DEV Community: Subhash Tyler 🚀

GUI container on the Docker

Subhash Tyler 🚀 — Tue, 01 Jun 2021 12:29:55 +0000

GUI container on the Docker
🔷 Launch a container on Docker in GUI mode
🔷 Run any GUI software on the container
In this Article I have explained how to launch firefox and Jupyter Notebook GUI application inside Docker container.

step 1: docker run -it --net=host --env=DISPLAY centos
this will pull centos image and run both at a time.

step 2: yum install firefox -y

step 3: yum install python3 -y

step 4: pip3 install jupyter

step 4: jupyter notebook --allow-root

This will successfully run Jupyter notebook on Firefox browser.

Running Machine Learning Model On top of Docker Container.

Subhash Tyler 🚀 — Sun, 30 May 2021 17:56:08 +0000

First build model in Jupyter or any other dev environment.
sample code:

import pandas
db = pandas.read_csv('marks.csv')
y = db["marks"]
x = db['hrs']
x = x.values
x = x.reshape(4,1)
from sklearn.linear_model import LinearRegression
mind = LinearRegression()
mind.fit( x, y)
mind.predict([[ 6 ]] )
mind.coef_

import joblib
mind = joblib.load('marks.pk1')
y = int(input("enter no. of hours: "))
output = mind.predict([[y]])
print(output[0])

save the model as savemodel.py file.

open WinSCP, to transfer these files(marks.csv,savemodel.py) from windows to VirtualBox Linux.

Now open your linux terminal.

systemctl start docker # this will start docker service

docker run -dit --name ML_model centos # this will pull and run docker with latest centos version and having a interactive terminal

docker attach ML_model # this will attach docker container running

we have to install some packages now:
yum install python3
pip install scikit-learn

now go to Base linux os for copying Ml model saved .pk1 file to running docker container.

docker cp savemodel.py ML_Model_check:/ # this will copy model file from base os to container

now come to container,

python3 savemodel.py # this will run the model file and ask for input, give input and get the prediction.

Awesome MongoDB .

Subhash Tyler 🚀 — Mon, 10 May 2021 18:27:29 +0000

MongoDB is an open-source document based database and leading NoSQL database. It is written in C++.
MongoDB uses JSON-like documents with optional schemas.

some frequently used MongoDB commands:-

db.createCollection("testcc")
db.testcc.drop()

show collections
db.student.find()

use test
db.dropDatabase()

Date()
db.student.find().pretty()

Industry use case on -Automation Using Ansible

Subhash Tyler 🚀 — Wed, 30 Dec 2020 18:27:16 +0000

Being an ARTH learner, I have attended a live practical workshop organized by LinuxWorld on the industrial application of ansible.

The session was delivered by two best experts in the industry
Mr. Sreejith Anujan (The Principal Instructor at Red Hat)
and Mr. Arun Eapen (Director, APAC Service Delivery, GLS RedHat Asia Pacific)

It was a very informative session, I learned about:

What is Automation?
Automation means reducing manual work as much as possible for a smooth and fast process. Using a Script in Automation engine that automates cloud provisioning, configuration management, application deployment, intra-service orchestration, and many other IT needs.

Ansible - Ansible is a and agentless Automation tool that helps in automating the deployment of various applications.

Ansible tower - Ansible tower is a bundle of multiple features that provide different services like WebUI, Jobs, Schedule Jobs, Notification feature, Credential Management, Workflow Management, Templates, and other integrations. It also helps in CI and CD where we can manage the entire workflow which includes the git SCM using webhooks, node scaling, credential security, and this whole thing can be done by using playbooks called Ansible Collections.

What are the Ansible Tower Challenges?
Ansible tower provides us with an API, with Role-based access control along with many more features and not to mention simple to control GUI interface.

🔴 we can execute multiple playbooks in a single click.
🔴 we can do scaling using an ansible tower.
🔴 we can integrate other technologies easily.
🔴 Maintaining Workflow (Always run Ansible playbooks one after other)
🔴 Run Playbook based on permission by admin (allow/deny)
🔴 Integration of Ansible with any other technology (If any technology not supported, We can integrate after changing the python Virtual Environment)

How can we use credentials in Ansible Tower?
🔴 we use credentials to authenticate.
🔴 we use credentials when we integrate Ansible with cloud tech and others.

ChatOps
ChatOps is a new operational paradigm - work that is already happening in the background today is brought into a common chatroom.

How can we integrate Ansible Tower with Cloud & Notification based Applications?
🔴 we can integrate ansible tower with cloud and notification based app (ChatOps).
🔴 like we can integrate ansible with Slack notifications using slack tokens.

#RedHat #vimaldaga #righteducation #educationredefine #rightmentor #linuxworld #makingindiafutureready #righeducation #arthbylw #ansiblebylw #ansible #expertsession #sreejith #arun #practicalimplementation #rh294

Why Billion Dollar Companies betting big on AWS Cloud

Subhash Tyler 🚀 — Tue, 22 Sep 2020 13:33:12 +0000

Setting up physical datacenters, servers, infrastructure and managing them is a bit hard and expansive for companies.

Amazon launches its public cloud services namely AWS cloud in March 14, 2006, combining the three initial service offerings of Amazon S3 cloud storage, SQS, and EC2, and if we see AWS offering in 2020 it's 175 products.
Over the last couple of years, from small startups to large enterprises have shown a huge growing interest in public clouds such as AWS.

Managing operational expenses on public clouds is very easy for starting a business or even migrating and existing one to it.

Let's take a look at SLACK which uses AWS cloud services. Slack is the cloud-based team collaboration tool developed in 2013 under the team of Silicon Valley entrepreneurs. It was initially built for a company to work in a team. Now, it is used all over the leading enterprises with an estimate of over a million daily active users. I caught headlines on the Internet like Slack to spend at least $250 million on Amazon Web Services over five years and there s one more that caught my eye Slack and AWS join forces to drive agility in software development.

"The future of enterprise software will be driven by the combination of cloud services and workstream collaboration tools," said Stewart Butterfield, CEO, and co-founder of Slack.

A business-like Slack needed a scalable infrastructure, low implementation costs. So they turned to Amazon Web Services. Slack migration with AWS took less than an hour which in other case take weeks to scale an upgrade. Costs were lower and less staff was needed to maintain the minimal on-site hardware.

Slack relies on simple cloud architecture (EC2 instances behind Elastic Load Balancers, S3, etc.), but it benefits greatly from the resource availability that AWS can provide. While adding capacity in traditional data centers took weeks, it is a non-issue with AWS. For a fast-growing company like Slack, the cloud was not only the best choice, it was probably the only one. In addition, the managed nature of most of AWS’ services has allowed Slack to minimize their IT management involvement in order to focus on their much sought-after product instead.

What If Companies Wanted to Opt-Out of AWS?

Even if we don't care about their contractual obligations, they have to face a huge task for re-architecting their components, and of course, they don't do it because they don't want to focus on building infrastructure so they are going to avoid it, also migrating data from AWS is also costly.

Recently, DROPBOX moved out of Amazon's AWS for its own infrastructure. but still, not many companies are willing to take that chance.

follow me at https://twitter.com/SubhashTyler

sources:
https://aws.amazon.com/solutions/case-studies/slack/
https://www.cnbc.com/2019/04/26/slack-to-spend-at-least-250-million-on-aws-over-five-years.html
https://n2ws.com/blog/aws-cloud/aws-case-studies-lyft-pinterest-slack
#aws #awscloud #awscsa #arthbylw #vimaldaga #righteducation #educationredefine #rightmentor
#worldrecordholder #ARTH #linuxworld #makingindiafutureready #righeudcation

Distributed Storage Cluster and Hadoop

Subhash Tyler 🚀 — Thu, 17 Sep 2020 17:32:31 +0000

How big giant MNC's like Google, Facebook, Instagram, etc stores, manages, and manipulates thousands of Terabytes of data with High Speed and High Efficiency.

The story starts with the buzzword Big data, which refers to a large volume of data that is large, fast, or complex and with different variety, variability, veracity – both structured and unstructured. The importance of big data doesn’t revolve around how much data you have, but what you do with it.

Sources of big data: Streaming data(Youtube, Twitch, Netflix), Social media(Twitter, Facebook), Publicly available data(US government and other agencies data), others come from data lakes, cloud data sources, suppliers, and customers.

How to Access, Manage, and Store Big Data:

Modern computing systems like Distributed Storage Cluster and Hadoop provide the speed, power, and flexibility needed to quickly
manages and manipulate Thousands of Terabytes of data with High Speed and High-Efficiency. Along with reliable access, companies also need methods for integrating the data, ensuring data quality, providing data governance and storage, and preparing the data for analytics. Some data is stored on local premises in a traditional data warehouse and data center – but there are several flexible, low-cost options for storing and handling Big Data via Cloud solutions, Data Lakes, and Hadoop.

Big Data analytics is the technique with which companies gain value and insights from data. With the coming of AI, Augmented Analytics, and Augmented Data Management the problem can be solvable and be managed easily.

Tip: follow https://twitter.com/SubhashTyler for creating Hadoop master-slave architecture or Distributed Storage Cluster on your system in next blog.
#ARTH #bigdata #hadoop

Distributed Storage Cluster and Hadoop

Subhash Tyler 🚀 — Thu, 17 Sep 2020 17:30:27 +0000

How big giant MNC's like Google, Facebook, Instagram, etc stores, manages, and manipulates thousands of Terabytes of data with High Speed and High Efficiency.

Sources of big data: Streaming data(Youtube, Twitch, Netflix), Social media(Twitter, Facebook), Publicly available data(US government and other agencies data), others come from data lakes, cloud data sources, suppliers, and customers.

How to Access, Manage, and Store Big Data:

Big Data analytics is the technique with which companies gain value and insights from data. With the coming of AI, Augmented Analytics, and Augmented Data Management the problem can be solvable and be managed easily.

Tip:
follow https://twitter.com/SubhashTyler for creating Hadoop master-slave architecture or Distributed Storage Cluster on your system in the next blog.