github – DevopsCurry

DevOps Release Orchestration

Shiwani Sharma — Sat, 27 Apr 2024 06:28:56 +0000

What is Release Orchestration?

Release orchestration is a process that coordinates, plans, and organizes automated tasks executed by numerous systems, updating them for users and various environments such as testing, production, and staging. It also helps reduce risks and ensures high-quality outcomes.

Throughout the process, release orchestration may also involve planning, including clarifying the scope of the release, organizing release dates, and coordinating activities with team members and stakeholders involved in the release process.

One of the main purposes of release orchestration is to make software releases efficient, reliable, and predictable. It ensures that new updates and features in the software product are delivered in a controlled manner, providing a positive user experience.

Image Credit: https://www.xenonstack.com/blog/application-release-orchestration

By this above image you will get some idea about the release orchestration platform, it has five stage or you can say ways, the first is plan next one is code, then build, test and then release.

What is Application Release Orchestration (ARO)?

ARO is the process that regulates, coordinates, and plans the deployment of software applications across various environments, including production, testing, development, and staging. The primary purpose of ARO is to ensure the efficient and smooth deployment of software releases.

ARO also assists organizations in automating deployment pipelines, enabling controlled, fast, and consistent software updates. In the IT industry, ARO is a new term that has emerged to enhance release management and regulate CI/CD pipelines and release workflows.

ARO reduces manual intervention, minimizing the impact of human errors. It ensures that changes in the code are appropriately arranged and tracked, facilitating the identification and resolution of any issues.

Benefits of DevOps Release Management and Orchestration

Here is a list of benefits:

Decreased Manual Errors: It reduces human errors during deployment and configuration processes.
Quick Release Cycle: Automated software delivery pipelines speed up the release cycle, allowing organizations to respond effectively to customer demands.
Enhanced Stability and Dependability: Thorough testing and verification before production enhance software release stability and dependability.
Continuous Improvement: DevOps release management promotes a culture of continuous improvement, with teams receiving feedback and making necessary enhancements to the release process.
Customer Satisfaction: Improved software features lead to enhanced customer satisfaction.

5 Release Orchestration Tools

GitLab CI/CD: This tool offers release orchestration features and an integrated CI/CD platform, catering to software engineers and small to mid-sized businesses. Similar to GitHub, GitLab is a web-based platform that provides CI (continuous integration), version control, and collaboration for software development. It also offers additional features, including CI/CD abilities. GitLab is available in both free and paid versions, providing end-to-end support for software development.
GitHub: GitHub provides code deployment, compilation, and testing functionalities, including CI/CD features. GitHub is a web-based platform that offers collaboration and version control for all software development projects. Developers use it to organize and collaborate on code.
Jenkins: Jenkins is an open-source tool for on-premise CI automation, suitable for Linux, macOS, and Windows platforms. Among the top priorities of CI/CD tools, Jenkins holds a position since its inauguration in 2011. It is an open-source tool for on-premise CI automation and is web-based, available for free. Jenkins can be used on Linux, macOS platforms, and Windows. It facilitates the automation of CI/CD in the cloud and is designed for cloud providers and Kubernetes clusters. It possesses the capability to enhance building and testing through the development of machine networks.
Circle CI: An open-source project offering CI/CD pipelines and supporting various programming languages and cloud-based platforms. A large-scale open-source project, Circle CI provides CI/CD pipelines as Workflow. It supports programming languages across various platforms, including macOS, Linux, and Windows. Additionally, it is compatible with cloud-based platforms such as Google Cloud, Azure, and AWS. Circle CI is focused on scalability and speed, making it easily recognizable. It can be accessed for free or purchased at $30 per month.
Bamboo: Created by Atlassian, Bamboo is a comprehensive tool for organizing automated pipelines, supporting multiple programming languages and available in both cloud-based and self-hosted variants. Developed by Atlassian, Bamboo is organized into five slabs: storage, tasks, project, plan, and jobs. This comprehensive tool is designed to manage automated pipelines. Bamboo is not available for free and starts with a pricing of $10 per year. It supports programming languages like SVN, AWS, and Git, and comes in various variants, including cloud-based or self-hosted.

Conclusion: Release orchestration and application release orchestration play crucial roles in ensuring efficient, reliable, and controlled software releases. The adoption of proper tools, such as GitLab CI/CD, GitHub, Jenkins, Circle CI, and Bamboo, can further enhance these processes and contribute to successful DevOps practices.

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Tensorflow

Shiwani Sharma — Mon, 18 Mar 2024 06:14:41 +0000

What is Tensorflow?

An open-source software library under Apache Open Source License that is utilized for machine learning as well deep learning such as RNN, DBN, Feed Forward Neural Network and CNN. Right now in the whole world, Google’s TensorFlow is the popular deep learning library. TensorFlow was firstly introduced on 9 November 2015 and laterally it is introduced permanently on 14 May 2021and it is formulated by Google. It operates on several platforms like Microsoft Windows, JavaScript, macOS, Android. It can be utilized to develop algorithms to imagine the item as well it provides the activity to identify the item. Tensorflow appeals to flexibility on modularity in the system and it provides the training which is known as Parallel Neural Network Training and it is beneficial for big organizations and which work is to create the models profitably.

As we already discussed Tensorflow utilized machine learning and several groups practice machine learning that is Data scientists, Programmers and Researchers. These three are using a similar toolset to enhance their efficiency and teamwork. It is created to operate on numerous mobile operating systems, GPU’s and CPU. Google formulated Tensorflow to improve its services as Photo, Gmail, Google search engine.

How Tensorflow Works

Developers create the graph of data flow and this graph describes how the information flows through a graph and a procession of processing nodes. In a graph, every node shows the mathematical systems and in a surrounding or all around the nodes is a multidimensional record collection or tensor. Python has some devices in TensorFlow, two of them are Nodes and Tensors as well the TensorFlow is a program of Python. As we already mentioned that TensorFlow was introduced in 2015 and the next version of TensorFlow that is TensorFlow 2.0 is introduced in 2019. It permit the developers to make the dataflow graph that means have to create a graph that structure express how the data shift through a graph. Every single moves or nodes reminds a mathematical operations and a particular connection between nodes is known as tensor. When we write the TensorFlow code, we will explain the operations and their connection to make the computational graph. This consist of variables for identify the operations, input data such as activation functions, matrix multiplication and it will also define how any data flow through the graph.

After making the computational graph, a sessions is created to perform. This session also assign resources like GPU, CPU and operate the operations that can define the graph.

Image Credit: https://www.datasciencecentral.com/how-tensorflow-works/

Advantage And Disadvantage of Tensorflow

Advantage of Tensorflow

Image Credit: https://www.javatpoint.com/advantage-and-disadvantage-of-tensorflow

In the above images you can see there are five advantage of TensorFlow that is Graphs, Library Management, Debugging, Scalability & Pipelining and we have explain some of the other advantage that is mentioned below with few lines.

Some of the advantages of Tensorflow are as discuss below:

Graphs

It has a computational graph visualization and that is ingrained when the comparison happened with different libraries such as Theano and Torch. One of the best advantage of TensorFlow is it has a good graph visualization that you can see on computer or in other word you can say it is best for computerized graphics visualizations.

Open-source platform

It is an open-source platform that is used for machine learning and deep learning and for all the users that are accessible and available for the development of any procedure on it.

Graphical support

For the growth of TensorFlow deep learning utilized it as well it permits creating a neural system with the support of graphs that affect the system as a node

With the statute of a user TensorFlow ordinance in several domains like motion detection, voice detection, image recognition etc.

Customer-centric

TensorFlow is built to utilize several software as in the backend and it consists of model parallelism and data, so you can operate them parallelly and distribute the model into a different portion. The architecture TensorFlow gives TensorFlow, TensorBoard that helps to regain the data and observe the errors by using TensorBoard.

Supply Centric

With the involvement of setups, it originated from cellular tools to computer systems and the libraries can be deployed on an expanse of hardware machines.

Disadvantage Of Tensorflow:

Image Credit: https://www.javatpoint.com/advantage-and-disadvantage-of-tensorflow

As you can see in the above image there are five disadvantage that are mentioned but we will discuss few more as mentioned below:

There are many software tools in the market, if they have the benefit definitely have some drawbacks, same happened with Tensorflow and some of the drawbacks are Debugging Challenges, Ecosystem and Community, Verbose Code, Performance on Small Datasets.

Debugging Challenges: This can be difficult due to the computational graph nature. Here mistake are arrived some time instantly but it is not easy to pinpoint and find the problem that arrived.

Ecosystem and Community: As we have already discuss that Tensorflow has big and developing community, there are some techniques that are new or unique techniques might have good contribution or in other word you can say have more wider resources in other frameworks.

Problem Arrived On Small Datasets: Sometime TensorFlow has to faces the problem when small datasets arrived as compared to other frameworks. TensorFlow is best for the tasks which is related to large-scale machine learning.

Verbose Code: When you are writing any code on TensorFlow, sometime you will realize that it will voluble that means you need to write more code as compared to other frameworks that effect on its readability.

Model Deployment Complexity: When you are establishing the model of TensorFlow it might be become more difficult in comparison to some other framework.

Conclusion: At the end of this blog the conclusion came for TensorFlow is stand as a versatile as well robust framework and a tool that work for machine learning. Right now in the whole world, Google’s TensorFlow is the popular deep learning library. TensorFlow was firstly introduced on 9 November 2015 and laterally it is introduced permanently on 14 May 2021and it is formulated by Google. It operates on several platforms like Microsoft Windows, JavaScript, macOS, Android.

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DevOps Toolchain – Starting with Git

Namrata Joshi — Tue, 13 Oct 2020 14:26:52 +0000

What is Git? Advantages of Git

Switching from a centralized VCS to Git will change the whole process of creating software, in a good way. Let us discuss what is Git and why Git is the most preferred Version Control in Devops toolchain.

What is Git?

Git is a Version Control System developed by Linus Torvalds, the same person who founded the Linux Operating System. Git was originally designed to help manage the Linux Kernel.

Why Git is so Popular?

If you want to find out why Git is so popular, let us first discuss about its capability. So, the Linux Kernel has 15 million lines of code Around 3500 words of code is added to it every day.

If Git can manage Linux Kernel very well, it can definitely manage any other project efficiently. Furthermore, Git architecture is a Distributed Version Control System. Rather than storing the entire project in a central server (Centralized VCS), Git does not require a network connection to work with. As Git is a distributed VCS, the entire project and its history is mirrored on everyone’ s computer.

Advantages of Git

Git benefits the whole business, especially if your company relies on the software. Switching to Git will change the way your development team creates the software.Â

Feature Branch Workflow  the feature branch workflow provides an isolated environment for every change made. The biggest advantage of Git is Branching. Git branches are affordable and easy to merge.
Distributed Development  as we discussed earlier, Git is a distributed Version Control System. Each developer gets its own local repository with a full local history. Having the access to full history makes Git faster. Eventually, it will create a reliable environment for everyone. Distributed Development will make it easier to scale your engineering team.
Pull Requests  tools like Bitbucket enhance core Git functionalities with Pull requests. A pull request is a way to ask another developer to merge one of your branches into their repository. This will help to keep track of the changes.
Community  Git is a very popular VCS among Open source projects available in the market. Thus, you won’t have to train the new joiners about your workflow. Why? Because they are already familiar with distributed development.
Faster Release Cycle  feature branches, distributed development, pull requests and a stable community will result in a faster result cycle for the project. It will create an agile workflow where even smaller changes are shared frequently. Also, Git works very well with Continuous Integration and Continuous Delivery.

What is the difference between Git and GitHub?

Git is one of the most popular open-source Version Control Systems. It works smoothly with small to large projects with speed and efficiency.

GitHub is a Git Repository Hosting Service. It is a web-based service. GitHub offers all features of a distributed VCS and source code management of Git.

Git	GitHub
It is an open source distributed tool for version control.	Github is a web-based platform for hosting Git repositories.
Git is focused on version control and code sharing by individual developers locally.	GitHub is more refocused on a centralized source code handling.With GitHub, developers can share their repositories, access other developers’ repositories, and also store remote copies of repositories to serve as backups.
In Git most of the commands are run through CLI, though we have GUI options as well.	GitHub is administrated through an interactive web GUI.
Git can work independently.	Github is dependent on Git and cannot be used without it.
Git works in local environment, on a developers local system.	Github works in a cloud environment and needs internet.
It offers a desktop interface called Git GUI.	It also offers a desktop interface called GitHub GUI.
Git has a minimal tool configuration feature.	GitHub has a market place for tool configuration.
Git does not have user-management functionality	GitHub has a in-built user management feature.

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Understanding Version Control in DevOps

Namrata Joshi — Wed, 23 Sep 2020 13:36:40 +0000

What is Version Control or Source Control?

Think about your files as a book. It has chapters, pages, beginning, middle, and end. Version Control or Source Control in DevOps helps you to make changes to that while maintaining the entire flow and working with the team.

A Version Control System (VCS) is a way to make changes to files without worrying about something that will get lost or things will fall out of the flow. Version Control also offers backup and history of any changes for any files line-by-line.

The success of the DevOps depends on the Source Control. Version Control or Source Control in DevOps helps to manage the changes done during development process in a project. It can be versions of code, documents, or even environment configuration.

What is Source Code Management?

Source Code Management or SCM is a Devops automation tool that maintains a track of versions (revisions) made to the program. Each version has a timestamp and the person who made the changes. These versions can be compared and merged. SCM is also known as Version Control.

Benefits of using Version Control (VCS)

Provides a complete history of every file, made by the individuals over the years. These changes include the creation, deletion, and edits to the files. History also displays the author, date, and notes of why the change was made. This will help to find the root cause of the problem in the future.
VC protects the source code from any accidental changes, human mistakes, and unintended circumstances. For any software project, the source code is the most important asset. Hence it is crucial to protect it. Because source code contains all the data and revision history related to a project.
The facility of Branching and Merging. A Branch in VCS will keep various streams of work independent from each other. At the same time it will also provide the facility to merge that work back together. There are many workflows to choose from. The team can choose the workflow depending on how to make use of branching and merging facility in the VCS.
Source Control or Version Control helps to understand the difference between various versions and also the purpose of making the changes.
Version Control is especially beneficial in case of large distributed teams, where developers work on multiple projects and its difficult to keep a track of changes and updates across teams.
Version Control ensures that Dev and Ops are in there in the source control. This is the beauty of DevOps. This makes it clear and transparent to everyone to see what is there in the source and each version of it.

Some Popular Version Control Tools in DevOps

There are many Version Control tools for DevOps available in the market. But here we have listed down some of the most popular Version Control tools used in DevOps which will make things easy for you and your team.

1.GitHub: Git is an open-source Version Control System (VCS), it is completely free. Git is designed to work in small to large level projects. Git will help to merge and maintain the history of code changes. Github is the repository where all the source code is kept by Git users. GitHub offers local branching and multiple workflows. It is easy to learn and offers faster operation speed.

2.GitLab: GitLab is an open-source Version Control System,written in Ruby and Golang. It comes with features like in integrated project, a project website, etc. One can automatically test and deliver the code using the Continuous Integration (CI) facility of GitLab. GitLab is repository management tool hosted on the free hosting service GitLab.com. It is easy to use to link projects via GitLab API. It works with various OS like Windows, Linux, OSX, etc.

3.BitBucket: BitBucket is a paid Version Control System. Its a part of the Atlassian’s software suite. It offers features like code branches, in-line commenting and discussions and pull requests. BitBucket is specifically developed for the professional teams. It not just enables users to code but also to manage and collaborate on GIT projects. It can be deployed on the local server as well as on the cloud.

4.Perforce: Perforce is an open-source enterprise version control tool. Here, users connect to a shared file repository. Perforce applications are used to transfer files between the file repository and individual user workstations. It provides branching and merging, integrations, web-based repository management, and artifacts management. It delivers version control through its HelixCore. It is a security solutions that protects the most important parts of the project.

5.Apache Subversion: Apache Subversion aka SVN is another popular open-source Version Control System (VCS). However, it also have an enterprise version. Initially created by CollabNet in 2000, SVN is now maintained as a project by the Apache Software Foundation. Apache Subversion supports locking of files so that users can be warned when many people try to edit the same file.Apache Subversion provides features like inventory management, security management, history tracking, workflow management etc. SVN supports empty directories and has a better windows support compared to Git.

6.Mercurial: Mercurial aka Hg, is a distributed version-control tool for developers. It is a free tool that boasts of scalability and high performance for distributed teams. As compared to Git, developers find Mercurial easy to setup and use.Its mostly developed in Python.

So Version Control or Source Control is an integral part of the Devops lifecycle and one of the initial phases. Once the developer checks-in his code into a VCS tool, the Devops chain starts. The version control is then followed by automated testing, CI/CD, deployment and monitoring phases of DevOps.

So for a successful DevOps implementation within an organization, a good version control process plays a crucial role.

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