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+# Paddle On Kubernetes
+
+>In this article, we will introduce how to run Paddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run Paddle training job on distributed cluster.
+
+## Build Docker Image
+
+In distributed Kubernetes cluster, we will use Ceph or other shared storage system for storing training related data so that all processes in Paddle training can retrieve data from Ceph. In this example, we will only demo training job on single machine. In order to simplify the requirement of the environment, we will directly put training data into Paddle's Docker Image, so we need to create a Paddle Docker image that already includes the training data.
+
+Paddle's [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html) introduces how to download and train data by using script from Paddle's source code.
+And `paddledev/paddle:cpu-demo-latest` image has the Paddle source code and demo. (Caution: Default Paddle image `paddledev/paddle:cpu-latest` doesn't include the source code, Paddle's different versions of image can be referred here: [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html)), so we run this container and download the training data, and then commit the whole container to be a new Docker image.
+  
+### Run Docker Container
+
+```
+$ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest
+```
+
+### Download Training Data
+
+Getting into `/root/paddle/demo/quick_start/data` Directory，using `get_data.sh` to download training data.
+
+```
+$ root@fbd1f2bb71f4:~/paddle/demo/quick_start/data# ./get_data.sh
+
+Downloading Amazon Electronics reviews data...
+--2016-10-31 01:33:43--  http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
+Resolving snap.stanford.edu (snap.stanford.edu)... 171.64.75.80
+Connecting to snap.stanford.edu (snap.stanford.edu)|171.64.75.80|:80... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 495854086 (473M) [application/x-gzip]
+Saving to: 'reviews_Electronics_5.json.gz'
+
+ 10% [=======>                                         ] 874,279     64.7KB/s  eta 2h 13m
+
+```
+
+### Modify Startup Script
+
+After downloading the data，modify `/root/paddle/demo/quick_start/train.sh` file contents are as follows (one more cd cmd):
+```
+set -e
+cd /root/paddle/demo/quick_start
+cfg=trainer_config.lr.py
+#cfg=trainer_config.emb.py
+#cfg=trainer_config.cnn.py
+#cfg=trainer_config.lstm.py
+#cfg=trainer_config.bidi-lstm.py
+#cfg=trainer_config.db-lstm.py
+paddle train \
+  --config=$cfg \
+  --save_dir=./output \
+  --trainer_count=4 \
+  --log_period=20 \
+  --num_passes=15 \
+  --use_gpu=false \
+  --show_parameter_stats_period=100 \
+  --test_all_data_in_one_period=1 \
+  2>&1 | tee 'train.log'
+```
+
+### Commit Docker Image
+
+```
+$ docker commit quick_start_data mypaddle/paddle:quickstart
+```
+
+## Use Kubernetes For Training
+
+>We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes.
+
+### Create Yaml Files
+
+The output result in container will be demolished when job finished (container stopped running), so we need to mount the volume out to the local disk when creating the container to store the training result. Using our previously created image, we can create a [Kubernetes Job](http://kubernetes.io/docs/user-guide/jobs/#what-is-a-job), the yaml contents are as follows:
+
+```
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: quickstart
+spec:
+  parallelism: 1
+  completions: 1
+  template:
+    metadata:
+      name: quickstart
+    spec:
+      volumes:
+      - name: output
+        hostPath: 
+          path: /home/work/paddle_output     
+      containers:
+      - name: pi
+        image: mypaddle/paddle:quickstart
+        command: ["bin/bash",  "-c", "/root/paddle/demo/quick_start/train.sh"]
+        volumeMounts:
+        - name: output
+          mountPath: /root/paddle/demo/quick_start/output
+      restartPolicy: Never
+```
+
+### Start Paddle Job
+
+Using the above yaml file to start the Kubernetes job.
+
+```
+$ kubectl  create -f paddle.yaml
+```
+
+Get the detailed status of the job:
+
+```
+$ kubectl  get job
+NAME         DESIRED   SUCCESSFUL   AGE
+quickstart   1         0            58s
+
+$ kubectl  describe job quickstart
+Name:		quickstart
+Namespace:	default
+Image(s):	registry.baidu.com/public/paddle:cpu-demo-latest
+Selector:	controller-uid=f120da72-9f18-11e6-b363-448a5b355b84
+Parallelism:	1
+Completions:	1
+Start Time:	Mon, 31 Oct 2016 11:20:16 +0800
+Labels:		controller-uid=f120da72-9f18-11e6-b363-448a5b355b84,job-name=quickstart
+Pods Statuses:	0 Running / 1 Succeeded / 0 Failed
+Volumes:
+  output:
+    Type:	HostPath (bare host directory volume)
+    Path:	/home/work/paddle_output
+Events:
+  FirstSeen	LastSeen	Count	From			SubobjectPath	Type		Reason			Message
+  ---------	--------	-----	----			-------------	--------	------			-------
+  1m		1m		1	{job-controller }			Normal		SuccessfulCreate	Created pod: quickstart-fa0wx
+```
+
+### Get Training Result
+
+We can use kubectl command to take a look at the status of related pod.
+
+```
+$ kubectl  describe pod quickstart-fa0wx
+Name:		quickstart-fa0wx
+Namespace:	default
+Node:		paddle-demo-let02/10.206.202.44
+Start Time:	Mon, 31 Oct 2016 11:20:17 +0800
+Labels:		controller-uid=f120da72-9f18-11e6-b363-448a5b355b84,job-name=quickstart
+Status:		Succeeded
+IP:		10.0.0.9
+Controllers:	Job/quickstart
+Containers:
+  quickstart:
+    Container ID:	docker://b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465
+    Image:		registry.baidu.com/public/paddle:cpu-demo-latest
+    Image ID:		docker://18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750
+    Port:
+    Command:
+      bin/bash
+      -c
+      /root/paddle/demo/quick_start/train.sh
+    QoS Tier:
+      cpu:		BestEffort
+      memory:		BestEffort
+    State:		Terminated
+      Reason:		Completed
+      Exit Code:	0
+      Started:		Mon, 31 Oct 2016 11:20:20 +0800
+      Finished:		Mon, 31 Oct 2016 11:21:46 +0800
+    Ready:		False
+    Restart Count:	0
+    Environment Variables:
+Conditions:
+  Type		Status
+  Ready 	False
+Volumes:
+  output:
+    Type:	HostPath (bare host directory volume)
+    Path:	/home/work/paddle_output
+```
+
+We can also ssh to Kubernetes node to take a look at the training result.
+
+```
+[root@paddle-demo-let02 paddle_output]# ll
+total 60
+drwxr-xr-x 2 root root 4096 Oct 31 11:20 pass-00000
+drwxr-xr-x 2 root root 4096 Oct 31 11:20 pass-00001
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00002
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00003
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00004
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00005
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00006
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00007
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00008
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00009
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00010
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00011
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00012
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00013
+drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00014
+```