paddle on aws with kubernetes tutorial now works

doc/howto/usage/k8s/src/job.yaml

-apiVersion: batch/v1
-kind: Job
-metadata:
-  name: paddle-cluster-job
-spec:
-  parallelism: 3
-  completions: 3
-  template:
-    metadata:
-      name: paddle-cluster-job
-    spec:
-      volumes:
-      - name: jobpath
-        hostPath: 
-          path: /home/work/paddle_output              
-      containers:
-      - name: trainer
-        image: registry.baidu.com/public/paddle:mypaddle
-        command: ["bin/bash",  "-c", "/root/start.sh"]        
-        env:
-        - name: JOB_NAME
-          value: paddle-cluster-job
-        - name: JOB_PATH
-          value: /home/jobpath     
-        - name: JOB_NAMESPACE
-          value: default         
-        - name: TRAIN_CONFIG_DIR
-          value: recommendation
-        - name: CONF_PADDLE_NIC
-          value: eth0  
-        - name: CONF_PADDLE_PORT
-          value: "7164"
-        - name: CONF_PADDLE_PORTS_NUM
-          value: "2"     
-        - name: CONF_PADDLE_PORTS_NUM_SPARSE
-          value: "2"  
-        - name: CONF_PADDLE_GRADIENT_NUM
-          value: "3"                                                               
-        volumeMounts:
-        - name: jobpath
-          mountPath: /home/jobpath       
-      restartPolicy: Never
-    

doc/howto/usage/k8s/src/k8s_data/Dockerfile

+FROM alpine
+
+RUN apk update && apk upgrade && apk add coreutils
+ADD quick_start /quick_start
+ADD get_data.sh /bin/
+RUN chmod +x /bin/get_data.sh
+ENTRYPOINT ["/bin/get_data.sh"]

doc/howto/usage/k8s/src/k8s_data/README.md

+To build PaddlePaddle data preparation image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../k8s_aws_en.md), run following commands:
+
+```
+cp -r ../../../../../../demo/quick_start .
+docker build . -t prepare-data-image-name
+```

doc/howto/usage/k8s/src/k8s_data/get_data.sh

+#!/bin/sh
+
+out_dir=$OUT_DIR
+split_count=$SPLIT_COUNT
+
+set -e
+
+mkdir -p $out_dir
+cp -r /quick_start $out_dir/
+
+mkdir -p $out_dir/0/data
+cd $out_dir/0/data
+wget http://paddlepaddle.bj.bcebos.com/demo/quick_start_preprocessed_data/preprocessed_data.tar.gz
+tar zxvf preprocessed_data.tar.gz
+rm preprocessed_data.tar.gz
+
+split -d --number=l/$split_count -a 5 train.txt train.
+mv train.00000 train.txt
+
+cd $out_dir
+end=$(expr $split_count - 1)
+for i in $(seq 1 $end); do
+    mkdir -p $i/data
+    cp -r 0/data/* $i/data
+    mv $i/data/train.`printf %05d $i` $i/data/train.txt
+done;

doc/howto/usage/k8s/src/k8s_train/Dockerfile

+FROM paddledev/paddle:cpu-latest
+
+COPY start.sh /root/
+COPY start_paddle.py /root/
+RUN chmod +x /root/start.sh
+CMD ["bash"," -c","/root/start.sh"]

doc/howto/usage/k8s/src/k8s_train/README.md

+To build PaddlePaddle training image in tutorial [Distributed PaddlePaddle Training on AWS with Kubernetes](../k8s_aws_en.md), run following command:
+
+```
+docker build . -t train-image-name
+```

doc/howto/usage/k8s/src/start.sh → doc/howto/usage/k8s/src/k8s_train/start.sh

 #!/bin/sh
+
 set -eu
 
 jobconfig=${JOB_PATH}"/"${JOB_NAME}"/"${TRAIN_CONFIG_DIR}
 cd /root
-cp -rf $jobconfig .
-cd $TRAIN_CONFIG_DIR
-
+cp -rf $jobconfig/* .
 
 python /root/start_paddle.py \
   --dot_period=10 \
-  --ports_num_for_sparse=$CONF_PADDLE_PORTS_NUM \
+  --ports_num=$CONF_PADDLE_PORTS_NUM \
+  --ports_num_for_sparse=$CONF_PADDLE_PORTS_NUM_SPARSE \
   --log_period=50 \
   --num_passes=10 \
-  --trainer_count=4 \
+  --trainer_count=$TRAINER_COUNT \
   --saving_period=1 \
   --local=0 \
-  --config=./trainer_config.py \
+  --config=trainer_config.lr.py \
   --use_gpu=0

doc/howto/usage/k8s/src/start_paddle.py → doc/howto/usage/k8s/src/k8s_train/start_paddle.py

@@ -23,7 +23,6 @@ import argparse
 API = "/api/v1/namespaces/"
 JOBSELECTOR = "labelSelector=job-name="
 JOB_PATH = os.getenv("JOB_PATH") + "/" + os.getenv("JOB_NAME")
-JOB_PATH_DATA = JOB_PATH + "/data"
 JOB_PATH_OUTPUT = JOB_PATH + "/output"
 JOBNAME = os.getenv("JOB_NAME")
 NAMESPACE = os.getenv("JOB_NAMESPACE")
@@ -33,6 +32,8 @@ PADDLE_PORTS_NUM = os.getenv("CONF_PADDLE_PORTS_NUM")
 PADDLE_PORTS_NUM_SPARSE = os.getenv("CONF_PADDLE_PORTS_NUM_SPARSE")
 PADDLE_SERVER_NUM = os.getenv("CONF_PADDLE_GRADIENT_NUM")
 
+tokenpath = '/var/run/secrets/kubernetes.io/serviceaccount/token'
+
 
 def refine_unknown_args(cmd_args):
     '''
@@ -64,6 +65,7 @@ def isPodAllRunning(podlist):
     for pod in podlist["items"]:
         if pod["status"]["phase"] == "Running":
             running += 1
+    print "waiting for pods running, require:", require, "running:", running
     if require == running:
         return True
     return False
@@ -79,6 +81,15 @@ def getPodList():
 
     pod = API + NAMESPACE + "/pods?"
     job = JOBNAME
+    if os.path.isfile(tokenpath):
+        tokenfile = open(tokenpath, mode='r')
+        token = tokenfile.read()
+        Bearer = "Bearer " + token
+        headers = {"Authorization": Bearer}
+        return requests.get(apiserver + pod + JOBSELECTOR + job,
+                            headers=headers,
+                            verify=False).json()
+    else:
         return requests.get(apiserver + pod + JOBSELECTOR + job,
                             verify=False).json()
 
@@ -122,8 +133,8 @@ def startPaddle(idMap={}, train_args_dict=None):
     if not os.path.exists(JOB_PATH_OUTPUT):
         os.makedirs(JOB_PATH_OUTPUT)
     os.mkdir(logDir)
-    copyCommand = 'cp -rf ' + JOB_PATH_DATA + \
-        "/" + str(trainerId) + " ./data"
+    copyCommand = 'cp -rf ' + JOB_PATH + \
+        "/" + str(trainerId) + "/data/*" + " ./data/"
     os.system(copyCommand)
     startPserver = 'nohup paddle pserver' + \
         " --port=" + str(PADDLE_PORT) + \
@@ -136,9 +147,9 @@ def startPaddle(idMap={}, train_args_dict=None):
     print startPserver
     os.system(startPserver)
     # wait until pservers completely start
-    time.sleep(10)
-    startTrainer = program + args + " > " + \
-        logDir + "/train.log 2>&1 < /dev/null"
+    time.sleep(20)
+    startTrainer = program + args + " 2>&1 | tee " + \
+        logDir + "/train.log"
     print startTrainer
     os.system(startTrainer)
 
@@ -152,7 +163,7 @@ if __name__ == '__main__':
     podlist = getPodList()
     # need to wait until all pods are running
     while not isPodAllRunning(podlist):
-        time.sleep(10)
+        time.sleep(20)
         podlist = getPodList()
     idMap = getIdMap(podlist)
     startPaddle(idMap, train_args_dict)