ddlePaddle on AWS with Kubernetes ##Prerequisites You need an Amazon account and your user account needs the following privileges to continue: * AmazonEC2FullAccess * AmazonS3FullAccess * AmazonRoute53FullAccess * AmazonRoute53DomainsFullAccess * AmazonElasticFileSystemFullAccess * AmazonVPCFullAccess * IAMUserSSHKeys * IAMFullAccess * NetworkAdministrator ![managed_policy](managed_policy.png =800x)) If you are not in Unites States, we also recommend creating a jump server VM instance with default amazon AMI in the same available zone as your cluster and login to jump server for the following operations, otherwise there will be some issues related to account authentication. ##PaddlePaddle on AWS If you are new to Kubernetes or AWS and just want to run PaddlePaddle, you can follow these steps to start up a new cluster. ###AWS Login First configure your AWS account information: ``` aws configure ``` Fill in the required fields: ``` 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 ``` ###Kubernetes Cluster Start Up And then type the following command: ``` export KUBERNETES_PROVIDER=aws; curl -sS https://get.k8s.io | bash ``` By default, the script will provision a new VPC and a 4 node k8s cluster in us-west-2a (Oregon) with EC2 instances running on Debian. You can override the variables defined in `/cluster/config-default.sh` to change this behavior as follows: ``` export KUBE_AWS_ZONE=us-west-2a export NUM_NODES=3 export MASTER_SIZE=m3.medium export NODE_SIZE=m3.large export AWS_S3_REGION=us-west-2a export AWS_S3_BUCKET=mycompany-kubernetes-artifacts export KUBE_AWS_INSTANCE_PREFIX=k8s ... ``` This process takes about 5 to 10 minutes. ``` [ec2-user@ip-172-31-27-229 ~]$ export KUBERNETES_PROVIDER=aws; curl -sS https://get.k8s.io | bash 'kubernetes' directory already exist. Should we skip download step and start to create cluster based on it? [Y]/n Skipping download step. Creating a kubernetes on aws... ... Starting cluster in us-west-2a using provider aws ... calling verify-prereqs ... calling kube-up Starting cluster using os distro: jessie Uploading to Amazon S3 +++ Staging server tars to S3 Storage: kubernetes-staging-9996f910edd9ec30ed3f8e3a9db7466c/devel upload: ../../../tmp/kubernetes.KsacFg/s3/bootstrap-script to s3://kubernetes-staging-9996f910edd9ec30ed3f8e3a9db7466c/devel/bootstrap-script Uploaded server tars: SERVER_BINARY_TAR_URL: https://s3.amazonaws.com/kubernetes-staging-9996f910edd9ec30ed3f8e3a9db7466c/devel/kubernetes-server-linux-amd64.tar.gz SALT_TAR_URL: https://s3.amazonaws.com/kubernetes-staging-9996f910edd9ec30ed3f8e3a9db7466c/devel/kubernetes-salt.tar.gz BOOTSTRAP_SCRIPT_URL: https://s3.amazonaws.com/kubernetes-staging-9996f910edd9ec30ed3f8e3a9db7466c/devel/bootstrap-script INSTANCEPROFILE arn:aws:iam::330323714104:instance-profile/kubernetes-master 2016-12-01T03:19:54Z AIPAIQDDLSMLWJ2QDXM6I kubernetes-master / ROLES arn:aws:iam::330323714104:role/kubernetes-master 2016-12-01T03:19:52Z / AROAJDKKDIYHJTTEJM73M kubernetes-master ASSUMEROLEPOLICYDOCUMENT 2012-10-17 STATEMENT sts:AssumeRole Allow PRINCIPAL ec2.amazonaws.com INSTANCEPROFILE arn:aws:iam::330323714104:instance-profile/kubernetes-minion 2016-12-01T03:19:57Z AIPAJGNG4GYTNVP3UQU4S kubernetes-minion / ROLES arn:aws:iam::330323714104:role/kubernetes-minion 2016-12-01T03:19:55Z / AROAIZVAWWBIVUENE5XB4 kubernetes-minion ASSUMEROLEPOLICYDOCUMENT 2012-10-17 STATEMENT sts:AssumeRole Allow PRINCIPAL ec2.amazonaws.com Using SSH key with (AWS) fingerprint: 70:66:c6:3d:53:3b:e5:3d:1d:7f:cd:c9:d1:87:35:81 Creating vpc. Adding tag to vpc-e01fc087: Name=kubernetes-vpc Adding tag to vpc-e01fc087: KubernetesCluster=kubernetes Using VPC vpc-e01fc087 Adding tag to dopt-807151e4: Name=kubernetes-dhcp-option-set Adding tag to dopt-807151e4: KubernetesCluster=kubernetes Using DHCP option set dopt-807151e4 Creating subnet. Adding tag to subnet-4a9a642d: KubernetesCluster=kubernetes Using subnet subnet-4a9a642d Creating Internet Gateway. Using Internet Gateway igw-821a73e6 Associating route table. Creating route table Adding tag to rtb-0d96fa6a: KubernetesCluster=kubernetes Associating route table rtb-0d96fa6a to subnet subnet-4a9a642d Adding route to route table rtb-0d96fa6a Using Route Table rtb-0d96fa6a Creating master security group. Creating security group kubernetes-master-kubernetes. Adding tag to sg-a47564dd: KubernetesCluster=kubernetes Creating minion security group. Creating security group kubernetes-minion-kubernetes. Adding tag to sg-9a7564e3: KubernetesCluster=kubernetes Using master security group: kubernetes-master-kubernetes sg-a47564dd Using minion security group: kubernetes-minion-kubernetes sg-9a7564e3 Creating master disk: size 20GB, type gp2 Adding tag to vol-0eba023cc1874c790: Name=kubernetes-master-pd Adding tag to vol-0eba023cc1874c790: KubernetesCluster=kubernetes Allocated Elastic IP for master: 35.165.155.60 Adding tag to vol-0eba023cc1874c790: kubernetes.io/master-ip=35.165.155.60 Generating certs for alternate-names: IP:35.165.155.60,IP:172.20.0.9,IP:10.0.0.1,DNS:kubernetes,DNS:kubernetes.default,DNS:kubernetes.default.svc,DNS:kubernetes.default.svc.cluster.local,DNS:kubernetes-master Starting Master Adding tag to i-097f358631739e01c: Name=kubernetes-master Adding tag to i-097f358631739e01c: Role=kubernetes-master Adding tag to i-097f358631739e01c: KubernetesCluster=kubernetes Waiting for master to be ready Attempt 1 to check for master nodeWaiting for instance i-097f358631739e01c to be running (currently pending) Sleeping for 3 seconds... Waiting for instance i-097f358631739e01c to be running (currently pending) Sleeping for 3 seconds... Waiting for instance i-097f358631739e01c to be running (currently pending) Sleeping for 3 seconds... Waiting for instance i-097f358631739e01c to be running (currently pending) Sleeping for 3 seconds... [master running] Attaching IP 35.165.155.60 to instance i-097f358631739e01c Attaching persistent data volume (vol-0eba023cc1874c790) to master 2016-12-13T10:56:50.378Z /dev/sdb i-097f358631739e01c attaching vol-0eba023cc1874c790 cluster "aws_kubernetes" set. user "aws_kubernetes" set. context "aws_kubernetes" set. switched to context "aws_kubernetes". user "aws_kubernetes-basic-auth" set. Wrote config for aws_kubernetes to /home/ec2-user/.kube/config Creating minion configuration Creating autoscaling group 0 minions started; waiting 0 minions started; waiting 0 minions started; waiting 0 minions started; waiting 0 minions started; waiting 3 minions started; ready Waiting for cluster initialization. This will continually check to see if the API for kubernetes is reachable. This might loop forever if there was some uncaught error during start up. ...........................................................................................................................................................................Kubernetes cluster created. Sanity checking cluster... Attempt 1 to check Docker on node @ 35.165.35.181 ...working Attempt 1 to check Docker on node @ 35.165.79.208 ...working Attempt 1 to check Docker on node @ 35.163.90.67 ...working Kubernetes cluster is running. The master is running at: https://35.165.155.60 The user name and password to use is located in /home/ec2-user/.kube/config. ... calling validate-cluster Waiting for 3 ready nodes. 0 ready nodes, 3 registered. Retrying. Waiting for 3 ready nodes. 0 ready nodes, 3 registered. Retrying. Found 3 node(s). NAME STATUS AGE ip-172-20-0-186.us-west-2.compute.internal Ready 33s ip-172-20-0-187.us-west-2.compute.internal Ready 34s ip-172-20-0-188.us-west-2.compute.internal Ready 34s Validate output: NAME STATUS MESSAGE ERROR scheduler Healthy ok controller-manager Healthy ok etcd-1 Healthy {"health": "true"} etcd-0 Healthy {"health": "true"} Cluster validation succeeded Done, listing cluster services: Kubernetes master is running at https://35.165.155.60 Elasticsearch is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/elasticsearch-logging Heapster is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/heapster Kibana is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/kibana-logging KubeDNS is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/kube-dns kubernetes-dashboard is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/kubernetes-dashboard Grafana is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/monitoring-grafana InfluxDB is running at https://35.165.155.60/api/v1/proxy/namespaces/kube-system/services/monitoring-influxdb To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'. Kubernetes binaries at /home/ec2-user/kubernetes/cluster/ You may want to add this directory to your PATH in $HOME/.profile Installation successful! ``` Once the cluster is up, the IP addresses of your master and node(s) will be printed, as well as information about the default services running in the cluster (monitoring, logging, dns). User credentials and security tokens are written in `~/.kube/config`, they will be necessary to use the CLI or the HTTP Basic Auth. And then concate the kubernetes binaries directory into PATH: ``` export PATH=/platforms/linux/amd64:$PATH ``` Now you can use administration tool `kubectl` to operate the cluster. By default, `kubectl` will use the kubeconfig file generated during the cluster startup for authenticating against the API, the location is in `~/.kube/config`. ###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 add the AmazonElasticFileSystemFullAccess policy into your AWS account. 1. Create the Elastic File System in AWS console, and attach the Kubernetes VPC with it. ![create_efs](create_efs.png =800x) 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. ![add_security_group](add_security_group.png =800x) 1. Follow the EC2 mount instruction to mount the disk onto all the Kubernetes nodes, we recommend to mount EFS disk onto ~/efs. ![efs_mount](efs_mount.png =800x) 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 node distributed training 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 (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 run this PaddlePaddle recommendation training demo on three 2 core 8 GB EC2 machine (m3.large), and the results will be 8 trained models. ###Kubernetes Cluster Tear Down If you want to tear down the running cluster, make sure to *delete* the EFS volume first, and then use the following command: ``` export KUBERNETES_PROVIDER=aws; /cluster/kube-down.sh ``` This process takes about 2 to 5 minutes. ``` ec2-user@ip-172-31-27-229 ~]$ export KUBERNETES_PROVIDER=aws; ./kubernetes/cluster/kube-down.sh Bringing down cluster using provider: aws Deleting instances in VPC: vpc-e01fc087 Deleting auto-scaling group: kubernetes-minion-group-us-west-2a Deleting auto-scaling launch configuration: kubernetes-minion-group-us-west-2a Deleting auto-scaling group: kubernetes-minion-group-us-west-2a Deleting auto-scaling group: kubernetes-minion-group-us-west-2a Waiting for instances to be deleted Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... Waiting for instance i-04e973f1d6d56d580 to be terminated (currently shutting-down) Sleeping for 3 seconds... All instances deleted Releasing Elastic IP: 35.165.155.60 Deleting volume vol-0eba023cc1874c790 Cleaning up resources in VPC: vpc-e01fc087 Cleaning up security group: sg-9a7564e3 Cleaning up security group: sg-a47564dd Deleting security group: sg-9a7564e3 Deleting security group: sg-a47564dd Deleting VPC: vpc-e01fc087 Done ``` ## 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 Debian, the official IAM, and the filesystem is aufs instead of ext4. * Kubernetes node use instance storage, no EBS get mounted. Master use a persistent volume for etcd. * Nodes are running in an Auto Scaling Group on AWS, auto-scaling itself is disabled, but if some node get terminated, it will launch another node instead. * For networking, we use ip-per-pod model here, each pod get assigned a /24 CIDR. And the whole vpc is a /16 CIDR, No overlay network at this moment, we will add 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. * Kube-proxy sets up two IAM roles, one for master called kubernetes-master, one for nodes called kubernetes-node. * All AWS resources are tagged with a tag named "KubernetesCluster", with a value that is the unique cluster-id. ###Script Details * Create an s3 bucket for binaries and scripts. * Create two iam roles: kubernetes-master, kubernetes-node. * Create an AWS SSH key named kubernetes-YOUR_RSA_FINGERPRINT. * Create a vpc with 172.20.0.0/16 CIDR, and enables dns-support and dns-hostnames options in vpc settings. * Create Internet gateway, route table, a subnet with CIDR of 172.20.0.0/24, and associate the subnet to the route table. * Create and configure security group for master and nodes. * Create an EBS for master, it will be attached after the master node get up. * Launch the master with fixed ip address 172.20.0.9, and the node is initialized with Salt script, all the components get started as docker containers. * Create an auto-scaling group, it has the min and max size, it can be changed by using aws api or console, it will auto launch the kubernetes node and configure itself, connect to master, assign an internal CIDR, and the master configures the route table with the assigned CIDR.