From fe4f45685bef59d5d4d79cd1c9dfab8ef71a7212 Mon Sep 17 00:00:00 2001 From: Jongseob Jeon Date: Sun, 13 Mar 2022 15:00:06 +0900 Subject: [PATCH] new menu for prerequisites (#107) * update Signed-off-by: Jongseob Jeon * add preprequisites docker (#108) * rebase * add docker posts Signed-off-by: Jongseob Jeon * add post and fix pre-commit Signed-off-by: Jongseob Jeon * update Signed-off-by: Jongseob Jeon * finalize docker Signed-off-by: Jongseob Jeon * change title requirements to install Signed-off-by: Jongseob Jeon * Apply suggestions from code review Co-authored-by: Jaeyeon Kim * Apply suggestions from code review Co-authored-by: Jaeyeon Kim * update Signed-off-by: Jongseob Jeon Co-authored-by: Jongseob Jeon Co-authored-by: Jaeyeon Kim Co-authored-by: Jongseob Jeon Co-authored-by: Jaeyeon Kim --- config/_default/menus/menus.en.toml | 41 +- config/_default/menus/menus.kor.toml | 31 +- config/_default/params.toml | 1 + .../en/docs/api-deployment/seldon-children.md | 2 +- .../en/docs/api-deployment/seldon-fields.md | 2 +- content/en/docs/api-deployment/seldon-iris.md | 4 +- .../en/docs/api-deployment/seldon-mlflow.md | 36 +- content/en/docs/api-deployment/seldon-pg.md | 42 ++ content/en/docs/appendix/metallb.md | 117 ++--- content/en/docs/further-readings/info.md | 21 + content/en/docs/help/_index.md | 4 +- .../help/{discussions.md => community.md} | 3 +- content/en/docs/help/how-to-contribute.md | 25 +- .../en/docs/kubeflow-dashboard-guide/intro.md | 8 +- .../en/docs/kubeflow/advanced-component.md | 4 +- content/en/docs/kubeflow/advanced-mlflow.md | 386 ++++++++++++++- content/en/docs/kubeflow/advanced-run.md | 111 ++++- .../setup-components/install-components-kf.md | 24 +- .../setup-components/install-components-pg.md | 18 - .../setup-kubernetes/install-prerequisite.md | 19 +- content/en/docs/setup-kubernetes/intro.md | 19 +- .../kubernetes-with-kubeadm.md | 54 +-- .../en/docs/setup-kubernetes/kubernetes.md | 10 +- .../docs/setup-kubernetes/setup-nvidia-gpu.md | 16 +- content/en/docs/{prologue => }/welcome.md | 3 - content/en/prerequisites/_index.md | 7 + content/en/prerequisites/docker/_index.md | 8 + content/en/prerequisites/intro.md | 10 + content/en/prerequisites/kubernetes/_index.md | 8 + content/kor/docs/{prologue => }/welcome.md | 3 - content/kor/prerequisites/_index.md | 7 + content/kor/prerequisites/docker/_index.md | 8 + content/kor/prerequisites/docker/advanced.md | 280 +++++++++++ content/kor/prerequisites/docker/command.md | 440 ++++++++++++++++++ content/kor/prerequisites/docker/docker.md | 59 +++ content/kor/prerequisites/docker/images.md | 245 ++++++++++ content/kor/prerequisites/docker/install.md | 37 ++ .../kor/prerequisites/docker/introduction.md | 104 +++++ content/kor/prerequisites/intro.md | 10 + .../kor/prerequisites/kubernetes/_index.md | 8 + layouts/index.html | 4 +- layouts/prerequisites/list.html | 22 + layouts/prerequisites/single.html | 53 +++ .../prerequisites/docker/cncf-survey.png | Bin 0 -> 117921 bytes .../prerequisites/docker/docker-layer.png | Bin 0 -> 27117 bytes static/images/prerequisites/docker/layers.png | Bin 0 -> 108782 bytes .../images/prerequisites/docker/storage.png | Bin 0 -> 22623 bytes .../prerequisites/docker/sysdig-2019.png | Bin 0 -> 437287 bytes .../prerequisites/docker/sysdig-2021.png | Bin 0 -> 33416 bytes static/images/prerequisites/docker/t4-ai.png | Bin 0 -> 57718 bytes 50 files changed, 2126 insertions(+), 188 deletions(-) create mode 100644 content/en/docs/api-deployment/seldon-pg.md rename content/en/docs/help/{discussions.md => community.md} (98%) rename content/en/docs/{prologue => }/welcome.md (97%) create mode 100644 content/en/prerequisites/_index.md create mode 100644 content/en/prerequisites/docker/_index.md create mode 100644 content/en/prerequisites/intro.md create mode 100644 content/en/prerequisites/kubernetes/_index.md rename content/kor/docs/{prologue => }/welcome.md (97%) create mode 100644 content/kor/prerequisites/_index.md create mode 100644 content/kor/prerequisites/docker/_index.md create mode 100644 content/kor/prerequisites/docker/advanced.md create mode 100644 content/kor/prerequisites/docker/command.md create mode 100644 content/kor/prerequisites/docker/docker.md create mode 100644 content/kor/prerequisites/docker/images.md create mode 100644 content/kor/prerequisites/docker/install.md create mode 100644 content/kor/prerequisites/docker/introduction.md create mode 100644 content/kor/prerequisites/intro.md create mode 100644 content/kor/prerequisites/kubernetes/_index.md create mode 100644 layouts/prerequisites/list.html create mode 100644 layouts/prerequisites/single.html create mode 100644 static/images/prerequisites/docker/cncf-survey.png create mode 100644 static/images/prerequisites/docker/docker-layer.png create mode 100644 static/images/prerequisites/docker/layers.png create mode 100644 static/images/prerequisites/docker/storage.png create mode 100644 static/images/prerequisites/docker/sysdig-2019.png create mode 100644 static/images/prerequisites/docker/sysdig-2021.png create mode 100644 static/images/prerequisites/docker/t4-ai.png diff --git a/config/_default/menus/menus.en.toml b/config/_default/menus/menus.en.toml index d25ca3f8..7b1cb733 100644 --- a/config/_default/menus/menus.en.toml +++ b/config/_default/menus/menus.en.toml @@ -1,9 +1,15 @@ +# Main Section + [[main]] name = "Docs" - url = "/docs/prologue/welcome/" -# url = "/docs/1.0/prologue/introduction/" + url = "/docs/welcome/" weight = 10 +[[main]] + name = "Prerequisites" + url = "/prerequisites/intro" + weight = 15 + # [[main]] # name = "Blog" # url = "/blog/" @@ -19,11 +25,12 @@ url = "/contact/" weight = 30 -[[docs]] - name = "Prologue" - url = "/docs/prologue/" - identifier = "prologue" - weight = 1 +[[main]] + name = "Help" + url = "/help/" + weight = 100 + +# Docs side menu [[docs]] name = "Introduction" @@ -73,11 +80,21 @@ identifier = "appendix" weight = 60 -[[docs]] - name = "Help" - url = "/docs/help/" - identifier = "help" - weight = 100 +# Prerequisites side menu + +[[prerequisites]] + name = "Docker" + url = "/prerequisites/docker/" + identifier = "docker" + weight = 10 + +[[prerequisites]] + name = "kubernetes" + url = "/prerequisites/kubernetes/" + identifier = "kubernetes" + weight = 20 + +# Social Badges [[social]] name = "GitHub" diff --git a/config/_default/menus/menus.kor.toml b/config/_default/menus/menus.kor.toml index 6b558205..7b1cb733 100644 --- a/config/_default/menus/menus.kor.toml +++ b/config/_default/menus/menus.kor.toml @@ -1,8 +1,15 @@ +# Main Section + [[main]] name = "Docs" - url = "/docs/prologue/welcome/" + url = "/docs/welcome/" weight = 10 +[[main]] + name = "Prerequisites" + url = "/prerequisites/intro" + weight = 15 + # [[main]] # name = "Blog" # url = "/blog/" @@ -23,11 +30,7 @@ url = "/help/" weight = 100 -[[docs]] - name = "Prologue" - url = "/docs/prologue/" - identifier = "prologue" - weight = 1 +# Docs side menu [[docs]] name = "Introduction" @@ -77,6 +80,22 @@ identifier = "appendix" weight = 60 +# Prerequisites side menu + +[[prerequisites]] + name = "Docker" + url = "/prerequisites/docker/" + identifier = "docker" + weight = 10 + +[[prerequisites]] + name = "kubernetes" + url = "/prerequisites/kubernetes/" + identifier = "kubernetes" + weight = 20 + +# Social Badges + [[social]] name = "GitHub" pre = "" diff --git a/config/_default/params.toml b/config/_default/params.toml index 60f44f5d..816ad840 100644 --- a/config/_default/params.toml +++ b/config/_default/params.toml @@ -94,3 +94,4 @@ lastMod = false [menu.section] auto = false collapsibleSidebar = true + mainSections = ["docs", "prerequisites"] diff --git a/content/en/docs/api-deployment/seldon-children.md b/content/en/docs/api-deployment/seldon-children.md index 5d982ea8..a1afd8db 100644 --- a/content/en/docs/api-deployment/seldon-children.md +++ b/content/en/docs/api-deployment/seldon-children.md @@ -1,5 +1,5 @@ --- -title : "5. Multi Models" +title : "6. Multi Models" description: "" lead: "" draft: false diff --git a/content/en/docs/api-deployment/seldon-fields.md b/content/en/docs/api-deployment/seldon-fields.md index f08264fd..3e09d18c 100644 --- a/content/en/docs/api-deployment/seldon-fields.md +++ b/content/en/docs/api-deployment/seldon-fields.md @@ -1,5 +1,5 @@ --- -title : "3. Seldon Fields" +title : "4. Seldon Fields" description: "" lead: "" draft: false diff --git a/content/en/docs/api-deployment/seldon-iris.md b/content/en/docs/api-deployment/seldon-iris.md index 9628b496..15806323 100644 --- a/content/en/docs/api-deployment/seldon-iris.md +++ b/content/en/docs/api-deployment/seldon-iris.md @@ -6,7 +6,7 @@ date: 2021-12-22 lastmod: 2021-12-22 draft: false weight: 411 -contributors: ["Youngcheol Jang"] +contributors: ["Youngcheol Jang", "SeungTae Kim"] menu: docs: parent: "api-deployment" @@ -97,7 +97,7 @@ echo "NODE_IP"=$NODE_IP echo "NODE_PORT"=$NODE_PORT ``` -다음과 비슷하게 출력되어야 합니다. +다음과 비슷하게 출력되어야 하며, 클라우드 등을 통해 설정할 경우, internal ip 주소가 설정되는 것을 확인할 수 있습니다. ```text NODE_IP=192.168.0.19 diff --git a/content/en/docs/api-deployment/seldon-mlflow.md b/content/en/docs/api-deployment/seldon-mlflow.md index e77fba33..29066859 100644 --- a/content/en/docs/api-deployment/seldon-mlflow.md +++ b/content/en/docs/api-deployment/seldon-mlflow.md @@ -1,5 +1,5 @@ --- -title : "4. Model from MLflow" +title : "5. Model from MLflow" description: "" lead: "" draft: false @@ -221,6 +221,9 @@ spec: - name: model_uri type: STRING value: "/mnt/models" + - name: xtype + type: STRING + value: "dataframe" children: [] EOF ``` @@ -249,3 +252,34 @@ kubectl get po -n kubeflow-user-example-com | grep seldon ```text seldon-example-model-0-model-5c949bd894-c5f28 3/3 Running 0 69s ``` + +CLI를 이용해 생성된 API에는 다음 request를 통해 실행을 확인할 수 있습니다. + +```text +curl -X POST http://$NODE_IP:$NODE_PORT/seldon/seldon-deploy/sklearn/api/v1.0/predictions \ +-H 'Content-Type: application/json' \ +-d '{ + "data": { + "ndarray": [ + [ + 143.0, + 0.0, + 30.0, + 30.0 + ] + ], + "names": [ + "sepal length (cm)", + "sepal width (cm)", + "petal length (cm)", + "petal width (cm)" + ] + } +}' +``` + +정상적으로 실행될 경우 다음과 같은 결과를 받을 수 있습니다. + +```text +{"data":{"names":[],"ndarray":["Virginica"]},"meta":{"requestPath":{"model":"ghcr.io/mlops-for-all/mlflowserver:e141f57"}}} +``` diff --git a/content/en/docs/api-deployment/seldon-pg.md b/content/en/docs/api-deployment/seldon-pg.md new file mode 100644 index 00000000..e662685f --- /dev/null +++ b/content/en/docs/api-deployment/seldon-pg.md @@ -0,0 +1,42 @@ +--- +title : "3. Seldon Monitoring" +description: "Prometheus & Grafana 확인하기" +date: 2021-12-24 +lastmod: 2021-12-24 +draft: false +weight: 412 +contributors: ["Jongseob Jeon"] +menu: + docs: + parent: "api-deployment" +--- + +## Grafana & Prometheus + +이제, [지난 페이지]({{< relref "docs/api-deployment/seldon-iris.md" >}})에서 생성했던 SeldonDeployment 로 API Request 를 반복적으로 수행해보고, 대시보드에 변화가 일어나는지 확인해봅니다. + +### 대시보드 + +[앞서 생성한 대시보드]({{< relref "docs/setup-components/install-components-pg.md" >}})를 포트 포워딩합니다. + +```text +kubectl port-forward svc/seldon-core-analytics-grafana -n seldon-system 8090:80 +``` + +### API 요청 + +[앞서 생성한 Seldon Deployment]({{< relref "docs/api-deployment/seldon-iris.md#using-cli" >}})에 요청을 **반복해서** 보냅니다. + +```text +curl -X POST http://$NODE_IP:$NODE_PORT/seldon/seldon-deploy/sklearn/api/v1.0/predictions \ +-H 'Content-Type: application/json' \ +-d '{ "data": { "ndarray": [[1,2,3,4]] } }' +``` + +그리고 그라파나 대시보드를 확인하면 다음과 같이 Global Request Rate 이 `0 ops` 에서 순간적으로 상승하는 것을 확인할 수 있습니다. + +

+ +

+ +이렇게 프로메테우스와 그라파나가 정상적으로 설치된 것을 확인할 수 있습니다. diff --git a/content/en/docs/appendix/metallb.md b/content/en/docs/appendix/metallb.md index a08a0b9a..3147adc8 100644 --- a/content/en/docs/appendix/metallb.md +++ b/content/en/docs/appendix/metallb.md @@ -9,26 +9,28 @@ weight: 8001 toc: true --- -## What is MetalLB? +## MetalLB란? -When using Kubernetes, cloud platforms such as AWS, GCP, and Azure provide their load balancer. On-premise clusters, however, require additional installations of modules that provide load balancing. -[MetalLB](https://metallb.universe.tf/) is an open-source project that provides load balancers in bare-metal environments. +Kubernetes 사용 시 AWS, GCP, Azure 와 같은 클라우드 플랫폼에서는 자체적으로 로드 벨런서(Load Balancer)를 제공해 주지만, 온프레미스 클러스터에서는 로드 벨런싱 기능을 제공하는 모듈을 추가적으로 설치해야 합니다. +[MetalLB](https://metallb.universe.tf/)는 베어메탈 환경에서 사용할 수 있는 로드 벨런서를 제공하는 오픈소스 프로젝트 입니다. -## Requirements +## 요구사항 -| requirments | version and content | +| 요구 사항 | 버전 및 내용 | | ------------------------------------------------------------ | ------------------------------------------------------------ | -| Kubernetes | Kubernetes 1.13.0 or later, that does not already have network load-balancing functionality | -| [cluster network configuration](https://metallb.universe.tf/installation/network-addons/) | Calico, Canal, Cilium, Flannel, Kube-ovn, Kube-router, Weave Net | -| IPv4 addresses | for MetalLB to hand out | -| When using the BGP operating mode | need one or more routers capable of speaking [BGP](https://en.wikipedia.org/wiki/Border_Gateway_Protocol) | -| Traffic on port 7946 (TCP & UDP) must be allowed between nodes | required by [memberlist](https://github.com/hashicorp/memberlist). +| Kubernetes | 로드 벨런싱 기능이 없는 >= v1.13.0 | +| [호환가능한 네트워크 CNI](https://metallb.universe.tf/installation/network-addons/) | Calico, Canal, Cilium, Flannel, Kube-ovn, Kube-router, Weave Net | +| IPv4 주소 | MetalLB 배포에 사용 | +| BGP 모드를 사용할 경우 | BGP 기능을 지원하는 하나 이상의 라우터 | +| 노드 간 포트 TCP/UDP 7946 오픈 | memberlist 요구 사항 -## Installation +## MetalLB 설치 ### Preparation -If you use kube-proxy in IPVS mode, you must enable strict ARP mode after Kubernetes v1.14.2. Kube-router basically activates strict ARP, so this function is not required when used as a service proxy. Before applying the strict ARP mode, check whether the current mode is a strict ARP mode or not. +IPVS 모드에서 kube-proxy를 사용하는 경우 Kubernetes v1.14.2 이후부터는 엄격한 ARP(strictARP) 모드를 사용하도록 설정해야 합니다. +Kube-router는 기본적으로 엄격한 ARP를 활성화하므로 서비스 프록시로 사용할 경우에는 이 기능이 필요하지 않습니다. +엄격한 ARP 모드를 적용하기에 앞서, 현재 모드를 확인합니다. ```text # see what changes would be made, returns nonzero returncode if different @@ -40,7 +42,8 @@ grep strictARP strictARP: false ``` -If the output is strickARP: false, change it to strickARP:true by executing the following command. (If the result is already strickARP:true, you don't have to run the below command.) +strictARP: false 가 출력되는 경우 다음을 실행하여 strictARP: true로 변경합니다. +(strictARP: true가 이미 출력된다면 다음 커맨드를 수행하지 않으셔도 됩니다.) ```text # actually apply the changes, returns nonzero returncode on errors only @@ -49,31 +52,31 @@ sed -e "s/strictARP: false/strictARP: true/" | \ kubectl apply -f - -n kube-system ``` -You will see the following result as output: +정상적으로 수행되면 다음과 같이 출력됩니다. ```text Warning: resource configmaps/kube-proxy is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by kubectl apply. kubectl apply should only be used on resources created declaratively by either kubectl create --save-config or kubectl apply. The missing annotation will be patched automatically. configmap/kube-proxy configured ``` -### Installation by manifest +### 설치 - Manifest -#### 1. Apply the manifest +#### 1. MetalLB 를 설치합니다. ```text kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/namespace.yaml kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/metallb.yaml ``` -#### 2. Check the result +#### 2. 정상 설치 확인 -Wait until both pods of the metallb-system namespace become Running. +metallb-system namespace 의 2 개의 pod 이 모두 Running 이 될 때까지 기다립니다. ```text kubectl get pod -n metallb-system ``` -When all pods are running, you will see the following result as output: +모두 Running 이 되면 다음과 비슷한 결과가 출력됩니다. ```text NAME READY STATUS RESTARTS AGE @@ -81,34 +84,34 @@ controller-7dcc8764f4-8n92q 1/1 Running 1 1m speaker-fnf8l 1/1 Running 1 1m ``` -The components in the manifest are: +매니페스트의 구성 요소는 다음과 같습니다. - metallb-system/controller - - distributed as deployment and is responsible for handling the allocation of external IP addresses to perform load balancing. + - deployment 로 배포되며, 로드 벨런싱을 수행할 external IP 주소의 할당을 처리하는 역할을 담당합니다. - metallb-system/speaker - - distributed as daemonset and is responsible for configuring network communication by connecting external traffic and services. + - daemonset 형태로 배포되며, 외부 트래픽과 서비스를 연결해 네트워크 통신이 가능하도록 구성하는 역할을 담당합니다. -The service includes the RBAC permissions required for controllers, speakers, and components to operate. +서비스에는 컨트롤러 및 스피커와 구성 요소가 작동하는 데 필요한 RBAC 사용 권한이 포함됩니다. ## Configuration -MetalLB's load balancing policy setting can be set by deploying a configmap containing related setting information. +MetalLB 의 로드 벨런싱 정책 설정은 관련 설정 정보를 담은 configmap 을 배포하여 설정할 수 있습니다. -MetalLB has two configurations: +MetalLB 에서 구성할 수 있는 모드로는 다음과 같이 2가지가 있습니다. -1. [Layer 2](https://metallb.universe.tf/concepts/layer2/) -2. [BGP](https://metallb.universe.tf/concepts/bgp/) +1. [Layer 2 모드](https://metallb.universe.tf/concepts/layer2/) +2. [BGP 모드](https://metallb.universe.tf/concepts/bgp/) -We will explain how to set up MetalLB with Layer 2 mode. +여기에서는 Layer 2 모드로 진행하겠습니다. ### Layer 2 Configuration -Layer 2 mode is the simplest to configure: in many cases, you don’t need any protocol-specific configuration, only IP addresses. +Layer 2 모드는 간단하게 사용할 IP 주소의 대역만 설정하면 됩니다. +Layer 2 모드를 사용할 경우 워커 노드의 네트워크 인터페이스에 IP를 바인딩 하지 않아도 되는데 로컬 네트워크의 ARP 요청에 직접 응답하여 컴퓨터의 MAC주소를 클라이언트에 제공하는 방식으로 작동하기 때문입니다. -Layer 2 mode does not require the IPs to be bound to the network interfaces of your worker nodes. It works by responding to ARP requests on your local network directly, to give the machine’s MAC address to clients. +다음 `metallb_config.yaml` 파일은 MetalLB 가 192.168.35.100 ~ 192.168.35.110의 IP에 대한 제어 권한을 제공하고 Layer 2 모드를 구성하는 설정입니다. -The following configuration `metallb_config.yaml` gives MetalLB control over IPs from 192.168.35.100 to 192.168.35.110 and configures Layer 2 mode. -If the cluster node and the client node are separated, the 192.168.35.100 to 192.168.35.110 bands must be accessible to both the client node and the cluster node. +클러스터 노드와 클라이언트 노드가 분리된 경우, 192.168.35.100 ~ 192.168.35.110 대역이 클라이언트 노드와 클러스터 노드 모두 접근 가능한 대역이어야 합니다. #### metallb_config.yaml @@ -127,36 +130,37 @@ data: - 192.168.35.100-192.168.35.110 # IP 대역폭 ``` -Apply the above settings. +위의 설정을 적용합니다. ```test kubectl apply -f metallb_config.yaml ``` -You will see the following result as output: +정상적으로 배포하면 다음과 같이 출력됩니다. ```test configmap/config created ``` -## Usage +## MetalLB 사용 ### Kubeflow Dashboard -First, check the current status before receiving load balancing from MetalLB by changing the type of istio-ingressgateway service to `LoadBalancer` in the istio-system namespace that provides kubeflow's dashboard. +먼저 kubeflow의 Dashboard 를 제공하는 istio-system 네임스페이스의 istio-ingressgateway 서비스의 타입을 `LoadBalancer`로 변경하여 MetalLB로부터 로드 벨런싱 기능을 제공받기 전에, 현재 상태를 확인합니다. ```text kubectl get svc/istio-ingressgateway -n istio-system ``` -You can see that the type of service is ClusterIP, and the External-IP value is `none`. +해당 서비스의 타입은 ClusterIP이며, External-IP 값은 `none` 인 것을 확인할 수 있습니다. ```text NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE istio-ingressgateway ClusterIP 10.103.72.5 15021/TCP,80/TCP,443/TCP,31400/TCP,15443/TCP 4h21m ``` -Change the type to Load Balancer and add a `loadBalancerIP` key-value pair if you want to enter the desired IP address. If not added, IP addresses will be assigned sequentially from the IP address pool set from `metallb_config.yaml`. +type 을 LoadBalancer 로 변경하고 원하는 IP 주소를 입력하고 싶은 경우 loadBalancerIP 항목을 추가합니다. +추가 하지 않을 경우에는 위에서 설정한 IP 주소풀에서 순차적으로 IP 주소가 배정됩니다. ```text kubectl edit svc/istio-ingressgateway -n istio-system @@ -201,7 +205,7 @@ status: loadBalancer: {} ``` -If we check again, you can see the External-IP value is `192.168.35.100`. +다시 확인을 해보면 External-IP 값이 `192.168.35.100` 인 것을 확인합니다. ```text kubectl get svc/istio-ingressgateway -n istio-system @@ -212,7 +216,7 @@ NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) istio-ingressgateway LoadBalancer 10.103.72.5 192.168.35.100 15021:31054/TCP,80:30853/TCP,443:30443/TCP,31400:30012/TCP,15443:31650/TCP 5h1m ``` -Open the Web Browser and access [http://192.168.35.100](http://192.168.35.100) to confirm that the following screen is output. +Web Browser 를 열어 [http://192.168.35.100](http://192.168.35.100) 으로 접속하여, 다음과 같은 화면이 출력되는 것을 확인합니다.

@@ -220,20 +224,21 @@ Open the Web Browser and access [http://192.168.35.100](http://192.168.35.100) t ### minio Dashboard -First, check the current status before receiving load balancing from MetalLB by changing the type of minio-service service to `LoadBalancer` in the kubeflow namespace that provides minio’s dashboard. +먼저 minio 의 Dashboard 를 제공하는 kubeflow 네임스페이스의 minio-service 서비스의 타입을 LoadBalancer로 변경하여 MetalLB로부터 로드 벨런싱 기능을 제공받기 전에, 현재 상태를 확인합니다. ```text kubectl get svc/minio-service -n kubeflow ``` -You can see that the type of service is ClusterIP, and the External-IP value is `none`. +해당 서비스의 타입은 ClusterIP이며, External-IP 값은 `none` 인 것을 확인할 수 있습니다. ```text NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE minio-service ClusterIP 10.109.209.87 9000/TCP 5h14m ``` -Change the type to Load Balancer and add a loadBalancerIP key-value pair if you want to enter the desired IP address. If not added, IP addresses will be assigned sequentially from the IP address pool set from metallb_config.yaml. +type 을 LoadBalancer 로 변경하고 원하는 IP 주소를 입력하고 싶은 경우 loadBalancerIP 항목을 추가합니다. +추가 하지 않을 경우에는 위에서 설정한 IP 주소풀에서 순차적으로 IP 주소가 배정됩니다. ```text kubectl edit svc/minio-service -n kubeflow @@ -275,7 +280,7 @@ status: loadBalancer: {} ``` -If we check again, you can see the External-IP value is `192.168.35.101`. +다시 확인을 해보면 External-IP 값이 `192.168.35.101` 인 것을 확인할 수 있습니다. ```text kubectl get svc/minio-service -n kubeflow @@ -286,7 +291,7 @@ NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) minio-service LoadBalancer 10.109.209.87 192.168.35.101 9000:31371/TCP 5h21m ``` -Open the Web Browser and access [http://192.168.35.101:9000](http://192.168.35.101:9000) to confirm that the following screen is output. +Web Browser 를 열어 [http://192.168.35.101:9000](http://192.168.35.101:9000) 으로 접속하여, 다음과 같은 화면이 출력되는 것을 확인합니다.

@@ -294,20 +299,21 @@ Open the Web Browser and access [http://192.168.35.101:9000](http://192.168.35.1 ### mlflow Dashboard -First, check the current status before receiving load balancing from MetalLB by changing the type of mlflow-server-service service to `LoadBalancer` in the mlflow-system namespace that provides mlflow’s dashboard. +먼저 mlflow 의 Dashboard 를 제공하는 mlflow-system 네임스페이스의 mlflow-server-service 서비스의 타입을 LoadBalancer로 변경하여 MetalLB로부터 로드 벨런싱 기능을 제공받기 전에, 현재 상태를 확인합니다. ```text kubectl get svc/mlflow-server-service -n mlflow-system ``` -You can see that the type of service is ClusterIP, and the External-IP value is `none`. +해당 서비스의 타입은 ClusterIP이며, External-IP 값은 `none` 인 것을 확인할 수 있습니다. ```text NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE mlflow-server-service ClusterIP 10.111.173.209 5000/TCP 4m50s ``` -Change the type to Load Balancer and add a loadBalancerIP key-value pair if you want to enter the desired IP address. If not added, IP addresses will be assigned sequentially from the IP address pool set from metallb_config.yaml. +type 을 LoadBalancer 로 변경하고 원하는 IP 주소를 입력하고 싶은 경우 loadBalancerIP 항목을 추가합니다. +추가 하지 않을 경우에는 위에서 설정한 IP 주소풀에서 순차적으로 IP 주소가 배정됩니다. ```text kubectl edit svc/mlflow-server-service -n mlflow-system @@ -347,7 +353,7 @@ status: loadBalancer: {} ``` -If we check again, you can see the External-IP value is `192.168.35.102`. +다시 확인을 해보면 External-IP 값이 `192.168.35.102` 인 것을 확인할 수 있습니다. ```text kubectl get svc/mlflow-server-service -n mlflow-system @@ -358,7 +364,7 @@ NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) mlflow-server-service LoadBalancer 10.111.173.209 192.168.35.102 5000:32287/TCP 6m11s ``` -Open the Web Browser and access [http://192.168.35.102:5000](http://192.168.35.102:5000) to confirm that the following screen is output. +Web Browser 를 열어 [http://192.168.35.102:5000](http://192.168.35.102:5000) 으로 접속하여, 다음과 같은 화면이 출력되는 것을 확인합니다.

@@ -366,20 +372,21 @@ Open the Web Browser and access [http://192.168.35.102:5000](http://192.168.35.1 ### Grafana Dashboard -First, check the current status before receiving load balancing from MetalLB by changing the type of seldon-core-analytics-grafana service to `LoadBalancer` in the seldon-system namespace that provides Grafana’s dashboard. +먼저 Grafana 의 Dashboard 를 제공하는 seldon-system 네임스페이스의 seldon-core-analytics-grafana 서비스의 타입을 LoadBalancer로 변경하여 MetalLB로부터 로드 벨런싱 기능을 제공받기 전에, 현재 상태를 확인합니다. ```text kubectl get svc/seldon-core-analytics-grafana -n seldon-system ``` -You can see that the type of service is ClusterIP, and the External-IP value is `none`. +해당 서비스의 타입은 ClusterIP이며, External-IP 값은 `none` 인 것을 확인할 수 있습니다. ```text NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE seldon-core-analytics-grafana ClusterIP 10.109.20.161 80/TCP 94s ``` -Change the type to Load Balancer and add a loadBalancerIP key-value pair if you want to enter the desired IP address. If not added, IP addresses will be assigned sequentially from the IP address pool set from metallb_config.yaml. +type 을 LoadBalancer 로 변경하고 원하는 IP 주소를 입력하고 싶은 경우 loadBalancerIP 항목을 추가합니다. +추가 하지 않을 경우에는 위에서 설정한 IP 주소풀에서 순차적으로 IP 주소가 배정됩니다. ```text kubectl edit svc/seldon-core-analytics-grafana -n seldon-system @@ -425,7 +432,7 @@ status: loadBalancer: {} ``` -If we check again, you can see the External-IP value is `192.168.35.103`. +다시 확인을 해보면 External-IP 값이 `192.168.35.103` 인 것을 확인할 수 있습니다. ```text kubectl get svc/seldon-core-analytics-grafana -n seldon-system @@ -436,7 +443,7 @@ NAME TYPE CLUSTER-IP EXTERNAL-IP seldon-core-analytics-grafana LoadBalancer 10.109.20.161 192.168.35.103 80:31191/TCP 5m14s ``` -Open the Web Browser and access [http://192.168.35.103:80](http://192.168.35.103:80) to confirm that the following screen is output. +Web Browser 를 열어 [http://192.168.35.103:80](http://192.168.35.103:80) 으로 접속하여, 다음과 같은 화면이 출력되는 것을 확인합니다.

diff --git a/content/en/docs/further-readings/info.md b/content/en/docs/further-readings/info.md index c7a5071d..14d205e7 100644 --- a/content/en/docs/further-readings/info.md +++ b/content/en/docs/further-readings/info.md @@ -32,3 +32,24 @@ toc: true

+ +세부 내용은 다음과 같습니다. + +| Mgmt. | Component | Open Soruce | +| -------------------------- | --------------------------- | ------------------------------------- | +| Data Mgmt. | Collection | [Kafka](https://kafka.apache.org/) | +| | Validation | [Beam](https://beam.apache.org/) | +| | Feature Store | [Flink](https://flink.apache.org/) | +| ML Model Dev. & Experiment | Modeling | [Jupyter](https://jupyter.org/) | +| | Analysis & Experiment Mgmt. | [MLflow](https://mlflow.org/) | +| | HPO Tuning & AutoML | [Katib](https://github.com/kubeflow/katib) | +| Deploy Mgmt. | Serving Framework | [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/index.html) | +| | A/B Test | [Iter8](https://iter8.tools/) | +| | Monitoring | [Grafana](https://grafana.com/oss/grafana/), [Prometheus](https://prometheus.io/) | +| Process Mgmt. | pipeline | [Kubeflow](https://www.kubeflow.org/) | +| | CI/CD | [Github Action](https://docs.github.com/en/actions) | +| | Continuous Training | [Argo Events](https://argoproj.github.io/events/) | +| Platform Mgmt. | Configuration Mgmt. | [Consul](https://www.consul.io/) | +| | Code Version Mgmt. | [Github](https://github.com/), [Minio](https://min.io/) | +| | Logging | (EFK) [Elastic Search](https://www.elastic.co/kr/elasticsearch/), [Fluentd](https://www.fluentd.org/), [Kibana](https://www.elastic.co/kr/kibana/) | +| | Resource Mgmt. | [Kubernetes](https://kubernetes.io/) | diff --git a/content/en/docs/help/_index.md b/content/en/docs/help/_index.md index d41793bd..55f69c43 100644 --- a/content/en/docs/help/_index.md +++ b/content/en/docs/help/_index.md @@ -1,10 +1,10 @@ --- title: "Help" description: "Help MLOps for ALL." -lead: "" date: 2021-12-21 lastmod: 2021-12-21 draft: false -weight: 9000 +description: "커뮤니티에 대한 설명과 기여하는 법에 대한 안내 페이지입니다." images: [] +weight: 9000 --- diff --git a/content/en/docs/help/discussions.md b/content/en/docs/help/community.md similarity index 98% rename from content/en/docs/help/discussions.md rename to content/en/docs/help/community.md index 60fee5be..323a7eef 100644 --- a/content/en/docs/help/discussions.md +++ b/content/en/docs/help/community.md @@ -4,11 +4,10 @@ date: 2021-12-21 lastmod: 2021-12-21 draft: false images: [] +toc: true menu: docs: parent: "help" -weight: 1001 -toc: true --- ### *모두의 MLOps* 릴리즈 소식 diff --git a/content/en/docs/help/how-to-contribute.md b/content/en/docs/help/how-to-contribute.md index 5c62a353..24c1bb77 100644 --- a/content/en/docs/help/how-to-contribute.md +++ b/content/en/docs/help/how-to-contribute.md @@ -4,28 +4,33 @@ date: 2021-12-21 lastmod: 2021-12-21 draft: false images: [] +toc: true menu: docs: parent: "help" -weight: 9001 -toc: true --- ## How to Start -1. 필요한 node module을 설치합니다. +1. [*모두의 MLOps* GitHub Repository](https://github.com/mlops-for-all/mlops-for-all.github.io)에 접속합니다. + +2. 여러분의 개인 Repository로 `Fork`합니다. + +3. Forked Repository를 여러분의 작업 환경으로 `git clone`합니다. + +4. 필요한 node module을 설치합니다. ```text npm install ``` -2. 글 수정 및 추가를 후 ci 를 실행합니다. +5. 글 수정 및 추가를 후 ci 를 실행합니다. ```text npm ci ``` -3. 로컬에서 실행 후 수정한 글이 정상적으로 나오는지 확인합니다. +6. 로컬에서 실행 후 수정한 글이 정상적으로 나오는지 확인합니다. ```text npm run start @@ -53,10 +58,10 @@ contributors: ["John Doe", "Adam Smith"] ### 3. 프로젝트에 처음 기여할 때 -만약 프로젝트에 처음 기여 할 때 `content/en/contributors`에 본인의 이름의 마크다운 파일을 작성합니다. +만약 프로젝트에 처음 기여 할 때 `content/kor/contributors`에 본인의 이름으로 폴더를 생성한 후, `_index.md`라는 파일을 작성합니다. -예를 들어, `minsoo kim`이 본인의 영어 이름이라면, 마크다운 파일은 `minsoo-kim.md`을 파일명으로 하여 다음의 내용을 작성합니다. -파일명은 하이픈(-)으로 연결한 소문자로, title은 띄어쓰기를 포함한 CamelCase로 작성합니다. +예를 들어, `minsoo kim`이 본인의 영어 이름이라면, 폴더명은 `minsoo-kim`으로 하여 해당 폴더 내부의 `_index.md`파일에 다음의 내용을 작성합니다. +폴더명은 하이픈(-)으로 연결한 소문자로, title은 띄어쓰기를 포함한 CamelCase로 작성합니다. ```markdown --- @@ -73,3 +78,7 @@ draft: false ```text npm test ``` + +## After Pull Request + +Pull Request를 생성하면 프로젝트에서는 자동으로 *모두의 MLOps* 운영진에게 리뷰 요청이 전해집니다. 최대 일주일 이내로 확인 후 Comment를 드릴 예정입니다. diff --git a/content/en/docs/kubeflow-dashboard-guide/intro.md b/content/en/docs/kubeflow-dashboard-guide/intro.md index 7a8690a0..ff81b196 100644 --- a/content/en/docs/kubeflow-dashboard-guide/intro.md +++ b/content/en/docs/kubeflow-dashboard-guide/intro.md @@ -4,13 +4,17 @@ description: "" lead: "" draft: false weight: 271 -contributors: ["Jaeyeon Kim"] +contributors: ["Jaeyeon Kim", "SeungTae Kim"] menu: docs: parent: kubeflow-ui-guide --- -[Kubeflow 설치]({{< relref "docs/setup-components/install-components-kf.md" >}})를 완료하면, 다음과 같은 대시보드에 접속할 수 있습니다. +[Kubeflow 설치]({{< relref "docs/setup-components/install-components-kf.md" >}})를 완료하면, 다음 커맨드를 통해 대시보드에 접속할 수 있습니다. + +```text +kubectl port-forward --address 0.0.0.0 svc/istio-ingressgateway -n istio-system 8080:80 +```

diff --git a/content/en/docs/kubeflow/advanced-component.md b/content/en/docs/kubeflow/advanced-component.md index 913535d0..817a82ba 100644 --- a/content/en/docs/kubeflow/advanced-component.md +++ b/content/en/docs/kubeflow/advanced-component.md @@ -4,7 +4,7 @@ description: "" lead: "" draft: false weight: 321 -contributors: ["Jongseob Jeon"] +contributors: ["Jongseob Jeon", "SeungTae Kim"] menu: docs: parent: "kubeflow" @@ -67,7 +67,7 @@ def train_from_csv( 파이썬에서 함수간에 값을 전달할 때, 객체를 반환해도 그 값이 호스트의 메모리에 저장되어 있으므로 다음 함수에서도 같은 객체를 사용할 수 있습니다. 하지만 kubeflow에서 컴포넌트들은 각각 컨테이너 위에서 서로 독립적으로 실행됩니다. 즉, 같은 메모리를 공유하고 있지 않기 때문에, 보통의 파이썬 함수에서 사용하는 방식과 같이 객체를 전달할 수 없습니다. 컴포넌트 간에 넘겨 줄 수 있는 정보는 `json` 으로만 가능합니다. 따라서 Model이나 DataFrame과 같이 json 형식으로 변환할 수 없는 타입의 객체는 다른 방법을 통해야 합니다. -Kubeflow에서는 이를 해결하기 위해 json-serializable 하지 않은 타입의 객체는 메모리 대신 파일에 데이터를 저장한 뒤, 그 파일을 이용해 정보를 전달합니다. 저장된 파일의 경로는 str이기 때문에 컴포넌트 간에 전달할 수 있기 때문입니다. 그런데 kubeflow에서는 minio를 이용해 파일을 저장하는데 유저는 실행을 하기 전에는 각 파일의 경로를 알 수 없습니다. 이를 위해서 kubeflow에서는 입력롸 출력의 경로와 관련된 매직을 제공하는데 바로 `InputPath`와 `OutputPath` 입니다. +Kubeflow에서는 이를 해결하기 위해 json-serializable 하지 않은 타입의 객체는 메모리 대신 파일에 데이터를 저장한 뒤, 그 파일을 이용해 정보를 전달합니다. 저장된 파일의 경로는 str이기 때문에 컴포넌트 간에 전달할 수 있기 때문입니다. 그런데 kubeflow에서는 minio를 이용해 파일을 저장하는데 유저는 실행을 하기 전에는 각 파일의 경로를 알 수 없습니다. 이를 위해서 kubeflow에서는 입력과 출력의 경로와 관련된 매직을 제공하는데 바로 `InputPath`와 `OutputPath` 입니다. `InputPath`는 단어 그대로 입력 경로를 `OutputPath` 는 단어 그대로 출력 경로를 의미합니다. diff --git a/content/en/docs/kubeflow/advanced-mlflow.md b/content/en/docs/kubeflow/advanced-mlflow.md index cdae336d..6c6d8f05 100644 --- a/content/en/docs/kubeflow/advanced-mlflow.md +++ b/content/en/docs/kubeflow/advanced-mlflow.md @@ -6,7 +6,7 @@ date: 2021-12-13 lastmod: 2021-12-20 draft: false weight: 330 -contributors: ["Jongseob Jeon"] +contributors: ["Jongseob Jeon", "SeungTae Kim"] menu: docs: parent: "kubeflow" @@ -552,6 +552,390 @@ if __name__ == "__main__": kfp.compiler.Compiler().compile(mlflow_pipeline, "mlflow_pipeline.yaml") ``` +

+

+ mlflow_pipeline.yaml + +```text +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: mlflow-pipeline- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10, pipelines.kubeflow.org/pipeline_compilation_time: '2022-01-19T14:14:11.999807', + pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"name": "kernel", "type": + "String"}, {"name": "model_name", "type": "String"}], "name": "mlflow_pipeline"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10} +spec: + entrypoint: mlflow-pipeline + templates: + - name: load-iris-data + container: + args: [--data, /tmp/outputs/data/data, --target, /tmp/outputs/target/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'pandas' 'scikit-learn' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip + install --quiet --no-warn-script-location 'pandas' 'scikit-learn' --user) + && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def _make_parent_dirs_and_return_path(file_path: str): + import os + os.makedirs(os.path.dirname(file_path), exist_ok=True) + return file_path + + def load_iris_data( + data_path, + target_path, + ): + import pandas as pd + from sklearn.datasets import load_iris + + iris = load_iris() + + data = pd.DataFrame(iris["data"], columns=iris["feature_names"]) + target = pd.DataFrame(iris["target"], columns=["target"]) + + data.to_csv(data_path, index=False) + target.to_csv(target_path, index=False) + + import argparse + _parser = argparse.ArgumentParser(prog='Load iris data', description='') + _parser.add_argument("--data", dest="data_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--target", dest="target_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = load_iris_data(**_parsed_args) + image: python:3.7 + outputs: + artifacts: + - {name: load-iris-data-data, path: /tmp/outputs/data/data} + - {name: load-iris-data-target, path: /tmp/outputs/target/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.10 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--data", {"outputPath": "data"}, "--target", {"outputPath": "target"}], + "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip + install --quiet --no-warn-script-location ''pandas'' ''scikit-learn'' || + PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''pandas'' ''scikit-learn'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), + exist_ok=True)\n return file_path\n\ndef load_iris_data(\n data_path,\n target_path,\n):\n import + pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data + = pd.DataFrame(iris[\"data\"], columns=iris[\"feature_names\"])\n target + = pd.DataFrame(iris[\"target\"], columns=[\"target\"])\n\n data.to_csv(data_path, + index=False)\n target.to_csv(target_path, index=False)\n\nimport argparse\n_parser + = argparse.ArgumentParser(prog=''Load iris data'', description='''')\n_parser.add_argument(\"--data\", + dest=\"data_path\", type=_make_parent_dirs_and_return_path, required=True, + default=argparse.SUPPRESS)\n_parser.add_argument(\"--target\", dest=\"target_path\", + type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = load_iris_data(**_parsed_args)\n"], + "image": "python:3.7"}}, "name": "Load iris data", "outputs": [{"name": + "data", "type": "csv"}, {"name": "target", "type": "csv"}]}', pipelines.kubeflow.org/component_ref: '{}'} + - name: mlflow-pipeline + inputs: + parameters: + - {name: kernel} + - {name: model_name} + dag: + tasks: + - {name: load-iris-data, template: load-iris-data} + - name: train-from-csv + template: train-from-csv + dependencies: [load-iris-data] + arguments: + parameters: + - {name: kernel, value: '{{inputs.parameters.kernel}}'} + artifacts: + - {name: load-iris-data-data, from: '{{tasks.load-iris-data.outputs.artifacts.load-iris-data-data}}'} + - {name: load-iris-data-target, from: '{{tasks.load-iris-data.outputs.artifacts.load-iris-data-target}}'} + - name: upload-sklearn-model-to-mlflow + template: upload-sklearn-model-to-mlflow + dependencies: [train-from-csv] + arguments: + parameters: + - {name: model_name, value: '{{inputs.parameters.model_name}}'} + artifacts: + - {name: train-from-csv-conda_env, from: '{{tasks.train-from-csv.outputs.artifacts.train-from-csv-conda_env}}'} + - {name: train-from-csv-input_example, from: '{{tasks.train-from-csv.outputs.artifacts.train-from-csv-input_example}}'} + - {name: train-from-csv-model, from: '{{tasks.train-from-csv.outputs.artifacts.train-from-csv-model}}'} + - {name: train-from-csv-signature, from: '{{tasks.train-from-csv.outputs.artifacts.train-from-csv-signature}}'} + - name: train-from-csv + container: + args: [--train-data, /tmp/inputs/train_data/data, --train-target, /tmp/inputs/train_target/data, + --kernel, '{{inputs.parameters.kernel}}', --model, /tmp/outputs/model/data, + --input-example, /tmp/outputs/input_example/data, --signature, /tmp/outputs/signature/data, + --conda-env, /tmp/outputs/conda_env/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'dill' 'pandas' 'scikit-learn' 'mlflow' || PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location 'dill' 'pandas' 'scikit-learn' + 'mlflow' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def _make_parent_dirs_and_return_path(file_path: str): + import os + os.makedirs(os.path.dirname(file_path), exist_ok=True) + return file_path + + def train_from_csv( + train_data_path, + train_target_path, + model_path, + input_example_path, + signature_path, + conda_env_path, + kernel, + ): + import dill + import pandas as pd + from sklearn.svm import SVC + + from mlflow.models.signature import infer_signature + from mlflow.utils.environment import _mlflow_conda_env + + train_data = pd.read_csv(train_data_path) + train_target = pd.read_csv(train_target_path) + + clf = SVC(kernel=kernel) + clf.fit(train_data, train_target) + + with open(model_path, mode="wb") as file_writer: + dill.dump(clf, file_writer) + + input_example = train_data.sample(1) + with open(input_example_path, "wb") as file_writer: + dill.dump(input_example, file_writer) + + signature = infer_signature(train_data, clf.predict(train_data)) + with open(signature_path, "wb") as file_writer: + dill.dump(signature, file_writer) + + conda_env = _mlflow_conda_env( + additional_pip_deps=["dill", "pandas", "scikit-learn"] + ) + with open(conda_env_path, "wb") as file_writer: + dill.dump(conda_env, file_writer) + + import argparse + _parser = argparse.ArgumentParser(prog='Train from csv', description='') + _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--input-example", dest="input_example_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--signature", dest="signature_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--conda-env", dest="conda_env_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = train_from_csv(**_parsed_args) + image: python:3.7 + inputs: + parameters: + - {name: kernel} + artifacts: + - {name: load-iris-data-data, path: /tmp/inputs/train_data/data} + - {name: load-iris-data-target, path: /tmp/inputs/train_target/data} + outputs: + artifacts: + - {name: train-from-csv-conda_env, path: /tmp/outputs/conda_env/data} + - {name: train-from-csv-input_example, path: /tmp/outputs/input_example/data} + - {name: train-from-csv-model, path: /tmp/outputs/model/data} + - {name: train-from-csv-signature, path: /tmp/outputs/signature/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.10 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--train-data", {"inputPath": "train_data"}, "--train-target", + {"inputPath": "train_target"}, "--kernel", {"inputValue": "kernel"}, "--model", + {"outputPath": "model"}, "--input-example", {"outputPath": "input_example"}, + "--signature", {"outputPath": "signature"}, "--conda-env", {"outputPath": + "conda_env"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''dill'' ''pandas'' + ''scikit-learn'' ''mlflow'' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m + pip install --quiet --no-warn-script-location ''dill'' ''pandas'' ''scikit-learn'' + ''mlflow'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), + exist_ok=True)\n return file_path\n\ndef train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n kernel,\n):\n import + dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from + mlflow.models.signature import infer_signature\n from mlflow.utils.environment + import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target + = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, + train_target)\n\n with open(model_path, mode=\"wb\") as file_writer:\n dill.dump(clf, + file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, + \"wb\") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature + = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, + \"wb\") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env + = _mlflow_conda_env(\n additional_pip_deps=[\"dill\", \"pandas\", + \"scikit-learn\"]\n )\n with open(conda_env_path, \"wb\") as file_writer:\n dill.dump(conda_env, + file_writer)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Train + from csv'', description='''')\n_parser.add_argument(\"--train-data\", dest=\"train_data_path\", + type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--train-target\", + dest=\"train_target_path\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--kernel\", + dest=\"kernel\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--model\", + dest=\"model_path\", type=_make_parent_dirs_and_return_path, required=True, + default=argparse.SUPPRESS)\n_parser.add_argument(\"--input-example\", dest=\"input_example_path\", + type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--signature\", + dest=\"signature_path\", type=_make_parent_dirs_and_return_path, required=True, + default=argparse.SUPPRESS)\n_parser.add_argument(\"--conda-env\", dest=\"conda_env_path\", + type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = train_from_csv(**_parsed_args)\n"], + "image": "python:3.7"}}, "inputs": [{"name": "train_data", "type": "csv"}, + {"name": "train_target", "type": "csv"}, {"name": "kernel", "type": "String"}], + "name": "Train from csv", "outputs": [{"name": "model", "type": "dill"}, + {"name": "input_example", "type": "dill"}, {"name": "signature", "type": + "dill"}, {"name": "conda_env", "type": "dill"}]}', pipelines.kubeflow.org/component_ref: '{}', + pipelines.kubeflow.org/arguments.parameters: '{"kernel": "{{inputs.parameters.kernel}}"}'} + - name: upload-sklearn-model-to-mlflow + container: + args: [--model-name, '{{inputs.parameters.model_name}}', --model, /tmp/inputs/model/data, + --input-example, /tmp/inputs/input_example/data, --signature, /tmp/inputs/signature/data, + --conda-env, /tmp/inputs/conda_env/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'dill' 'pandas' 'scikit-learn' 'mlflow' 'boto3' || PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location 'dill' 'pandas' 'scikit-learn' + 'mlflow' 'boto3' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def upload_sklearn_model_to_mlflow( + model_name, + model_path, + input_example_path, + signature_path, + conda_env_path, + ): + import os + import dill + from mlflow.sklearn import save_model + + from mlflow.tracking.client import MlflowClient + + os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000" + os.environ["AWS_ACCESS_KEY_ID"] = "minio" + os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123" + + client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000") + + with open(model_path, mode="rb") as file_reader: + clf = dill.load(file_reader) + + with open(input_example_path, "rb") as file_reader: + input_example = dill.load(file_reader) + + with open(signature_path, "rb") as file_reader: + signature = dill.load(file_reader) + + with open(conda_env_path, "rb") as file_reader: + conda_env = dill.load(file_reader) + + save_model( + sk_model=clf, + path=model_name, + serialization_format="cloudpickle", + conda_env=conda_env, + signature=signature, + input_example=input_example, + ) + run = client.create_run(experiment_id="0") + client.log_artifact(run.info.run_id, model_name) + + import argparse + _parser = argparse.ArgumentParser(prog='Upload sklearn model to mlflow', description='') + _parser.add_argument("--model-name", dest="model_name", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--input-example", dest="input_example_path", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--signature", dest="signature_path", type=str, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--conda-env", dest="conda_env_path", type=str, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = upload_sklearn_model_to_mlflow(**_parsed_args) + image: python:3.7 + inputs: + parameters: + - {name: model_name} + artifacts: + - {name: train-from-csv-conda_env, path: /tmp/inputs/conda_env/data} + - {name: train-from-csv-input_example, path: /tmp/inputs/input_example/data} + - {name: train-from-csv-model, path: /tmp/inputs/model/data} + - {name: train-from-csv-signature, path: /tmp/inputs/signature/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.10 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--model-name", {"inputValue": "model_name"}, "--model", {"inputPath": + "model"}, "--input-example", {"inputPath": "input_example"}, "--signature", + {"inputPath": "signature"}, "--conda-env", {"inputPath": "conda_env"}], + "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip + install --quiet --no-warn-script-location ''dill'' ''pandas'' ''scikit-learn'' + ''mlflow'' ''boto3'' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location ''dill'' ''pandas'' ''scikit-learn'' ''mlflow'' + ''boto3'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def upload_sklearn_model_to_mlflow(\n model_name,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n):\n import + os\n import dill\n from mlflow.sklearn import save_model\n\n from + mlflow.tracking.client import MlflowClient\n\n os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] + = \"http://minio-service.kubeflow.svc:9000\"\n os.environ[\"AWS_ACCESS_KEY_ID\"] + = \"minio\"\n os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minio123\"\n\n client + = MlflowClient(\"http://mlflow-server-service.mlflow-system.svc:5000\")\n\n with + open(model_path, mode=\"rb\") as file_reader:\n clf = dill.load(file_reader)\n\n with + open(input_example_path, \"rb\") as file_reader:\n input_example + = dill.load(file_reader)\n\n with open(signature_path, \"rb\") as file_reader:\n signature + = dill.load(file_reader)\n\n with open(conda_env_path, \"rb\") as file_reader:\n conda_env + = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format=\"cloudpickle\",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run + = client.create_run(experiment_id=\"0\")\n client.log_artifact(run.info.run_id, + model_name)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Upload + sklearn model to mlflow'', description='''')\n_parser.add_argument(\"--model-name\", + dest=\"model_name\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--model\", + dest=\"model_path\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--input-example\", + dest=\"input_example_path\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--signature\", + dest=\"signature_path\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--conda-env\", + dest=\"conda_env_path\", type=str, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = upload_sklearn_model_to_mlflow(**_parsed_args)\n"], + "image": "python:3.7"}}, "inputs": [{"name": "model_name", "type": "String"}, + {"name": "model", "type": "dill"}, {"name": "input_example", "type": "dill"}, + {"name": "signature", "type": "dill"}, {"name": "conda_env", "type": "dill"}], + "name": "Upload sklearn model to mlflow"}', pipelines.kubeflow.org/component_ref: '{}', + pipelines.kubeflow.org/arguments.parameters: '{"model_name": "{{inputs.parameters.model_name}}"}'} + arguments: + parameters: + - {name: kernel} + - {name: model_name} + serviceAccountName: pipeline-runner +``` + +
+

+ 실행후 생성된 mlflow_pipeline.yaml 파일을 파이프라인 업로드한 후, 실행하여 run 의 결과를 확인합니다.

diff --git a/content/en/docs/kubeflow/advanced-run.md b/content/en/docs/kubeflow/advanced-run.md index 4b827e95..fd0bbbc9 100644 --- a/content/en/docs/kubeflow/advanced-run.md +++ b/content/en/docs/kubeflow/advanced-run.md @@ -4,7 +4,7 @@ description: "" lead: "" draft: false weight: 325 -contributors: ["Jongseob Jeon"] +contributors: ["Jongseob Jeon", "SeungTae Kim"] menu: docs: parent: "kubeflow" @@ -130,6 +130,115 @@ if __name__ == "__main__": kfp.compiler.Compiler().compile(plot_pipeline, "plot_pipeline.yaml") ``` +이 스크립트를 실행해서 나온 `plot_pipeline.yaml`을 확인하면 다음과 같습니다. + +

+

+ plot_pipeline.yaml + +```text +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: plot-pipeline- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2 +022-01-17T13:31:32.963214', + pipelines.kubeflow.org/pipeline_spec: '{"name": "plot_pipeline"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: plot-pipeline + templates: + - name: plot-linear + container: + args: [--mlpipeline-ui-metadata, /tmp/outputs/mlpipeline_ui_metadata/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'matplotlib' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet + --no-warn-script-location 'matplotlib' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def _make_parent_dirs_and_return_path(file_path: str): + import os + os.makedirs(os.path.dirname(file_path), exist_ok=True) + return file_path + def plot_linear(mlpipeline_ui_metadata): + import base64 + import json + from io import BytesIO + import matplotlib.pyplot as plt + plt.plot([1, 2, 3], [1, 2, 3]) + tmpfile = BytesIO() + plt.savefig(tmpfile, format="png") + encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8") + html = f"" + metadata = { + "outputs": [ + { + "type": "web-app", + "storage": "inline", + "source": html, + }, + ], + } + with open(mlpipeline_ui_metadata, "w") as html_writer: + json.dump(metadata, html_writer) + + import argparse + _parser = argparse.ArgumentParser(prog='Plot linear', description='') + _parser.add_argument("--mlpipeline-ui-metadata", dest="mlpipeline_ui_metadata", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + _outputs = plot_linear(**_parsed_args) + image: python:3.7 + outputs: + artifacts: + - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline_ui_metadata/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--mlpipeline-ui-metadata", {"outputPath": "mlpipeline_ui_metadata"}], + "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip + install --quiet --no-warn-script-location ''matplotlib'' || PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''matplotlib'' + --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), + exist_ok=True)\n return file_path\n\ndef plot_linear(mlpipeline_ui_metadata):\n import + base64\n import json\n from io import BytesIO\n\n import matplotlib.pyplot + as plt\n\n plt.plot([1, 2, 3], [1, 2, 3])\n\n tmpfile = BytesIO()\n plt.savefig(tmpfile, + format=\"png\")\n encoded = base64.b64encode(tmpfile.getvalue()).decode(\"utf-8\")\n\n html + = f\"\"\n metadata = {\n \"outputs\": + [\n {\n \"type\": \"web-app\",\n \"storage\": + \"inline\",\n \"source\": html,\n },\n ],\n }\n with + open(mlpipeline_ui_metadata, \"w\") as html_writer:\n json.dump(metadata, + html_writer)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Plot + linear'', description='''')\n_parser.add_argument(\"--mlpipeline-ui-metadata\", + dest=\"mlpipeline_ui_metadata\", type=_make_parent_dirs_and_return_path, + required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs + = plot_linear(**_parsed_args)\n"], "image": "python:3.7"}}, "name": "Plot + linear", "outputs": [{"name": "mlpipeline_ui_metadata", "type": "UI_Metadata"}]}', + pipelines.kubeflow.org/component_ref: '{}'} + - name: plot-pipeline + dag: + tasks: + - {name: plot-linear, template: plot-linear} + arguments: + parameters: [] + serviceAccountName: pipeline-runner +``` + +
+

+ 실행 후 Visualization을 클릭합니다.

diff --git a/content/en/docs/setup-components/install-components-kf.md b/content/en/docs/setup-components/install-components-kf.md index 4672fb34..5a36242a 100644 --- a/content/en/docs/setup-components/install-components-kf.md +++ b/content/en/docs/setup-components/install-components-kf.md @@ -5,7 +5,7 @@ date: 2021-12-13 lastmod: 2021-12-20 draft: false weight: 252 -contributors: ["Jaeyeon Kim"] +contributors: ["Jaeyeon Kim", "SeungTae Kim"] menu: docs: parent: "setup-components" @@ -464,6 +464,28 @@ kubectl port-forward svc/ml-pipeline-ui -n kubeflow 8888:80

+- localhost 연결 거부 이슈 + +

+ +

+ +만약 다음과 같이 `localhost에서 연결을 거부했습니다` 라는 에러가 출력될 경우, 커맨드로 address 설정을 통해 접근하는 것이 가능합니다. + +**보안상의 문제가 되지 않는다면,** 아래와 같이 `0.0.0.0` 로 모든 주소의 bind를 열어주는 방향으로 ml-pipeline UI가 정상적으로 접속되는지 확인합니다. + +```text +kubectl port-forward --address 0.0.0.0 svc/ml-pipeline-ui -n kubeflow 8888:80 +``` + +- 위의 옵션으로 실행했음에도 여전히 연결 거부 이슈가 발생할 경우 + +방화벽 설정으로 접속해 모든 tcp 프로토콜의 포트에 대한 접속을 허가 또는 8888번 포트의 접속 허가를 추가해 접근 권한을 허가해줍니다. + +웹 브라우저를 열어 `http://<당신의 가상 인스턴스 공인 ip 주소>:8888/#/pipelines/` 경로에 접속하면, ml-pipeline UI 화면이 출력되는 것을 확인할 수 있습니다. + +하단에서 진행되는 다른 포트의 경로에 접속할 때도 위의 절차와 동일하게 커맨드를 실행하고, 방화벽에 포트 번호를 추가해주면 실행하는 것이 가능합니다. + ### Katib Katib 를 설치합니다. diff --git a/content/en/docs/setup-components/install-components-pg.md b/content/en/docs/setup-components/install-components-pg.md index 545caeb2..f6e9c703 100644 --- a/content/en/docs/setup-components/install-components-pg.md +++ b/content/en/docs/setup-components/install-components-pg.md @@ -136,24 +136,6 @@ Seldon Core API Dashboard 가 보이고, 다음과 같이 출력되는 것을

-이제, [지난 페이지]({{< relref "docs/setup-components/install-components-seldon" >}})에서 생성했던 SeldonDeployment 로 API Request 를 반복적으로 수행해보고, 대시보드에 변화가 일어나는지 확인해봅니다. - -```text -curl -X POST http://172.25.0.129:32193/seldon/seldon-system/sklearn/api/v1.0/predictions -H 'Content-Type: application/json' -d '{ "data": { "ndarray": [[1,2,3,4]] } }' -``` - -

- -

- -다음과 같이 Global Request Rate 이 `0 ops` 에서 순간적으로 상승하는 것을 확인할 수 있습니다. - -

- -

- -이렇게 프로메테우스와 그라파나가 정상적으로 설치된 것을 확인할 수 있습니다. - ## References - [Seldon-Core-Analytics Helm Chart](https://github.com/SeldonIO/seldon-core/tree/master/helm-charts/seldon-core-analytics) diff --git a/content/en/docs/setup-kubernetes/install-prerequisite.md b/content/en/docs/setup-kubernetes/install-prerequisite.md index 6f1ea80d..c4f8fa77 100644 --- a/content/en/docs/setup-kubernetes/install-prerequisite.md +++ b/content/en/docs/setup-kubernetes/install-prerequisite.md @@ -5,7 +5,7 @@ date: 2021-12-13 lastmod: 2021-12-20 draft: false weight: 210 -contributors: ["Jaeyeon Kim", "Jongsun Shinn"] +contributors: ["Jaeyeon Kim", "Jongsun Shinn", "Sangwoo Shim"] menu: docs: parent: "setup-kubernetes" @@ -30,7 +30,7 @@ sudo apt-get install -y socat 1. 도커 설치에 필요한 APT 패키지들을 설치합니다. ```text - sudo apt-get update && sudo apt-get install ca-certificates curl gnupg lsb-release + sudo apt-get update && sudo apt-get install -y ca-certificates curl gnupg lsb-release ``` 2. 도커의 공식 GPG key를 추가합니다. @@ -174,24 +174,13 @@ kubectl 은 쿠버네티스 클러스터에 API를 요청할 때 사용하는 3. 정상적으로 설치되었는지 확인합니다. ```text - kubectl --help + kubectl version --client ``` 다음과 같은 메시지가 보이면 정상적으로 설치된 것을 의미합니다. ```text - kubectl controls the Kubernetes cluster manager. - - Find more information at: - https://kubernetes.io/docs/reference/kubectl/overview/ - - Basic Commands (Beginner): - create Create a resource from a file or from stdin - expose Take a replication controller, service, deployment or pod and - expose it as a new Kubernetes service - run Run a particular image on the cluster - set Set specific features on objects - ... + Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.7", GitCommit:"1f86634ff08f37e54e8bfcd86bc90b61c98f84d4", GitTreeState:"clean", BuildDate:"2021-11-17T14:41:19Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"} ``` 4. 여러 개의 쿠버네티스 클러스터를 사용하는 경우, 여러 개의 kubeconfig 파일을 관리해야 하는 경우가 있습니다. diff --git a/content/en/docs/setup-kubernetes/intro.md b/content/en/docs/setup-kubernetes/intro.md index d8c29574..6335d4bf 100644 --- a/content/en/docs/setup-kubernetes/intro.md +++ b/content/en/docs/setup-kubernetes/intro.md @@ -6,7 +6,7 @@ date: 2021-12-13 lastmod: 2021-12-13 draft: false weight: 201 -contributors: ["Jaeyeon Kim", "Jongsun Shinn"] +contributors: ["Jaeyeon Kim", "Jongsun Shinn", "Youngdon Tae", "SeungTae Kim"] menu: docs: parent: "setup-kubernetes" @@ -19,6 +19,23 @@ MLOps를 공부하는 데 있어서 가장 큰 장벽은 MLOps 시스템을 구 이런 이유들로 MLOps를 선뜻 시작해보지 못하시는 분들을 위해, *모두의 MLOps*에서는 우분투가 설치되는 데스크톱 하나만 준비되어 있다면 MLOps 시스템을 밑바닥부터 구축하고 사용해 볼 수 있는 방법을 다룰 예정입니다. +

+

+ 우분투 데스크탑 환경을 준비할 수 없는 경우, 가상머신을 활용하여 환경을 구성하기 + + Windows 혹은 Intel Mac을 사용해 `모두의 MLops` 실습을 진행 중인 분들은 `Virtual Box`, `VMware` 등의 가상머신 소프트웨어를 이용하여 우분투 데스크탑 환경을 준비할 수 있습니다. 이 때, 권장 사양을 맞춰 가상 머신을 생성해주시기 바랍니다. + + 또한, M1 Mac을 사용하시는 분들은 작성일(2022년 2월) 기준으로는 Virtual Box, VMware 는 이용할 수 없습니다. ([M1 Apple Silicone Mac에 최적화된 macOS 앱 지원 확인하기](https://isapplesiliconready.com/kr)) + + 따라서, 클라우드 환경을 이용해 실습하는 것이 아니라면, [UTM , Virtual machines for Mac](https://mac.getutm.app/)을 설치하여 가상 머신을 이용해주세요. + + (앱스토어에서 구매하여 다운로드 받는 소프트웨어는 일종의 Donation 개념의 비용 지불입니다. 무료 버전과 자동 업데이트 정도의 차이가 있어, 무료버전을 사용해도 무방합니다.) + + 해당 가상머신 소프트웨어는 `Ubuntu 20.04.3 LTS` 실습 운영체제를 지원하고 있어, M1 Mac에서 실습을 수행하는 것을 가능하게 합니다. + +
+

+ 하지만 [MLOps의 구성요소]({{< relref "docs/introduction/component.md" >}})에서 설명하는 요소들을 모두 사용해볼 수는 없기에, *모두의 MLOps*에서는 대표적인 오픈소스만을 설치한 뒤, 서로 연동하여 사용하는 부분을 주로 다룰 예정입니다. *모두의 MLOps*에서 설치하는 오픈소스가 표준을 의미하는 것은 아니며, 여러분의 상황에 맞게 적절한 툴을 취사선택하는 것을 권장합니다. diff --git a/content/en/docs/setup-kubernetes/kubernetes-with-kubeadm.md b/content/en/docs/setup-kubernetes/kubernetes-with-kubeadm.md index 7ebfa390..597847fd 100644 --- a/content/en/docs/setup-kubernetes/kubernetes-with-kubeadm.md +++ b/content/en/docs/setup-kubernetes/kubernetes-with-kubeadm.md @@ -5,7 +5,7 @@ date: 2021-12-13 lastmod: 2021-12-20 draft: false weight: 223 -contributors: ["Youngcheol Jang", "Jongsun Shinn"] +contributors: ["Youngcheol Jang"] menu: docs: parent: "setup-kubernetes" @@ -45,44 +45,29 @@ sudo sysctl --system ```text sudo apt-get update -sudo apt-get install -y apt-transport-https ca-certificates curl -sudo curl -fsSLo /usr/share/keyrings/kubernetes-archive-keyring.gpg https://packages.cloud.google.com/apt/doc/apt-key.gpg -echo "deb [signed-by=/usr/share/keyrings/kubernetes-archive-keyring.gpg] https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee /etc/apt/sources.list.d/kubernetes.list +sudo apt-get install -y apt-transport-https ca-certificates curl && +sudo curl -fsSLo /usr/share/keyrings/kubernetes-archive-keyring.gpg https://packages.cloud.google.com/apt/doc/apt-key.gpg && +echo "deb [signed-by=/usr/share/keyrings/kubernetes-archive-keyring.gpg] https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee /etc/apt/sources.list.d/kubernetes.list && sudo apt-get update -sudo apt-get install -y kubelet=1.21.7-00 kubeadm=1.21.7-00 kubectl=1.21.7-00 +sudo apt-get install -y kubelet=1.21.7-00 kubeadm=1.21.7-00 kubectl=1.21.7-00 && sudo apt-mark hold kubelet kubeadm kubectl ``` kubeadm, kubelet, kubectl 이 잘 설치되었는지 확인합니다. ```text -mlops@ubuntu:~$ kubeadm - - - ┌──────────────────────────────────────────────────────────┐ - │ KUBEADM │ - │ Easily bootstrap a secure Kubernetes cluster │ - │ │ - │ Please give us feedback at: │ - │ https://github.com/kubernetes/kubeadm/issues │ - └──────────────────────────────────────────────────────────┘ -... -mlops@ubuntu:~$ kubelet -h -The kubelet is the primary "node agent" that runs on each -node. It can register the node with the apiserver using one of: the hostname; a flag to -override the hostname; or specific logic for a cloud provider. -... -mlops@ubuntu:~$ kubectl -kubectl controls the Kubernetes cluster manager. - - Find more information at: https://kubernetes.io/docs/reference/kubectl/overview/ - -Basic Commands (Beginner): - create Create a resource from a file or from stdin. - expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service - run Run a particular image on the cluster - set Set specific features on objects -... +mlops@ubuntu:~$ kubeadm version +kubeadm version: &version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.7", GitCommit:"1f86634ff08f37e54e8bfcd86bc90b61c98f84d4", GitTreeState:"clean", BuildDate:"2021-11-17T14:40:08Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"} +``` + +```text +mlops@ubuntu:~$ kubelet --version +Kubernetes v1.21.7 +``` + +```text +mlops@ubuntu:~$ kubectl version --client +Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.7", GitCommit:"1f86634ff08f37e54e8bfcd86bc90b61c98f84d4", GitTreeState:"clean", BuildDate:"2021-11-17T14:41:19Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"} ``` 이제 kubeadm을 사용하여 쿠버네티스를 설치합니다. @@ -141,6 +126,11 @@ scp -p {CLUSTER_USER_ID}@{CLUSTER_IP}:~/.kube/config ~/.kube/config ```text kubectl get nodes +``` + +Ready 가 되면 다음과 비슷한 결과가 출력됩니다. + +```text NAME STATUS ROLES AGE VERSION ubuntu Ready control-plane,master 2m55s v1.21.7 ``` diff --git a/content/en/docs/setup-kubernetes/kubernetes.md b/content/en/docs/setup-kubernetes/kubernetes.md index 7e327f3f..abc2e670 100644 --- a/content/en/docs/setup-kubernetes/kubernetes.md +++ b/content/en/docs/setup-kubernetes/kubernetes.md @@ -5,7 +5,7 @@ date: 2021-12-13 lastmod: 2021-12-13 draft: false weight: 202 -contributors: ["Jaeyeon Kim", "Kyunghwan Kim"] +contributors: ["Jaeyeon Kim"] menu: docs: parent: "setup-kubernetes" @@ -21,6 +21,10 @@ images: [] 각각의 도구는 장단점이 다르기에 사용자마다 선호하는 도구가 다른 점을 고려하여, 본 글에서는 kubeadm, k3s, minikube의 3가지 도구를 활용하여 쿠버네티스 클러스터를 구축하는 방법을 다룹니다. 각 도구에 대한 자세한 비교는 다음 쿠버네티스 [공식 문서](https://kubernetes.io/ko/docs/tasks/tools/)를 확인해주시기를 바랍니다. +*모두의 MLOps*에서 권장하는 툴은 **k3s**로 쿠버네티스 클러스터를 구축할 때 쉽게 할 수 있다는 장점이 있습니다. +만약 쿠버네티스의 모든 기능을 사용하고 노드 구성까지 활용하고 싶다면 **kubeadm**을 권장해 드립니다. +minikube는 저희가 설명하는 컴포넌트 외에도 다른 쿠버네티스를 add-on 형식으로 쉽게 설치할 수 있다는 장점이 있습니다. + 본 *모두의 MLOps*에서는 구축하게 될 MLOps 구성 요소들을 원활히 사용하기 위해, 각각의 도구를 활용해 쿠버네티스 클러스터를 구축할 때, 추가로 설정해 주어야 하는 부분이 추가되어 있습니다. Ubuntu OS까지는 설치되어 있는 데스크탑을 k8s cluster로 구축한 뒤, 외부 클라이언트 노드에서 쿠버네티스 클러스터에 접근하는 것을 확인하는 것까지가 본 **Setup Kubernetes**단원의 범위입니다. @@ -30,9 +34,9 @@ Ubuntu OS까지는 설치되어 있는 데스크탑을 k8s cluster로 구축한 ```text 3. Setup Prerequisite 4. Setup Kubernetes - 4.1. with kubeadm + 4.1. with k3s 4.2. with minikube - 4.3. with k3s + 4.3. with kubeadm 5. Setup Kubernetes Modules ``` diff --git a/content/en/docs/setup-kubernetes/setup-nvidia-gpu.md b/content/en/docs/setup-kubernetes/setup-nvidia-gpu.md index 90c2fa5e..f46e7438 100644 --- a/content/en/docs/setup-kubernetes/setup-nvidia-gpu.md +++ b/content/en/docs/setup-kubernetes/setup-nvidia-gpu.md @@ -5,7 +5,7 @@ date: 2021-12-13 lastmod: 2021-12-13 draft: false weight: 231 -contributors: ["Jaeyeon Kim", "Jongsun Shinn"] +contributors: ["Jaeyeon Kim"] menu: docs: parent: "setup-kubernetes" @@ -68,7 +68,7 @@ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt-get update -sudo apt-get install -y nvidia-docker2 +sudo apt-get install -y nvidia-docker2 && sudo systemctl restart docker ``` @@ -106,18 +106,6 @@ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi +-----------------------------------------------------------------------------+ ``` -만약 `could not select device driver "" with capabilities: [[gpu]]..` 와 같은 에러가 나는 경우, nvidia-container-toolkit을 같이 설치합니다. -관련 issue는 [여기서](https://github.com/NVIDIA/nvidia-docker/issues/1034) 확인 가능합니다. - -```text -distribution=$(. /etc/os-release;echo $ID$VERSION_ID) -curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - -curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list - -sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit -sudo systemctl restart docker -``` - ## 3. NVIDIA-Docker를 Default Container Runtime으로 설정 쿠버네티스는 기본적으로 Docker-CE를 Default Container Runtime으로 사용합니다. diff --git a/content/en/docs/prologue/welcome.md b/content/en/docs/welcome.md similarity index 97% rename from content/en/docs/prologue/welcome.md rename to content/en/docs/welcome.md index 5c33bed4..c2982f35 100644 --- a/content/en/docs/prologue/welcome.md +++ b/content/en/docs/welcome.md @@ -7,9 +7,6 @@ lastmod: 2021-12-13 draft: false images: [] weight: 1 -menu: - docs: - parent: "prologue" --- ## 모두의 MLOps diff --git a/content/en/prerequisites/_index.md b/content/en/prerequisites/_index.md new file mode 100644 index 00000000..6348a198 --- /dev/null +++ b/content/en/prerequisites/_index.md @@ -0,0 +1,7 @@ +--- +title : "Prerequisites" +description: "Prerequisites" +lead: "" +draft: false +images: [] +--- diff --git a/content/en/prerequisites/docker/_index.md b/content/en/prerequisites/docker/_index.md new file mode 100644 index 00000000..bfe8c602 --- /dev/null +++ b/content/en/prerequisites/docker/_index.md @@ -0,0 +1,8 @@ +--- +title : "Docker" +description: "How to use Docker." +lead: "" +draft: false +weight: 100 +images: [] +--- diff --git a/content/en/prerequisites/intro.md b/content/en/prerequisites/intro.md new file mode 100644 index 00000000..25bf27d3 --- /dev/null +++ b/content/en/prerequisites/intro.md @@ -0,0 +1,10 @@ +--- +title : "Introduction to Prerequisites" +description: "Tutorials for prerequisites." +lead: "" +draft: false +weight: 1 +images: [] +--- + +모두의 MLOps를 이해하기 위해 필요한 선수 지식들에 대해서 설명합니다. diff --git a/content/en/prerequisites/kubernetes/_index.md b/content/en/prerequisites/kubernetes/_index.md new file mode 100644 index 00000000..9323cb82 --- /dev/null +++ b/content/en/prerequisites/kubernetes/_index.md @@ -0,0 +1,8 @@ +--- +title : "Kubernetes" +description: "How to use Kubernetes." +lead: "" +draft: false +weight: 200 +images: [] +--- diff --git a/content/kor/docs/prologue/welcome.md b/content/kor/docs/welcome.md similarity index 97% rename from content/kor/docs/prologue/welcome.md rename to content/kor/docs/welcome.md index 5c33bed4..c2982f35 100644 --- a/content/kor/docs/prologue/welcome.md +++ b/content/kor/docs/welcome.md @@ -7,9 +7,6 @@ lastmod: 2021-12-13 draft: false images: [] weight: 1 -menu: - docs: - parent: "prologue" --- ## 모두의 MLOps diff --git a/content/kor/prerequisites/_index.md b/content/kor/prerequisites/_index.md new file mode 100644 index 00000000..6348a198 --- /dev/null +++ b/content/kor/prerequisites/_index.md @@ -0,0 +1,7 @@ +--- +title : "Prerequisites" +description: "Prerequisites" +lead: "" +draft: false +images: [] +--- diff --git a/content/kor/prerequisites/docker/_index.md b/content/kor/prerequisites/docker/_index.md new file mode 100644 index 00000000..bfe8c602 --- /dev/null +++ b/content/kor/prerequisites/docker/_index.md @@ -0,0 +1,8 @@ +--- +title : "Docker" +description: "How to use Docker." +lead: "" +draft: false +weight: 100 +images: [] +--- diff --git a/content/kor/prerequisites/docker/advanced.md b/content/kor/prerequisites/docker/advanced.md new file mode 100644 index 00000000..bcebc064 --- /dev/null +++ b/content/kor/prerequisites/docker/advanced.md @@ -0,0 +1,280 @@ +--- +title : "[Practice] Docker Advanced" +description: "Practice to use docker more advanced way." +lead: "" +draft: false +weight: 106 +images: [] +contributors: ["Jongseob Jeon", "Jaeyeon Kim"] +menu: + prerequisites: + parent: "docker" +--- + +## 도커 이미지 잘 만들기 + +### 도커 이미지를 만들 때 고려해야 될 점 + +Dockerfile 을 활용하여 도커 이미지를 만들 때는 명령어의 **순서**가 중요합니다. +그 이유는 도커 이미지는 여러 개의 Read-Only Layer 로 구성되어있고, 이미지를 빌드할 때 이미 존재하는 레이어는 **캐시되어** 재사용되기 때문에, 이를 생각해서 Dockerfile 을 구성한다면 **빌드 시간을 줄일 수 있습니다.** + +Dockerfile에서 `RUN`, `ADD`, `COPY` 명령어 하나가 하나의 레이어로 저장됩니다. + +예를 들어서 다음과 같은 `Dockerfile`이 있습니다. + +```docker +# Layer 1 +FROM ubuntu:latest + +# Layer 2 +RUN apt-get update && apt-get install python3 pip3 -y + +# Layer 3 +RUN pip3 install -U pip && pip3 install torch + +# Layer 4 +COPY src/ src/ + +# Layer 5 +CMD python src/app.py +``` + +위의 `Dockerfile`로 빌드된 이미지를 `docker run -it app:latest /bin/bash` 명령어로 실행하면 다음과 같은 레이어로 표현할 수 있습니다. + +

+ +

+ +최상단의 R/W Layer 는 이미지에 영향을 주지 않습니다. 즉, 컨테이너 내부에서 작업한 내역은 모두 휘발성입니다. + +하단의 레이어가 변경되면, 그 위의 레이어는 모두 새로 빌드됩니다. 그래서 Dockerfile 내장 명령어의 순서가 중요합니다. +예를 들면, **자주 변경**되는 부분은 **최대한 뒤쪽으로** 정렬하는 것을 추천합니다. (ex. `COPY src/ app/src/`) + +그렇기 때문에 반대로 변경되지 않는 부분은 최대한 앞쪽으로 정렬하는게 좋습니다. + +만약 거의 **변경되지 않지만**, 여러 곳에서 **자주** 쓰이는 부분을 공통화할 수도 있습니다. +해당 공통부분만 묶어서 별도의 이미지는 미리 만들어둔 다음, **베이스 이미지** 로 활용하는 것이 좋습니다. + +예를 들어, 다른 건 거의 똑같은데, tensorflow-cpu 를 사용하는 이미지와, tensorflow-gpu 를 사용하는 환경을 분리해서 이미지로 만들고 싶은 경우에는 다음과 같이 할 수 있습니다. +python 과 기타 기본적인 패키지가 설치된 [`ghcr.io/makinarocks/python:3.8-base`](http://ghcr.io/makinarocks/python:3.8-base-cpu) 를 만들어두고, **tensorflow cpu 버전과 gpu 버전이** 설치된 이미지 새로 만들때는, 위의 이미지를 `FROM` 으로 불러온 다음, tensorflow install 하는 부분만 별도로 작성해서 Dockerfile 을 2 개로 관리한다면 가독성도 좋고 빌드 시간도 줄일 수 있습니다. + +**합칠 수 있는 Layer 는 합치는 것**이 Old version 의 도커에서는 성능 향상 효과를 이끌었습니다. 여러분의 도커 컨테이너가 어떤 도커 버전에서 실행될 것인지 보장할 수 없으며, **가독성**을 위해서도 합칠 수 있는 Layer 는 적절히 합치는 것이 좋습니다. + +예를 들면, 다음과 같이 작성된 `Dockerfile`이 있습니다. + +```docker +# Bad Case +RUN apt-get update +RUN apt-get install build-essential -y +RUN apt-get install curl -y +RUN apt-get install jq -y +RUN apt-get install git -y +``` + +이를 아래와 같이 합쳐서 적을 수 있습니다. + +```docker +# Better Case +RUN apt-get update && \ + apt-get install -y \ + build-essential \ + curl \ + jq \ + git +``` + +편의를 위해서는 `.dockerignore` 도 사용하는게 좋습니다. +`.dockerignore`는 `.gitignore` 와 비슷한 역할을 한다고 이해하면 됩니다. (git add 할 때 제외할 수 있듯이, docker build 할 때 자동으로 제외) + +더 많은 정보는 [Docker 공식 문서](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/)에서 확인하실 수 있습니다. + +### ENTRYPOINT vs CMD + +`ENTRYPOINT` 와 `CMD` 는 모두 컨테이너의 실행 시점에서 어떤 명령어를 실행시키고 싶을 때 사용합니다. +그리고 이 둘 중 하나는 반드시 존재해야 합니다. + +- **차이점** + - `CMD`: docker run 을 수행할 때, 쉽게 변경하여 사용할 수 있음 + - `ENTRYPOINT`: `--entrypoint` 를 사용해야 변경할 수 있음 + +`ENTRYPOINT` 와 `CMD` 가 함께 쓰일 때는 보통 `CMD`는 `ENTRYPOINT` 에서 적은 명령의 arguments(parameters) 를 의미합니다. + +예를 들어서 다음과 같은 `Dockerfile` 이 있습니다. + +```docker +FROM ubuntu:latest + +# 아래 4 가지 option 을 바꿔가며 직접 테스트해보시면 이해하기 편합니다. +# 단, NO ENTRYPOINT 옵션은 base image 인 ubuntu:latest 에 이미 있어서 테스트해볼 수는 없고 나머지 v2, 3, 5, 6, 8, 9, 11, 12 를 테스트해볼 수 있습니다. +# ENTRYPOINT echo "Hello ENTRYPOINT" +# ENTRYPOINT ["echo", "Hello ENTRYPOINT"] +# CMD echo "Hello CMD" +# CMD ["echo", "Hello CMD"] +``` + +위의 `Dockerfile`에서 주석으로 표시된 부분들을 해제하며 빌드하고 실행하면 다음과 같은 결과를 얻을 수 있습니다. + +| | No ENTRYPOINT | ENTRYPOINT a b | ENTRYPOINT ["a", "b"] | +| ------------------ | -------------- | -------------- | --------------------- | +| **NO CMD** | Error! | /bin/sh -c a b | a b | +| **CMD ["x", "y"]** | x y | /bin/sh -c a b | a b x y | +| **CMD x y** | /bin/sh -c x y | /bin/sh -c a b | a b /bin/sh -c x y | + +- In Kubernetes pod + - `ENTRYPOINT` → command + - `CMD` → args + +### Docker tag 이름 짓기 + +도커 이미지의 tag 로 **latest 는 사용하지 않는 것을 권장**합니다. +이유는 latest 는 default tag name 이므로 **의도치 않게 overwritten** 되는 경우가 너무 많이 발생하기 때문입니다. + +하나의 이미지는 하나의 태그를 가짐(**uniqueness**)을 보장해야 추후 Production 단계에서 **협업/디버깅**에 용이합니다. +내용은 다르지만, 동일한 tag 를 사용하게 되면 추후 dangling image 로 취급되어 관리하기 어려워집니다. +dangling image는 `docker images`에는 나오지 않지만 계속해서 저장소를 차지하고 있습니다. + +### ETC + +1. log 등의 정보는 container 내부가 아닌 곳에 따로 저장합니다. + container 내부에서 write 한 data 는 언제든지 사라질 수 있기 때문입니다. +2. secret 한 정보, 환경(dev/prod) dependent 한 정보 등은 Dockerfile 에 직접 적는 게 아니라, env var 또는 .env config file 을 사용합니다. +3. Dockerfile **linter** 도 존재하므로, 협업 시에는 활용하면 좋습니다. + [https://github.com/hadolint/hadolint](https://github.com/hadolint/hadolint) + +## docker run 의 다양한 옵션 + +### docker run with volume + +Docker container 사용 시 불편한 점이 있습니다. +바로 Docker는 기본적으로 Docker **container 내부에서 작업한 모든 사항은 저장되지 않습니다.** +이유는 Docker container 는 각각 격리된 파일시스템을 사용합니다. 따라서, **여러 docker container 끼리 데이터를 공유하기 어렵습니다.** + +이 문제를 해결하기 위해서 Docker에서 제공하는 방식은 **2 가지**가 있습니다. + +

+ +

+ +#### Docker volume + +- docker cli 를 사용해 `volume` 이라는 리소스를 직접 관리 +- host 에서 Docker area(`/var/lib/docker`) 아래에 특정 디렉토리를 생성한 다음, 해당 경로를 docker container 에 mount + +#### Bind mount + +- host 의 특정 경로를 docker container 에 mount + +#### How to use? + +사용 방식은 **동일한 인터페이스**로 `-v` 옵션을 통해 사용할 수 있습니다. +다만, volume 을 사용할 때에는 `docker volume create`, `docker volume ls`, `docker volume rm` 등을 수행하여 직접 관리해주어야 합니다. + +- Docker volume + + ```bash + docker run \ + -v my_volume:/app \ + nginx:latest + ```` + +- Blind mount + + ```bash + docker run \ + -v /home/user/some/path:/app \ + nginx:latest + ``` + +로컬에서 개발할 때는 bind mount 가 편하긴 하지만, 환경을 깔끔하게 유지하고 싶다면 docker volume 을 사용하여 create, rm 을 명시적으로 수행하는 것도 하나의 방법입니다. + +쿠버네티스에서 스토리지를 제공하는 방식도 결국 docker 의 bind mount 를 활용하여 제공합니다. + +### docker run with resource limit + +기본적으로 docker container 는 **host OS 의 cpu, memory 자원을 fully 사용**할 수 있습니다. 하지만 이렇게 사용하게 되면 host OS 의 자원 상황에 따라서 **OOM** 등의 이슈로 docker container 가 비정상적으로 종료되는 상황이 발생할 수 있습니다. +이런 문제를 다루기 위해 **docker container 실행 시, cpu 와 memory 의 사용량 제한**을 걸 수 있는 `-m` [옵션](https://docs.docker.com/config/containers/resource_constraints/#limit-a-containers-access-to-memory)을 제공합니다. + +```bash +docker run -d -m 512m --memory-reservation=256m --name 512-limit ubuntu sleep 3600 +docker run -d -m 1g --memory-reservation=256m --name 1g-limit ubuntu sleep 3600 +``` + +위의 도커를 실행 후 `docker stats` 커맨드를 통해 사용량을 확인할 수 있습니다. + +```bash +CONTAINER ID NAME CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O PIDS +4ea1258e2e09 1g-limit 0.00% 300KiB / 1GiB 0.03% 1kB / 0B 0B / 0B 1 +4edf94b9a3e5 512-limit 0.00% 296KiB / 512MiB 0.06% 1.11kB / 0B 0B / 0B 1 +``` + +쿠버네티스에서 pod 라는 리소스에 cpu, memory 제한을 줄 때, 이 방식을 활용하여 제공합니다. + +### docker run with restart policy + +특정 컨테이너가 계속해서 running 상태를 유지시켜야 하는 경우가 존재합니다. 이런 경우를 위해서 해당 컨테이너가 종료되자마자 바로 재생성을 시도할 수 있는 `--restart=always` 옵션을 제공하고 있습니다. + +옵션 입력 후 도커를 실행합니다. + +```bash +docker run --restart=always ubuntu +``` + +`watch -n1 docker ps`를 통해 재실행이 되고 있는지 확인합니다. +정상적으로 수행되고 있다면 다음과 같이 STATUS에 `Restarting (0)` 이 출력됩니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +a911850276e8 ubuntu "bash" 35 seconds ago Restarting (0) 6 seconds ago hungry_vaughan +``` + +- [https://docs.docker.com/engine/reference/commandline/run/#restart-policies---restart](https://docs.docker.com/engine/reference/commandline/run/#restart-policies---restart) + - on-failure with max retries + - always 등의 선택지 제공 + +쿠버네티스에서 job 이라는 resource 의 restart 옵션을 줄 때, 이 방식을 활용하여 제공합니다. + +### docker run as a background process + +도커 컨테이너를 실행할 때는 기본적으로 foreground process 로 실행됩니다. 즉, 컨테이너를 실행한 터미널이 해당 컨테이너에 자동으로 attach 되어 있어, 다른 명령을 실행할 수 없습니다. + +다음과 같은 예시를 수행해봅니다. +우선 터미널 2 개를 열어, 하나의 터미널에서는 `docker ps` 를 지켜보고, 다른 하나의 터미널에서는 다음과 같은 명령을 차례로 실행해보며 동작을 지켜봅니다. + +#### First Practice + +```bash +docker run -it ubuntu sleep 10 +``` + +10 초동안 멈춰 있어야 하고, 해당 컨테이너에서 다른 명령을 수행할 수 없습니다. 10초 뒤에는 docker ps 에서 container 가 종료되는 것을 확인할 수 있습니다. + +#### Second Pratice + +```bash +docker run -it ubuntu sleep 10 +``` + +이후, `ctrl + p` -> `ctrl + q` + +해당 터미널에서 이제 다른 명령을 수행할 수 있게 되었으며, docker ps 로도 10초까지는 해당 컨테이너가 살아있는 것을 확인할 수 있습니다. +이렇게 docker container 내부에서 빠져나온 상황을 detached 라고 부릅니다. +도커에서는 run 을 실행함과 동시에 detached mode 로 실행시킬 수 있는 옵션을 제공합니다. + +#### Third Pratice + +```bash +docker run -d ubuntu sleep 10 +``` + +detached mode 이므로 해당 명령을 실행시킨 터미널에서 다른 액션을 수행시킬 수 있습니다. + +상황에 따라 detached mode 를 적절히 활용하면 좋습니다. +예를 들어, DB 와 통신하는 Backend API server 를 개발할 때 Backend API server 는 source code 를 변경시켜가면서 hot-loading 으로 계속해서 로그를 확인해봐야 하지만, DB 는 로그를 지켜볼 필요는 없는 경우라면 다음과 같이 실행할 수 있습니다. +DB 는 docker container 를 detached mode 로 실행시키고, Backend API server 는 attached mode 로 log 를 following 하면서 실행시키면 효율적입니다. + +## References + +- [https://towardsdatascience.com/docker-storage-598e385f4efe](https://towardsdatascience.com/docker-storage-598e385f4efe) +- [https://vsupalov.com/docker-latest-tag/](https://vsupalov.com/docker-latest-tag/) +- [https://docs.microsoft.com/ko-kr/azure/container-registry/container-registry-image-tag-version](https://docs.microsoft.com/ko-kr/azure/container-registry/container-registry-image-tag-version) +- [https://stevelasker.blog/2018/03/01/docker-tagging-best-practices-for-tagging-and-versioning-docker-images/](https://stevelasker.blog/2018/03/01/docker-tagging-best-practices-for-tagging-and-versioning-docker-images/) diff --git a/content/kor/prerequisites/docker/command.md b/content/kor/prerequisites/docker/command.md new file mode 100644 index 00000000..af90d951 --- /dev/null +++ b/content/kor/prerequisites/docker/command.md @@ -0,0 +1,440 @@ +--- +title : "[Practice] Docker command" +description: "Practice to use docker command." +lead: "" +draft: false +weight: 104 +images: [] +contributors: ["Jongseob Jeon", "Jaeyeon Kim"] +menu: + prerequisites: + parent: "docker" +--- + +## 1. 정상 설치 확인 + +```bash +docker run hello-world +``` + +정상적으로 설치된 경우 다음과 같은 메시지를 확인할 수 있습니다. + +```text +Hello from Docker! +This message shows that your installation appears to be working correctly. +.... +``` + +**(For ubuntu)** sudo 없이 사용하고 싶다면 아래 사이트를 참고합니다. + +- [https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user](https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user) + +## 2. Docker Pull + +docker image registry(도커 이미지를 저장하고 공유할 수 있는 저장소)로부터 Docker image 를 로컬에 다운로드 받는 커맨드입니다. + +아래 커맨드를 통해 docker pull에서 사용 가능한 argument들을 확인할 수 있습니다. + +```bash +docker pull --help +``` + +정상적으로 수행되면 아래와 같이 출력됩니다. + +```text +Usage: docker pull [OPTIONS] NAME[:TAG|@DIGEST] + +Pull an image or a repository from a registry + +Options: + -a, --all-tags Download all tagged images in the repository + --disable-content-trust Skip image verification (default true) + --platform string Set platform if server is multi-platform capable + -q, --quiet Suppress verbose output +``` + +여기서 알 수 있는 것은 바로 docker pull은 두 개 타입의 argument를 받는다는 것을 알 수 있습니다. + +1. `[OPTIONS]` +2. `NAME[:TAG|@DIGEST]` + +help에서 나온 `-a`, -`q` 옵션을 사용하기 위해서는 NAME 앞에서 사용해야 합니다. + +직접 `ubuntu:18.04` 이미지를 pull 해보겠습니다. + +```bash +docker pull ubuntu:18.04 +``` + +위 명령어를 해석하면 `ubuntu` 라는 이름을 가진 이미지 중 `18.04` 태그가 달려있는 이미지를 가져오라는 뜻입니다. + +만약, 정상적으로 수행된다면 다음과 비슷하게 출력됩니다. + +```text +18.04: Pulling from library/ubuntu +20d796c36622: Pull complete +Digest: sha256:42cd9143b6060261187a72716906187294b8b66653b50d70bc7a90ccade5c984 +Status: Downloaded newer image for ubuntu:18.04 +docker.io/library/ubuntu:18.04 +``` + +위의 명령어를 수행하면 [docker.io/library](http://docker.io/library/) 라는 이름의 registry 에서 ubuntu:18.04 라는 image 를 여러분의 노트북에 다운로드 받게됩니다. + +- 참고사항 + - 추후 [docker.io](http://docker.io) 나 public 한 docker hub 와 같은 registry 대신에, 특정 **private** 한 registry 에서 docker image 를 가져와야 하는 경우에는, [`docker login`](https://docs.docker.com/engine/reference/commandline/login/) 을 통해서 특정 registry 를 바라보도록 한 뒤, docker pull 을 수행하는 형태로 사용합니다. 혹은 insecure registry 를 설정하는 [방안](https://stackoverflow.com/questions/42211380/add-insecure-registry-to-docker)도 활용할 수 있습니다. + - 폐쇄망에서 docker image 를 `.tar` 파일과 같은 형태로 저장하고 공유할 수 있도록 [`docker save`](https://docs.docker.com/engine/reference/commandline/save/), [`docker load`](https://docs.docker.com/engine/reference/commandline/load/) 와 같은 명령어도 존재합니다. + +## 3. Docker images + +로컬에 존재하는 docker image 리스트를 출력하는 커맨드입니다. + +```bash +docker images --help +``` + +docker images에서 사용할 수 있는 argument는 다음과 같습니다. + +```text +Usage: docker images [OPTIONS] [REPOSITORY[:TAG]] + +List images + +Options: + -a, --all Show all images (default hides intermediate images) + --digests Show digests + -f, --filter filter Filter output based on conditions provided + --format string Pretty-print images using a Go template + --no-trunc Don't truncate output + -q, --quiet Only show image IDs +``` + +아래 명령어를 이용해 직접 실행해 보겠습니다. + +```bash +docker images +``` + +만약 도커를 최초 설치 후 이 실습을 진행한다면 다음과 비슷하게 출력됩니다. + +```text +REPOSITORY TAG IMAGE ID CREATED SIZE +ubuntu 18.04 29e70752d7b2 2 days ago 56.7MB +``` + +줄 수 있는 argument중 `-q`를 사용하면 `IMAGE ID` 만 출력됩니다. + +```bash +docker images -q +``` + +```text +29e70752d7b2 +``` + +## 4. Docker ps + +현재 실행 중인 도커 컨테이너 리스트를 출력하는 커맨드입니다. + +```bash +docker ps --help +``` + +docker ps에서 사용할 수 있는 argument는 다음과 같습니다. + +```text +Usage: docker ps [OPTIONS] + +List containers + +Options: + -a, --all Show all containers (default shows just running) + -f, --filter filter Filter output based on conditions provided + --format string Pretty-print containers using a Go template + -n, --last int Show n last created containers (includes all states) (default -1) + -l, --latest Show the latest created container (includes all states) + --no-trunc Don't truncate output + -q, --quiet Only display container IDs + -s, --size Display total file sizes +``` + +아래 명령어를 이용해 직접 실행해 보겠습니다. + +```bash +docker ps +``` + +현재 실행 중인 컨테이너가 없다면 다음과 같이 나옵니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +``` + +만약 실행되는 컨테이너가 있다면 다음과 비슷하게 나옵니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +c1e8f5e89d8d ubuntu "sleep 3600" 13 seconds ago Up 12 seconds trusting_newton +``` + +## 5. Docker run + +도커 컨테이너를 실행시키는 커맨드입니다. + +```bash +docker run --help +``` + +docker run을 실행하는 명령어는 다음과 같습니다. + +```text +Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...] + +Run a command in a new container +``` + +여기서 우리가 확인해야 하는 것은 바로 docker run은 세 개 타입의 argument를 받는다는 것을 알 수 있습니다. + +1. `[OPTIONS]` +2. `[COMMAND]` +3. `[ARG...]` + +직접 도커 컨테이너를 실행해 보겠습니다. + +```bash +## Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...] +docker run -it --name demo1 ubuntu:18.04 /bin/bash +``` + +- `-it` : `-i` 옵션 + `-t` 옵션 + - container 를 실행시킴과 동시에 interactive 한 terminal 로 접속시켜주는 옵션 +- `--name` : name + - 컨테이너 id 대신, 구분하기 쉽도록 지정해주는 이름 +- `/bin/bash` + - 컨테이너를 실행시킴과 동시에 실행할 커맨드로, `/bin/bash` 는 bash 쉘을 여는 것을 의미합니다. + +실행 후 `exit` 명령어를 통해 컨테이너를 종료합니다. + +이 제 앞서 배웠던 `docker ps` 명령어를 치면 다음과 같이 나옵니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +``` + +실행되고 있는 컨테이너가 나온다고 했지만 어째서인지 방금 실행한 컨테이너가 보이지 않습니다. +그 이유는 `docker ps`는 기본값으로 현재 실행 중인 컨테이너를 보여주기 때문입니다. + +만약 종료된 컨테이너들도 보고 싶다면 `-a` 옵션을 주어야 합니다. + +```bash +docker ps -a +``` + +그러면 다음과 같이 종료된 컨테이너 목록도 나옵니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +4c1aa74a382a ubuntu:18.04 "/bin/bash" 2 minutes ago Exited (0) 2 minutes ago demo1 +``` + +## 6. Docker exec + +Docker 컨테이너 내부에서 명령을 내리거나, 내부로 접속하는 커맨드입니다. + +```bash +docker exec --help +``` + +예를 들어서 다음과 같은 명령어를 실행해 보겠습니다. + +```bash +docker run -d --name demo2 ubuntu:18.04 sleep 3600 +``` + +여기서 `-d` 옵션은 도커 컨테이너를 백그라운드에서 실행시켜서, 컨테이너에서 접속 종료를 하더라도, 계속 실행 중이 되도록 하는 커맨드입니다. + +`docker ps`를 통해 현재 실행중인지 확인합니다. + +다음과 같이 실행 중임을 확인할 수 있습니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +fc88a83e90f0 ubuntu:18.04 "sleep 3600" 4 seconds ago Up 3 seconds demo2 +``` + +이제 `docker exec` 명령어를 통해서 실행중인 도커 컨테이너에 접속해 보겠습니다. + +```bash +docker exec -it demo2 /bin/bash +``` + +이 전의 `docker run`과 동일하게 container 내부에 접속할 수 있습니다. + +`exit`을 통해 종료합니다. + +## 7. Docker logs + +도커 컨테이너의 log를 확인하는 커맨드 입니다. + +```bash +docker logs --help +``` + +다음과 같은 컨테이너를 실행시키도록 하겠습니다. + +```bash +docker run --name demo3 -d busybox sh -c "while true; do $(echo date); sleep 1; done" +``` + +위 명령어를 통해서 test 라는 이름의 busybox 컨테이너를 백그라운드에서 도커 컨테이너로 실행하여, 1초에 한 번씩 현재 시간을 출력하도록 했습니다. + +이제 아래 명령어를 통해 log를 확인해 보겠습니다. + +```bash +docker logs demo3 +``` + +정상적으로 수행되면 아래와 비슷하게 나옵니다. + +```text +Sun Mar 6 11:06:49 UTC 2022 +Sun Mar 6 11:06:50 UTC 2022 +Sun Mar 6 11:06:51 UTC 2022 +Sun Mar 6 11:06:52 UTC 2022 +Sun Mar 6 11:06:53 UTC 2022 +Sun Mar 6 11:06:54 UTC 2022 +``` + +그런데 이렇게 사용할 경우 여태까지 찍힌 log 밖에 확인할 수 없습니다. +이 때 `-f` 옵션을 이용해 계속 watch 하며 출력할 수 있습니다. + +```bash +docker logs demo3 -f +``` + +## 8. Docker stop + +실행 중인 도커 컨테이너를 중단시키는 커맨드입니다. + +```bash +docker stop --help +``` + +`docker ps`를 통해 현재 실행 중인 컨테이너를 확인하면 다음과 같습니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +730391669c39 busybox "sh -c 'while true; …" About a minute ago Up About a minute demo3 +fc88a83e90f0 ubuntu:18.04 "sleep 3600" 4 minutes ago Up 4 minutes demo2 +``` + +이제 `docker stop` 을 통해 도커를 정지해 보겠습니다. + +```bash +docker stop demo2 +``` + +실행 후 `docker ps`를 다시 입력합니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +730391669c39 busybox "sh -c 'while true; …" 2 minutes ago Up 2 minutes demo3 +``` + +위의 결과와 비교했을 때 demo2 컨테이너가 현재 실행 중인 컨테이너 목록에서 사라진 것을 확인할 수 있습니다. + +나머지 컨테이너도 정지합니다. + +```bash +docker stop demo3 +``` + +## 9. Docker rm + +도커 컨테이너를 삭제하는 커맨드입니다. + +```bash +docker rm --help +``` + +도커 컨테이너는 기본적으로 종료가 된 상태로 있습니다. 그래서 `docker ps -a`를 통해서 종료된 컨테이너도 볼 수 있습니다. +그런데 종료된 컨테이너는 왜 지워야 할까요? +종료되어 있는 도커에는 이전에 사용한 데이터가 아직 컨테이너 내부에 남아있습니다. +그래서 restart 등을 통해서 컨테이너를 재시작할 수 있습니다. +그런데 이 과정에서 disk를 사용하게 됩니다. + +그래서 완전히 사용하지 않는 컨테이너를 지우기 위해서는 `docker rm` 명령어를 사용해야 합니다. + +우선 현재 컨테이너들을 확인합니다. + +```text +docker ps -a +``` + +다음과 같이 3개의 컨테이너가 있습니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +730391669c39 busybox "sh -c 'while true; …" 4 minutes ago Exited (137) About a minute ago demo3 +fc88a83e90f0 ubuntu:18.04 "sleep 3600" 7 minutes ago Exited (137) 2 minutes ago demo2 +4c1aa74a382a ubuntu:18.04 "/bin/bash" 10 minutes ago Exited (0) 10 minutes ago demo1 +``` + +아래 명령어를 통해 `demo3` 컨테이너를 삭제해 보겠습니다. + +```bash +docker rm demo3 +``` + +`docker ps -a` 명령어를 치면 다음과 같이 2개로 줄었습니다. + +```text +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +fc88a83e90f0 ubuntu:18.04 "sleep 3600" 13 minutes ago Exited (137) 8 minutes ago demo2 +4c1aa74a382a ubuntu:18.04 "/bin/bash" 16 minutes ago Exited (0) 16 minutes ago demo1 +``` + +나머지 컨테이너들도 삭제합니다. + +```text +docker rm demo2 +docker rm demo1 +``` + +## 10. Docker rmi + +도커 이미지를 삭제하는 커맨드입니다. + +```bash +docker rmi --help +``` + +아래 명령어를 통해 현재 어떤 이미지들이 로컬에 있는지 확인합니다. + +```bash +docker images +``` + +다음과 같이 출력됩니다. + +```bash +REPOSITORY TAG IMAGE ID CREATED SIZE +busybox latest a8440bba1bc0 32 hours ago 1.41MB +ubuntu 18.04 29e70752d7b2 2 days ago 56.7MB +``` + +`busybox` 이미지를 삭제해 보겠습니다. + +```bash +docker rmi busybox +``` + +다시 `docker images`를 칠 경우 다음과 같이 나옵니다. + +```bash +REPOSITORY TAG IMAGE ID CREATED SIZE +ubuntu 18.04 29e70752d7b2 2 days ago 56.7MB +``` + +## References + +- [https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry) diff --git a/content/kor/prerequisites/docker/docker.md b/content/kor/prerequisites/docker/docker.md new file mode 100644 index 00000000..449c97ad --- /dev/null +++ b/content/kor/prerequisites/docker/docker.md @@ -0,0 +1,59 @@ +--- +title : "What is Docker?" +description: "Introduction to Docker." +lead: "" +draft: false +weight: 103 +images: [] +contributors: ["Jongseob Jeon", "Jaeyeon Kim"] +menu: + prerequisites: + parent: "docker" +--- + + +## 컨테이너 + +- 컨테이너 가상화 + - 어플리케이션을 어디에서나 동일하게 실행하는 기술 +- 컨테이너 이미지 + - 어플리케이션을 실행시키기 위해 필요한 모든 파일들의 집합 + - → 붕어빵 틀 +- 컨테이너란? + - 컨테이너 이미지를 기반으로 실행된 한 개의 프로세스 + - → 붕어빵 틀로 찍어낸 붕어빵 + +## 도커 + +도커는 **컨테이너를 관리**하고 사용할 수 있게 해주는 플랫폼입니다. +이러한 도커의 슬로건은 바로 **Build Once, Run Anywhere** 로 어디에서나 동일한 실행 결과를 보장합니다. + +도커 내부에서 동작하는 과정을 보자면 실제로 container 를 위한 리소스를 분리하고, lifecycle 을 제어하는 기능은 linux kernel 의 cgroup 등이 수행합니다. +하지만 이러한 인터페이스를 바로 사용하는 것은 **너무 어렵기 때문에** 다음과 같은 추상화 layer를 만들게 됩니다. + +

+ +

+ +이를 통해 사용자는 사용자 친화적인 API 인 **Docker CLI** 만으로 쉽게 컨테이너를 제어할 수 있습니다. + +## Layer 해석 + +위에서 나온 layer들의 역할은 다음과 같습니다. + +1. runC: linux kernel 의 기능을 직접 사용해서, container 라는 하나의 프로세스가 사용할 네임스페이스와 cpu, memory, filesystem 등을 격리시켜주는 기능을 수행합니다. +2. containerd: runC(OCI layer) 에게 명령을 내리기 위한 추상화 단계이며, 표준화된 인터페이스(OCI)를 사용합니다. +3. dockerd: containerd 에게 명령을 내리는 역할만 합니다. +4. docker cli: 사용자는 docker cli 로 dockerd (Docker daemon)에게 명령을 내리기만 하면 됩니다. + - 이 통신 과정에서 unix socket 을 사용하기 때문에 가끔 도커 관련 에러가 나면 `/var/run/docker.sock` 가 사용 중이다, 권한이 없다 등등의 에러 메시지가 나오는 것입니다. + +이처럼 도커는 많은 단계를 감싸고 있지만, 흔히 도커라는 용어를 사용할 때는 Docker CLI 를 말할 때도 있고, Dockerd 를 말할 때도 있고 Docker Container 하나를 말할 때도 있어서 혼란이 생길 수 있습니다. +앞으로 나오는 글에서도 도커가 여러가지 의미로 쓰일 수 있습니다. + +## For ML Engineer + +머신러닝 엔지니어가 도커를 사용하는 이유는 다음과 같습니다. + +1. 나의 ML 학습/추론 코드를 OS, python version, python 환경, 특정 python package 버전에 independent 하도록 해야 한다. +2. 그래서 코드 뿐만이 아닌 **해당 코드가 실행되기 위해 필요한 모든 종속적인 패키지, 환경 변수, 폴더명 등등을 하나의 패키지로** 묶을 수 있는 기술이 컨테이너화 기술이다. +3. 이 기술을 쉽게 사용하고 관리할 수 있는 소프트웨어 중 하나가 도커이며, 패키지를 도커 이미지라고 부른다. diff --git a/content/kor/prerequisites/docker/images.md b/content/kor/prerequisites/docker/images.md new file mode 100644 index 00000000..56ec54a7 --- /dev/null +++ b/content/kor/prerequisites/docker/images.md @@ -0,0 +1,245 @@ +--- +title : "[Practice] Docker images" +description: "Practice to use docker image." +lead: "" +draft: false +weight: 105 +images: [] +contributors: ["Jongseob Jeon", "Jaeyeon Kim"] +menu: + prerequisites: + parent: "docker" +--- + +## 1. Dockerfile 만들기 + +도커 이미지를 만드는 가장 쉬운 방법은 도커에서 제공하는 템플릿인 Dockerfile을 사용하는 것입니다. +이외에는 running container 를 docker image 로 만드는 `docker commit` 등을 활용하는 방법이 있습니다. + +- `Dockerfile` + - 사용자가 도커 이미지를 쉽게 만들 수 있도록, 제공하는 템플릿 + - 파일명은 꼭 `Dockerfile` 이 아니어도 상관없지만, `docker build` 수행 시, default 로 사용하는 파일명이 `Dockerfile` 입니다. + - 도커 이미지를 만드는 `docker build` 를 수행할 때, `-f` 옵션을 주면 다른 파일명으로도 사용 가능합니다. + - ex) `docker build -f dockerfile-asdf .` 도 가능 + +1. 실습을 위해서 편한 디렉토리로 이동합니다. + + ```bash + cd + ``` + +2. docker-practice 라는 이름의 폴더를 생성합니다. + + ```bash + mkdir docker-practice + ``` + +3. docker-practice 폴더로 이동합니다. + + ```bash + cd docker-practice + ``` + +4. Dockerfile 이라는 빈 파일을 생성합니다. + + ```bash + touch Dockerfile + ``` + +5. 정상적으로 생성되었는지 확인합니다. + + ```bash + ls + ``` + +## 2. Dockerfile 내장 명령어 + +Dockerfile 에서 사용할 수 있는 기본적인 명령어에 대해서 하나씩 알아보겠습니다. + +### FROM + +Dockerfile 이 base image 로 어떠한 이미지를 사용할 것인지를 명시하는 명령어입니다. +도커 이미지를 만들 때, 아무것도 없는 빈 환경에서부터 하나하나씩 제가 의도한 환경을 만들어가는게 아니라, python 3.9 버전이 설치된 환경을 베이스로해두고, 저는 pytorch 를 설치하고, 제 소스코드만 넣어두는 형태로 활용할 수가 있습니다. +이러한 경우에는 `python:3.9`, `python-3.9-alpine`, ... 등의 잘 만들어진 이미지를 베이스로 활용합니다. + +```docker +FROM [:] [AS ] + +# 예시 +FROM ubuntu +FROM ubuntu:18.04 +FROM nginx:latest AS ngx +``` + +### COPY + +**host(로컬)에서의 ** 경로의 파일 혹은 디렉토리를 **container 내부에서의 ** 경로에 복사하는 명령어입니다. + +```docker +COPY ... + +# 예시 +COPY a.txt /some-directory/b.txt +COPY my-directory /some-directory-2 +``` + +`ADD` 는 `COPY` 와 비슷하지만 추가적인 기능을 품고 있습니다. + +```docker +# 1 - 호스트에 압축되어있는 파일을 풀면서 컨테이너 내부로 copy 할 수 있음 +ADD scripts.tar.gz /tmp +# 2 - Remote URLs 에 있는 파일을 소스 경로로 지정할 수 있음 +ADD http://www.example.com/script.sh /tmp + +# 위 두 가지 기능을 사용하고 싶을 경우에만 COPY 대신 ADD 를 사용하는 것을 권장 +``` + +### RUN + +명시한 커맨드를 도커 컨테이너 내부에서 실행하는 명령어입니다. +도커 이미지는 해당 커맨드들이 실행된 상태를 유지합니다. + +```docker +RUN +RUN ["executable-command", "parameter1", "parameter2"] + +# 예시 +RUN pip install torch +RUN pip install -r requirements.txt +``` + +### CMD + +명시한 커맨드를 도커 컨테이너가 **시작될 때**, 실행하는 것을 명시하는 명령어입니다. +비슷한 역할을 하는 명령어로 **ENTRYPOINT** 가 있습니다. 이 둘의 차이에 대해서는 **뒤에서** 다룹니다. +하나의 도커 이미지에서는 하나의 **CMD** 만 실행할 수 있다는 점에서 **RUN** 명령어와 다릅니다. + +```docker +CMD +CMD ["executable-command", "parameter1", "parameter2"] +CMD ["parameter1", "parameter2"] # ENTRYPOINT 와 함께 사용될 때 + +# 예시 +CMD python main.py +``` + +### WORKDIR + +이후 추가될 명령어를 컨테이너 내의 어떤 디렉토리에서 수행할 것인지를 명시하는 명령어입니다. +만약, 해당 디렉토리가 없다면 생성합니다. + +```docker +WORKDIR /path/to/workdir + +# 예시 +WORKDIR /home/demo +RUN pwd # /home/demo 가 출력됨 +``` + +### ENV + +컨테이너 내부에서 지속적으로 사용될 environment variable 의 값을 설정하는 명령어입니다. + +```docker +ENV +ENV = + +# 예시 +# default 언어 설정 +RUN locale-gen ko_KR.UTF-8 +ENV LANG ko_KR.UTF-8 +ENV LANGUAGE ko_KR.UTF-8 +ENV LC_ALL ko_KR.UTF-8 +``` + +### EXPOSE + +컨테이너에서 뚫어줄 포트/프로토콜을 지정할 수 있습니다. + 을 지정하지 않으면 TCP 가 디폴트로 설정됩니다. + +```docker +EXPOSE +EXPOSE / + +# 예시 +EXPOSE 8080 +``` + +## 3. 간단한 Dockerfile 작성해보기 + +`vim Dockerfile` 혹은 vscode 등 본인이 사용하는 편집기로 `Dockerfile` 을 열어 다음과 같이 작성해줍니다. + +```docker +# base image 를 ubuntu 18.04 로 설정합니다. +FROM ubuntu:18.04 + +# apt-get update 명령을 실행합니다. +RUN apt-get update + +# TEST env var의 값을 hello 로 지정합니다. +ENV TEST hello + +# DOCKER CONTAINER 가 시작될 때, 환경변수 TEST 의 값을 출력합니다. +CMD echo $TEST +``` + +## 4. Docker build from Dockerfile + +`docker build` 명령어로 Dockerfile 로부터 Docker Image 를 만들어봅니다. + +```bash +docker build --help +``` + +Dockerfile 이 있는 경로에서 다음 명령을 실행합니다. + +```bash +docker build -t my-image:v1.0.0 . +``` + +위 커맨드를 설명하면 다음과 같습니다. + +- `.` : **현재 경로**에 있는 Dockerfile 로부터 +- `-t` : my-image 라는 **이름**과 v1.0.0 이라는 **태그**로 **이미지**를 +- 빌드하겠다라는 명령어 + +정상적으로 이미지 빌드되었는지 확인해 보겠습니다. + +```bash +# grep : my-image 가 있는지를 잡아내는 (grep) 하는 명령어 +docker images | grep my-image +``` + +정상적으로 수행된다면 다음과 같이 출력됩니다. + +```text +my-image v1.0.0 143114710b2d 3 seconds ago 87.9MB +``` + +## 5. Docker run from Dockerfile + +그럼 이제 방금 빌드한 `my-image:v1.0.0` 이미지로 docker 컨테이너를 **run** 해보겠습니다. + +```bash +docker run my-image:v1.0.0 +``` + +정상적으로 수행된다면 다음과 같이 나옵니다. + +```text +hello +``` + +## 6. Docker run with env + +이번에는 방금 빌드한 `my-image:v1.0.0` 이미지를 실행하는 시점에, `TEST` env var 의 값을 변경하여 docker 컨테이너를 run 해보겠습니다. + +```bash +docker run -e TEST=bye my-image:v1.0.0 +``` + +정상적으로 수행된다면 다음과 같이 나옵니다. + +```text +bye +``` diff --git a/content/kor/prerequisites/docker/install.md b/content/kor/prerequisites/docker/install.md new file mode 100644 index 00000000..839c3cee --- /dev/null +++ b/content/kor/prerequisites/docker/install.md @@ -0,0 +1,37 @@ +--- +title : "Install Docker" +description: "Install docker to start." +lead: "" +draft: false +weight: 101 +images: [] +contributors: ["Jongseob Jeon", "Jaeyeon Kim"] +menu: + prerequisites: + parent: "docker" +--- + +## Docker + +도커 실습을 위해 도커를 설치해야 합니다. +도커 설치는 어떤 OS를 사용하는지에 따라 달라집니다. +각 환경에 맞는 도커 설치는 공식 홈페이지를 참고해주세요. + +- [ubuntu](https://docs.docker.com/engine/install/ubuntu/) +- [mac](https://docs.docker.com/desktop/mac/install/) +- [windows](https://docs.docker.com/desktop/windows/install/) + +## 설치 확인 + +`docker run hello-world` 가 정상적으로 수행되는 OS, 터미널 환경이 필요합니다. + +| OS | Docker Engine | Terminal | +| ------- | -------------- | ------------------ | +| MacOS | Docker Desktop | zsh | +| Windows | Docker Desktop | Powershell | +| Windows | Docker Desktop | WSL2 | +| Ubuntu | Docker Engine | bash | + +## 들어가기 앞서서.. + +MLOps를 사용하기 위해 필요한 도커 사용법을 설명하니 많은 비유와 예시가 MLOps 쪽으로 치중되어 있을 수 있습니다. diff --git a/content/kor/prerequisites/docker/introduction.md b/content/kor/prerequisites/docker/introduction.md new file mode 100644 index 00000000..741cae0d --- /dev/null +++ b/content/kor/prerequisites/docker/introduction.md @@ -0,0 +1,104 @@ +--- +title : "Why Docker & Kubernetes ?" +description: "Introduction to Docker." +lead: "" +draft: false +weight: 102 +images: [] +contributors: ["Jongseob Jeon", "Jaeyeon Kim"] +menu: + prerequisites: + parent: "docker" +--- + +## Why Kubernetes ? + +머신러닝 모델을 서비스화하기 위해서는 모델 개발 외에도 많은 **부가적인** 기능들이 필요합니다. + +1. 학습 단계 + - 모델 학습 명령의 스케줄 관리 + - 학습된 모델의 Reproducibility 보장 +2. 배포 단계 + - 트래픽 분산 + - 서비스 장애 모니터링 + - 장애 시 트러블슈팅 + +다행히도 이런 기능들에 대한 needs는 소프트웨어 개발 쪽에서 이미 많은 고민을 거쳐 발전되어 왔습니다. +따라서 머신러닝 모델을 배포할 때도 이런 고민의 결과물들을 활용하면 큰 도움을 받을 수 있습니다. +MLOps에서 대표적으로 활용하는 소프트웨어 제품이 바로 도커와 쿠버네티스입니다. + +## 도커와 쿠버네티스 + +### 기술 이름이 아니라 제품 이름 + +도커와 쿠버네티스는 각각 컨테이너라이제이션(Containerization) 기능과 컨테이너 오케스트레이션(Container Orchestration) 기능을 제공하는 대표 소프트웨어(제품)입니다. + +#### 도커 + +도커는 과거에 대세였지만 유료화 관련 정책들을 하나씩 추가하면서 점점 사용 빈도가 하락세입니다. +하지만 2022년 3월 기준으로 아직까지도 가장 일반적으로 사용되는 컨테이너 가상화 소프트웨어입니다. + +

+ +

+ +
[from sysdig 2019]
+ +

+ +

+ +
[from sysdig 2021]
+ +#### 쿠버네티스 + +쿠버네티스는 지금까지는 비교 대상조차 거의 없는 제품입니다. + +

+ +

+ +
[from cncf survey]
+ +

+ +

+ +
[from t4.ai]
+ +### **재미있는 오픈소스 역사 이야기** + +#### 초기 도커 & 쿠버네티스 + +초기 도커 개발시에는 Docker Engine이라는 **하나의 패키지**에 API, CLI, 네트워크, 스토리지 등 여러 기능들을 모두 포함했으나, **MSA** 의 철학을 담아 **하나씩 분리**하기 시작했습니다. +하지만 초기의 쿠버네티스는 컨테이너 가상화를 위해 Docker Engine을 내장하고 있었습니다. +따라서 도커 버전이 업데이트될 때마다 Docker Engine 의 인터페이스가 변경되어 쿠버네티스에서 크게 영향을 받는 일이 계속해서 발생하였습니다. + +#### Open Container Initiative + +그래서 **이런 불편함을 해소**하고자, 도커를 중심으로 구글 등 컨테이너 기술에 관심있는 **여러 집단**들이 한데 모여 **Open Container Initiative,** 이하 **OCI**라는 프로젝트를 시작하여 컨테이너에 관한 **표준**을 정하는 일들을 시작하였습니다. +도커에서도 인터페이스를 **한 번 더 분리**해서, OCI 표준을 준수하는 **containerd**라는 Container Runtime 를 개발하고, **dockerd** 가 containerd 의 API 를 호출하도록 추상화 레이어를 추가하였습니다. + +이러한 흐름에 맞추어서 쿠버네티스에서도 이제부터는 도커만을 지원하지 않고, **OCI 표준을** 준수하고, 정해진 스펙을 지키는 컨테이너 런타임은 무엇이든 쿠버네티스에서 사용할 수 있도록, Container Runtime Interface, 이하 **CRI 스펙**을 버전 1.5부터 제공하기 시작했습니다. + +#### CRI-O + +Red Hat, Intel, SUSE, IBM에서 **OCI 표준+CRI 스펙을** 따라 Kubernetes 전용 Container Runtime 을 목적으로 개발한 컨테이너 런타임입니다. + +#### 지금의 도커 & 쿠버네티스 + +쿠버네티스는 Docker Engine 을 디폴트 컨테이너 런타임으로 사용해왔지만, 도커의 API 가 **CRI** 스펙에 맞지 않아(*OCI 는 따름*) 도커의 API를 **CRI**와 호환되게 바꿔주는 **dockershim**을 쿠버네티스 자체적으로 개발 및 지원해왔었는데,(*도커 측이 아니라 쿠버네티스 측에서 지원했다는 점이 굉장히 큰 짐이었습니다.*) 이걸 쿠버네티스 **v1.20 부터는 Deprecated하고,** **v1.23 부터는 지원을 포기**하기로 결정하였습니다. + +- v1.23 은 2021 년 12월 릴리즈 + +그래서 쿠버네티스 v1.23 부터는 도커를 native 하게 쓸 수 없습니다다. +그렇지만 **사용자들은 이런 변화에 크게 관련이 있진 않습니다.** +왜냐하면 Docker Engine을 통해 만들어진 도커 이미지는 OCI 표준을 준수하기 때문에, 쿠버네티스가 어떤 컨테이너 런타임으로 이루어져있든 사용 가능하기 때문입니다. + +### References + +- [*https://www.linkedin.com/pulse/containerd는-무엇이고-왜-중요할까-sean-lee/?originalSubdomain=kr*](https://www.linkedin.com/pulse/containerd%EB%8A%94-%EB%AC%B4%EC%97%87%EC%9D%B4%EA%B3%A0-%EC%99%9C-%EC%A4%91%EC%9A%94%ED%95%A0%EA%B9%8C-sean-lee/?originalSubdomain=kr) +- [https://kubernetes.io/blog/2021/12/07/kubernetes-1-23-release-announcement/](https://kubernetes.io/blog/2021/12/07/kubernetes-1-23-release-announcement/) +- [https://kubernetes.io/blog/2020/12/02/dockershim-faq/](https://kubernetes.io/blog/2020/12/02/dockershim-faq/) +- [https://kubernetes.io/blog/2020/12/02/dont-panic-kubernetes-and-docker/](https://kubernetes.io/blog/2020/12/02/dont-panic-kubernetes-and-docker/) +- [https://kubernetes.io/ko/blog/2020/12/02/dont-panic-kubernetes-and-docker/](https://kubernetes.io/ko/blog/2020/12/02/dont-panic-kubernetes-and-docker/) diff --git a/content/kor/prerequisites/intro.md b/content/kor/prerequisites/intro.md new file mode 100644 index 00000000..25bf27d3 --- /dev/null +++ b/content/kor/prerequisites/intro.md @@ -0,0 +1,10 @@ +--- +title : "Introduction to Prerequisites" +description: "Tutorials for prerequisites." +lead: "" +draft: false +weight: 1 +images: [] +--- + +모두의 MLOps를 이해하기 위해 필요한 선수 지식들에 대해서 설명합니다. diff --git a/content/kor/prerequisites/kubernetes/_index.md b/content/kor/prerequisites/kubernetes/_index.md new file mode 100644 index 00000000..9323cb82 --- /dev/null +++ b/content/kor/prerequisites/kubernetes/_index.md @@ -0,0 +1,8 @@ +--- +title : "Kubernetes" +description: "How to use Kubernetes." +lead: "" +draft: false +weight: 200 +images: [] +--- diff --git a/layouts/index.html b/layouts/index.html index 8488b029..b526d374 100644 --- a/layouts/index.html +++ b/layouts/index.html @@ -6,7 +6,7 @@

{{ .Title }}

{{ .Params.lead | safeHTML }}

- Get Started + Get Started

Open-source MIT Licensed.

이 프로젝트는 MakinaRocks의 지원을 받아 제작되었습니다.

@@ -16,7 +16,7 @@

{{ .Title }}

{{ end }} {{ define "sidebar-prefooter" }} - {{ if eq $.Site.Language.LanguageName "English" }} + {{ if eq $.Site.Language.LanguageName "Not Use" }}
diff --git a/layouts/prerequisites/list.html b/layouts/prerequisites/list.html new file mode 100644 index 00000000..ea3ed6df --- /dev/null +++ b/layouts/prerequisites/list.html @@ -0,0 +1,22 @@ +{{ define "main" }} +
+
+
+

{{ if eq .CurrentSection .FirstSection }}{{ .Section | humanize }}{{ else }}{{ .Title }}{{ end }}

+
{{ .Content }}
+
+ {{ $currentSection := .CurrentSection }} + {{ range where .Site.RegularPages.ByTitle "Section" .Section }} + {{ if in (.RelPermalink | string) $currentSection.RelPermalink }} + + {{ end }} + {{ end }} +
+
+
+
+{{ end }} diff --git a/layouts/prerequisites/single.html b/layouts/prerequisites/single.html new file mode 100644 index 00000000..d1aec300 --- /dev/null +++ b/layouts/prerequisites/single.html @@ -0,0 +1,53 @@ +{{ define "main" }} +
+
+ +
+ {{ if ne .Params.toc false -}} + + {{ end -}} + {{ if .Params.toc -}} +
+ {{ else -}} +
+ {{ end -}} + {{ if .Site.Params.options.breadCrumb -}} + + + {{ end }} +

{{ .Title }}

+

{{ .Params.lead | safeHTML }}

+ {{ if ne .Params.toc false -}} + + {{ end -}} + {{ .Content }} + + {{ partial "main/docs-navigation.html" . }} + +
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