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7905e367
编写于
1月 18, 2018
作者:
A
Abhinav Arora
提交者:
GitHub
1月 18, 2018
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Implement Book chapter 02 in distributed framework (#7629)
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python/paddle/v2/fluid/tests/book_distribute/notest_recognize_digits_mlp_dist.py
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python/paddle/v2/fluid/tests/book_distribute/notest_recognize_digits_mlp_dist.py
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7905e367
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from
__future__
import
print_function
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
import
os
BATCH_SIZE
=
128
PASS_NUM
=
100
images
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
784
],
dtype
=
'float32'
)
# TODO(aroraabhinav) Add regularization and error clipping after
# Issue 7432(https://github.com/PaddlePaddle/Paddle/issues/7432) is resolved.
hidden1
=
fluid
.
layers
.
fc
(
input
=
images
,
size
=
128
,
act
=
'relu'
)
hidden2
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
64
,
act
=
'relu'
)
predict
=
fluid
.
layers
.
fc
(
input
=
hidden2
,
size
=
10
,
act
=
'softmax'
)
label
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'int64'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
0.001
,
momentum
=
0.9
)
optimize_ops
,
params_grads
=
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
8192
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
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
)
elif
training_role
==
"TRAINER"
:
trainer_prog
=
t
.
get_trainer_program
()
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
images
,
label
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
batch_id
=
0
for
data
in
train_reader
():
loss
,
acc
=
exe
.
run
(
trainer_prog
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
if
batch_id
%
100
==
0
:
print
(
"batch_id %d, loss: %f, acc: %f"
%
(
batch_id
,
loss
,
pass_acc
))
batch_id
+=
1
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"pass_id="
+
str
(
pass_id
)
+
" pass_acc="
+
str
(
pass_acc
))
else
:
print
(
"environment var TRAINER_ROLE should be TRAINER os PSERVER"
)
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