def set_runtime_envs(cluster_envs, engine_yaml):
if cluster_envs is None:
cluster_envs = {}
envs.set_runtime_environs(cluster_envs)
need_print = {}
for k, v in os.environ.items():
if k.startswith("train.trainer."):
need_print[k] = v
print(envs.pretty_print_envs(need_print, ("Runtime Envs", "Value")))
def master():
role = "MASTER"
from paddlerec.core.engine.cluster.cluster import ClusterEngine
_envs = envs.load_yaml(args.backend)
flattens = envs.flatten_environs(_envs, "_")
flattens["engine_role"] = role
flattens["engine_run_config"] = args.model
flattens["engine_temp_path"] = tempfile.mkdtemp()
envs.set_runtime_environs(flattens)
print(envs.pretty_print_envs(flattens, ("Submit Envs", "Value")))
launch = ClusterEngine(None, args.model)
return launch
def _build_trainer(yaml_path):
print(envs.pretty_print_envs(envs.get_global_envs()))
train_mode = envs.get_trainer()
trainer_abs = trainers.get(train_mode, None)
if trainer_abs is None:
if not os.path.isfile(train_mode):
raise IOError("trainer {} can not be recognized".format(
train_mode))
trainer_abs = train_mode
train_mode = "UserDefineTrainer"
trainer_class = envs.lazy_instance_by_fliename(trainer_abs, train_mode)
trainer = trainer_class(yaml_path)
return trainer
def set_runtime_envs(cluster_envs, engine_yaml):
if cluster_envs is None:
cluster_envs = {}
engine_extras = get_inters_from_yaml(engine_yaml, "train.trainer.")
if "train.trainer.threads" in engine_extras and "CPU_NUM" in cluster_envs:
cluster_envs["CPU_NUM"] = engine_extras["train.trainer.threads"]
envs.set_runtime_environs(cluster_envs)
envs.set_runtime_environs(engine_extras)
need_print = {}
for k, v in os.environ.items():
if k.startswith("train.trainer."):
need_print[k] = v
print(envs.pretty_print_envs(need_print, ("Runtime Envs", "Value")))
def master():
role = "MASTER"
from paddlerec.core.engine.cluster.cluster import ClusterEngine
with open(args.backend, 'r') as rb:
_envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
flattens = envs.flatten_environs(_envs, "_")
flattens["engine_role"] = role
flattens["engine_run_config"] = args.model
flattens["engine_temp_path"] = tempfile.mkdtemp()
update_workspace(flattens)
envs.set_runtime_environs(flattens)
print(
envs.pretty_print_envs(flattens, ("Submit Runtime Envs", "Value")))
launch = ClusterEngine(None, args.model)
return launch
def env_set(self):
envs.set_runtime_environs(self.cluster_env)
flattens = envs.flatten_environs(self.cluster_env)
print(envs.pretty_print_envs(flattens, ("Cluster Envs", "Value")))