Tech Edic

WhatsApp Privacy Policy Update : Concerned about privacy policy , Users Look at Alternative like Signal, Telegram

2021-01-10T08:48:00.002-08:00

Recently, WhatsApp began turning out in-application notices to Android and iOS clients uncovering an update in its Terms of Service and protection strategy. The warning educated clients regarding an update in the manner client information is taken care of by the stage, how organizations can utilize Facebook facilitated administrations to store and oversee, and the organization accomplices with Facebook to offer incorporations across the Facebook Company Product. It further cautioned clients that they need to consent to the new terms and strategy by 8 February 2021 to keep utilizing the application.

Since the update, there has been a great deal of discussion on the web about WhatsApp offering client data to Facebook. Numerous clients on Twitter have been persuading each other into getting off WhatsApp and making a move to other scrambled couriers like Signal and Telegram. Tesla, SpaceX, and Boring Company CEO Elon Musk participated in the discussion as well, requesting that individuals leave WhatsApp. Toward the end of last night, he posted on Twitter, "Utilize Signal".

Use Signal
— Elon Musk (@elonmusk) January 7, 2021

Signal has also shared how users can transfer their group chat from some other messaging app to Signal.

A lot of people have been asking how to move their group chats from other apps to Signal, and Signal group links are a great way to get started. Drop a group link into your former chat app of choice like you're dropping the mic on the way out. pic.twitter.com/q49DeZufBG
— Signal (@signalapp) January 7, 2021

TechCrunch supervisor Mike Butcher as of late tweeted about how Signal and Telegram are presently better than other options on the off chance that you are worried about your security.

WhatsApp will share its users' personal information, including phone numbers, IP addresses, contacts, & more with Facebook from Feb. 8, according to the new T&Cs. No opt-out. The only way to object is to leave the service & move to a service like Signal or Telegram. https://t.co/FbGcq5T3JB
— Mike Butcher (@mikebutcher) January 6, 2021

Also now currently number 1 trending app on App Store is the Signal app.

Look at what you've done. 🇮🇳 pic.twitter.com/0YuqyZXtgP
— Signal (@signalapp) January 8, 2021

U.S. Robot Strikes: Should Terrorism Suspects be given a Fair Trial?

2020-12-16T08:34:00.002-08:00

"They state Lewis Carol is fiction," Senator Rand Paul starts his delay, restricting robot strikes on American soil. "Alice never tumbled down a bunny opening, and the White Queen's scathing decisions are not generally a danger to your security. Has America the lovely become Alice's wonderland?" Paul continues with an exchange passage from exemplary writing, finishing with frivolity:

“No no!” said the Queen, “Sentence first, verdict afterwards!”
“Stuff and nonsense!” said Alice loudly. “The idea of having the sentence first!”
“Hold your tongue!” said the Queen, turning purple.
“I won’t!” said Alice.
“Release the drones!” the Queen shouted at the top of her voice.

Congressperson Rand Paul makes a practically equivalent association between fiction and reality. In spite of the fact that Senator's contention, at last, contradicts drone strikes on American soil, maybe it should be made a stride further, serving likewise as contention for restricting strikes on outsiders. For similarly as the Queen demanded condemning a suspect before the jury got an opportunity to consider, so President Barack Obama executes the utilization of robot strikes to condemn unfamiliar suspects to death without a decision.

Robots have been utilized to strike residents in far off nations since 2004 when it was first approved by George W. Hedge. From that point forward, there have been several robot strikes in Pakistan, Libya, Yemen, Somalia, Iraq, and Afghanistan. The reason for these assaults is to wipe out outsiders associated with psychological oppression. For what reason would they say they are stuck somewhere around rambles, rather than given a free preliminary? Obama's thinking for denying them a reasonable and quick preliminary is basically on the grounds that we need more time or space. Does this thinking legitimize the outcomes?

Since psychological oppression is quite a delicate issue, the same number of Americans have been harmed by occasions, for example, 9/11, some might be slanted to state that denying illegal intimidation presumes a preliminary is irrelevant. Maybe, insofar as they're being gotten, it doesn't make a difference how their sentence is being completed. There are numerous issues with this way of thinking, in any case, one being that a president's expected set of responsibilities does exclude being an adjudicator, jury, and killer. There is no higher force than deciding whether a man lives or passes on, and it a force excessively extraordinary for the judgment of one man alone. Notwithstanding this, President Obama has heightened the utilization of robots dramatically, and even dispatched a battle on Libya unlawfully in 2011, with no respect for legislative endorsement. Strangely, even Obama himself concurs with its unlawfulness, as he wrote in 2007:

“The president does not have power under the Constitution to unilaterally authorize a military attack in a situation that does not involve stopping an actual or imminent threat to the nation … History has shown us time and again, however, that military action is most successful when it is authorized and supported by the Legislative branch.”

Furthermore, U.S. drones are not just focusing on Al Qaeda and their partners they are focusing on assailants crossing the Afghan outskirt. This negates the Obama Administration's explanation that the robots were just being utilized to target individuals from al-Qaeda or the individuals who represent an 'unavoidable danger'. Furthermore, focusing on Taliban infantrymen was not legitimized under the Authorization to Use Military Force (AUMF) that was passed by Congress in 2001.

What is Deep Learning?

2020-12-15T09:51:00.013-08:00

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990s and early 2000s.

The leaders and experts in the field have ideas of what deep learning is and these specific and nuanced perspectives shed a lot of light on what deep learning is all about.

In this post, you will discover exactly what deep learning is by hearing from a range of experts and leaders in the field.

Deep Learning is Large Neural Networks

Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.

He has spoken and written a lot about what deep learning is and is a good place to start.

In early talks on deep learning, Andrew described deep learning in the context of traditional artificial neural networks. In the 2013 talk titled “Deep Learning, Self-Taught Learning and Unsupervised Feature Learning” he described the idea of deep learning as:

Using brain simulations, hope to:
– Make learning algorithms much better and easier to use.
– Make revolutionary advances in machine learning and AI.
I believe this is our best shot at progress towards real AI

Later his comments became more nuanced.

The core of deep learning according to Andrew is that we now have fast enough computers and enough data to actually train large neural networks. When discussing why now is the time that deep learning is taking off at ExtractConf 2015 in a talk titled “What data scientists should know about deep learning“, he commented:

very large neural networks we can now have and … huge amounts of data that we have access to

He also commented on the important point that it is all about scale. That as we construct larger neural networks and train them with more and more data, their performance continues to increase. This is generally different from other machine learning techniques that reach a plateau in performance.

for most flavors of the old generations of learning algorithms … performance will plateau. … deep learning … is the first class of algorithms … that is scalable. … performance just keeps getting better as you feed them more data

Finally, he is clear to point out that the benefits from deep learning that we are seeing in practice come from supervised learning. From the 2015 ExtractConf talk, he commented:

almost all the value today of deep learning is through supervised learning or learning from labeled data

Earlier at a talk to Stanford University titled “Deep Learning” in 2014, he made a similar comment:

one reason that deep learning has taken off like crazy is because it is fantastic at supervised learning

Andrew often mentions that we should and will see more benefits coming from the unsupervised side of the tracks as the field matures to deal with the abundance of unlabeled data available.

Jeff Dean is a Wizard and Google Senior Fellow in the Systems and Infrastructure Group at Google and has been involved and perhaps partially responsible for the scaling and adoption of deep learning within Google. Jeff was involved in the Google Brain project and the development of large-scale deep learning software DistBelief and later TensorFlow.

In a 2016 talk titled “Deep Learning for Building Intelligent Computer Systems,” he made a comment in a similar vein, that deep learning is really all about large neural networks.

When you hear the term deep learning, just think of a large deep neural net. Deep refers to the number of layers typically and so this kind of the popular term that’s been adopted in the press. I think of them as deep neural networks generally.

He has given this talk a few times, and in a modified set of slides for the same talk, he highlights the scalability of neural networks indicating that results get better with more data and larger models, which in turn require more computation to train.

Java Tutorial 01 - Overview

2020-12-14T09:39:00.001-08:00

Java programming language was originally developed by Sun Microsystems which was initiated by James Gosling and released in 1995 as a core component of Sun Microsystems' Java platform (Java 1.0 [J2SE]).

The latest release of the Java Standard Edition is Java SE 8. With the advancement of Java and its widespread popularity, multiple configurations were built to suit various types of platforms. For example J2EE for Enterprise Applications, J2ME for Mobile Applications.

The new J2 versions were renamed Java SE, Java EE, and Java ME respectively. Java is guaranteed to be Write Once, Run Anywhere.

Java is −

Object-Oriented − In Java, everything is an Object. Java can be easily extended since it is based on the Object model.
Platform Independent − Unlike many other programming languages including C and C++, when Java is compiled, it is not compiled into the platform-specific machine, rather into platform independent byte code. This byte code is distributed over the web and interpreted by the Virtual Machine (JVM) on whichever platform it is being run on.
Simple − Java is designed to be easy to learn. If you understand the basic concept of OOP Java, it would be easy to master.
Secure − With Java's secure feature it enables to development of virus-free, tamper-free systems. Authentication techniques are based on public-key encryption.
Architecture-neutral − Java compiler generates an architecture-neutral object file format, which makes the compiled code executable on many processors, with the presence of Java runtime system.
Portable − Being architecture-neutral and having no implementation dependent aspects of the specification makes Java portable. Compiler in Java is written in ANSI C with a clean portability boundary, which is a POSIX subset.
Robust − Java makes an effort to eliminate error-prone situations by emphasizing mainly compile-time error checking and runtime checking.
Multithreaded − With Java's multithreaded feature it is possible to write programs that can perform many tasks simultaneously. This design feature allows the developers to construct interactive applications that can run smoothly.
Interpreted − Java byte code is translated on the fly to native machine instructions and is not stored anywhere. The development process is more rapid and analytical since linking is an incremental and light-weight process.
High Performance − With the use of Just-In-Time compilers, Java enables high performance.
Distributed − Java is designed for the distributed environment of the internet.
Dynamic − Java is considered to be more dynamic than C or C++ since it is designed to adapt to an evolving environment. Java programs can carry an extensive amount of run-time information that can be used to verify and resolve access to objects on run-time.

History of Java

James Gosling initiated the Java language project in June 1991 for use in one of his many set-top box projects. The language, initially called ‘Oak’ after an oak tree that stood outside Gosling's office, also went by the name ‘Green’ and ended up later being renamed as Java, from a list of random words.

Sun released the first public implementation as Java 1.0 in 1995. It promised to Write Once, Run Anywhere (WORA), providing no-cost run-times on popular platforms.

On 13 November 2006, Sun released much of Java as free and open-source software under the terms of the GNU General Public License (GPL).

On 8 May 2007, Sun finished the process, making all of Java's core code free and open-source, aside from a small portion of code to which Sun did not hold the copyright.

Tools You Will Need

For performing the examples discussed in this tutorial, you will need a Pentium 200-MHz computer with a minimum of 64 MB of RAM (128 MB of RAM recommended).

You will also need the following software −

Linux 7.1 or Windows xp/7/8 operating system
Java JDK 8
Microsoft Notepad or any other text editor

This tutorial will provide the necessary skills to create GUI, networking, and web applications using Java.

Yes, Google's Gmail, YouTube, Maps and other services are down around the world

2020-12-14T04:32:00.003-08:00

Yes, Google’s two of the major services, Gmail and YouTube are down for millions across the globe. Several are also complaining about Google Docs going down. While Google Docs users are seeing the message “Google Docs encountered an error. Please try reloading this page, or coming back to it in a few minutes,” YouTube just shows its error page with a monkey and states ‘Something went wrong’ along with a search bar.

As for Gmail, the page says ‘We’re sorry, but your account is temporarily unavailable. We apologize for the inconvenience and suggest trying again in a few minutes. You can view the G Suite Status Dashboard for the current status of the service.’

According to down detector website, almost all the Google services seem to be affected. This includes Google Drive, Hangouts, Meet, Play, and Duo.

Google Maps users can open the page but cannot sign in to get personalized results.

Google's Pixel phones catch up with iPhones as they get this basic camera feature

2020-12-13T08:52:00.006-08:00

Google recently rolled out an update for the camera app in Pixel phones. This update brought features like Audio Zoom and Cinematic Pan to older Pixel phones. It also added a storage saver and new video effects. Google also improved the camera app in Pixel phones by adding two level indicators for getting that perfectly leveled shot.

The new leveling tool in the camera app for Pixels was first spotted by Android Police. There’s now a yellow line in addition to the white one that already exists. Google Pixel users will see the two-level indicators appear after a few seconds of holding the phone still. To get the perfect level shot, you will need to make the yellow and white lines touch each other to form a single yellow line. You can capture your photo once this single level is achieved.

Android Police notes that on Pixel 4 and above, users will feel a faint vibration when it reaches the perfect level. The two-level indicators also appear as crosshairs when you’re taking top-down shots. The same process should be followed for this too by getting both the indicators to appear as one. This way you can get the perfect shot in both landscape and portrait mode. The new feature is turned on by default, and there’s no option to turn it off.

This feature is new for Pixel phones but it’s something that has existed for years on iPhones. iPhone users can turn on the grid from the camera settings to take shots following the rule of thirds, and also adjust the frame with the leveling tool.

Samsung Galaxy M12 Reportedly Gets Certified in India, Geekbench Listing Suggests Exynos 850 SoC

2020-12-12T23:43:00.003-08:00

Samsung Galaxy M12 is said to have secured a certification from the Bureau of Indian Standards (BIS). The latest development suggests that the new Samsung phone may soon make its way to India. Separately, a Samsung phone, believed to be the Galaxy M12, has been listed on the benchmarking portal Geekbench. The listing indicates that the smartphone could be powered by Exynos 850 SoC that debuted on the Galaxy A21s earlier. The Galaxy M12 is likely to succeed the Galaxy M11, which was launched in March with entry-level specifications. It is also speculated to launch in some markets as Galaxy F12.

MySmartPrice has reported that a Samsung phone has been listed on the BIS website with a model number SM-M127G/DS that is so far associated with the Galaxy M12. The same listing is also said to include a model number SM-F127G/DS that seems to be related to the Galaxy F12.

The BIS certification site doesn't provide any particular details about the Galaxy M12 or the Galaxy F12, according to the report. However, some previous reports suggest that both phones could be identical, and the Galaxy M12 may just come as the Galaxy F12 in some markets.

Samsung Galaxy M12 specifications (expected)

In addition to the BIS certification, a Samsung phone carrying model number SM-M127F, associated with the Galaxy M12, has surfaced on Geekbench. It appears to have Exynos 850 SoC and 3GB of RAM. The listing on the Geekbench site also suggests Android 11 on the Galaxy smartphone.

Earlier this month, the Samsung phone with the same model number SM-M127F appeared on the Bluetooth SIG and Wi-Fi Alliance websites. Those listings suggested Bluetooth v5.0 and single-band (2.4GHz) Wi-Fi.

Some early renders of the Samsung Galaxy M12 suggest a side-mounted fingerprint sensor and a square-shaped camera module with multiple sensors. The renders also hinted at a USB Type-C port as well as a 3.5mm headphone jack. Further, the phone appeared to have a waterdrop-style display notch.

The Samsung Galaxy M12 is rumored to have a 6.7-inch display and a massive 7,000mAh battery. The phone is expected to launch in early 2021.

Samsung to Get Incentives for Setting Up Display Factory in Uttar Pradesh

2020-12-12T23:40:00.003-08:00

Uttar Pradesh government on Friday said it will give financial incentives to South Korea's Samsung Electronics to set up a $654.36 million (Rs. 4,825 crores) display factory.

Samsung is relocating the factory to the state from China, the Uttar Pradesh administration said in a statement, a move that will help boost Prime Minister Narendra Modi's flagship drive to make India a manufacturing hub.

India is the world's second-biggest smartphone market with significant growth potential, which has driven companies such as Samsung to expand locally.

New Delhi earlier this year also approved financial incentives — under a $6.65 billion (roughly Rs. 49,035 crores) federal plan to boost domestic smartphone production — for 16 companies, including Samsung and top Apple suppliers Foxconn, Wistron, and Pegatron.

On Friday, Uttar Pradesh said Samsung will receive Rs. 700 crores in financial benefits and will also get an exemption from a tax payable on the transfer of land for the factory.

Samsung had sought tax and other incentives from Uttar Pradesh for this smartphone display manufacturing plant, Reuters previously reported.

The unit, expected to create 510 direct jobs, is expected to be operational next year.

Samsung already operates one of the world's biggest mobile phone manufacturing plants in Uttar Pradesh.

Understanding Kubernetes networking: pods

2020-12-12T12:53:00.006-08:00

how does the Kubernetes networking work

This post is going to attempt to demystify the several layers of networking operating in a Kubernetes cluster. Kubernetes is a powerful platform embodying many intelligent design choices, but discussing the way things interact can get confusing: pod networks, service networks, cluster IPs, container ports, host ports, node ports… I’ve seen a few eyes glaze over. We mostly talk about these things at work, cutting across all layers at once because something is broken and someone wants it fixed. If you take it a piece at a time and get clear on how each layer works it all makes sense in a rather elegant way.

In order to keep things focused, I’m going to split the post into three parts. This first part will look at containers and pods. The second will examine services, which are the abstraction layer that allows pods to be ephemeral. The last post will look at ingress and getting traffic to your pods from outside the cluster. A few disclaimers first. This post isn’t intended to be a basic intro to containers, Kubernetes, or pods. To learn more about how containers work to see this overview from Docker. A high-level overview of Kubernetes can be found here, and an overview of pods specifically is here. Lastly, a basic familiarity with networking and IP address spaces will be helpful.

Pods

So what is a pod? A pod consists of one or more containers that are collocated on the same host, and are configured to share a network stack and other resources such as volumes. Pods are the basic unit Kubernetes applications are built from. What does “share a network stack” actually mean? In practical terms, it means that all the containers in a pod can reach each other on the localhost. If I have a container running nginx and listening on port 80 and another container running scrapyd the second container can connect to the first as http://localhost:80. But how does that really work? Let's look at the typical situation when we start a docker container on our local machine:

From the top down we have a physical network interface eth0. Attached to that is a bridge docker0, and attached to that is a virtual network interface veth0. Note that docker0 and veth0 are both on the same network, 172.17.0.0/24 in this example. On this network, docker0 is assigned 172.17.0.1 and is the default gateway for veth0, which is assigned 172.17.0.2. Due to how network namespaces are configured when the container is launched processes inside it sees only veth0 and communicates with the outside world through docker0 and eth0. Now let’s launch a second container:

As shown above, the second container gets a new virtual network interface veth1, connected to the same docker0 bridge.* This interface is assigned 172.17.0.3, so it is on the same logical network as the bridge and the first container, and both containers can communicate through the bridge as long as they can discover the other container’s IP address somehow.

That’s fine and all but it doesn’t get us to the “shared network stack” of a Kubernetes pod. Fortunately, the namespaces are very flexible. Docker can start a container and rather than creating a new virtual network interface for it, specify that it shares an existing interface. In this case, the drawing above looks a little different:

Now the second container sees veth0 rather than getting its own veth1 as in the previous example. This has a few implications: first, both containers are addressable from the outside on 172.17.0.2, and on the inside, each can hit ports opened by the other on localhost. This also means that the two containers cannot open the same port, which is a restriction but no different than the situation when running multiple processes on a single host. In this way, a set of processes can take full advantage of the decoupling and isolation of containers, while at the same time collaborating together in the simplest possible networking environment.

The Pod Network

That’s all pretty cool, but one pod full of containers that can talk to each other does not get us a system. For reasons that will become even clearer in the next post where I discuss services, the very heart of Kubernetes’ design requires that pods be able to communicate with other pods whether they are running on the same localhost or separate hosts. To look at how that happens we need to step up a level and look at nodes in a cluster. This section will contain some unfortunate references to network routing and routes, a subject I realize all of humanity would prefer to avoid. Finding a clear, brief tutorial on IP routing is difficult, but if you want a decent review of Wikipedia's article on the topic isn’t horrible.

A Kubernetes cluster consists of one or more nodes. A node is a host system, whether physical or virtual, with a container runtime and its dependencies (i.e. docker mostly) and several Kubernetes system components, that are connected to a network that allows it to reach other nodes in the cluster. A simple cluster of two nodes might look like this:

If you’re running your cluster on a cloud platform like GCP or AWS that drawing pretty well approximates the default networking architecture for a single project environment. For the purposes of illustration, I’ve used the private network 10.100.0.0/24 for this example, so the router is 10.100.0.1 and the two instances are 10.100.0.2 and 10.100.0.3 respectively. Given this setup, each instance can communicate with the other on eth0. That’s great, but recall that the pod we looked at above is not on this private network: it’s hanging off a bridge on a different network entirely, one that is virtual and exists only on a specific node. The host on the left has interface eth0 with an address of 10.100.0.2, whose default gateway is the router at 10.100.0.1. Connected to that interface is bridge docker0 with an address of 172.17.0.1, and connected to that is interface veth0 with address 172.17.0.2. The veth0 interface was created with the pause container and is visible inside all three containers by virtue of the shared network stack. Because of local routing rules set up when the bridge has created any packet arriving at eth0 with a destination address of 172.17.0.2 will be forwarded to the bridge, which will then send it on to veth0. Sounds ok so far. If we know we have a pod at 172.17.0.2 on this host we can add rules to our router setting the next hop for that address to 10.100.0.2 and they will get forwarded from there to veth0.

The host on the right also has eth0, with an address of 10.100.0.3, using the same default gateway at 10.100.0.1, and again connected to it is the docker0 bridge with an address of 172.17.0.1. Hmm. That’s going to be an issue. Now, this address might not actually be the same as the other bridge on host 1. I’ve made it the same here because that’s a pathological worst case, and it might very well work out that way if you just installed docker and let it do its thing. But even if the chosen network is different this highlights the more fundamental problem which is that one node typically has no idea what private address space was assigned to a bridge on another node, and we need to know that if we’re going to send packets to it and have them arrive at the right place. Clearly some structure is required.

Kubernetes provides that structure in two ways. First, it assigns an overall address space for the bridges on each node, and then assigns the bridge's addresses within that space, based on the node the bridge is built on. Secondly, it adds routing rules to the gateway at 10.100.0.1 telling it how packets destined for each bridge should be routed, i.e. which node’s eth0 the bridge can be reached through. Such a combination of virtual network interfaces, bridges, and routing rules is usually called an overlay network. When talking about Kubernetes I usually call this network the “pod network”

Kubernetes Overview

2020-12-12T12:45:00.000-08:00

Kubernetes

The name Kubernetes comes from a Greek word and this is a word for helmsman or vessel pilot. Kubernetes is commanding a ship of containers for our applications. Kubernetes began as a secret project funded by Google called Brog. brog Eventually evolved into Kubernetes, powered by many of the Borg support staff. In 2015 Google donated the project to a community called Cloud Native Computing Foundation (CNCF). Kubernetes solves the problem of the application monolith, which is defined as a software product running on dedicated hardware. Monoliths are expensive and quickly drift to legacy hardware. Upgrades to virtual environments and Clouds are expensive and complicated. The advent of microservices and APIs such as RESt services and Service Oriented Architecture (SOA) and the Cloud has made the monolith obsolete and unnecessary.

Monolithic Architecture and Microservices

Kubernetes microservices include:

Deployment - Application Distribution
Sensitive Information - Certificates, Password
Registration and scaling
Load Balancing-Managing requests, data load, traffic, and computing processing power for running applications.
Job scheduling and Management.
DevOps
Continuity and Fault Tolerance

Before we go further it is important to discuss Container technologies. Kubernetes and other products host applications running in Containers. Containers are a solution for reliably deploying to and running software products in different computing environments. Containers are standardized packages consisting of an application and the items needed to execute it, including runtimes, system tools, libraries, and settings.

Also, multiple containers can run on the same operating system(OS)and share the OS kernel. The application and dependencies are packaged in their own container. The difference is supporting infrastructure are abstracted away. The underlying infrastructure only has to run the containers.

Container management

One of the Kubernetes key features is Container management or Orchestration.

Containers :

Are not unique to Kubernetes.
Are application-centric, Platform-as-a-Service (PaaS) operating system (OS) level virtualized packages that include their own microservices, and other dependencies.
Run in isolation from one another, and do not interact
Run with their own set of services
Are not full virtual machines

All Containers connect to and run using the same OS kernel.

Container Orchestration

The automatic process of managing or scheduling the work of individual containers for applications based on microservices within multiple clusters. Following are the Container orchestration supports,

Fault-Tolerance: Business continuity
Optimization
real-time scalability
Automatic discovery of other Containers
On-lines updates, recovery, and rollbacks without downtime

Kubernetes is an open-source container orchestration tool. however, Amazon Web services, Azure, Docker, and other vendors also offer Containers Orchestration solution.

Deployment and installation of the container orchestrators can be performed on many types of infrastructure. Organizations and users can almost always select the environment with which they are most familiar: “bare metal”, virtual machines, private or public Clouds. A very popular model is installing Kubernetes on infrastructure as a service (IaaS) solution like Amazon Web Services, Google, etc. Kubernetes can be installed quite readily on these laaS solutions with only a few commands.

Kubernetes features

Some Kubernetes features for supporting Container Orchestration:

Fault tolerance / Self-healing -As a multi-node cluster solution, Kubernetes can automatically backup and replace containers in failed nodes.
Automatic Bin Packaging -Tomaximize resource utilization.
Horizontal Scaling -Kubernetes can dynamically add containers as needed based on processing utilization rules and criteria
In Kubernetes, Clusters run Pods -Each Pod receives its own IP address, However, all containers communicate using localhost to support load balancing across the containers set.
Automatic Recovery and Rollbacks -Kubertnets saves application and environment configuration version. When needed, Kubernetes can restore or rollback to a stable version of the app or/or the environment configuration.
Secret and Configuration management -Kubernetes manages application secrets and configuration data separately to avoid extensive image rebuilds. Secrets consist of confidential information passed to the application without revealing the sensitive content to the stack configuration.
Storage Orchestration -Kubernetes automatically mounts storage for containers from local appliance storage or virtual storage.
Batch Execution -Kubernetes supports batch operations, long-running processes, and container failover.

Kubernetes has a flexible modular architecture, that supports plugins and microservice APIs. Kubernetes is extensile and can be augmented by writing custom software programs, API calls, plugins, etc. Kubernetes is supported by a community of more than 2,000 contributors. The Kubernetes community also includes local groups such as meet-ups and interest Groups focused on scaling, networking, and other features.

Kubernetes architecture

For Kubernetes, the CNCF offerings include:

Licensing and proper use.
Scanning for vendor code
Marketing and conferences
Legal guidance
Certification standards
Other items

The Kubernetes host, the Cloud Native Computing Foundation (CNCF) is part of the Linux foundation. The CNCF also host several generally available(GA) products, and incubating projects. Besides Kubernetes, the CNCF’s GA products include Prometheus for monitoring, Fluentd for logging, and several others.

Kubernetes Architecture

In general, Kubernetes Architecture consists of three major tiers :

The User Interface
The Master node
Worker Node
The etcd or Key store

Kubernetes is a node-based architecture, with two types of nodes.

1. Master Node:

The master node provides connections to the user interface (UI) and executes the control plane service. the control plane services manage and operate the Kubernetes cluster. Each of the control plane services run with distinct roles in cluster operation. the UI includes a command-line interface (CLI), a web dashboard, and an API. Since the Kubernetes Master node runs the cluster, downtime is not tolerated. For redundancy, a replication strategy is used for the Master node. the master node replicas are added to the cluster in High Availability (HA)mode. State data for the cluster is stored in the etcd which is part of the key store. HA mode keeps all master nodes replicas synchronized. If the currently active Master nodes go offline, a replica will start up to continue operating the master node. the master node runs 4 components. They are the API server, the scheduler, the controller, and etcd, or key store.

The API server manages all administrative tasks in the master node. The API server receives users' REST calls. The API server accesses the state data from the etcd. After the completion of any API processing, the new state of the cluster is written to the etcd, key-store. The API server can be configured and customized.

The Scheduler assigns objects to the nodes. Node assignment is largely based on the Kubernetes cluster's current state and the object's runtime requirements. As expected, the scheduler queries the state of the cluster from the etcd, and new object requirements from the objects configuration files. The scheduler is also very configurable and runs complex operations in multi-node clusters.

The Controller component manages the state of the Kubernetes cluster and the currently running pods. The controller manages a configuration called a DEployments. A Pod is a group of one or more containers. The controller acts when pods go offline. The optimal cluster state or required number of running pods is configured into the controller.

The etcd is the key-value repository where Kubernetes stores the clusters state. As stated, earlier, only the Controller can access the etcd key-store. The etcd can run in HA-mode to support fault tolerance.

Worker Node

Just as master nodes, Worker nodes have four components:

Container runtime
Kubelet
Kube-proxy
Plugins for DNS, Dashboard, cluster monitoring, and cluster logging.

The worker node is the runtime for the application. Through containerization and microservices, the applications run out of pods. Pods are managed on worker nodes, where they consume computing resources such as memory, storage, networking.

The Kubelet is a service that communicates with the master node and the containers. The kubelet connects to the container using a container runtime interface(CRI)

Kubernetes is a container orchestration engine, however, interestingly enough Kubernetes does not directly execute containers. Kubernetes needs a container runtime, on a node where it is executing a pod and containers. Kubernetes supports several container runtimes including;

Docker: a very popular runtime for Kubernetes
CRI-O: Considered a lightweight container runtime
Containerd: A very general Container runtime built for many solutions
rklet : Apod native Container runtime.

The Kube-proxy is the networking agent for each node. The plugins and Addons:

DNS -cluster DNS is a DNS server required to assign DNS records to Kubernetes resources.
Dashboard -general purposed web-based user interface for cluster management
Monitoring-Collects cluster level container metrics
Logging — collects cluster level container logs.

Jenkins Tutorials For Beginners — Part 05

2020-12-12T12:27:00.005-08:00

Jenkins is a powerful application that allows continuous integration and continuous delivery of projects, regardless of the platform you are working on. It is a free source that can handle any kind of build or continuous integration. You can integrate Jenkins with a number of testing and deployment technologies. In this tutorial, we would explain how you can use Jenkins to build and test your software projects continuously.

How to Create and builds With the Jenkins FreeStyle Project

Jenkins

What is a Jenkins freestyle project?

A Jenkins project is a repeatable build job which contains steps and post-build actions. The types of actions you can perform in a build step or post-build action are quite limited. There are many standard plugins available within a Jenkins freestyle project to help you overcome this problem. They allow you to configure build triggers and offer project-based security for your Jenkins project.

Creating a Freestyle Build Job

The freestyle build job is a highly flexible and easy-to-use option. You can use it for any type of project; it is easy to set up, and many of its options appear in other build jobs.

Step 1) To create a Jenkins freestyle job, log on to your Jenkins dashboard by visiting your Jenkins installation path. Usually, it will be hosted on localhost at http://localhost:8000 If you have installed Jenkins in another path, use the appropriate URL to access your dashboard.

Step 2) Click on “New Item” at the top left-hand side of your dashboard.

Step 3) In the next screen,

Enter the name of the item you want to create. We shall use the “Hello world” for this demo.
Select Freestyle project
Click Okay

Step 4) Enter the details of the project you want to test.

Step 5) Under Source Code Management, Enter your repository URL. We have a test repository located at https://github.com/LakmalAsh/JenkinsFreestyle.git

It is also possible for you to use a local repository.

If your GitHub repository is private, Jenkins will first validate your login credentials with GitHub and only then pull the source code from your GitHub repository.

Step 6) Now that you have provided all the details, it’s time to build the code. Tweak the settings under the build section to build the code at the time you want. You can even schedule the build to happen periodically, at set times.

Underbuild,

1. Click on “Add build step”

2. Click on “Execute Windows batch command” and add the commands you want to execute during the build process.

Here, I have added the java commands to compile the java code.

I have added the following windows commands:

javac HelloWorld.java

java HelloWorld

Step 7) When you have entered all the data,

Click Apply
Save the project.

Step 8) Now, on the main screen, click the Build Now button on the left-hand side to build the source code.

Step 9) After clicking on Build now, you can see the status of the build you run under Build History.

Step 10) Click on the build number and then Click on console output to see the status of the build you run. It should show you a success message, provided you have followed the setup properly

In sum, we have executed a HelloWorld program hosted on GitHub. Jenkin pulls the code from the remote repository and builds continuously at a frequency you define.

So we discuss now until the freestyle project creation. Next, we discuss the Jenkins Pipeline. Thank you.

Jenkins Tutorials For Beginners — Part 04

2020-12-12T12:25:00.006-08:00

How to Create Users & Manage Permissions: Role Strategy Plugin

Jenkins

Generally, in a large organization, there are multiple, separate teams to manage and run jobs in Jenkins. But managing this crowd of users and assigning roles to them can prove troublesome.

By default, Jenkins comes with very basic user creation options. You can create multiple users but can only assign the same global roles and privileges to them. This not ideal, especially for a large organization.

The Role Strategy Plugin enables you to assign different roles and privileges to different users. You will first need to install the plugin in your Jenkins mange environment.

How to Create/Add a User

Step 1) log in to your Jenkins dashboard by visiting http://localhost:8000/

Step 2) You will now see options to create new users and manage current users.

Step 3)

Under Manage Jenkins, Click Create User
Enter User details like password, name, email, etc.
Click Create User

Step 4) User is created

Install Role Strategy Plugin

There are two methods for installing plugins in Jenkins:

Installing it through your Jenkins dashboard
Downloading the plugin from the Jenkins website and installing it manually.

Step 1)

1. Go to Manage Jenkins

2. Click on the Manage Plugins option

Step 2)

In the available section, screen Search for “role”.
Select Role-based Authorization Strategy plugin
Click on “Install without restart” (make sure you have an active internet connection)

Step 3)

Once the plugin is installed, a “success” status will be displayed.

Click on Go back to the top page.

Step 4) Go to Manage Jenkins -> Configure Global Security -> Under Authorization, select Role-Based Strategy. Click on Save.

Create Roles

In this step, we shall learn to certain roles to a single user or a group of users.

Step 1)

1. Go to Manage Jenkins

2. Select Manage and Assign Roles

Note: that the Manage and Assign Roles option will only be visible if you’ve installed the role strategy plugin.

Step 2) Click on Manage Roles to add new roles based on your organization.

Step 3) To create a new role called “developer”,

Type “developer” under “role”.
Click on “Add” to create a new role.
Now, select the permissions you want to assign to the “Developer” role.
Click Save

Assign a Role

Step 1) Now that you have created roles, let us assign them to specific users.

Go to Manage Jenkins
Select Manage and Assign Roles

Step 2) We shall add the new role “developer” to the user “what you enter in user”

Selector developer role checkbox
Click Save

You can assign any role to any user, as per your need.

Project Roles

You can create project-specific roles under Project Roles.

Step 1) In Jenkin’s Manage and Assign Roles

Enter a role as “tester”
Add a pattern to this by adding tester.*, so that any username starting with “tester” will be assigned the project role you specify.
Click Add
Select privileges
Click Save

So now you can see how the Roles are doing in Jenkins. In the next part, we will discuss How to Create Builds with the Jenkins Freestyle Project.

Creating a Model for Weather Forecasting Using Linear Regression

2020-12-12T11:29:00.006-08:00

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on — the kind of relationship between dependent and independent variables, they are considering and the number of independent variables being used. creates a model that forecasts weather/temperature based upon some features mainly including Humidity, Ppm, and Air quality index AQI or PM2.5. We searched for a lot of datasets that contain all those features but found none. What we found were two different datasets containing the following features: -

Data-set-1

This data set contains features like Weather temperature, humidity, and AQI(PM2.5) as the target variable. This data set was too short just 150 entries.

Data-set-2:

This data set contains almost 24 features including Ppm, Humidity, and weather temperature as a target variable.

Solution

We cannot record real-time data due to pandemic, nor do we have any data set that contains all required features in one place. One possible solution was to combine both data sets so that we can create a final data set that contains all the required features. In order to do so, we must create two separate models. One model that is trained on data-set-1 and predicts the AQI(PM2.5) value and the other data set into which the values of PM2.5 will be embedded to get the final desired data set.

Program Structure

We created a linear regression model and train it on data set-1 to predict PM2.5 values. Before that, we plotted a heat map to check the correlation between features and target variables and found out that only temperature and humidity were in some correlation with PM2.5 (target variable ). So the model was trained using these two features. The following shows the heat map of it.

Heat map

We saved this model as a pickle file to use it later. Pickle is a python module used to store objects. We save the model at maximum accuracy because every time we ran the program accuracy varies a little bit, so it is always a nice practice to save the best accuracy model to use it again.

Gather data

Data-set2 now needed to be embedded with PM2.5 values. So we picked temperature and humidity columns from dataset-2 and give it to our trained linear regression model to get values of PM2.5. in this way we created a final data set that now has all features including Ppm, Humidity, PM2.5.Now we trained another linear regression model on this final data set with Temperature as the target variable. As before we again plotted the heat maps to check the correlation of features and target variables to throw out unnecessary features.

The model when trained gave 93% accuracy which is quite good. But as it is not real data rather it is just sample data so the model might not predict very accurately on real-time data. To overcome this, we must retrain our model on real-time data, and then it will be good to go.

Here is the code snippet of the training method that uses here.

lis_drop = [ 'Date2','Time3','Weather_Temperature6', 'Exterior_Entalpic_120','Exterior_Entalpic_221', 'Exterior_Entalpic_turbo22','Day_Of_Week\n'
'Lighting_Comedor_Sensor11' , 'Lighting_Habitacion_Sensor12',   'Precipitacion13'  , 'Meteo_Exterior_Crepusculo14']

features = []
for i in data:
  # print(i)
  if i not in lis_drop:
   features.append( i )

print(len(features))
print (features)

x = np.array(data[features  ])
y = np.array(data['Weather_Temperature6'])

print(x.shape , y.shape)

#x_train , x_test , y_train , y_test = sklearn.model_selection.train_test_split(x , y , test_size = 0.1 )
#print(x_train.shape , y_train.shape) 
#print(x_test.shape , y_test.shape)

while True:
  x_train , x_test , y_train , y_test =  sklearn.model_selection.train_test_split(x , y , test_size = 0.2 )
  linear = linear_model.LinearRegression()
  linear.fit(x_train,  y_train)
  acc = linear.score(x_test , y_test)
  if int(acc*100) > 94:
    #print(acc*100)
  break

predictions = linear.predict(x_test)

for i in range(len(predictions)):
  print('PREDICTED WEATHER : '+str(predictions[i])  ,'\t','ACTUAL    WEATEHR : '+str(y_test[i]))

print(acc)

Conclusion

From these code snippets, we can train the data and get an approximately 93% accurate model for weather prediction. Also, forget the more accuracy need to improve the algorithm with neural networks with the Keras LSTM model. That will be worked fine rather than going with the linear regression

Deep Learning for Air Quality Prediction

2020-12-12T11:28:00.004-08:00

As we all know air pollution is one of the major problems in urban cities, where particulate matter is the most dangerous part of air pollution which affects humans than any other substance. Time Series Forecasting to predict air quality using Deep Learning with Long Short-Term Memory (LSTM) Neural Network. First, let's talk about what's called LSTM and the theory of it.

Recurrent Neural Networks

Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

LSTM Networks

Long Short Term Memory networks — usually just called “LSTMs” — are a special kind of RNN, capable of learning long-term dependencies.LSTMs are explicitly designed to avoid the long-term dependency problem. All recurrent neural networks have the form of a chain of repeating modules of the neural networks. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer.

The repeating module in an LSTM contains four interacting layers.

In the above diagram, yellow boxes denote the neural network layer, Pink circle denote Pointwise Operation, and arrows mention the Transfer, copy. Each line carries an entire vector, from the output of one node to the inputs of others. The pink circles represent pointwise operations, like vector addition, while the yellow boxes are learned neural network layers. Lines merging denote concatenation, while a line forking denotes its content being copied and the copies going to different locations. The key to LSTMs is the cell state, the horizontal line running through the top of the diagram. The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along with it unchanged.

The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates. Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation. The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero means “let nothing through,” while a value of one means “let everything through!”

An LSTM has three of these gates, to protect and control the cell state.

Now From the above part, we can go through some ideas of the LSTM neural networks. So next move on to the Coding part. Here we choose python as the programming language using the following libraries.

import datetime, warnings, scipy 
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.core.interactiveshell import InteractiveShell
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.metrics import mean_squared_error

After importing the libraries then move to the data importing a part of the algorithm.

df = pd.read_csv('Data.csv', sep=' ')

So after importing the data the main process of the algorithm comes next. The calculation part for the quantile from each attribute. The following code snippet tells how the calculation was done.

def calculate_quantile (i, df2):
    Q1 = df2[[i]].quantile(0.25)[0]
    Q3 = df2[[i]].quantile(0.75)[0]
    IQR = Q3 - Q1
    min = df2[[i]].min()[0]
    max = df2[[i]].max()[0]
    min_IQR = Q1 - 1.5*IQR
    max_IQR = Q3 + 1.5*IQR
    
    return Q1, Q3, min, max, min_IQR, max_IQR

# delete first and last rows to avoid missing value extrapolation
df2.drop(index=[df2.index[0], df2.index[df2.shape[0]-1]], inplace=True)

# find and interpolate the outliers
for i in df2.columns:
    print('\nAttribute-',i,':')
    Q1, Q3, min, max, min_IQR, max_IQR = calculate_quantile(i, df2)
    print('Q1 = %.2f' % Q1)
    print('Q3 = %.2f' % Q3)
    print('min IQR = %.2f' % min_IQR)
    print('max IQR = %.2f' % max_IQR)
    if (min < min_IQR):
        print('Low outlier = %.2f' % min)
    if (max > max_IQR):
        print('High outlier= %.2f' % max)
    
    def convert_nan (x, max_IQR=max_IQR, min_IQR=min_IQR):
        if ((x > max_IQR) | (x < min_IQR)):
            x = np.nan
        else:
            x = x
        return x

def convert_nan_HUM (x, max_IQR=100.0, min_IQR=min_IQR):
        if ((x > max_IQR) | (x < min_IQR)):
            x = np.nan
        else:
            x = x
        return x
    
    if (i == 'HUM'):
        df2[i] = df2[i].map(convert_nan_HUM)
        df2[i] = df2[i].interpolate(method='linear')
    if (i != 'HUM'):
        df2[i] = df2[i].map(convert_nan)
        df2[i] = df2[i].interpolate(method='linear')
    
    if (len(df2[df2[i].isnull()][i]) == 0):

After the calculation then move to the next part which most important part of here the prediction part. So for this step, the data will split into some stages. Here we split the data 75% for training and 25% for testing.

train_size = int(len(dataset) * 0.75)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))

After doing that, Now the LSTM neural network will create. Following is how the LSTM neural network was made.

model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=1500, batch_size=32, verbose=2)

After creating a neural network all the prediction work is done. Now the results will tell how good was the prediction. Here is some example that I have done to analyze the temperature.

# shift train predictions for plotting
trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

# shift test predictions for plotting
testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

# plot original dataset and predictions
time_axis = np.linspace(0, dataset.shape[0]-1, 15)
time_axis = np.array([int(i) for i in time_axis])
time_axisLab = np.array(df2.index, dtype='datetime64[D]')

fig = plt.figure()
ax = fig.add_axes([0, 0, 2.1, 2])
ax.plot(np.expm1(dataset), label='Original Dataset')
ax.plot(trainPredictPlot, color='orange', label='Train Prediction')
ax.plot(testPredictPlot, color='red', label='Test Prediction')
ax.set_xticks(time_axis)
ax.set_xticklabels(time_axisLab[time_axis], rotation=45)
ax.set_xlabel('\nDate', fontsize=27, fontweight='bold')
ax.set_ylabel('TEMP', fontsize=27, fontweight='bold')
ax.legend(loc='best', prop= {'size':20})
ax.tick_params(size=10, labelsize=15)
ax.set_xlim([-1,1735])

ax1 = fig.add_axes([2.3, 1.3, 1, 0.7])
ax1.plot(np.expm1(dataset), label='Original Dataset')
ax1.plot(testPredictPlot, color='red', label='Test Prediction')
ax1.set_xticks(time_axis)
ax1.set_xticklabels(time_axisLab[time_axis], rotation=45)
ax1.set_xlabel('Date', fontsize=27, fontweight='bold')
ax1.set_ylabel('TEMP', fontsize=27, fontweight='bold')
ax1.tick_params(size=10, labelsize=15)
ax1.set_xlim([1360,1735]);

Here is the output of it

The output of the temperature Prediction

Conclusion

From these code snippets, we can train the data and get an approximately 95% accurate model from the neural network using LSTM. In my last story, I was trying to predict the weather using Linear regression. From that, I got 93% accuracy. So, I'm still working get as more accuracy as possible for implement the successful neural network for the Air Quality prediction

Jenkins Tutorials For Beginners — Part 03

2020-12-12T11:25:00.005-08:00

Install Git Plugin

Jenkins has outstanding plugin support. There are thousands of third-party application plugins available on their website. To know if Jenkins supports the third-party applications you have in mind, check their plugins directory at this link.

Installation of Plugins in Jenkins

Jenkins comes with a pretty basic setup, so you will need to install the required plugins to enable respective third-party application support.

GitHub is a web-based repository of code that plays a major role in DevOps. It provides a common platform for multiple developers working on the same code/project to upload and retrieve updated code, thereby facilitating continuous integration.

Jenkins needs to have a GitHub plugin installed to be able to pull code from the GitHub repository.

You need not install a GitHub plugin if you have already installed the Git plugin in response to the prompt during the Jenkins installation setup. But if not, here is how you install GitHub plugins in Jenkins and pull code from a GitHub repository.

Install GIT Plugin

Step 1: Click on the Manage Jenkins button on your Jenkins dashboard:

Step 2: Click on Manage Plugins:

Step 3: In the Plugins Page

Select the GIT Plugin
Click on Install without restart. The plugin will take a few moments to finish downloading depending on your internet connection, and will be installed automatically.
You can also select the option Download now and install after the restart button. In which plugin is installed after the restart
You will be shown a “No updates available” message if you already have the Git plugin installed.

Step 4: Once the plugins have been installed, go to Manage Jenkins on your Jenkins dashboard. You will see your plugins listed among the rest.

Integrating Jenkins with GitHub

We shall now discuss the process of integrating GitHub into Jenkins in a Windows system.

Step 1) Create a new job in Jenkins, open the Jenkins dashboard with your Jenkins URL. For example, http://localhost:8000/ (this depend on the local port that you enter in the setup)

Click on create new jobs:

Create new job

Step 2) Enter the item name, select job type, and click OK. We shall create a Freestyle project as an example.

Creating an item.

tep 3) Once you click OK, the page will be redirected to its project form. Here you will need to enter the project information:

Jenkin setup

Step 4) You will see a Git option under Source Code Management if your Git plugin has been installed in Jenkins:

Source type selection

NOTE: If the Git option does not appear, try re-installing the plugins, followed by a restart and a re-login into your Jenkins dashboard. You will now be able to see the Git option as mentioned above.

Step 5) Enter the Git repository URL to pull the code from GitHub.

Jenkins Git

Step 6) You might get an error message the first time you enter the repository URL.

This happens if you do not have Git installed on your local machine. To install Git on your local machine, go to this link.

Download the appropriate Git file for your Operating System, in this case, Windows, and install it onto your local machine running Jenkins. Complete the on-screen instructions to install GIT.

Git Install Interface

Step 7) You can execute Git repositories in your Jenkins once Git has been installed on your machine. To check if it has been successfully installed onto your system, open your command prompt, type “Git” and press enter. You should see different options come up for Git:

git in Command prompt

This means that Git has been installed in your system.

Note: If you have GIT already installed in your system, just add the git.exe path in Global Tool Configuration.

Step 8) Once you have everything in place, try adding the Git URL into Jenkins. You will not see any error messages:

After git install

Git is now properly configured on your system.

So now we did up to the git install. In the next part, we will discuss how to Create Users & Manage Permissions.

Jenkins Tutorials For Beginners — Part 02

2020-12-12T11:24:00.004-08:00

How to Download & Install Jenkins on Windows

Jenkins

Jenkins may be installed on either Windows or Unix platforms, but we will focus on Windows installation only.

Prerequisites:

Before you proceed to install Jenkins in your windows system, there are some prerequisites for Jenkins to install Jenkins on your computer.

Hardware requirements:

You need a minimum of 256 MB of RAM on your computer or laptop to install Jenkins
You need at least 1 GB of space in your hard drive for Jenkins.

Software Requirements:

Since Jenkins runs on Java, you need either the latest version of Java Development Kit (JDK) or Java Runtime Environment (JRE).

Release Types

Jenkins releases two types of versions based on the organization's needs.

Long-term support release
Weekly release

Long term support release (LTS) :

Long-term support releases are available every 12 weeks. They are stable and are widely tested. This release is intended for end-users.

Weekly release:

Weekly releases are made available every week by fixing bugs in their earlier version. These releases are intended for plugin developers.

We will use the LTS release though the process remains the same for the Weekly release.

How to Download Jenkins?

Following steps should be followed so that to install Jenkins successfully:

Step 1) Got to https://jenkins.io/download/ and select the platform. In our case Windows

Downloading window

Step 2) Go to the download location from the local computer and unzip the downloaded package. Double-click on unzipped jenkins.msi. You can also Jenkin using a WAR (Web application ARchive) but that is not recommended.

Step 3) In the Jenkin Setup screen, click Next.

Step 4) Choose the location where you want to have the Jenkins instance installed (default location is C:\Program Files (x86)\Jenkins), then click on Next button.

Step 5)Click on the Install button. (In 2.249.2 they ask for the port number you can check whether that the port is already there by clicking the test button use)

Step 6) Once install is complete, click Finish.

Step 7) During the installation process an info panel may pop-up to inform the user that for a complete setup, the system should be rebooted at the end of the current installation. Click on the OK button when the Info panel is popping-up:

How to Unblock Jenkins?

After completing the Jenkins installation phase, you should proceed further and start its configuration. The next steps will guide you on how you can unblock the Jenkins application:

Step 1) After completing the Jenkins installation process, a browser tab will pop-up asking for the initial Administrator password. To access Jenkins, you need to go to browse the following path in your web browser.

http://localhost:8080

If you can access the above URL, then it confirms that Jenkins is successfully installed in your system.

Step 2) The initial Administrator password should be found under the Jenkins installation path (set at Step 4 in Jenkins Installation).

For default installation location to C:\Program Files (x86)\Jenkins, a file called initialAdminPassword can be found under C:\Program Files (x86)\Jenkins\secrets.

However, If a custom path for Jenkins installation was selected, then you should check that location for initialAdminPassword file.

Step 3) Open the highlighted file and copy the content of the initialAdminPassword file.

Step 4) Paste the password into the browser’s pop-up tab (http://localhost:8080/login?form=%2F) and click on the Continue button.

Customize Jenkins

You can also customize your Jenkins environment by the below-given steps:

Step 1) Click on the “Install suggested plugins button” so Jenkins will retrieve and install the essential plugins

Jenkins will start to download and install all the necessary plugins needed to create new Jenkins Jobs.

Step 2) After all the suggested plugins were installed, the “Create First Admin User” panel will show up. Fill all the fields with desired account details and hit the “Save and Finish” button.

Step 3) Once you have filled the data, finally it will ask for URL information where you can configure the default instance path for Jenkins. Leave it as it is to avoid any confusion later. However, if another application is already using the 8080 port, you can use another port for Jenkins and finally save the settings, and you are done with the installation of Jenkins. Hit the “Save and Continue” button:

Below you can find the Jenkins instance up and run, ready to create the first Jenkins jobs:

the dashboard of Jenkins

So these are the steps that you need to do the installation for Jenkins. So in the next part, we discuss how to install the git plugin to Jenkins.

Jenkins Tutorials For Beginners — Part 01

2020-12-12T11:21:00.002-08:00

What is Jenkins?

Jenkins

Jenkins is an open-source Continuous Integration server capable of orchestrating a chain of actions that help to achieve the Continuous Integration process (and not only) in an automated fashion.

Jenkins is free and is entirely written in Java. Jenkins is a widely used application around the world that has around 300k installations and growing day by day.

It is a server-based application and requires a web server like Apache Tomcat. The reason Jenkins became so popular is that of its monitoring of repeated tasks that arise during the development of a project. For example, if your team is developing a project, Jenkins will continuously test your project builds and show you the errors in the early stages of your development.

By using Jenkins, software companies can accelerate their software development process, as Jenkins can automate build and test at a rapid rate. Jenkins supports the complete development lifecycle of software from building, testing, documenting the software, deploying, and other stages of a software development lifecycle.

What is Continuous Integration?

In Continuous Integration after a code commit, the software is built and tested immediately. In a large project with many developers, commits are made many times during a day. With each commit code is built and tested. If the test is passed, the build is tested for deployment. If the deployment is a success, the code is pushed to production. This commit, build, test, and deploy is a continuous process and hence the name continuous integration/deployment.

A Continuous Integration Pipeline is a powerful instrument that consists of a set of tools designed to host, monitor, compile and test code, or code changes, like:

Continuous Integration Server (Jenkins, Bamboo, CruiseControl, TeamCity, and others)
Source Control Tool (e.g., CVS, SVN, GIT, Mercurial, Perforce, ClearCase and others)
Build tool (Make, Ant, Maven, Ivy, Gradle, and others)
Automation testing framework (Selenium, Appium, TestComplete, UFT, and others)

Jenkin History

Kohsuke Kawaguchi, a Java developer, working at SUN Microsystems, was tired of building the code and fixing errors repetitively. In 2004, created an automation server called Hudson that automates build and test tasks.
In 2011, Oracle who owned Sun Microsystems had a dispute with Hudson's open source community, so they forked Hudson and renamed it, Jenkins.
Both Hudson and Jenkins continued to operate independently. But in a short span of time, Jenkins acquired a lot of projects and contributors while Hudson remained with only 32 projects. With time, Jenkins became more popular, and Hudson is not maintained anymore.

Real-world case study of Continuous Integration

I am sure all of you aware of the old phone Nokia. Nokia used to implement a procedure called nightly build. After multiple commits from diverse developers during the day, the software built every night. Since the software was built only once a day, it’s a huge pain to isolate, identify, and fix the errors in a large codebase.

Later, they adopted the Continuous Integration approach. The software was built and tested as soon as a developer committed code. If any error is detected, the respective developer can quickly fix the defect.

Jenkins Plugins

By default, Jenkins comes with a limited set of features. If you want to integrate your Jenkins installation with version control tools like Git, then you need to install plugins related to Git. In fact, for integration with tools like Maven, Amazon EC2, you need to install respective plugins in your Jenkins.

Jenkin Plugins integration

Advantages of using Jenkins

Jenkins is being managed by the community which is very open. Every month, they hold public meetings and take inputs from the public for the development of the Jenkins project.
So far around 280 tickets are closed, and the project publishes a stable release every three months.
As technology grows, so does Jenkins. So far Jenkins has around 320 plugins published in its plugins database. With plugins, Jenkins becomes even more powerful and feature-rich.
Jenkins also supports cloud-based architecture so that you can deploy Jenkins in cloud-based platforms.
The reason why Jenkins became popular is that it was created by a developer for developers.

Disadvantages of using Jenkins

Though Jenkins is a very powerful tool, it has its flaws.

Its interface is outdated and not user friendly compared to current UI trends.
Though Jenkins is loved by many developers, it’s not that easy to maintain it because Jenkins runs on a server and requires some skills as a server administrator to monitor its activity.
One of the reasons why many people don’t implement Jenkins is due to its difficulty in installing and configuring Jenkins.
Continuous integrations regularly break due to some small setting changes. Continuous integration will be paused and therefore requires some developer attention.

So In this part, we are talking about the brief of Jenkins. So In the next Part, we will discuss How to Download & Install Jenkins on Windows.