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体验新版 GitCode,发现更多精彩内容 >>
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034c2657
编写于
3月 07, 2019
作者:
Z
zhang wenhui
提交者:
GitHub
3月 07, 2019
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差异文件
Merge pull request #1840 from zhengya01/ce_gru4rec
Ce gru4rec
上级
156b8996
2a4ecb48
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
136 addition
and
0 deletion
+136
-0
fluid/PaddleRec/gru4rec/.run_ce.sh
fluid/PaddleRec/gru4rec/.run_ce.sh
+22
-0
fluid/PaddleRec/gru4rec/__init__.py
fluid/PaddleRec/gru4rec/__init__.py
+0
-0
fluid/PaddleRec/gru4rec/_ce.py
fluid/PaddleRec/gru4rec/_ce.py
+66
-0
fluid/PaddleRec/gru4rec/train.py
fluid/PaddleRec/gru4rec/train.py
+48
-0
未找到文件。
fluid/PaddleRec/gru4rec/.run_ce.sh
0 → 100755
浏览文件 @
034c2657
#!/bin/bash
export
MKL_NUM_THREADS
=
1
export
OMP_NUM_THREADS
=
1
export
CPU_NUM
=
1
export
NUM_THREADS
=
1
FLAGS_benchmark
=
true
python train.py
--train_dir
train_big_data
--vocab_path
vocab_big.txt
--use_cuda
0
--batch_size
500
--model_dir
model_output
--pass_num
2
--enable_ce
--step_num
10 | python _ce.py
cudaid
=
${
gru4rec
:
=0
}
# use 0-th card as default
export
CUDA_VISIBLE_DEVICES
=
$cudaid
FLAGS_benchmark
=
true
python train.py
--train_dir
train_big_data
--vocab_path
vocab_big.txt
--use_cuda
1
--batch_size
500
--model_dir
model_output
--pass_num
2
--enable_ce
--step_num
1000 | python _ce.py
cudaid
=
${
gru4rec_4
:
=0,1,2,3
}
# use 0-th card as default
export
CUDA_VISIBLE_DEVICES
=
$cudaid
FLAGS_benchmark
=
true
python train.py
--train_dir
train_big_data
--vocab_path
vocab_big.txt
--use_cuda
1
--parallel
1
--num_devices
2
--batch_size
500
--model_dir
model_output
--pass_num
2
--enable_ce
--step_num
1000 | python _ce.py
fluid/PaddleRec/gru4rec/__init__.py
0 → 100644
浏览文件 @
034c2657
fluid/PaddleRec/gru4rec/_ce.py
0 → 100644
浏览文件 @
034c2657
# 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_cpu1_thread1_kpi
=
DurationKpi
(
'each_pass_duration_cpu1_thread1'
,
0.08
,
0
,
actived
=
True
)
train_ppl_cpu1_thread1_kpi
=
CostKpi
(
'train_ppl_cpu1_thread1'
,
0.08
,
0
)
each_pass_duration_gpu1_kpi
=
DurationKpi
(
'each_pass_duration_gpu1'
,
0.08
,
0
,
actived
=
True
)
train_ppl_gpu1_kpi
=
CostKpi
(
'train_ppl_gpu1'
,
0.08
,
0
)
each_pass_duration_gpu4_kpi
=
DurationKpi
(
'each_pass_duration_gpu4'
,
0.08
,
0
,
actived
=
True
)
train_ppl_gpu4_kpi
=
CostKpi
(
'train_ppl_gpu4'
,
0.08
,
0
)
tracking_kpis
=
[
each_pass_duration_cpu1_thread1_kpi
,
train_ppl_cpu1_thread1_kpi
,
each_pass_duration_gpu1_kpi
,
train_ppl_gpu1_kpi
,
each_pass_duration_gpu4_kpi
,
train_ppl_gpu4_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
)
fluid/PaddleRec/gru4rec/train.py
浏览文件 @
034c2657
...
...
@@ -40,6 +40,12 @@ def parse_args():
'--base_lr'
,
type
=
float
,
default
=
0.01
,
help
=
'learning rate'
)
parser
.
add_argument
(
'--num_devices'
,
type
=
int
,
default
=
1
,
help
=
'Number of GPU devices'
)
parser
.
add_argument
(
'--step_num'
,
type
=
int
,
default
=
1000
,
help
=
'Number of steps'
)
parser
.
add_argument
(
'--enable_ce'
,
action
=
'store_true'
,
help
=
'If set, run the task with continuous evaluation logs.'
)
args
=
parser
.
parse_args
()
return
args
...
...
@@ -51,6 +57,9 @@ def get_cards(args):
def
train
():
""" do training """
args
=
parse_args
()
if
args
.
enable_ce
:
fluid
.
default_startup_program
().
random_seed
=
SEED
fluid
.
default_main_program
().
random_seed
=
SEED
hid_size
=
args
.
hid_size
train_dir
=
args
.
train_dir
vocab_path
=
args
.
vocab_path
...
...
@@ -84,6 +93,7 @@ def train():
model_dir
=
args
.
model_dir
fetch_list
=
[
avg_cost
.
name
]
ce_info
=
[]
total_time
=
0.0
for
pass_idx
in
six
.
moves
.
xrange
(
pass_num
):
epoch_idx
=
pass_idx
+
1
...
...
@@ -105,8 +115,11 @@ def train():
fetch_list
=
fetch_list
)
avg_ppl
=
np
.
exp
(
ret_avg_cost
[
0
])
newest_ppl
=
np
.
mean
(
avg_ppl
)
ce_info
.
append
(
newest_ppl
)
if
i
%
args
.
print_batch
==
0
:
print
(
"step:%d ppl:%.3f"
%
(
i
,
newest_ppl
))
if
args
.
enable_ce
and
i
>
args
.
step_num
:
break
t1
=
time
.
time
()
total_time
+=
t1
-
t0
...
...
@@ -117,8 +130,43 @@ def train():
fetch_vars
=
[
avg_cost
,
acc
]
fluid
.
io
.
save_inference_model
(
save_dir
,
feed_var_names
,
fetch_vars
,
exe
)
print
(
"model saved in %s"
%
save_dir
)
# only for ce
if
args
.
enable_ce
:
ce_ppl
=
0
try
:
ce_ppl
=
ce_info
[
-
2
]
except
:
print
(
"ce info error"
)
epoch_idx
=
args
.
pass_num
device
=
get_device
(
args
)
if
args
.
use_cuda
:
gpu_num
=
device
[
1
]
print
(
"kpis
\t
each_pass_duration_gpu%s
\t
%s"
%
(
gpu_num
,
total_time
/
epoch_idx
))
print
(
"kpis
\t
train_ppl_gpu%s
\t
%s"
%
(
gpu_num
,
ce_ppl
))
else
:
cpu_num
=
device
[
1
]
threads_num
=
device
[
2
]
print
(
"kpis
\t
each_pass_duration_cpu%s_thread%s
\t
%s"
%
(
cpu_num
,
threads_num
,
total_time
/
epoch_idx
))
print
(
"kpis
\t
train_ppl_cpu%s_thread%s
\t
%s"
%
(
cpu_num
,
threads_num
,
ce_ppl
))
print
(
"finish training"
)
def
get_device
(
args
):
if
args
.
use_cuda
:
gpus
=
os
.
environ
.
get
(
"CUDA_VISIBLE_DEVICES"
,
1
)
gpu_num
=
len
(
gpus
.
split
(
','
))
return
"gpu"
,
gpu_num
else
:
threads_num
=
os
.
environ
.
get
(
'NUM_THREADS'
,
1
)
cpu_num
=
os
.
environ
.
get
(
'CPU_NUM'
,
1
)
return
"cpu"
,
int
(
cpu_num
),
int
(
threads_num
)
if
__name__
==
"__main__"
:
train
()
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