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29d464d3
P
PaddleRec
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体验新版 GitCode,发现更多精彩内容 >>
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29d464d3
编写于
4月 20, 2020
作者:
T
tangwei
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差异文件
update code
上级
3a3fe12f
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
73 addition
and
47 deletion
+73
-47
fleetrec/core/factory.py
fleetrec/core/factory.py
+28
-20
fleetrec/core/utils/envs.py
fleetrec/core/utils/envs.py
+2
-5
fleetrec/models/ctr_dnn/model.py
fleetrec/models/ctr_dnn/model.py
+3
-3
fleetrec/run.py
fleetrec/run.py
+40
-19
未找到文件。
fleetrec/core/factory.py
浏览文件 @
29d464d3
...
...
@@ -19,6 +19,19 @@ import yaml
from
fleetrec.core.utils
import
envs
trainer_abs
=
os
.
path
.
join
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
)),
"trainers"
)
trainers
=
{}
def
trainer_registry
():
trainers
[
"SingleTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"single_trainer.py"
)
trainers
[
"ClusterTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"cluster_trainer.py"
)
trainers
[
"CtrCodingTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"ctr_coding_trainer.py"
)
trainers
[
"CtrModulTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"ctr_modul_trainer.py"
)
trainer_registry
()
class
TrainerFactory
(
object
):
def
__init__
(
self
):
...
...
@@ -28,26 +41,21 @@ class TrainerFactory(object):
def
_build_trainer
(
yaml_path
):
print
(
envs
.
pretty_print_envs
(
envs
.
get_global_envs
()))
train_mode
=
envs
.
get_training_mode
()
if
train_mode
==
"SingleTraining"
:
from
fleetrec.core.trainers.single_trainer
import
SingleTrainer
trainer
=
SingleTrainer
(
yaml_path
)
elif
train_mode
==
"ClusterTraining"
:
from
fleetrec.core.trainers.cluster_trainer
import
ClusterTrainer
trainer
=
ClusterTrainer
(
yaml_path
)
elif
train_mode
==
"CtrTraining"
:
from
fleetrec.core.trainers.ctr_coding_trainer
import
CtrPaddleTrainer
trainer
=
CtrPaddleTrainer
(
yaml_path
)
elif
train_mode
==
"UserDefineTraining"
:
train_location
=
envs
.
get_global_env
(
"train.location"
)
train_dirname
=
os
.
path
.
dirname
(
train_location
)
base_name
=
os
.
path
.
splitext
(
os
.
path
.
basename
(
train_location
))[
0
]
sys
.
path
.
append
(
train_dirname
)
trainer_class
=
envs
.
lazy_instance
(
base_name
,
"UserDefineTrainer"
)
trainer
=
trainer_class
(
yaml_path
)
else
:
raise
ValueError
(
"trainer only support SingleTraining/ClusterTraining"
)
train_mode
=
envs
.
get_trainer
()
trainer_abs
=
trainers
.
get
(
train_mode
,
None
)
if
trainer_abs
is
None
:
if
not
os
.
path
.
exists
(
train_mode
)
or
os
.
path
.
isfile
(
train_mode
):
raise
ValueError
(
"trainer {} can not be recognized"
)
trainer_abs
=
train_mode
train_mode
=
"UserDefineTrainer"
train_location
=
envs
.
get_global_env
(
"train.location"
)
train_dirname
=
os
.
path
.
dirname
(
trainer_abs
)
base_name
=
os
.
path
.
splitext
(
os
.
path
.
basename
(
train_location
))[
0
]
sys
.
path
.
append
(
train_dirname
)
trainer_class
=
envs
.
lazy_instance
(
base_name
,
train_mode
)
trainer
=
trainer_class
(
yaml_path
)
return
trainer
@
staticmethod
...
...
fleetrec/core/utils/envs.py
浏览文件 @
29d464d3
...
...
@@ -29,11 +29,8 @@ def get_runtime_envion(key):
return
os
.
getenv
(
key
,
None
)
def
get_training_mode
():
train_mode
=
get_global_env
(
"train.trainer"
)
if
train_mode
is
None
:
train_mode
=
get_runtime_envion
(
"train.trainer"
)
def
get_trainer
():
train_mode
=
get_runtime_envion
(
"trainer.trainer"
)
return
train_mode
...
...
fleetrec/models/ctr_dnn/model.py
浏览文件 @
29d464d3
...
...
@@ -60,12 +60,12 @@ class Model(ModelBase):
self
.
_data_var
.
append
(
self
.
label_input
)
def
net
(
self
):
train
_mode
=
envs
.
get_training_mode
()
train
er
=
envs
.
get_trainer
()
is_distributed
=
True
if
train
_mode
==
"CtrTraining
"
else
False
is_distributed
=
True
if
train
er
==
"CtrTrainer
"
else
False
sparse_feature_number
=
envs
.
get_global_env
(
"hyper_parameters.sparse_feature_number"
,
None
,
self
.
namespace
)
sparse_feature_dim
=
envs
.
get_global_env
(
"hyper_parameters.sparse_feature_dim"
,
None
,
self
.
namespace
)
sparse_feature_dim
=
9
if
train
_mode
==
"CtrTraining
"
else
sparse_feature_dim
sparse_feature_dim
=
9
if
train
er
==
"CtrTrainer
"
else
sparse_feature_dim
def
embedding_layer
(
input
):
emb
=
fluid
.
layers
.
embedding
(
...
...
fleetrec/run.py
浏览文件 @
29d464d3
import
argparse
import
os
import
sys
import
yaml
from
paddle.fluid.incubate.fleet.parameter_server
import
version
...
...
@@ -10,6 +9,19 @@ from fleetrec.core.utils import envs
from
fleetrec.core.utils
import
util
engines
=
{
"TRAINSPILER"
:
{},
"PSLIB"
:
{}}
clusters
=
[
"SINGLE"
,
"LOCAL_CLUSTER"
,
"CLUSTER"
]
def
set_runtime_envs
(
cluster_envs
,
engine_yaml
):
if
engine_yaml
is
not
None
:
with
open
(
engine_yaml
,
'r'
)
as
rb
:
_envs
=
yaml
.
load
(
rb
.
read
(),
Loader
=
yaml
.
FullLoader
)
if
cluster_envs
is
None
:
cluster_envs
=
{}
cluster_envs
.
update
(
_envs
)
envs
.
set_runtime_envions
(
cluster_envs
)
print
(
envs
.
pretty_print_envs
(
cluster_envs
,
(
"Runtime Envs"
,
"Value"
)))
def
engine_registry
():
...
...
@@ -34,35 +46,38 @@ def get_engine(engine):
def
single_engine
(
args
):
print
(
"use
SingleTraining
to run model: {}"
.
format
(
args
.
model
))
single_envs
=
{
"train.trainer"
:
"SingleTraining"
}
print
(
"use
single engine
to run model: {}"
.
format
(
args
.
model
))
single_envs
=
{
"train
er
.trainer"
:
"SingleTraining"
}
print
(
envs
.
pretty_print_envs
(
single_envs
,
(
"Single Envs"
,
"Value"
)))
envs
.
set_runtime_envions
(
single_envs
)
set_runtime_envs
(
single_envs
,
args
.
engine_extras
)
trainer
=
TrainerFactory
.
create
(
args
.
model
)
return
trainer
def
cluster_engine
(
args
):
print
(
"launch ClusterTraining with cluster to run model: {}"
.
format
(
args
.
model
))
print
(
"launch cluster engine with cluster to run model: {}"
.
format
(
args
.
model
))
cluster_envs
=
{
"trainer.trainer"
:
"ClusterTraining"
}
set_runtime_envs
(
cluster_envs
,
args
.
engine_extras
)
cluster_envs
=
{
"train.trainer"
:
"ClusterTraining"
}
envs
.
set_runtime_envions
(
cluster_envs
)
trainer
=
TrainerFactory
.
create
(
args
.
model
)
return
trainer
def
cluster_mpi_engine
(
args
):
print
(
"launch ClusterTraining with cluster to run model: {}"
.
format
(
args
.
model
))
print
(
"launch cluster engine with cluster to run model: {}"
.
format
(
args
.
model
))
cluster_envs
=
{
"trainer.trainer"
:
"CtrTraining"
}
set_runtime_envs
(
cluster_envs
,
args
.
engine_extras
)
cluster_envs
=
{
"train.trainer"
:
"CtrTraining"
}
envs
.
set_runtime_envions
(
cluster_envs
)
trainer
=
TrainerFactory
.
create
(
args
.
model
)
return
trainer
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
=
{}
...
...
@@ -70,17 +85,17 @@ def local_cluster_engine(args):
cluster_envs
[
"worker_num"
]
=
1
cluster_envs
[
"start_port"
]
=
36001
cluster_envs
[
"log_dir"
]
=
"logs"
cluster_envs
[
"train.trainer"
]
=
"ClusterTraining"
cluster_envs
[
"train.strategy.mode"
]
=
"async"
cluster_envs
[
"train
er
.trainer"
]
=
"ClusterTraining"
cluster_envs
[
"train
er
.strategy.mode"
]
=
"async"
print
(
envs
.
pretty_print_envs
(
cluster_envs
,
(
"Local Cluster Envs"
,
"Value"
)))
envs
.
set_runtime_envions
(
cluster_envs
)
set_runtime_envs
(
cluster_envs
,
args
.
engine_extras
)
launch
=
LocalClusterEngine
(
cluster_envs
,
args
.
model
)
return
launch
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
))
...
...
@@ -89,10 +104,8 @@ def local_mpi_engine(args):
if
not
mpi
:
raise
RuntimeError
(
"can not find mpirun, please check environment"
)
cluster_envs
=
{
"mpirun"
:
mpi
,
"train.trainer"
:
"CtrTraining"
,
"log_dir"
:
"logs"
}
print
(
envs
.
pretty_print_envs
(
cluster_envs
,
(
"Local MPI Cluster Envs"
,
"Value"
)))
envs
.
set_runtime_envions
(
cluster_envs
)
cluster_envs
=
{
"mpirun"
:
mpi
,
"trainer.trainer"
:
"CtrTraining"
,
"log_dir"
:
"logs"
}
set_runtime_envs
(
cluster_envs
,
args
.
engine_extras
)
launch
=
LocalMPIEngine
(
cluster_envs
,
args
.
model
)
return
launch
...
...
@@ -118,13 +131,21 @@ if __name__ == "__main__":
parser
=
argparse
.
ArgumentParser
(
description
=
'fleet-rec run'
)
parser
.
add_argument
(
"-m"
,
"--model"
,
type
=
str
)
parser
.
add_argument
(
"-e"
,
"--engine"
,
type
=
str
)
parser
.
add_argument
(
"-ex"
,
"--engine_extras"
,
type
=
str
)
parser
.
add_argument
(
"-ex"
,
"--engine_extras"
,
default
=
None
,
type
=
str
)
args
=
parser
.
parse_args
()
if
not
os
.
path
.
exists
(
args
.
model
)
or
not
os
.
path
.
isfile
(
args
.
model
):
raise
ValueError
(
"argument model: {} error, must specify an existed YAML file"
.
format
(
args
.
model
))
if
args
.
engine
.
upper
()
not
in
clusters
:
raise
ValueError
(
"argument engine: {} error, must in {}"
.
format
(
args
.
engine
,
clusters
))
if
args
.
engine_extras
is
not
None
:
if
not
os
.
path
.
exists
(
args
.
engine_extras
)
or
not
os
.
path
.
isfile
(
args
.
engine_extras
):
raise
ValueError
(
"argument engine_extras: {} error, must specify an existed YAML file"
.
format
(
args
.
engine_extras
))
which_engine
=
get_engine
(
args
.
engine
)
engine
=
which_engine
(
args
)
engine
.
run
()
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