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Merge pull request #370 from tizhou86/develop 

Added paddle on kubernetes tutorial.
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# PaddlePaddle on AWS with Kubernetes
## Create AWS Account and IAM Account
To use AWS, we need to sign up an AWS account on Amazon's Web site.
An AWS account allows us to login to the AWS Console Web interface to
create IAM users and user groups. Usually, we create a user group with
privileges required to run PaddlePaddle, and we create users for
those who are going to run PaddlePaddle and add these users into the
group. IAM users can identify themselves using password and tokens,
where passwords allows users to log in to the AWS Console, and tokens
make it easy for users to submit and inspect jobs from the command
line.
To sign up an AWS account, please
follow
[this guide](http://docs.aws.amazon.com/lambda/latest/dg/setting-up.html).
To create users and user groups under an AWS account, please
follow
[this guide](http://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html).
Please be aware that this tutorial needs the following privileges in
the user group:
- AmazonEC2FullAccess
- AmazonS3FullAccess
- AmazonRoute53FullAccess
- AmazonRoute53DomainsFullAccess
- AmazonElasticFileSystemFullAccess
- AmazonVPCFullAccess
- IAMUserSSHKeys
- IAMFullAccess
- NetworkAdministrator
By the time we write this tutorial, we noticed that Chinese AWS users
might suffer from authentication problems when running this tutorial.
Our solution is that we create a VM instance with the default Amazon
AMI and in the same zone as our cluster runs, so we can SSH to this VM
instance as a tunneling server and control our cluster and jobs from
it.
## PaddlePaddle on AWS
Here we will show you step by step on how to run PaddlePaddle training on AWS cluster.
###Download kube-aws and kubectl
####kube-aws
Import the CoreOS Application Signing Public Key:
```
gpg2 --keyserver pgp.mit.edu --recv-key FC8A365E
```
Validate the key fingerprint:
```
gpg2 --fingerprint FC8A365E
```
The correct key fingerprint is `18AD 5014 C99E F7E3 BA5F 6CE9 50BD D3E0 FC8A 365E`
Go to the [releases](https://github.com/coreos/kube-aws/releases) and download the latest release tarball and detached signature (.sig) for your architecture.
Validate the tarball's GPG signature:
```
PLATFORM=linux-amd64
# Or
PLATFORM=darwin-amd64
gpg2 --verify kube-aws-${PLATFORM}.tar.gz.sig kube-aws-${PLATFORM}.tar.gz
```
Extract the binary:
```
tar zxvf kube-aws-${PLATFORM}.tar.gz
```
Add kube-aws to your path:
```
mv ${PLATFORM}/kube-aws /usr/local/bin
```
####kubectl
Go to the [releases](https://github.com/kubernetes/kubernetes/releases) and download the latest release tarball.
Extract the tarball and then concate the kubernetes binaries directory into PATH:
```
export PATH=<path/to/kubernetes-directory>/platforms/linux/amd64:$PATH
```
User credentials and security tokens will be generated later in user directory, not in `~/.kube/config`, they will be necessary to use the CLI or the HTTP Basic Auth.
###Configure AWS Credentials
First check out [this](http://docs.aws.amazon.com/cli/latest/userguide/installing.html) for installing the AWS command line interface, if you use ec2 instance with default amazon AMI, the cli tool has already been installed on your machine.
And then configure your AWS account information:
```
aws configure
```
Fill in the required fields (You can get your AWS aceess key id and AWS secrete access key by following [this](http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html) instruction):
```
AWS Access Key ID: YOUR_ACCESS_KEY_ID
AWS Secrete Access Key: YOUR_SECRETE_ACCESS_KEY
Default region name: us-west-2
Default output format: json
```
Test that your credentials work by describing any instances you may already have running on your account:
```
aws ec2 describe-instances
```
###Define Cluster Parameters
####EC2 key pair
The keypair that will authenticate SSH access to your EC2 instances. The public half of this key pair will be configured on each CoreOS node.
After creating a key pair, you will use the name you gave the keys to configure the cluster. Key pairs are only available to EC2 instances in the same region. More info in the [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html).
####KMS key
Amazon KMS keys are used to encrypt and decrypt cluster TLS assets. If you already have a KMS Key that you would like to use, you can skip creating a new key and provide the Arn string for your existing key.
You can create a KMS key in the AWS console, or with the aws command line tool:
```
$ aws kms --region=us-west-2 create-key --description="kube-aws assets"
{
"KeyMetadata": {
"CreationDate": 1458235139.724,
"KeyState": "Enabled",
"Arn": "arn:aws:kms:us-west-2:xxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx",
"AWSAccountId": "xxxxxxxxxxxxx",
"Enabled": true,
"KeyUsage": "ENCRYPT_DECRYPT",
"KeyId": "xxxxxxxxx",
"Description": "kube-aws assets"
}
}
```
You will use the `KeyMetadata.Arn` string to identify your KMS key in the init step.
And then you need to add several inline policies in your user permission.
kms inline policy:
```
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Stmt1482205552000",
"Effect": "Allow",
"Action": [
"kms:Decrypt",
"kms:Encrypt"
],
"Resource": [
"arn:aws:kms:*:xxxxxxxxx:key/*"
]
}
]
}
```
cloudformation inline policy:
```
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Stmt1482205746000",
"Effect": "Allow",
"Action": [
"cloudformation:CreateStack",
"cloudformation:UpdateStack",
"cloudformation:DeleteStack",
"cloudformation:DescribeStacks",
"cloudformation:DescribeStackResource",
"cloudformation:GetTemplate"
],
"Resource": [
"arn:aws:cloudformation:us-west-2:xxxxxxxxx:stack/YOUR_CLUSTER_NAME/*"
]
}
]
}
```
####External DNS name
When the cluster is created, the controller will expose the TLS-secured API on a public IP address. You will need to create an A record for the external DNS hostname you want to point to this IP address. You can find the API external IP address after the cluster is created by invoking kube-aws status.
####S3 bucket
You need to create an S3 bucket before startup the Kubernetes cluster.
####Initialize an asset directory
Create a directory on your local machine to hold the generated assets:
```
$ mkdir my-cluster
$ cd my-cluster
```
Initialize the cluster CloudFormation stack with the KMS Arn, key pair name, and DNS name from the previous step:
```
$ kube-aws init \
--cluster-name=my-cluster-name \
--external-dns-name=my-cluster-endpoint \
--region=us-west-1 \
--availability-zone=us-west-1c \
--key-name=key-pair-name \
--kms-key-arn="arn:aws:kms:us-west-2:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx"
```
There will now be a cluster.yaml file in the asset directory. This is the main configuration file for your cluster.
####Render contents of the asset directory
In the simplest case, you can have kube-aws generate both your TLS identities and certificate authority for you.
```
$ kube-aws render credentials --generate-ca
```
The next command generates the default set of cluster assets in your asset directory.
```
sh $ kube-aws render stack
```
Here's what the directory structure looks like:
```
$ tree
.
├── cluster.yaml
├── credentials
│ ├── admin-key.pem
│ ├── admin.pem
│ ├── apiserver-key.pem
│ ├── apiserver.pem
│ ├── ca-key.pem
│ ├── ca.pem
│ ├── worker-key.pem
│ └── worker.pem
│ ├── etcd-key.pem
│ └── etcd.pem
│ ├── etcd-client-key.pem
│ └── etcd-client.pem
├── kubeconfig
├── stack-template.json
└── userdata
├── cloud-config-controller
└── cloud-config-worker
```
These assets (templates and credentials) are used to create, update and interact with your Kubernetes cluster.
###Kubernetes Cluster Start Up
####Create the instances defined in the CloudFormation template
Now for the exciting part, creating your cluster:
```
$ kube-aws up --s3-uri s3://<your-bucket-name>/<prefix>
```
####Configure DNS
You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation, if necessary. This command can take a while. And then dig the load balancer hostname to get the ip address, use this ip to setup an A record for your external dns name.
####Access the cluster
Once the API server is running, you should see:
```
$ kubectl --kubeconfig=kubeconfig get nodes
NAME STATUS AGE
ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m
ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m
ip-10-0-0-xx.us-west-1.compute.internal Ready,SchedulingDisabled 5m
```
###Setup PaddlePaddle Environment on AWS
Now, we've created a cluster with following network capability:
1. All Kubernetes nodes can communicate with each other.
1. All Docker containers on Kubernetes nodes can communicate with each other.
1. All Kubernetes nodes can communicate with all Docker containers on Kubernetes nodes.
1. All other traffic loads from outside of Kubernetes nodes cannot reach to the Docker containers on Kubernetes nodes except for creating the services for containers.
For sharing the training data across all the Kubernetes nodes, we use EFS (Elastic File System) in AWS. Ceph might be a better solution, but it requires high version of Linux kernel that might not be stable enough at this moment. We haven't automated the EFS setup at this moment, so please do the following steps:
1. Make sure you added AmazonElasticFileSystemFullAccess policy in your group.
1. Create the Elastic File System in AWS console, and attach the new VPC with it.
<img src="create_efs.png" width="800">
1. Modify the Kubernetes security group under ec2/Security Groups, add additional inbound policy "All TCP TCP 0 - 65535 0.0.0.0/0" for Kubernetes default VPC security group.
<img src="add_security_group.png" width="800">
1. Follow the EC2 mount instruction to mount the disk onto all the Kubernetes nodes, we recommend to mount EFS disk onto ~/efs.
<img src="efs_mount.png" width="800">
Before starting the training, you should place your user config and divided training data onto EFS. When the training start, each task will copy related files from EFS into container, and it will also write the training results back onto EFS, we will show you how to place the data later in this article.
###Core Concept of PaddlePaddle Training on AWS
Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each node we've attached the EFS volume, in this training demo, we will create three Kubernetes pod and scheduling them on 3 node. Each pod contains a PaddlePaddle container. When container gets created, it will start pserver and trainer process, load the training data from EFS volume and start the distributed training task.
####Use Kubernetes Job
We use Kubernetes job to represent one time of distributed training. After the job get finished, Kubernetes will destroy job container and release all related resources.
We can write a yaml file to describe the Kubernetes job. The file contains lots of configuration information, for example PaddlePaddle's node number, `paddle pserver` open port number, the network card info etc., these information are passed into container for processes to use as environment variables.
In one time of distributed training, user will confirm the PaddlePaddle node number first. And then upload the pre-divided training data and configuration file onth EFS volume. And then create the Kubernetes job yaml file; submit to the Kubernetes cluster to start the training job.
####Create PaddlePaddle Node
After Kubernetes master gets the request, it will parse the yaml file and create several pods (defined by PaddlePaddle's node number)， Kubernetes will allocate these pods onto cluster's node. A pod represents a PaddlePaddle node, when pod is successfully allocated onto one physical/virtual machine, Kubernetes will startup the container in the pod, and this container will use the environment variables in yaml file and start up `paddle pserver` and `paddle trainer` processes.
####Start up Training
After container gets started, it starts up the distributed training by using scripts. We know `paddle train` process need to know other node's ip address and it's own trainer_id, since PaddlePaddle currently don't have the ability to do the service discovery, so in the start up script, each node will use job pod's name to query all to pod info from Kubernetes apiserver (apiserver's endpoint is an environment variable in container by default).
With pod information, we can assign each pod a unique trainer_id. Here we sort all the pods by pod's ip, and assign the index to each PaddlePaddle node as it's trainer_id. The workflow of starting up the script is as follows:
1. Query the api server to get pod information, and assign the trainer_id by sorting the ip.
1. Copy the training data from EFS sharing volume into container.
1. Parse the `paddle pserver` and 'paddle trainer' startup parameters from environment variables, and then start up the processes.
1. PaddlePaddle will automatically write the result onto the PaddlePaddle node with trainer_id:0, we set the output path to be the EFS volume to save the result data.
###Start PaddlePaddle Training Demo on AWS
Now we'll start a PaddlePaddle training demo on AWS, steps are as follows:
1. Build PaddlePaddle Docker image.
1. Divide the training data file and upload it onto the EFS sharing volume.
1. Create the training job yaml file, and start up the job.
1. Check the result after training.
####Build PaddlePaddle Docker Image
PaddlePaddle docker image need to provide the runtime environment for `paddle pserver` and `paddle train`, so the container use this image should have two main function:
1. Copy the training data into container.
1. Generate the startup parameter for `paddle pserver` and `paddle train` process, and startup the training.
Since official `paddledev/paddle:cpu-latest` have already included the PaddlePaddle binary, but lack of the above functionalities, so we will create the startup script based on this image, to achieve the work above. the detailed Dockerfile is as follows:
```
FROM paddledev/paddle:cpu-latest
MAINTAINER zjsxzong89@gmail.com
COPY start.sh /root/
COPY start_paddle.py /root/
CMD ["bash"," -c","/root/start.sh"]
```
At this point, we will copy our `start.sh` and `start_paddle.py` file into container, and then exec `start_paddle.py` script to start up the training, all the steps like assigning trainer_id, getting other nodes' ip are implemented in `start_paddle.py`.
`start_paddle.py` will start parsing the parameters.
```
parser = argparse.ArgumentParser(prog="start_paddle.py",
description='simple tool for k8s')
args, train_args_list = parser.parse_known_args()
train_args = refine_unknown_args(train_args_list)
train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2]))
podlist = getPodList()
```
And then using function `getPodList()` to query all the pod information from the job name through Kubernetes api server. When all the pods are in the running status, using `getIdMap(podlist)` to get the trainer_id.
```
podlist = getPodList()
# need to wait until all pods are running
while not isPodAllRunning(podlist):
time.sleep(10)
podlist = getPodList()
idMap = getIdMap(podlist)
```
In function `getIdMap(podlist)`, we use podlist to get the ip address for each pod and sort them, use the index as the trainer_id.
```
def getIdMap(podlist):
'''
generate tainer_id by ip
'''
ips = []
for pod in podlist["items"]:
ips.append(pod["status"]["podIP"])
ips.sort()
idMap = {}
for i in range(len(ips)):
idMap[ips[i]] = i
return idMap
```
After getting `idMap`, we use function `startPaddle(idMap, train_args_dict)` to generate `paddle pserver` and `paddle train` start up parameters and then start up the processes.
In function `startPaddle`, the most important work is to generate `paddle pserver` and `paddle train` start up parameters. For example, `paddle train` parameter parsing, we will get parameters like `PADDLE_NIC`, `PADDLE_PORT`, `PADDLE_PORTS_NUM`, and get the `trainer_id` from `idMap`.
```
program = 'paddle train'
args = " --nics=" + PADDLE_NIC
args += " --port=" + str(PADDLE_PORT)
args += " --ports_num=" + str(PADDLE_PORTS_NUM)
args += " --comment=" + "paddle_process_by_paddle"
ip_string = ""
for ip in idMap.keys():
ip_string += (ip + ",")
ip_string = ip_string.rstrip(",")
args += " --pservers=" + ip_string
args_ext = ""
for key, value in train_args_dict.items():
args_ext += (' --' + key + '=' + value)
localIP = socket.gethostbyname(socket.gethostname())
trainerId = idMap[localIP]
args += " " + args_ext + " --trainer_id=" + \
str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT
```
Use `docker build` to build toe Docker Image:
```
docker build -t your_repo/paddle:mypaddle .
```
And then push the built image onto docker registry.
```
docker push your_repo/paddle:mypaddle
```
####Upload Training Data File
Here we will use PaddlePaddle's official recommendation demo as the content for this training, we put the training data file into a directory named by job name, which located in EFS sharing volume, the tree structure for the directory looks like:
```
efs
└── paddle-cluster-job
├── data
│ ├── 0
│ │
│ ├── 1
│ │
│ └── 2
├── output
└── recommendation
```
The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the pre-divided data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory.
####Create Kubernetes Job
Kubernetes use yaml file to describe job details, and then use command line tool to create the job in Kubernetes cluster.
In yaml file, we describe the Docker image we use for this training, the node number we need to startup, the volume mounting information and all the necessary parameters we need for `paddle pserver` and `paddle train` processes.
The yaml file content is as follows:
```
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/admin/efs
containers:
- name: trainer
image: drinkcode/paddle:k8s-job
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
ports:
- name: jobport
hostPort: 30001
containerPort: 30001
restartPolicy: Never
```
In yaml file, the metadata's name is the job's name. `parallelism, completions` means this job will simultaneously start up 3 PaddlePaddle nodes, and this job will be finished when there are 3 finished pods. For the data store volume, we declare the path jobpath, it mount the /home/admin/efs on host machine into the container with path /home/jobpath. So in container, the /home/jobpath actually stores the data onto EFS sharing volume.
`env` field represents container's environment variables, we pass the PaddlePaddle parameters into containers by using the `env` field.
`JOB_PATH` represents the sharing volume path, `JOB_NAME` represents job name, `TRAIN_CONFIG_DIR` represents the training data file directory, we can these three parameters to get the file path for this training.
`CONF_PADDLE_NIC` represents `paddle pserver` process's `--nics` parameters, the NIC name.
`CONF_PADDLE_PORT` represents `paddle pserver` process's `--port` parameters, `CONF_PADDLE_PORTS_NUM` represents `--port_num` parameter.
`CONF_PADDLE_PORTS_NUM_SPARSE` represents the sparse updated port number, `--ports_num_for_sparse` parameter.
`CONF_PADDLE_GRADIENT_NUM` represents the training node number, `--num_gradient_servers` parameter.
After we create the yaml file, we can use Kubernetes command line tool to create the job onto the cluster.
```
kubectl create -f job.yaml
```
After we execute the above command, Kubernetes will create 3 pods and then pull the PaddlePaddle image, then start up the containers for training.
####Check Training Results
During the training, we can see the logs and models on EFS sharing volume, the output directory contains the training results. (Caution: node_0, node_1, node_2 directories represents PaddlePaddle node and train_id, not the Kubernetes node)
```
[root@paddle-kubernetes-node0 output]# tree -d
.
├── node_0
│ ├── server.log
│ └── train.log
├── node_1
│ ├── server.log
│ └── train.log
├── node_2
......
├── pass-00002
│ ├── done
│ ├── ___embedding_0__.w0
│ ├── ___embedding_1__.w0
......
```
We can always check the container training status through logs, for example:
```
[root@paddle-kubernetes-node0 node_0]# cat train.log
I1116 09:10:17.123121 50 Util.cpp:155] commandline:
/usr/local/bin/../opt/paddle/bin/paddle_trainer
--nics=eth0 --port=7164
--ports_num=2 --comment=paddle_process_by_paddle
--pservers=192.168.129.66,192.168.223.143,192.168.129.71
--ports_num_for_sparse=2 --config=./trainer_config.py
--trainer_count=4 --num_passes=10 --use_gpu=0
--log_period=50 --dot_period=10 --saving_period=1
--local=0 --trainer_id=0
--save_dir=/home/jobpath/paddle-cluster-job/output
I1116 09:10:17.123440 50 Util.cpp:130] Calling runInitFunctions
I1116 09:10:17.123764 50 Util.cpp:143] Call runInitFunctions done.
[WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config.
[INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating]
[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__regression_cost_0__]
I1116 09:10:17.392917 50 Trainer.cpp:170] trainer mode: Normal
I1116 09:10:17.613910 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
I1116 09:10:17.680917 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
I1116 09:10:17.681543 50 GradientMachine.cpp:134] Initing parameters..
I1116 09:10:18.012390 50 GradientMachine.cpp:141] Init parameters done.
I1116 09:10:18.018641 50 ParameterClient2.cpp:122] pserver 0 192.168.129.66:7164
I1116 09:10:18.018950 50 ParameterClient2.cpp:122] pserver 1 192.168.129.66:7165
I1116 09:10:18.019069 50 ParameterClient2.cpp:122] pserver 2 192.168.223.143:7164
I1116 09:10:18.019492 50 ParameterClient2.cpp:122] pserver 3 192.168.223.143:7165
I1116 09:10:18.019716 50 ParameterClient2.cpp:122] pserver 4 192.168.129.71:7164
I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7165
```
It'll take around 8 hours to finish this PaddlePaddle recommendation training demo on three 2 core 8 GB EC2 machine (m3.large).
###Kubernetes Cluster Tear Down
If you want to tear down the whole Kubernetes cluster, make sure to *delete* the EFS volume first (otherwise, you will get stucked on following steps), and then use the following command:
```
kube-aws destroy
```
It's an async call, it might take 5 min to tear down the whole cluster.
If you created any Kubernetes Services of type LoadBalancer, you must delete these first, as the CloudFormation cannot be fully destroyed if any externally-managed resources still exist.
## For Experts with Kubernetes and AWS
Sometimes we might need to create or manage the cluster on AWS manually with limited privileges, so here we will explain more on what’s going on with the Kubernetes setup script.
### Some Presumptions
* Instances run on CoreOS, the official IAM.
* Kubernetes node use instance storage, no EBS get mounted. Etcd is running on additional node.
* For networking, we use Flannel network at this moment, we will use Calico solution later on.
* When you create a service with Type=LoadBalancer, Kubernetes will create and ELB, and create a security group for the ELB.
doc/howto/usage/cluster/k8s/k8s_en.md 0 → 100644
浏览文件 @ 1a24be18
# 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.
Then getting into `/root/paddle/demo/quick_start` Directory, using `preprocess.sh` to pre-process 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
```
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