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提交 40d3c4a7 编写于 作者: W wangjiawei04
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add ctr+k8s+volcano yamls and save_program.py

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doc/resource/ctr.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: batch.volcano.sh/v1alpha1
kind: Job
metadata:
name: edl-demo
spec:
minAvailable: 6
schedulerName: volcano
policies:
- event: PodEvicted
action: RestartJob
- event: PodFailed
action: RestartJob
tasks:
- replicas: 3
name: pserver
template:
metadata:
labels:
paddle-job-pserver: fluid-ctr
spec:
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/
type: ""
name: seqdata
containers:
- image: wangjiawei1993/edldemo:v19
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: pserver
volumeMounts:
- mountPath: /data
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 1Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
value: ""
- name: TRAINER_PACKAGE
value: /workspace
- name: PADDLE_INIT_NICS
value: eth2
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: PADDLE_TRAINERS_NUM
value: "3"
- name: PADDLE_PSERVERS_NUM
value: "3"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: ENTRY
value: cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
- name: PADDLE_PORT
value: "30240"
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: PADDLE_TRAINING_ROLE
value: PSERVER
- name: TRAINING_ROLE
value: PSERVER
restartPolicy: OnFailure
- name: model-out-trainer
replicas: 1
policies:
- event: TaskCompleted
action: CompleteJob
template:
metadata:
labels:
paddle-job: fluid-ctr
spec:
nodeSelector:
nodeType: model
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/data
type: ""
name: seqdata
containers:
- image: wangjiawei1993/edldemo:v19
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: trainer
volumeMounts:
- mountPath: /data
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 10Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
- name: TRAINER_PACKAGE
value: /workspace
- name: PADDLE_INIT_NICS
value: eth2
- name: CPU_NUM
value: "2"
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: PADDLE_PORT
value: "30240"
- name: PADDLE_PSERVERS_NUM
value: "3"
- name: PADDLE_TRAINERS_NUM
value: "3"
- name: PADDLE_TRAINING_ROLE
value: TRAINER
- name: TRAINING_ROLE
value: TRAINER
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: ENTRY
value: (/postprocess &) && cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
restartPolicy: OnFailure
- replicas: 2
policies:
- event: TaskCompleted
action: CompleteJob
name: trainer
template:
metadata:
labels:
paddle-job: fluid-ctr
spec:
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/data
type: ""
name: seqdata
containers:
- image: wangjiawei1993/edldemo:v19
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: trainer
volumeMounts:
- mountPath: /data
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 10Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
- name: TRAINER_PACKAGE
value: /workspace
- name: PADDLE_INIT_NICS
value: eth2
- name: CPU_NUM
value: "2"
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: PADDLE_PORT
value: "30240"
- name: PADDLE_PSERVERS_NUM
value: "3"
- name: PADDLE_TRAINERS_NUM
value: "3"
- name: PADDLE_TRAINING_ROLE
value: TRAINER
- name: TRAINING_ROLE
value: TRAINER
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: ENTRY
value: (/postprocess &) && cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
restartPolicy: OnFailure
doc/resource/cube.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: v1
kind: Pod
metadata:
name: cube-0
labels:
app: cube-0
spec:
containers:
- name: cube-0
image: wangjiawei1993/cube:v11
workingDir: /cube
command: ['/bin/bash']
args: ['start.sh']
ports:
- containerPort: 8001
name: cube-agent
- containerPort: 8027
name: cube-server
---
apiVersion: v1
kind: Pod
metadata:
name: cube-1
labels:
app: cube-1
spec:
containers:
- name: cube-1
image: wangjiawei1993/cube:v11
workingDir: /cube
command: ['/bin/bash']
args: ['start.sh']
ports:
- containerPort: 8001
name: cube-agent
- containerPort: 8027
name: cube-server
---
kind: Service
apiVersion: v1
metadata:
name: cube-0
spec:
ports:
- name: agent
port: 8001
protocol: TCP
- name: server
port: 8027
protocol: TCP
selector:
app: cube-0
---
kind: Service
apiVersion: v1
metadata:
name: cube-1
spec:
ports:
- name: agent
port: 8001
protocol: TCP
- name: server
port: 8027
protocol: TCP
selector:
app: cube-1
doc/resource/ftp.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: v1
kind: Pod
metadata:
name: file-server
labels:
app: file-server
spec:
volumes:
- hostPath:
path: /home/work
type: ""
name: file-home
containers:
- name: file-server
image: halverneus/static-file-server
ports:
- containerPort: 8080
volumeMounts:
- mountPath: /web
name: file-home
nodeSelector:
nodeType: model
---
kind: Service
apiVersion: v1
metadata:
name: file-server
spec:
type: LoadBalancer
ports:
- name: file-server
port: 8080
targetPort: 8080
selector:
app: file-server
doc/resource/paddle-suite.sh 0 → 100755
浏览文件 @ 40d3c4a7
#!/bin/bash
kubectl apply -f https://raw.githubusercontent.com/volcano-sh/volcano/master/installer/volcano-development.yaml
OUTPUT_NODE=$(kubectl get no | awk '{print $1}' | sed -n '2p')
kubectl label nodes $OUTPUT_NODE nodeType=model --overwrite
kubectl apply -f paddle-suite.yaml
doc/resource/paddle-suite.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: v1
kind: Pod
metadata:
name: file-server
labels:
app: file-server
spec:
volumes:
- hostPath:
path: /home/work
type: ""
name: file-home
containers:
- name: file-server
image: halverneus/static-file-server
ports:
- containerPort: 8080
volumeMounts:
- mountPath: /web
name: file-home
nodeSelector:
nodeType: model
---
kind: Service
apiVersion: v1
metadata:
name: file-server
spec:
type: LoadBalancer
ports:
- name: file-server
port: 8080
targetPort: 8080
selector:
app: file-server
---
apiVersion: v1
kind: Pod
metadata:
name: cube-0
labels:
app: cube-0
spec:
containers:
- name: cube-0
image: wangjiawei1993/cube:v11
workingDir: /cube
command: ['/bin/bash']
args: ['start.sh']
ports:
- containerPort: 8001
name: cube-agent
- containerPort: 8027
name: cube-server
---
apiVersion: v1
kind: Pod
metadata:
name: cube-1
labels:
app: cube-1
spec:
containers:
- name: cube-1
image: wangjiawei1993/cube:v11
workingDir: /cube
command: ['/bin/bash']
args: ['start.sh']
ports:
- containerPort: 8001
name: cube-agent
- containerPort: 8027
name: cube-server
---
kind: Service
apiVersion: v1
metadata:
name: cube-0
spec:
ports:
- name: agent
port: 8001
protocol: TCP
- name: server
port: 8027
protocol: TCP
selector:
app: cube-0
---
kind: Service
apiVersion: v1
metadata:
name: cube-1
spec:
ports:
- name: agent
port: 8001
protocol: TCP
- name: server
port: 8027
protocol: TCP
selector:
app: cube-1
---
apiVersion: v1
kind: Pod
metadata:
name: cube-transfer
labels:
app: cube-transfer
spec:
containers:
- name: cube-transfer
image: wangjiawei1993/cube-transfer:v18
workingDir: /
env:
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
command: ['bash']
args: ['nonstop.sh']
ports:
- containerPort: 8099
name: cube-transfer
- containerPort: 8098
name: cube-http
---
apiVersion: v1
kind: Pod
metadata:
name: paddleserving
labels:
app: paddleserving
spec:
containers:
- name: paddleserving
image: wangjiawei1993/paddleserving:v7-debug
workingDir: /serving
command: ['/bin/bash']
args: ['run.sh']
ports:
- containerPort: 8010
name: serving
---
apiVersion: v1
kind: Service
metadata:
name: paddleserving
spec:
ports:
- name: serving
port: 8010
protocol: TCP
selector:
app: paddleserving
---
apiVersion: v1
kind: Pod
metadata:
name: pdservingclient
labels:
app: pdservingclient
spec:
containers:
- name: pdservingclient
image: wangjiawei1993/pdservingclient:v4
workingDir: /
command: ['bash']
args: ['nonstop.sh']
---
apiVersion: batch.volcano.sh/v1alpha1
kind: Job
metadata:
name: edl-demo
spec:
minAvailable: 6
schedulerName: volcano
policies:
- event: PodEvicted
action: RestartJob
- event: PodFailed
action: RestartJob
tasks:
- replicas: 3
name: pserver
template:
metadata:
labels:
paddle-job-pserver: fluid-ctr
spec:
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/
type: ""
name: seqdata
containers:
- image: wangjiawei1993/edldemo:v19
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: pserver
volumeMounts:
- mountPath: /data
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 1Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
value: ""
- name: TRAINER_PACKAGE
value: /workspace
- name: PADDLE_INIT_NICS
value: eth2
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: PADDLE_TRAINERS_NUM
value: "3"
- name: PADDLE_PSERVERS_NUM
value: "3"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: ENTRY
value: cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
- name: PADDLE_PORT
value: "30240"
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: PADDLE_TRAINING_ROLE
value: PSERVER
- name: TRAINING_ROLE
value: PSERVER
restartPolicy: OnFailure
- name: model-out-trainer
replicas: 1
policies:
- event: TaskCompleted
action: CompleteJob
template:
metadata:
labels:
paddle-job: fluid-ctr
spec:
nodeSelector:
nodeType: model
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/data
type: ""
name: seqdata
containers:
- image: wangjiawei1993/edldemo:v19
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: trainer
volumeMounts:
- mountPath: /data
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 10Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
- name: TRAINER_PACKAGE
value: /workspace
- name: PADDLE_INIT_NICS
value: eth2
- name: CPU_NUM
value: "2"
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: PADDLE_PORT
value: "30240"
- name: PADDLE_PSERVERS_NUM
value: "3"
- name: PADDLE_TRAINERS_NUM
value: "3"
- name: PADDLE_TRAINING_ROLE
value: TRAINER
- name: TRAINING_ROLE
value: TRAINER
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: ENTRY
value: (/postprocess &) && cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
restartPolicy: OnFailure
- replicas: 2
policies:
- event: TaskCompleted
action: CompleteJob
name: trainer
template:
metadata:
labels:
paddle-job: fluid-ctr
spec:
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/data
type: ""
name: seqdata
containers:
- image: wangjiawei1993/edldemo:v19
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: trainer
volumeMounts:
- mountPath: /data
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 10Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
- name: TRAINER_PACKAGE
value: /workspace
- name: PADDLE_INIT_NICS
value: eth2
- name: CPU_NUM
value: "2"
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: PADDLE_PORT
value: "30240"
- name: PADDLE_PSERVERS_NUM
value: "3"
- name: PADDLE_TRAINERS_NUM
value: "3"
- name: PADDLE_TRAINING_ROLE
value: TRAINER
- name: TRAINING_ROLE
value: TRAINER
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: ENTRY
value: (/postprocess &) && cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
restartPolicy: OnFailure
doc/resource/pdclient.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: v1
kind: Pod
metadata:
name: pdservingclient
labels:
app: pdservingclient
spec:
containers:
- name: pdservingclient
image: wangjiawei1993/pdservingclient:v4
workingDir: /
command: ['bash']
args: ['nonstop.sh']
doc/resource/pdserving.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: v1
kind: Pod
metadata:
name: paddleserving
labels:
app: paddleserving
spec:
containers:
- name: paddleserving
image: wangjiawei1993/paddleserving:v7-debug
workingDir: /serving
command: ['/bin/bash']
args: ['run.sh']
ports:
- containerPort: 8010
name: serving
---
apiVersion: v1
kind: Service
metadata:
name: paddleserving
spec:
ports:
- name: serving
port: 8010
protocol: TCP
selector:
app: paddleserving
doc/resource/save_program.py 0 → 100755
浏览文件 @ 40d3c4a7
from __future__ import print_function
import argparse
import logging
import os
import time
import math
import reader
import google.protobuf.text_format as text_format
import numpy as np
import six
import paddle
import paddle.fluid as fluid
import paddle.fluid.proto.framework_pb2 as framework_pb2
import paddle.fluid.core as core
from multiprocessing import cpu_count
# disable gpu training for this example
os.environ["CUDA_VISIBLE_DEVICES"] = ""
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger("fluid")
logger.setLevel(logging.INFO)
dense_feature_dim = 13
def parse_args():
parser = argparse.ArgumentParser(description="PaddlePaddle CTR example")
parser.add_argument(
'--train_data_path',
type=str,
default='./data/raw/train.txt',
help="The path of training dataset")
parser.add_argument(
'--batch_size',
type=int,
default=1000,
help="The size of mini-batch (default:1000)")
parser.add_argument(
'--embedding_size',
type=int,
default=10,
help="The size for embedding layer (default:10)")
parser.add_argument(
'--sparse_feature_dim',
type=int,
default=1000001,
help='sparse feature hashing space for index processing')
parser.add_argument(
'--model_output_dir',
type=str,
default='models',
help='The path for model to store (default: models)')
return parser.parse_args()
def ctr_dnn_model(embedding_size, sparse_feature_dim, use_py_reader=True):
def embedding_layer(input):
emb = fluid.layers.embedding(
input=input,
is_sparse=True,
# you need to patch https://github.com/PaddlePaddle/Paddle/pull/14190
# if you want to set is_distributed to True
is_distributed=False,
size=[sparse_feature_dim, embedding_size],
param_attr=fluid.ParamAttr(name="SparseFeatFactors",
initializer=fluid.initializer.Uniform()))
seq = fluid.layers.sequence_pool(input=emb, pool_type='average')
return emb, seq
dense_input = fluid.layers.data(
name="dense_input", shape=[dense_feature_dim], dtype='float32')
sparse_input_ids = [
fluid.layers.data(name="C" + str(i), shape=[1], lod_level=1, dtype='int64')
for i in range(1, 27)]
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
words = [dense_input] + sparse_input_ids + [label]
sparse_embed_and_seq = list(map(embedding_layer, words[1:-1]))
emb_list = [x[0] for x in sparse_embed_and_seq]
sparse_embed_seq = [x[1] for x in sparse_embed_and_seq]
concated = fluid.layers.concat(sparse_embed_seq + words[0:1], axis=1)
train_feed_vars = words
inference_feed_vars = emb_list + words[0:1]
fc1 = fluid.layers.fc(input=concated, size=400, act='relu',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(concated.shape[1]))))
fc2 = fluid.layers.fc(input=fc1, size=400, act='relu',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(fc1.shape[1]))))
fc3 = fluid.layers.fc(input=fc2, size=400, act='relu',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(fc2.shape[1]))))
predict = fluid.layers.fc(input=fc3, size=2, act='softmax',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(fc3.shape[1]))))
cost = fluid.layers.cross_entropy(input=predict, label=words[-1])
avg_cost = fluid.layers.reduce_sum(cost)
accuracy = fluid.layers.accuracy(input=predict, label=words[-1])
auc_var, batch_auc_var, auc_states = \
fluid.layers.auc(input=predict, label=words[-1], num_thresholds=2 ** 12, slide_steps=20)
fetch_vars = [predict]
return avg_cost, auc_var, batch_auc_var, train_feed_vars, inference_feed_vars, fetch_vars
def train_loop(args, train_program, feed_vars, loss, auc_var, batch_auc_var,
trainer_num, trainer_id):
dataset = reader.CriteoDataset(args.sparse_feature_dim)
train_reader = paddle.batch(
paddle.reader.shuffle(
dataset.train([args.train_data_path], trainer_num, trainer_id),
buf_size=args.batch_size * 100),
batch_size=args.batch_size)
feed_var_names = [var.name for var in feed_vars]
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
total_time = 0
pass_id = 0
batch_id = 0
feeder = fluid.DataFeeder(feed_var_names, place)
for data in train_reader():
loss_val, auc_val, batch_auc_val = exe.run(fluid.default_main_program(),
feed = feeder.feed(data),
fetch_list=[loss.name, auc_var.name, batch_auc_var.name])
break
loss_val = np.mean(loss_val)
auc_val = np.mean(auc_val)
batch_auc_val = np.mean(batch_auc_val)
logger.info("TRAIN --> pass: {} batch: {} loss: {} auc: {}, batch_auc: {}"
.format(pass_id, batch_id, loss_val/args.batch_size, auc_val, batch_auc_val))
def save_program():
args = parse_args()
if not os.path.isdir(args.model_output_dir):
os.mkdir(args.model_output_dir)
loss, auc_var, batch_auc_var, train_feed_vars, inference_feed_vars, fetch_vars = ctr_dnn_model(args.embedding_size, args.sparse_feature_dim, use_py_reader=False)
optimizer = fluid.optimizer.Adam(learning_rate=1e-4)
optimizer.minimize(loss)
main_program = fluid.default_main_program()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
train_loop(args, main_program, train_feed_vars, loss, auc_var, batch_auc_var, 1, 0)
model_dir = args.model_output_dir + "/inference_only"
feed_var_names = [var.name for var in inference_feed_vars]
fluid.io.save_inference_model(model_dir, feed_var_names, fetch_vars, exe, fluid.default_main_program())
def prune_program():
args = parse_args()
model_dir = args.model_output_dir + "/inference_only"
model_file = model_dir + "/__model__"
with open(model_file, "rb") as f:
protostr = f.read()
f.close()
proto = framework_pb2.ProgramDesc.FromString(six.binary_type(protostr))
block = proto.blocks[0]
kept_ops = [op for op in block.ops if op.type != "lookup_table"]
del block.ops[:]
block.ops.extend(kept_ops)
kept_vars = [var for var in block.vars if var.name != "SparseFeatFactors"]
del block.vars[:]
block.vars.extend(kept_vars)
with open(model_file, "wb") as f:
f.write(proto.SerializePartialToString())
f.close()
with open(model_file + ".prototxt.pruned", "w") as f:
f.write(text_format.MessageToString(proto))
f.close()
def remove_embedding_param_file():
args = parse_args()
model_dir = args.model_output_dir + "/inference_only"
embedding_file = model_dir + "/SparseFeatFactors"
os.remove(embedding_file)
if __name__ == '__main__':
save_program()
prune_program()
remove_embedding_param_file()
doc/resource/transfer.yaml 0 → 100755
浏览文件 @ 40d3c4a7
apiVersion: v1
kind: Pod
metadata:
name: cube-transfer
labels:
app: cube-transfer
spec:
containers:
- name: cube-transfer
image: wangjiawei1993/cube-transfer:v18
workingDir: /
env:
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
command: ['bash']
args: ['nonstop.sh']
ports:
- containerPort: 8099
name: cube-transfer
- containerPort: 8098
name: cube-http
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