def deploy(config_data):
model_id = config_data.get('model_id')
model_version = config_data.get('model_version')
local_role = config_data.get('local').get('role')
local_party_id = config_data.get('local').get('party_id')
child_model_version = config_data.get('child_model_version')
try:
party_model_id = model_utils.gen_party_model_id(
model_id=model_id, role=local_role, party_id=local_party_id)
model = PipelinedModel(model_id=party_model_id,
model_version=model_version)
model_data = model.collect_models(in_bytes=True)
if "pipeline.pipeline:Pipeline" not in model_data:
raise Exception("Can not found pipeline file in model.")
# check if the model could be executed the deploy process (parent/child)
if not check_before_deploy(model):
raise Exception('Child model could not be deployed.')
# copy proto content from parent model and generate a child model
deploy_model = PipelinedModel(model_id=party_model_id,
model_version=child_model_version)
shutil.copytree(src=model.model_path, dst=deploy_model.model_path)
pipeline = deploy_model.read_component_model('pipeline',
'pipeline')['Pipeline']
# modify two pipeline files (model version/ train_runtime_conf)
train_runtime_conf = json_loads(pipeline.train_runtime_conf)
adapter = JobRuntimeConfigAdapter(train_runtime_conf)
train_runtime_conf = adapter.update_model_id_version(
model_version=deploy_model.model_version)
pipeline.model_version = child_model_version
pipeline.train_runtime_conf = json_dumps(train_runtime_conf, byte=True)
parser = get_dsl_parser_by_version(
train_runtime_conf.get('dsl_version', '1'))
train_dsl = json_loads(pipeline.train_dsl)
parent_predict_dsl = json_loads(pipeline.inference_dsl)
if str(train_runtime_conf.get('dsl_version', '1')) == '1':
predict_dsl = json_loads(pipeline.inference_dsl)
else:
if config_data.get('dsl') or config_data.get('predict_dsl'):
predict_dsl = config_data.get('dsl') if config_data.get(
'dsl') else config_data.get('predict_dsl')
if not isinstance(predict_dsl, dict):
predict_dsl = json_loads(predict_dsl)
else:
if config_data.get('cpn_list', None):
cpn_list = config_data.pop('cpn_list')
else:
cpn_list = list(train_dsl.get('components', {}).keys())
parser_version = train_runtime_conf.get('dsl_version', '1')
if str(parser_version) == '1':
predict_dsl = parent_predict_dsl
else:
parser = schedule_utils.get_dsl_parser_by_version(
parser_version)
predict_dsl = parser.deploy_component(cpn_list, train_dsl)
# save predict dsl into child model file
parser.verify_dsl(predict_dsl, "predict")
inference_dsl = parser.get_predict_dsl(
role=local_role,
predict_dsl=predict_dsl,
setting_conf_prefix=file_utils.
get_federatedml_setting_conf_directory())
pipeline.inference_dsl = json_dumps(inference_dsl, byte=True)
if model_utils.compare_version(pipeline.fate_version, '1.5.0') == 'gt':
pipeline.parent_info = json_dumps(
{
'parent_model_id': model_id,
'parent_model_version': model_version
},
byte=True)
pipeline.parent = False
runtime_conf_on_party = json_loads(pipeline.runtime_conf_on_party)
runtime_conf_on_party['job_parameters'][
'model_version'] = child_model_version
pipeline.runtime_conf_on_party = json_dumps(runtime_conf_on_party,
byte=True)
# save model file
deploy_model.save_pipeline(pipeline)
shutil.copyfile(
os.path.join(deploy_model.model_path, "pipeline.pb"),
os.path.join(deploy_model.model_path, "variables", "data",
"pipeline", "pipeline", "Pipeline"))
model_info = model_utils.gather_model_info_data(deploy_model)
model_info['job_id'] = model_info['f_model_version']
model_info['size'] = deploy_model.calculate_model_file_size()
model_info['role'] = local_role
model_info['party_id'] = local_party_id
model_info['work_mode'] = adapter.get_job_work_mode()
model_info['parent'] = False if model_info.get(
'f_inference_dsl') else True
if model_utils.compare_version(model_info['f_fate_version'],
'1.5.0') == 'eq':
model_info['roles'] = model_info.get('f_train_runtime_conf',
{}).get('role', {})
model_info['initiator_role'] = model_info.get(
'f_train_runtime_conf', {}).get('initiator', {}).get('role')
model_info['initiator_party_id'] = model_info.get(
'f_train_runtime_conf', {}).get('initiator',
{}).get('party_id')
model_utils.save_model_info(model_info)
except Exception as e:
stat_logger.exception(e)
return 100, f"deploy model of role {local_role} {local_party_id} failed, details: {str(e)}"
else:
return 0, f"deploy model of role {local_role} {local_party_id} success"
def deploy(config_data):
model_id = config_data.get('model_id')
model_version = config_data.get('model_version')
local_role = config_data.get('local').get('role')
local_party_id = config_data.get('local').get('party_id')
child_model_version = config_data.get('child_model_version')
components_checkpoint = config_data.get('components_checkpoint', {})
warning_msg = ""
try:
party_model_id = gen_party_model_id(model_id=model_id,
role=local_role,
party_id=local_party_id)
model = PipelinedModel(model_id=party_model_id,
model_version=model_version)
model_data = model.collect_models(in_bytes=True)
if "pipeline.pipeline:Pipeline" not in model_data:
raise Exception("Can not found pipeline file in model.")
# check if the model could be executed the deploy process (parent/child)
if not check_before_deploy(model):
raise Exception('Child model could not be deployed.')
# copy proto content from parent model and generate a child model
deploy_model = PipelinedModel(model_id=party_model_id,
model_version=child_model_version)
shutil.copytree(src=model.model_path,
dst=deploy_model.model_path,
ignore=lambda src, names: {'checkpoint'}
if src == model.model_path else {})
pipeline_model = deploy_model.read_pipeline_model()
train_runtime_conf = json_loads(pipeline_model.train_runtime_conf)
runtime_conf_on_party = json_loads(
pipeline_model.runtime_conf_on_party)
dsl_version = train_runtime_conf.get("dsl_version", "1")
parser = get_dsl_parser_by_version(dsl_version)
train_dsl = json_loads(pipeline_model.train_dsl)
parent_predict_dsl = json_loads(pipeline_model.inference_dsl)
if config_data.get('dsl') or config_data.get('predict_dsl'):
inference_dsl = config_data.get('dsl') if config_data.get(
'dsl') else config_data.get('predict_dsl')
if not isinstance(inference_dsl, dict):
inference_dsl = json_loads(inference_dsl)
else:
if config_data.get('cpn_list', None):
cpn_list = config_data.pop('cpn_list')
else:
cpn_list = list(train_dsl.get('components', {}).keys())
if int(dsl_version) == 1:
# convert v1 dsl to v2 dsl
inference_dsl, warning_msg = parser.convert_dsl_v1_to_v2(
parent_predict_dsl)
else:
parser = get_dsl_parser_by_version(dsl_version)
inference_dsl = parser.deploy_component(cpn_list, train_dsl)
# convert v1 conf to v2 conf
if int(dsl_version) == 1:
components = parser.get_components_light_weight(inference_dsl)
from fate_flow.db.component_registry import ComponentRegistry
job_providers = parser.get_job_providers(
dsl=inference_dsl, provider_detail=ComponentRegistry.REGISTRY)
cpn_role_parameters = dict()
for cpn in components:
cpn_name = cpn.get_name()
role_params = parser.parse_component_role_parameters(
component=cpn_name,
dsl=inference_dsl,
runtime_conf=train_runtime_conf,
provider_detail=ComponentRegistry.REGISTRY,
provider_name=job_providers[cpn_name]["provider"]["name"],
provider_version=job_providers[cpn_name]["provider"]
["version"])
cpn_role_parameters[cpn_name] = role_params
train_runtime_conf = parser.convert_conf_v1_to_v2(
train_runtime_conf, cpn_role_parameters)
adapter = JobRuntimeConfigAdapter(train_runtime_conf)
train_runtime_conf = adapter.update_model_id_version(
model_version=deploy_model.model_version)
pipeline_model.model_version = child_model_version
pipeline_model.train_runtime_conf = json_dumps(train_runtime_conf,
byte=True)
# save inference dsl into child model file
parser = get_dsl_parser_by_version(2)
parser.verify_dsl(inference_dsl, "predict")
inference_dsl = JobSaver.fill_job_inference_dsl(
job_id=model_version,
role=local_role,
party_id=local_party_id,
dsl_parser=parser,
origin_inference_dsl=inference_dsl)
pipeline_model.inference_dsl = json_dumps(inference_dsl, byte=True)
if compare_version(pipeline_model.fate_version, '1.5.0') == 'gt':
pipeline_model.parent_info = json_dumps(
{
'parent_model_id': model_id,
'parent_model_version': model_version
},
byte=True)
pipeline_model.parent = False
runtime_conf_on_party['job_parameters'][
'model_version'] = child_model_version
pipeline_model.runtime_conf_on_party = json_dumps(
runtime_conf_on_party, byte=True)
# save model file
deploy_model.save_pipeline(pipeline_model)
shutil.copyfile(
os.path.join(deploy_model.model_path, "pipeline.pb"),
os.path.join(deploy_model.model_path, "variables", "data",
"pipeline", "pipeline", "Pipeline"))
model_info = gather_model_info_data(deploy_model)
model_info['job_id'] = model_info['f_model_version']
model_info['size'] = deploy_model.calculate_model_file_size()
model_info['role'] = local_role
model_info['party_id'] = local_party_id
model_info['parent'] = False if model_info.get(
'f_inference_dsl') else True
if compare_version(model_info['f_fate_version'], '1.5.0') == 'eq':
model_info['roles'] = model_info.get('f_train_runtime_conf',
{}).get('role', {})
model_info['initiator_role'] = model_info.get(
'f_train_runtime_conf', {}).get('initiator', {}).get('role')
model_info['initiator_party_id'] = model_info.get(
'f_train_runtime_conf', {}).get('initiator',
{}).get('party_id')
save_model_info(model_info)
for component_name, component in train_dsl.get('components',
{}).items():
step_index = components_checkpoint.get(component_name,
{}).get('step_index')
step_name = components_checkpoint.get(component_name,
{}).get('step_name')
if step_index is not None:
step_index = int(step_index)
step_name = None
elif step_name is None:
continue
checkpoint_manager = CheckpointManager(
role=local_role,
party_id=local_party_id,
model_id=model_id,
model_version=model_version,
component_name=component_name,
mkdir=False,
)
checkpoint_manager.load_checkpoints_from_disk()
if checkpoint_manager.latest_checkpoint is not None:
checkpoint_manager.deploy(
child_model_version,
component['output']['model'][0] if component.get(
'output', {}).get('model') else 'default',
step_index,
step_name,
)
except Exception as e:
stat_logger.exception(e)
return 100, f"deploy model of role {local_role} {local_party_id} failed, details: {str(e)}"
else:
msg = f"deploy model of role {local_role} {local_party_id} success"
if warning_msg:
msg = msg + f", warning: {warning_msg}"
return 0, msg