def __init__(self, config):
"""R
"""
self._cost = None
self._metrics = {}
self._data_var = []
self._data_loader = None
self._fetch_interval = 20
self._namespace = "train.model"
self._platform = envs.get_platform()
def processor_register(self):
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
if envs.get_platform() == "LINUX":
self.regist_context_processor('train_pass', self.dataset_train)
else:
self.regist_context_processor('train_pass', self.dataloader_train)
self.regist_context_processor('infer_pass', self.infer)
self.regist_context_processor('terminal_pass', self.terminal)
def single_engine(args):
print("use single engine to run model: {}".format(args.model))
single_envs = {}
single_envs["train.trainer.trainer"] = "SingleTrainer"
single_envs["train.trainer.threads"] = "2"
single_envs["train.trainer.engine"] = "single"
single_envs["train.trainer.device"] = args.device
single_envs["train.trainer.platform"] = envs.get_platform()
set_runtime_envs(single_envs, args.model)
trainer = TrainerFactory.create(args.model)
return trainer
def cluster_mpi_engine(args):
print("launch cluster engine with cluster to run model: {}".format(
args.model))
cluster_envs = {}
cluster_envs["train.trainer.trainer"] = "CtrCodingTrainer"
cluster_envs["train.trainer.device"] = args.device
cluster_envs["train.trainer.platform"] = envs.get_platform()
set_runtime_envs(cluster_envs, args.model)
trainer = TrainerFactory.create(args.model)
return trainer
def processor_register(self):
role = PaddleCloudRoleMaker()
fleet.init(role)
if fleet.is_server():
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
self.regist_context_processor('server_pass', self.server)
else:
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
if envs.get_platform() == "LINUX":
self.regist_context_processor('train_pass', self.dataset_train)
else:
self.regist_context_processor('train_pass', self.dataloader_train)
self.regist_context_processor('terminal_pass', self.terminal)
def local_mpi_engine(args):
print("launch cluster engine with cluster to run model: {}".format(
args.model))
from fleetrec.core.engine.local_mpi_engine import LocalMPIEngine
print("use 1X1 MPI ClusterTraining at localhost to run model: {}".format(
args.model))
mpi = util.run_which("mpirun")
if not mpi:
raise RuntimeError("can not find mpirun, please check environment")
cluster_envs = {}
cluster_envs["mpirun"] = mpi
cluster_envs["train.trainer.trainer"] = "CtrCodingTrainer"
cluster_envs["log_dir"] = "logs"
cluster_envs["train.trainer.engine"] = "local_cluster"
cluster_envs["train.trainer.device"] = args.device
cluster_envs["train.trainer.platform"] = envs.get_platform()
set_runtime_envs(cluster_envs, args.model)
launch = LocalMPIEngine(cluster_envs, args.model)
return launch
def local_cluster_engine(args):
print("launch cluster engine with cluster to run model: {}".format(
args.model))
from fleetrec.core.engine.local_cluster_engine import LocalClusterEngine
cluster_envs = {}
cluster_envs["server_num"] = 1
cluster_envs["worker_num"] = 1
cluster_envs["start_port"] = 36001
cluster_envs["log_dir"] = "logs"
cluster_envs["train.trainer.trainer"] = "ClusterTrainer"
cluster_envs["train.trainer.strategy"] = "async"
cluster_envs["train.trainer.threads"] = "2"
cluster_envs["train.trainer.engine"] = "local_cluster"
cluster_envs["train.trainer.device"] = args.device
cluster_envs["train.trainer.platform"] = envs.get_platform()
cluster_envs["CPU_NUM"] = "2"
set_runtime_envs(cluster_envs, args.model)
launch = LocalClusterEngine(cluster_envs, args.model)
return launch
