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Publish GitHub pages with GitHub Actions | itsopensource

Publish GitHub pages with GitHub Actions | itsopensource

itsopensource

Publish GitHub pages with GitHub Actions

December 12, 2020

automationcigithub-actions


GitHub pages are the best way to host static blogs like Gatsby. One of the most common ways to do this is, maintain your code in main/master branch, build it, and then push the code to gh-pages branch. There are various CI that easily automate this process like Travis CI, CircleCI, etc. With GitHub actions, this would be a piece of cake, and without depending on any third-party provider. From the docs:

Automate, customize, and execute your software development workflows right in your repository with GitHub Actions

If you are not sure what are GitHub actions please visit here.

Workflow

Prerequisites

  1. You have a static blog(let’s say gatsby) setup, the code for your blog is in master branch.
  2. You have a build script in your package.json
  3. You have setup your GitHub pages (or pointed to your custom domain) in gh-pages branch.

Process

Step 1

Install gh-pages npm package in your project, this is a small utility package which helps to publish your code to gh-pages branch. You can skip this step in case you want to push all contents from master to gh-pages branch.

Step 2

Add a deploy script in your package.json. This script should do 2 jobs

  1. Build the project, make it ready for being published.
  2. Push the changes to the gh-pages branch.

For example, this pushes the code to gh-pages via npm package.

"deploy": "gatsby build && gh-pages -d public -r https://$GH_TOKEN@github.com/<username>/<repository_name>.git"

Step 3

Generate an access token and add it to secret of your repository.

  1. Create an access token: goto https://github.com/settings/tokens, create a new token, give it access for repo and workflow.

  2. Add this token to secret of your repository: goto https://github.com/<username>/<repository_name>/settings/secrets/actions, click new repository secret.

Step 4

  1. Create a workflow file, you need to create a file in following path: .github/workflows/<some-name-you-like>.yml, its important to have .yml extension and have exact same path.

  2. Following action file is complete enough to publish gh-pages, every time a new commit is merged in master branch. ✅

    name: gh-pages publisher 🚀

on:
push:
  branches: [ master ]

jobs:
publish-gh-pages:
  runs-on: ubuntu-latest
  steps:
     - uses: actions/checkout@v2
     - uses: actions/setup-node@v1
     with:
        node-version: 12
     - run: npm ci
     - run: git config user.name "<user name>" && git config user.email "user email"
     - run: npm run deploy
     env:
        GH_TOKEN: ${{secrets.SECRET_GITHUB_TOKEN}}

This script is pretty self-explanatory, it performs the following tasks.

  • It specifies to work only on push on master
  • Sets up a node 12 environment
  • Installs dependencies via npm ci
  • Sets up git requirements for username and email (required to do a push to branch)
  • Run the deploy script
  • In case you don’t use gh-pages npm package, you can write another step for git push to gh-pages branch.
  • At last, we set up env variable GH_TOKEN from our action secret (which you set up in step 3), this env variable would be available in package.json

Step 5

Commit this file and see your first action in action (sorry for the pun 🙈)

Hope this helps 🙏🙏🙏

  • You can see the actions running here: https://github.com/<username>/<repository_name>/actions.
  • Workflow file for itsopensource.com can be viewed here.

Trishul Goel is a professional frontend developer; writes React code for living and volunteers for Mozilla to justify his existence.
@trishulgoel

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Setting up a Kubernetes cluster using Docker in Docker | Callista

Setting up a Kubernetes cluster using Docker in Docker | Callista 

Setting up a Kubernetes cluster using Docker in Docker | Callista



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Setting up a Kubernetes cluster using Docker in Docker

20 December 2017 // Magnus Larsson

In this blog post I will describe how to set up a local Kubernetes cluster for test purposes with a minimal memory usage and fast startup times, using Docker in Docker instead of traditional local virtual machines.

This blog post is part of the blog series - Trying out new features in Docker.

Background

For a background on how Docker in Docker can help us to set up a local Kubernetes cluster, see the Background section in the blog post Setting up a Docker Swarm cluster using Docker in Docker.

This blog post is not an introduction to Kubernetes and the components that builds up a Kubernetes cluster. For an introduction of the concepts used in Kubernetes see: kubernetes.io/docs/concepts/.

We are going to use the GitHub project Mirantis/kubeadm-dind-cluster to set up a Kubernetes cluster using Docker in Docker and we will use Docker for Mac to act as the Docker Host for the Kubernetes nodes (running as containers in Docker for Mac).


Source: http://nishadikirielle.blogspot.se/2016/02/kubernetes-at-first-glance.html

Installation

First, you need to have Docker for Mac installed, I’m on version 17.09.1-ce-mac42.

Next, you also need to have jq and md5sha1sum installed to be able to follow my instructions below. If you use Homebrew, they can be installed with:

brew install jq
brew install md5sha1sum

Finally, clone the Git repo Mirantis/kubeadm-dind-cluster from GitHub and jump into the fixed folder:

git clone https://github.com/Mirantis/kubeadm-dind-cluster.git
cd kubeadm-dind-cluster/fixed

We are good to go!

Setup

Start up a Kubernetes v1.8 cluster requesting 3 worker nodes in the cluster (default is 2):

NUM_NODES=3 ./dind-cluster-v1.8.sh up

The first time the up command is executed it will take a few minutes and produce lot of output in the terminal window…

…in the end it should say something like:

NAME          STATUS    ROLES     AGE       VERSION
kube-master   Ready     master    2m        v1.8.4
kube-node-1   Ready     <none>    1m        v1.8.4
kube-node-2   Ready     <none>    1m        v1.8.4
kube-node-3   Ready     <none>    47s       v1.8.4
* Access dashboard at: http://localhost:8080/ui

Note: If you start up the cluster again later on, it will only take a minute.

Verify that you can see the master and worker nodes as ordinary containers in Docker for Mac:

docker ps

It should report something like:

CONTAINER ID        IMAGE                                COMMAND                  CREATED             STATUS              PORTS                      NAMES
766582a93d1f        mirantis/kubeadm-dind-cluster:v1.8   "/sbin/dind_init s..."   9 hours ago         Up 9 hours          8080/tcp                   kube-node-3
e1fc6bec1f23        mirantis/kubeadm-dind-cluster:v1.8   "/sbin/dind_init s..."   9 hours ago         Up 9 hours          8080/tcp                   kube-node-2
b39509b9db77        mirantis/kubeadm-dind-cluster:v1.8   "/sbin/dind_init s..."   9 hours ago         Up 9 hours          8080/tcp                   kube-node-1
a01be2512423        mirantis/kubeadm-dind-cluster:v1.8   "/sbin/dind_init s..."   9 hours ago         Up 9 hours          127.0.0.1:8080->8080/tcp   kube-master

View

Ok, so let’s see if we actually have a Kubernetes cluster up and running:

kubectl get nodes

It should result in a response like:

NAME          STATUS    AGE       VERSION
kube-master   Ready     2m        v1.8.4
kube-node-1   Ready     55s       v1.8.4
kube-node-2   Ready     1m        v1.8.4
kube-node-3   Ready     1m        v1.8.4

Also try out Kubernetes Dashboard at: localhost:8080/ui

Click on the “Nodes” - link in the menu to the left and you should see something like:

Deploy

Now, let’s deploy a service and try it out!

I have a very simple Docker image magnuslarsson/quotes:go-22 (written in Go) that creates some random quotes about successful programming languages.

We will create a Deployment of this Docker Image and a Service that expose it on each node in the Kubernetes cluster using a dedicated port (31000). The creation of the Deployment object will automatically also create a Replica Set and a Pod.

Note: In more production like environment we should also set up an external load balancer, like HAProxy or NGINX in front of the Kubernetes cluster to be able to expose one single entry point to all services in the cluster. But that is out of scope for this blog post and left as an exercise for the interested reader :-)

First, switch to the default namespace:

kubectl config set-context $(kubectl config current-context) --namespace=default

The default namespace should only contain one pre-created object, run the command:

kubectl get all

It should report:

NAME             CLUSTER-IP   EXTERNAL-IP   PORT(S)   AGE
svc/kubernetes   10.96.0.1    <none>        443/TCP   5h

Create a file named quotes.yml with the following command:

cat <<EOF > quotes.yml
apiVersion: apps/v1beta1
kind: Deployment
metadata:
  name: quotes
  labels:
    app: quotes-app
spec:
  replicas: 1
  selector:
    matchLabels:
      app: quotes-app
  template:
    metadata:
      labels:
        app: quotes-app
    spec:
      containers:
      - name: quotes
        image: magnuslarsson/quotes:go-22
        ports:
        - containerPort: 8080
---

apiVersion: v1
kind: Service
metadata:
  name: quotes-service
spec:
  type: NodePort
  selector:
    app: quotes-app
  ports:
    - port: 8080
      targetPort: 8080
      nodePort: 31000
EOF

Create the Deployment and Service objects with the following command:

kubectl create -f quotes.yml

Verify that we got the expected objects created, using the following command:

kubectl get all

Expect output:

NAME                         READY     STATUS    RESTARTS   AGE
po/quotes-77776b5bbc-5lll7   1/1       Running   0          45s

NAME                 CLUSTER-IP       EXTERNAL-IP   PORT(S)          AGE
svc/kubernetes       10.96.0.1        <none>        443/TCP          5h
svc/quotes-service   10.105.185.117   <nodes>       8080:31000/TCP   45s

NAME            DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deploy/quotes   1         1         1            1           45s

NAME                   DESIRED   CURRENT   READY     AGE
rs/quotes-77776b5bbc   1         1         1         45s

Note: In the output above short names are used for object types:

  • po: Pod
  • svc: Service
  • deploy: Deployment
  • rs: Replica Set

We can now try it out using curl from one of the worker nodes:

docker exec kube-node-2 curl localhost:31000/api/quote -s -w "\n" | jq

Output should look like:

{
  "ipAddress": "quotes-77776b5bbc-5lll7/10.192.3.4",
  "quote": "In Go, the code does exactly what it says on the page.",
  "language": "EN"
}

The most interesting part of the response from the service is actually the field ipAddress, that contains the hostname and ip address of the pod that served the request, quotes-77776b5bbc-5lll7/10.192.3.4 in the sample response above.

Scale

This can be used to verify that scaling of a service actually works. In the output from a scaled service we expect different values in the ipAddress - field from subsequent requests, indicating that the request is load balanced over the available pods.

Let’s try it out, shall we?

First, start a loop that use curl to sends one request per second to the quote-service and prints out the ipAddress - field from the response:

while true; do docker exec kube-node-2 curl localhost:31000/api/quote -s -w "\n" | jq -r .ipAddress; sleep 1; done

Initially the output should return one and the same hostname and IP address, since we only have one pod running in the service:

quotes-77776b5bbc-5lll7/10.192.3.4
quotes-77776b5bbc-5lll7/10.192.3.4
quotes-77776b5bbc-5lll7/10.192.3.4
quotes-77776b5bbc-5lll7/10.192.3.4

Now, scale the quote-service by adding 8 new pods to it (9 in total):

kubectl scale --replicas=9 deployment/quotes

Verify that you can see all 9 quote-service pods and also to what node they are deployed:

kubectl get pods -o wide

Expected output:

NAME                      READY     STATUS    RESTARTS   AGE       IP            NODE
quotes-77776b5bbc-42wgk   1/1       Running   0          1m        10.192.4.9    kube-node-3
quotes-77776b5bbc-c8mkf   1/1       Running   0          1m        10.192.3.8    kube-node-2
quotes-77776b5bbc-dnpm8   1/1       Running   0          25m       10.192.3.4    kube-node-2
quotes-77776b5bbc-gpk85   1/1       Running   0          1m        10.192.2.8    kube-node-1
quotes-77776b5bbc-qmspm   1/1       Running   0          1m        10.192.4.11   kube-node-3
quotes-77776b5bbc-qr27h   1/1       Running   0          1m        10.192.3.9    kube-node-2
quotes-77776b5bbc-txpcq   1/1       Running   0          1m        10.192.2.9    kube-node-1
quotes-77776b5bbc-wb2qt   1/1       Running   0          1m        10.192.4.10   kube-node-3
quotes-77776b5bbc-wzhzz   1/1       Running   0          1m        10.192.2.7    kube-node-1

Note: We got three pods per node, as expected!

You can also use the Dashboard to see what pods that run in a specific node:

Now, the output from the curl - loop should report different hostnames and ip addresses as the requests are load balanced over the 9 pods:

quotes-77776b5bbc-gpk85/10.192.2.8
quotes-77776b5bbc-42wgk/10.192.4.9
quotes-77776b5bbc-txpcq/10.192.2.9
quotes-77776b5bbc-txpcq/10.192.2.9
quotes-77776b5bbc-wb2qt/10.192.4.10
quotes-77776b5bbc-txpcq/10.192.2.9

Great, isn’t it?

Resilience

Now, let’s expose the container orchestrator, i.e. Kubernetes, to some problems and see if it handles them as expected!

Kill some pods

First, let’s shut down some arbitrary pods and see if the orchestrator detects it and start new ones!

Note: We will actually kill the container that runs within the pod, not the pod itself.

Start a long running command, using the --watch flag, that continuously reports changes in the state of the Deployment object:

kubectl get deployment quotes --watch

Initially, it should report:

NAME      DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
quotes    9         9         9            9           1d

Note: The command hangs, waiting for state changes to be reported

To keep things relatively simple, let’s kill all quote-services running on the first worker node:

CIDS=$(docker exec kube-node-1 docker ps --filter name=k8s_quotes_quotes -q)
docker exec kube-node-1 docker rm -f $CIDS

The command should respond with the ids of the killed containers:

e780545ddd17
ddd260ba3f73
b4e07e736028

Now, go back to the “_deployment watch_” - command and see what output it produces!

It should be something like:

quotes    9         9         9         8         1d
quotes    9         9         9         7         1d
quotes    9         9         9         6         1d
quotes    9         9         9         7         1d
quotes    9         9         9         8         1d
quotes    9         9         9         9         1d

The output shows how Kubernetes detected that it got short of available pods and compensated that by scheduling new containers for the affected pods.

Worker node off line

Now, let’s make it even worse by removing a worker node, simulating that it is taken off line for maintenance work. Let’s mark kube-node-3 as no longer accepting either existing pods or scheduling of new pods:

kubectl drain kube-node-3 --ignore-daemonsets

The command reports back what pods that was evicted from the node:

pod "quotes-77776b5bbc-jlwtb" evicted
pod "quotes-77776b5bbc-7d6gc" evicted
pod "quotes-77776b5bbc-cz8sp" evicted

Kubernetes will however automatically detect this and start new ones on the remaining nodes:

kubectl get pods -o wide

Reports back:

NAME                      READY     STATUS    RESTARTS   AGE       IP            NODE
quotes-77776b5bbc-28r7w   1/1       Running   0          11s       10.192.2.10   kube-node-1
quotes-77776b5bbc-7hxd5   1/1       Running   0          11s       10.192.3.10   kube-node-2
quotes-77776b5bbc-c8mkf   1/1       Running   0          7m        10.192.3.8    kube-node-2
quotes-77776b5bbc-dnpm8   1/1       Running   0          31m       10.192.3.4    kube-node-2
quotes-77776b5bbc-gpk85   1/1       Running   0          7m        10.192.2.8    kube-node-1
quotes-77776b5bbc-grcqn   1/1       Running   0          11s       10.192.2.11   kube-node-1
quotes-77776b5bbc-qr27h   1/1       Running   0          7m        10.192.3.9    kube-node-2
quotes-77776b5bbc-txpcq   1/1       Running   0          7m        10.192.2.9    kube-node-1
quotes-77776b5bbc-wzhzz   1/1       Running   0          7m        10.192.2.7    kube-node-1

Note: The three pods with an age of 11 sec are the new ones.

We can also see that the node is reported to being unavailable for scheduling of pods:

kubectl get node

Reports:

NAME          STATUS                     AGE       VERSION
kube-master   Ready                      1d        v1.8.4
kube-node-1   Ready                      1d        v1.8.4
kube-node-2   Ready                      1d        v1.8.4
kube-node-3   Ready,SchedulingDisabled   1d        v1.8.4

Great!

Let’s wrap up by making the node available again:

kubectl uncordon kube-node-3

The node is now reported to be back on line:

kubectl get node

Results in:

NAME          STATUS    AGE       VERSION
kube-master   Ready     1d        v1.8.4
kube-node-1   Ready     1d        v1.8.4
kube-node-2   Ready     1d        v1.8.4
kube-node-3   Ready     1d        v1.8.4

But none of the existing pods are automatically rescheduled to the node:

kubectl get pods -o wide

Still reports that all pods runs on node 1 and 2:

NAME                      READY     STATUS    RESTARTS   AGE       IP            NODE
quotes-77776b5bbc-28r7w   1/1       Running   0          4m        10.192.2.10   kube-node-1
quotes-77776b5bbc-7hxd5   1/1       Running   0          4m        10.192.3.10   kube-node-2
quotes-77776b5bbc-c8mkf   1/1       Running   0          11m       10.192.3.8    kube-node-2
quotes-77776b5bbc-dnpm8   1/1       Running   0          36m       10.192.3.4    kube-node-2
quotes-77776b5bbc-gpk85   1/1       Running   0          11m       10.192.2.8    kube-node-1
quotes-77776b5bbc-grcqn   1/1       Running   0          4m        10.192.2.11   kube-node-1
quotes-77776b5bbc-qr27h   1/1       Running   0          11m       10.192.3.9    kube-node-2
quotes-77776b5bbc-txpcq   1/1       Running   0          11m       10.192.2.9    kube-node-1
quotes-77776b5bbc-wzhzz   1/1       Running   0          11m       10.192.2.7    kube-node-1

We can, however, manually rebalance our pods with the commands:

kubectl scale --replicas=6 deployment/quotes
kubectl scale --replicas=9 deployment/quotes

Verify:

kubectl get pods -o wide

Reports the expected three pod per node again:

NAME                      READY     STATUS    RESTARTS   AGE       IP            NODE
quotes-77776b5bbc-2q26w   1/1       Running   0          1s        10.192.4.13   kube-node-3
quotes-77776b5bbc-bbhcb   1/1       Running   0          1s        10.192.4.14   kube-node-3
quotes-77776b5bbc-c8mkf   1/1       Running   0          13m       10.192.3.8    kube-node-2
quotes-77776b5bbc-dnpm8   1/1       Running   0          37m       10.192.3.4    kube-node-2
quotes-77776b5bbc-gpk85   1/1       Running   0          13m       10.192.2.8    kube-node-1
quotes-77776b5bbc-qr27h   1/1       Running   0          13m       10.192.3.9    kube-node-2
quotes-77776b5bbc-trrdh   1/1       Running   0          1s        10.192.4.12   kube-node-3
quotes-77776b5bbc-txpcq   1/1       Running   0          13m       10.192.2.9    kube-node-1
quotes-77776b5bbc-wzhzz   1/1       Running   0          13m       10.192.2.7    kube-node-1

Teardown

That’s it, let’s remove the Kubernetes cluster:

./dind-cluster-v1.8.sh down

If you start up the cluster again with the up command, it will start up much faster than the first time!

If you don’t want to start up the cluster again, at least in any near time, you can also clean up some data created for the cluster:

./dind-cluster-v1.8.sh clean

If you start up the cluster again after a clean command you are back to the very long startup time.

Next up…

For more blog posts on new features in Docker, see the blog series - Trying out new features in Docker.

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bsycorp/kind: Kubernetes-in-Docker - A single node cluster to run your CI tests against thats ready in 30 seconds

来源：bsycorp/kind: Kubernetes-in-Docker - A single node cluster to run your CI tests against thats ready in 30 seconds

摘录内容

Other projects:

  1. https://github.com/kubernetes/minikube/tree/master/deploy/docker (Deprecated)
  2. https://github.com/kubernetes/test-infra/tree/master/dind (Replaces no.1)
  3. https://github.com/kubernetes-sigs/kubeadm-dind-cluster/ (more info: http://callistaenterprise.se/blogg/teknik/2017/12/20/kubernetes-on-docker-in-docker/)

想法

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deeplearning-ai/machine-learning-yearning-cn: Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著

来源：deeplearning-ai/machine-learning-yearning-cn: Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著

摘录内容

Machine Learning Yearning 中文版

访问此处 开始在线阅读《机器学习训练秘籍》样稿，希望这本书对你们有所帮助！当前样稿仅供内容预览，非最终版本（历史版本可以在 release 中找到）。

想法

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github不能使用账户密码登陆了？ github的token使用（超详细）_CPerdst的博客-CSDN博客_github token怎么用

然后使用git remote来查看自己的 url，git branch -v 查看自己的分支，使用git remote set-url <your_url> https://<your_token>@github.com//.git 来更新自己的 url
然后使用git remote来查看自己的 url，git branch -v 查看自己的分支，使用git remote set-url <your_url> https://<your_token>@github.com//.git 来更新自己的 url
使用git push <your_url> 来更新 github
使用git push <your_url> 来更新 github
最近想把自己写的几个小程序上传到github上面，但是 github 那端已经不让使用账号密码进行验证登录了，所以在此做一个自己总结的 github token 使用教程来记录一下，以防以后再不会用了。

一，生成自己的 token

首先选择 setting 进入设置，然后在进入 Developer setting，选择生成私人 token。

note 随便写，天数尽量选长点时间，选上 repo 才能用 git 指令操作自己的 repositories。

二，使用 token

可以从自己的 github 上面 clone 一个项目，例如：

值得注意的是它的格式是这样的：

你在 github 上的原始 url： https://github.com//.git

而你现在需要 clone 的则是：https://<your_token>@github.com//.git

<your_token>是你自己刚刚生成的 token，

是自己设置的，你可以从这里查看（是下面的（CPerdst））：

如果正常的话，现在你已经将自己在 github 上的项目下载下来了（如果没有，建议开一下代理，毕竟 github 是国外的网站），现在就可以继续写自己的项目了，我这里为了演示就直接随便创造一些文件来代替。

现在进入自己的项目：

使用git status查看当前的状态

使用git add ./来确定更改，然后使用git commit -m ‘your update message’添加提交信息。

然后使用git remote来查看自己的 url，git branch -v 查看自己的分支，使用git remote set-url <your_url> https://<your_token>@github.com//.git 来更新自己的 url

使用git push <your_url> 来更新 github

补充：如果当使用 git push 的时候没有显示账户密码可以使用 git config –system –unset credential.helper 更新。

或者使用 git reset 重置一下

引自： (21 条消息) github 开发人员在七夕搞事情：remote: Support for password authentication was removed on August 13, 2021._星空 - CSDN 博客_github 开发人员在七夕搞事情
https://blog.csdn.net/qq_42915526/article/details/122362565?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-122362565-blog-120060010.pc_relevant_antiscanv3&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-122362565-blog-120060010.pc_relevant_antiscanv3&utm_relevant_index=1

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mindsdb/mindsdb: In-Database Machine Learning

来源：mindsdb/mindsdb: In-Database Machine Learning

摘录内容

mindsdb：用 SQL 开启机器学习的数据库。把机器学习引入 SQL 数据库将模型作为虚拟表（AI-table），从而省去了数据准备、预处理等步骤，可以直接用 SQL 查询时间序列、回归、分类预测的结果，实现简化机器学习开发流程的效果
MindsDB ML-SQL Server enables machine learning workflows for the most powerful databases and datawarehouses using SQL.

  • Developers can quickly add AI capabilities to your applications.
  • Data Scientists can streamline MLOps by deploying ML models as AI Tables.
  • Data Analysts can easily make forecasts on complex data (like multivariate time-series with high cardinality) and visualize them in BI tools like Tableau.

If you like our project then we would really appreciate a Star ⭐!

Also, check-out the rewards and community programs.

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