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Commit 8314d6ee authored by Deshui Yu's avatar Deshui Yu Committed by fishyds
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Merge from dogfood branch to master

parent 98530fd2
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Showing with 128 additions and 87 deletions
tools/nnicmd/launcher_utils.py
View file @ 8314d6ee
......@@ -19,13 +19,23 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import os
import json
def expand_path(experiment_config, key):
'''Change '~' to user home directory'''
if experiment_config.get(key):
experiment_config[key] = os.path.expanduser(experiment_config[key])
def parse_relative_path(root_path, experiment_config, key):
'''Change relative path to absolute path'''
if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)):
experiment_config[key] = os.path.join(root_path, experiment_config.get(key))
def check_empty(experiment_config, key):
'''Check whether a key is in experiment_config and has non-empty value'''
if key not in experiment_config or experiment_config[key] is None:
raise ValueError('%s can not be empty' % key)
def check_digit(experiment_config, key, start, end):
'''Check whether a value in experiment_config is digit and in a range of [start, end]'''
if not str(experiment_config[key]).isdigit() or experiment_config[key] < start or \
......@@ -61,6 +71,41 @@ def parse_time(experiment_config, key):
parse_dict = {'s':1, 'm':60, 'h':3600, 'd':86400}
experiment_config[key] = int(time) * parse_dict[unit]
def parse_path(experiment_config, config_path):
'''Parse path in config file'''
expand_path(experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
expand_path(experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
expand_path(experiment_config['tuner'], 'codeDir')
if experiment_config.get('assessor'):
expand_path(experiment_config['assessor'], 'codeDir')
#if users use relative path, convert it to absolute path
root_path = os.path.dirname(config_path)
if experiment_config.get('searchSpacePath'):
parse_relative_path(root_path, experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
parse_relative_path(root_path, experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
parse_relative_path(root_path, experiment_config['tuner'], 'codeDir')
if experiment_config.get('assessor'):
parse_relative_path(root_path, experiment_config['assessor'], 'codeDir')
def validate_search_space_content(experiment_config):
'''Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file'''
try:
search_space_content = json.load(open(experiment_config.get('searchSpacePath'), 'r'))
for value in search_space_content.values():
if not value.get('_type') or not value.get('_value'):
raise ValueError('please use _type and _value to specify searchspace!')
except:
raise Exception('searchspace file is not a valid json format!')
def validate_common_content(experiment_config):
'''Validate whether the common values in experiment_config is valid'''
#validate authorName
......@@ -88,89 +133,83 @@ def validate_common_content(experiment_config):
def validate_tuner_content(experiment_config):
'''Validate whether tuner in experiment_config is valid'''
tuner_algorithm_dict = {'TPE': 'nni.hyperopt_tuner --algorithm_name tpe',\
'Random': 'nni.hyperopt_tuner --algorithm_name random_search',\
'Anneal': 'nni.hyperopt_tuner --algorithm_name anneal',\
'Evolution': 'nni.evolution_tuner'}
check_empty(experiment_config, 'tuner')
#TODO: use elegent way to detect keys
if experiment_config['tuner'].get('tunerCommand') and experiment_config['tuner'].get('tunerCwd')\
and (experiment_config['tuner'].get('tunerName') or experiment_config['tuner'].get('optimizationMode'))\
or experiment_config['tuner'].get('tunerName') and experiment_config['tuner'].get('optimizationMode')\
and (experiment_config['tuner'].get('tunerCommand') or experiment_config['tuner'].get('tunerCwd')):
raise Exception('Please choose to use (tunerCommand, tunerCwd) or (tunerName, optimizationMode)')
if experiment_config['tuner'].get('tunerCommand') and experiment_config['tuner'].get('tunerCwd'):
check_directory(experiment_config['tuner'], 'tunerCwd')
experiment_config['tuner']['tunerCwd'] = os.path.abspath(experiment_config['tuner']['tunerCwd'])
elif experiment_config['tuner'].get('tunerName') and experiment_config['tuner'].get('optimizationMode'):
check_choice(experiment_config['tuner'], 'tunerName', ['TPE', 'Random', 'Anneal', 'Evolution'])
check_choice(experiment_config['tuner'], 'optimizationMode', ['Maximize', 'Minimize'])
if experiment_config['tuner']['optimizationMode'] == 'Maximize':
experiment_config['tuner']['optimizationMode'] = 'maximize'
else:
experiment_config['tuner']['optimizationMode'] = 'minimize'
experiment_config['tuner']['tunerCommand'] = 'python3 -m %s --optimize_mode %s'\
% (tuner_algorithm_dict.get(experiment_config['tuner']['tunerName']), experiment_config['tuner']['optimizationMode'])
experiment_config['tuner']['tunerCwd'] = ''
tuner_class_name_dict = {'TPE': 'HyperoptTuner',\
'Random': 'HyperoptTuner',\
'Anneal': 'HyperoptTuner',\
'Evolution': 'EvolutionTuner'}
tuner_algorithm_name_dict = {'TPE': 'tpe',\
'Random': 'random_search',\
'Anneal': 'anneal'}
if experiment_config.get('tuner') is None:
raise ValueError('Please set tuner!')
if (experiment_config['tuner'].get('builtinTunerName') and \
(experiment_config['tuner'].get('codeDir') or experiment_config['tuner'].get('classFileName') or experiment_config['tuner'].get('className'))) or \
(experiment_config['tuner'].get('codeDir') and experiment_config['tuner'].get('classFileName') and experiment_config['tuner'].get('className') and \
experiment_config['tuner'].get('builtinTunerName')):
raise ValueError('Please check tuner content!')
if experiment_config['tuner'].get('builtinTunerName') and experiment_config['tuner'].get('classArgs'):
if tuner_class_name_dict.get(experiment_config['tuner']['builtinTunerName']) is None:
raise ValueError('Please set correct builtinTunerName!')
experiment_config['tuner']['className'] = tuner_class_name_dict.get(experiment_config['tuner']['builtinTunerName'])
if experiment_config['tuner']['classArgs'].get('optimize_mode') is None:
raise ValueError('Please set optimize_mode!')
if experiment_config['tuner']['classArgs']['optimize_mode'] not in ['maximize', 'minimize']:
raise ValueError('optimize_mode should be maximize or minimize')
if tuner_algorithm_name_dict.get(experiment_config['tuner']['builtinTunerName']):
experiment_config['tuner']['classArgs']['algorithm_name'] = tuner_algorithm_name_dict.get(experiment_config['tuner']['builtinTunerName'])
elif experiment_config['tuner'].get('codeDir') and experiment_config['tuner'].get('classFileName') and experiment_config['tuner'].get('className'):
if not os.path.exists(os.path.join(experiment_config['tuner']['codeDir'], experiment_config['tuner']['classFileName'])):
raise ValueError('Tuner file directory is not valid!')
else:
raise ValueError('Please complete tuner information!')
raise ValueError('Tuner format is not valid!')
if experiment_config['tuner'].get('tunerGpuNum'):
check_digit(experiment_config['tuner'], 'tunerGpuNum', 0, 100)
if experiment_config['tuner'].get('gpuNum'):
check_digit(experiment_config['tuner'], 'gpuNum', 0, 100)
def validate_assessor_content(experiment_config):
'''Validate whether assessor in experiment_config is valid'''
assessor_algorithm_dict = {'Medianstop': 'nni.medianstop_assessor'}
if 'assessor' in experiment_config:
if experiment_config['assessor']:
if experiment_config['assessor'].get('assessorCommand') and experiment_config['assessor'].get('assessorCwd')\
and (experiment_config['assessor'].get('assessorName') or experiment_config['assessor'].get('optimizationMode'))\
or experiment_config['assessor'].get('assessorName') and experiment_config['assessor'].get('optimizationMode')\
and (experiment_config['assessor'].get('assessorCommand') or experiment_config['assessor'].get('assessorCwd')):
raise Exception('Please choose to use (assessorCommand, assessorCwd) or (assessorName, optimizationMode)')
if experiment_config['assessor'].get('assessorCommand') and experiment_config['assessor'].get('assessorCwd'):
check_empty(experiment_config['assessor'], 'assessorCommand')
check_empty(experiment_config['assessor'], 'assessorCwd')
check_directory(experiment_config['assessor'], 'assessorCwd')
experiment_config['assessor']['assessorCwd'] = os.path.abspath(experiment_config['assessor']['assessorCwd'])
if 'assessorGpuNum' in experiment_config['assessor']:
if experiment_config['assessor']['assessorGpuNum']:
check_digit(experiment_config['assessor'], 'assessorGpuNum', 0, 100)
elif experiment_config['assessor'].get('assessorName') and experiment_config['assessor'].get('optimizationMode'):
check_choice(experiment_config['assessor'], 'assessorName', ['Medianstop'])
check_choice(experiment_config['assessor'], 'optimizationMode', ['Maximize', 'Minimize'])
if experiment_config['assessor']['optimizationMode'] == 'Maximize':
experiment_config['assessor']['optimizationMode'] = 'maximize'
else:
experiment_config['assessor']['optimizationMode'] = 'minimize'
experiment_config['assessor']['assessorCommand'] = 'python3 -m %s --optimize_mode %s'\
% (assessor_algorithm_dict.get(experiment_config['assessor']['assessorName']), experiment_config['assessor']['optimizationMode'])
experiment_config['assessor']['assessorCwd'] = ''
else:
raise ValueError('Please complete assessor information!')
if experiment_config['assessor'].get('assessorGpuNum'):
check_digit(experiment_config['assessor'], 'assessorGpuNum', 0, 100)
assessor_class_name_dict = {'Medianstop': 'MedianstopAssessor'}
if experiment_config.get('assessor'):
if (experiment_config['assessor'].get('builtinAssessorName') and \
(experiment_config['assessor'].get('codeDir') or experiment_config['assessor'].get('classFileName') or experiment_config['assessor'].get('className'))) or \
(experiment_config['assessor'].get('codeDir') and experiment_config['assessor'].get('classFileName') and experiment_config['assessor'].get('className') and \
experiment_config['assessor'].get('builtinAssessorName')):
raise ValueError('Please check assessor content!')
if experiment_config['assessor'].get('builtinAssessorName') and experiment_config['assessor'].get('classArgs'):
if assessor_class_name_dict.get(experiment_config['assessor']['builtinAssessorName']) is None:
raise ValueError('Please set correct builtinAssessorName!')
experiment_config['assessor']['className'] = assessor_class_name_dict.get(experiment_config['assessor']['builtinAssessorName'])
if experiment_config['assessor']['classArgs'].get('optimize_mode') is None:
raise ValueError('Please set optimize_mode!')
if experiment_config['assessor']['classArgs']['optimize_mode'] not in ['maximize', 'minimize']:
raise ValueError('optimize_mode should be maximize or minimize')
elif experiment_config['assessor'].get('codeDir') and experiment_config['assessor'].get('classFileName') and experiment_config['assessor'].get('className'):
if not os.path.exists(os.path.join(experiment_config['assessor']['codeDir'], experiment_config['assessor']['classFileName'])):
raise ValueError('Assessor file directory is not valid!')
else:
raise ValueError('Assessor format is not valid!')
if experiment_config['assessor'].get('gpuNum'):
check_digit(experiment_config['assessor'], 'gpuNum', 0, 100)
def validate_trail_content(experiment_config):
'''Validate whether trial in experiment_config is valid'''
check_empty(experiment_config, 'trial')
check_empty(experiment_config['trial'], 'trialCommand')
check_empty(experiment_config['trial'], 'trialCodeDir')
check_directory(experiment_config['trial'], 'trialCodeDir')
experiment_config['trial']['trialCodeDir'] = os.path.abspath(experiment_config['trial']['trialCodeDir'])
if experiment_config['trial'].get('trialGpuNum') is None:
experiment_config['trial']['trialGpuNum'] = 0
check_empty(experiment_config['trial'], 'command')
check_empty(experiment_config['trial'], 'codeDir')
check_directory(experiment_config['trial'], 'codeDir')
experiment_config['trial']['codeDir'] = os.path.abspath(experiment_config['trial']['codeDir'])
if experiment_config['trial'].get('gpuNum') is None:
experiment_config['trial']['gpuNum'] = 0
else:
check_digit(experiment_config['trial'], 'trialGpuNum', 0, 100)
check_digit(experiment_config['trial'], 'gpuNum', 0, 100)
def validate_machinelist_content(experiment_config):
......@@ -183,7 +222,12 @@ def validate_machinelist_content(experiment_config):
else:
check_digit(machine, 'port', 0, 65535)
check_empty(machine, 'username')
check_empty(machine, 'passwd')
if machine.get('passwd') is None and machine.get('sshKeyPath') is None:
raise ValueError('Please set passwd or sshKeyPath for remote machine!')
if machine.get('sshKeyPath') is None and machine.get('passphrase'):
raise ValueError('Please set sshKeyPath!')
if machine.get('sshKeyPath'):
check_file(machine, 'sshKeyPath')
def validate_annotation_content(experiment_config):
......@@ -194,16 +238,19 @@ def validate_annotation_content(experiment_config):
raise Exception('If you set useAnnotation=true, please leave searchSpacePath empty')
else:
# validate searchSpaceFile
check_empty(experiment_config, 'searchSpacePath')
check_file(experiment_config, 'searchSpacePath')
if experiment_config['tuner'].get('tunerName') and experiment_config['tuner'].get('optimizationMode'):
check_empty(experiment_config, 'searchSpacePath')
check_file(experiment_config, 'searchSpacePath')
validate_search_space_content(experiment_config)
def validate_all_content(experiment_config):
def validate_all_content(experiment_config, config_path):
'''Validate whether experiment_config is valid'''
parse_path(experiment_config, config_path)
validate_common_content(experiment_config)
validate_tuner_content(experiment_config)
validate_assessor_content(experiment_config)
validate_trail_content(experiment_config)
# validate_annotation_content(experiment_config)
validate_annotation_content(experiment_config)
if experiment_config['trainingServicePlatform'] == 'remote':
validate_machinelist_content(experiment_config)
tools/nnicmd/nnictl.py
View file @ 8314d6ee
......@@ -38,7 +38,6 @@ def parse_args():
# 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('--manager', '-m', default='nnimanager', dest='manager')
parser_start.add_argument('--webuiport', '-w', default=8080, dest='webuiport')
parser_start.set_defaults(func=create_experiment)
......@@ -103,13 +102,6 @@ def parse_args():
parser_config_show = parser_config_subparsers.add_parser('show', help='show the information of config')
parser_config_show.set_defaults(func=get_config)
#parse restful server command
parser_rest = subparsers.add_parser('rest', help='get restful server information')
#add subparsers for parser_rest
parser_rest_subparsers = parser_rest.add_subparsers()
parser_rest_check = parser_rest_subparsers.add_parser('check', help='check restful server')
parser_rest_check.set_defaults(func=check_rest)
#parse log command
parser_log = subparsers.add_parser('log', help='get log information')
# add subparsers for parser_rest
......
tools/nnicmd/webui_utils.py
View file @ 8314d6ee
......@@ -20,6 +20,7 @@
import psutil
import os
from socket import AddressFamily
from .rest_utils import rest_get
from .config_utils import Config
......@@ -29,7 +30,8 @@ from .constants import STDOUT_FULL_PATH, STDERR_FULL_PATH
def start_web_ui(port):
'''start web ui'''
cmds = ['serve', '-s', '-n', '/usr/share/nni/webui', '-l', str(port)]
web_ui = os.environ.get('WEB_UI_FOLDER')
cmds = ['serve', '-s', '-n', web_ui, '-l', str(port)]
stdout_file = open(STDOUT_FULL_PATH, 'a+')
stderr_file = open(STDERR_FULL_PATH, 'a+')
webui_process = Popen(cmds, stdout=stdout_file, stderr=stderr_file)
......@@ -80,7 +82,7 @@ def check_web_ui():
if not url_list:
return False
for url in url_list:
response = rest_get(url, 20)
response = rest_get(url, 3)
if response and response.status_code == 200:
return True
return False
\ No newline at end of file
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