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体验新版 GitCode,发现更多精彩内容 >>
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b1f24660
编写于
1月 11, 2018
作者:
X
Xi Chen
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差异文件
add dist demo fit_a_line
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5d9dcfc1
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python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
...le/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
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python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
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浏览文件 @
b1f24660
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
import
os
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
13
],
dtype
=
'float32'
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1
,
act
=
None
)
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
optimize_ops
,
params_grads
=
sgd_optimizer
.
minimize
(
avg_cost
)
BATCH_SIZE
=
20
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
uci_housing
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
x
,
y
])
exe
=
fluid
.
Executor
(
place
)
t
=
fluid
.
DistributeTranspiler
()
# all parameter server endpoints list for spliting parameters
pserver_endpoints
=
os
.
getenv
(
"PSERVERS"
)
# server endpoint for current node
current_endpoint
=
os
.
getenv
(
"SERVER_ENDPOINT"
)
# run as trainer or parameter server
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
# get the training role: trainer/pserver
t
.
transpile
(
optimize_ops
,
params_grads
,
pservers
=
pserver_endpoints
,
trainers
=
2
)
if
training_role
==
"PSERVER"
:
if
not
current_endpoint
:
print
(
"need env SERVER_ENDPOINT"
)
exit
(
1
)
pserver_prog
=
t
.
get_pserver_program
(
current_endpoint
,
optimize_ops
)
exe
.
run
(
fluid
.
default_startup_program
())
exe
.
run
(
pserver_prog
)
else
:
trainer_prog
=
t
.
get_trainer_program
()
exe
.
run
(
fluid
.
default_startup_program
())
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
fluid
.
io
.
save_persistables
(
exe
,
"./fit_a_line.model/"
)
fluid
.
io
.
load_persistables
(
exe
,
"./fit_a_line.model/"
)
for
data
in
train_reader
():
avg_loss_value
,
=
exe
.
run
(
trainer_prog
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
])
if
avg_loss_value
[
0
]
<
10.0
:
exit
(
0
)
# if avg cost less than 10.0, we think our code is good.
exit
(
1
)
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