'interact':
interact_model(pipeline_config_path)
elif args.mode == 'telegram':
interact_model_by_telegram(model_config=pipeline_config_path,
token=args.token)
elif args.mode == 'msbot':
start_ms_bf_server(model_config=pipeline_config_path,
app_id=args.ms_id,
app_secret=args.ms_secret,
port=args.port,
https=args.https,
ssl_key=args.key,
ssl_cert=args.cert)
elif args.mode == 'alexa':
start_alexa_server(model_config=pipeline_config_path,
port=args.port,
https=args.https,
ssl_key=args.key,
ssl_cert=args.cert)
elif args.mode == 'alice':
start_alice_server(model_config=pipeline_config_path,
port=args.port,
https=args.https,
ssl_key=args.key,
ssl_cert=args.cert)
elif args.mode == 'riseapi':
start_model_server(pipeline_config_path,
args.https,
args.key,
args.cert,
port=args.port)
elif args.mode == 'risesocket':
start_socket_server(pipeline_config_path,
args.socket_type,
port=args.port,
socket_file=args.socket_file)
elif args.mode == 'agent-rabbit':
start_rabbit_service(model_config=pipeline_config_path,
service_name=args.service_name,
agent_namespace=args.agent_namespace,
batch_size=args.batch_size,
utterance_lifetime_sec=args.utterance_lifetime,
rabbit_host=args.rabbit_host,
rabbit_port=args.rabbit_port,
rabbit_login=args.rabbit_login,
rabbit_password=args.rabbit_password,
rabbit_virtualhost=args.rabbit_virtualhost)
elif args.mode == 'predict':
predict_on_stream(pipeline_config_path, args.batch_size,
args.file_path)
elif args.mode == 'install':
install_from_config(pipeline_config_path)
elif args.mode == 'crossval':
if args.folds < 2:
log.error('Minimum number of Folds is 2')
else:
calc_cv_score(pipeline_config_path,
n_folds=args.folds,
is_loo=False)
from deeppavlov.core.commands.infer import interact_model
from deeppavlov.core.commands.utils import set_usr_dir, get_usr_dir
# HCN
# skills/hcn/config.json
# HCN_new
# skills/go_bot/config.json
# Speller
# models/spellers/error_model/config_en.json
# models/spellers/error_model/config_ru.json
# models/spellers/error_model/config_ru_custom_vocab.json
# Intents classifier
# models/classifiers/intents/config_dstc2.json
# NER
# models/ner/config.json
PIPELINE_CONFIG_PATH = 'skills/go_bot/config.json'
set_usr_dir(PIPELINE_CONFIG_PATH)
try:
train_model_from_config(PIPELINE_CONFIG_PATH)
interact_model(PIPELINE_CONFIG_PATH)
# remove if usr_dir is empty:
finally:
usr_dir = get_usr_dir()
if not list(usr_dir.iterdir()):
usr_dir.rmdir()