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e07edbcb
编写于
4月 08, 2019
作者:
Z
zhengya01
提交者:
hutuxian
4月 08, 2019
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add ce for gnn (#2003)
上级
5a81d8c2
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
114 addition
and
0 deletion
+114
-0
PaddleRec/gnn/.run_ce.sh
PaddleRec/gnn/.run_ce.sh
+13
-0
PaddleRec/gnn/__init__.py
PaddleRec/gnn/__init__.py
+0
-0
PaddleRec/gnn/_ce.py
PaddleRec/gnn/_ce.py
+60
-0
PaddleRec/gnn/train.py
PaddleRec/gnn/train.py
+41
-0
未找到文件。
PaddleRec/gnn/.run_ce.sh
0 → 100755
浏览文件 @
e07edbcb
#!/bin/bash
export
MKL_NUM_THREADS
=
1
export
OMP_NUM_THREADS
=
1
cudaid
=
${
gnn
:
=0
}
# use 0-th card as default
export
CUDA_VISIBLE_DEVICES
=
$cudaid
FLAGS_benchmark
=
true
python
-u
train.py
--use_cuda
1
--epoch_num
5
--enable_ce
| python _ce.py
PaddleRec/gnn/__init__.py
0 → 100644
浏览文件 @
e07edbcb
PaddleRec/gnn/_ce.py
0 → 100644
浏览文件 @
e07edbcb
# this file is only used for continuous evaluation test!
import
os
import
sys
sys
.
path
.
append
(
os
.
environ
[
'ceroot'
])
from
kpi
import
CostKpi
from
kpi
import
DurationKpi
from
kpi
import
AccKpi
each_pass_duration_card1_kpi
=
DurationKpi
(
'each_pass_duration_card1'
,
0.08
,
0
,
actived
=
True
)
train_loss_card1_kpi
=
CostKpi
(
'train_loss_card1'
,
0.08
,
0
)
train_acc_card1_kpi
=
AccKpi
(
'train_acc_card1'
,
0.08
,
0
)
tracking_kpis
=
[
each_pass_duration_card1_kpi
,
train_loss_card1_kpi
,
train_acc_card1_kpi
,
]
def
parse_log
(
log
):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost
\t
1.0
test_cost
\t
1.0
train_cost
\t
1.0
train_cost
\t
1.0
train_acc
\t
1.2
"
'''
for
line
in
log
.
split
(
'
\n
'
):
fs
=
line
.
strip
().
split
(
'
\t
'
)
print
(
fs
)
if
len
(
fs
)
==
3
and
fs
[
0
]
==
'kpis'
:
kpi_name
=
fs
[
1
]
kpi_value
=
float
(
fs
[
2
])
yield
kpi_name
,
kpi_value
def
log_to_ce
(
log
):
kpi_tracker
=
{}
for
kpi
in
tracking_kpis
:
kpi_tracker
[
kpi
.
name
]
=
kpi
for
(
kpi_name
,
kpi_value
)
in
parse_log
(
log
):
print
(
kpi_name
,
kpi_value
)
kpi_tracker
[
kpi_name
].
add_record
(
kpi_value
)
kpi_tracker
[
kpi_name
].
persist
()
if
__name__
==
'__main__'
:
log
=
sys
.
stdin
.
read
()
log_to_ce
(
log
)
PaddleRec/gnn/train.py
浏览文件 @
e07edbcb
...
...
@@ -16,6 +16,7 @@ import numpy as np
import
os
from
functools
import
partial
import
logging
import
time
import
paddle
import
paddle.fluid
as
fluid
import
argparse
...
...
@@ -55,11 +56,19 @@ def parse_args():
'--use_cuda'
,
type
=
int
,
default
=
0
,
help
=
'whether to use gpu'
)
parser
.
add_argument
(
'--use_parallel'
,
type
=
int
,
default
=
1
,
help
=
'whether to use parallel executor'
)
parser
.
add_argument
(
'--enable_ce'
,
action
=
'store_true'
,
help
=
'If set, run the task with continuous evaluation logs.'
)
return
parser
.
parse_args
()
def
train
():
args
=
parse_args
()
if
args
.
enable_ce
:
SEED
=
102
fluid
.
default_main_program
().
random_seed
=
SEED
fluid
.
default_startup_program
().
random_seed
=
SEED
batch_size
=
args
.
batch_size
items_num
=
reader
.
read_config
(
args
.
config_path
)
loss
,
acc
=
network
.
network
(
batch_size
,
items_num
,
args
.
hidden_size
,
...
...
@@ -102,6 +111,9 @@ def train():
logger
.
info
(
"begin train"
)
total_time
=
[]
ce_info
=
[]
start_time
=
time
.
time
()
loss_sum
=
0.0
acc_sum
=
0.0
global_step
=
0
...
...
@@ -116,16 +128,45 @@ def train():
epoch_sum
.
append
(
res
[
0
])
global_step
+=
1
if
global_step
%
PRINT_STEP
==
0
:
ce_info
.
append
([
loss_sum
/
PRINT_STEP
,
acc_sum
/
PRINT_STEP
])
total_time
.
append
(
time
.
time
()
-
start_time
)
logger
.
info
(
"global_step: %d, loss: %.4lf, train_acc: %.4lf"
%
(
global_step
,
loss_sum
/
PRINT_STEP
,
acc_sum
/
PRINT_STEP
))
loss_sum
=
0.0
acc_sum
=
0.0
start_time
=
time
.
time
()
logger
.
info
(
"epoch loss: %.4lf"
%
(
np
.
mean
(
epoch_sum
)))
save_dir
=
args
.
model_path
+
"/epoch_"
+
str
(
i
)
fetch_vars
=
[
loss
,
acc
]
fluid
.
io
.
save_inference_model
(
save_dir
,
feed_list
,
fetch_vars
,
exe
)
logger
.
info
(
"model saved in "
+
save_dir
)
# only for ce
if
args
.
enable_ce
:
gpu_num
=
get_cards
(
args
)
ce_loss
=
0
ce_acc
=
0
ce_time
=
0
try
:
ce_loss
=
ce_info
[
-
1
][
0
]
ce_acc
=
ce_info
[
-
1
][
1
]
ce_time
=
total_time
[
-
1
]
except
:
print
(
"ce info error"
)
print
(
"kpis
\t
each_pass_duration_card%s
\t
%s"
%
(
gpu_num
,
ce_time
))
print
(
"kpis
\t
train_loss_card%s
\t
%f"
%
(
gpu_num
,
ce_loss
))
print
(
"kpis
\t
train_acc_card%s
\t
%f"
%
(
gpu_num
,
ce_acc
))
def
get_cards
(
args
):
num
=
0
cards
=
os
.
environ
.
get
(
'CUDA_VISIBLE_DEVICES'
)
num
=
len
(
cards
.
split
(
","
))
return
num
if
__name__
==
"__main__"
:
train
()
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