If you’ve been anywhere near the IT industry, you’ve very likely heard the term containers🚢. The adoption of containers is growing exponentially as they are lightweight, portable, and revolutionary fast. Nowadays, deployments over containers are holding an upper hand over VMs for larger environments.
🔅Provision EC2 instances through ansible.
🔅 Retrieve the IP Address of instances using the dynamic inventory concept.
🔅Configure the web servers through the ansible role.
🔅Configure the load balancer through the ansible role.
🔅The target nodes of the load balancer should auto-update as per the status of web servers. …
→Pre-requisites:
— >RedHat Ansible downloaded and configured in the local system.
>Do check out my previous article for Ansible👇👇:
♦️ Deploy Web Server on AWS through ANSIBLE!
🔹 Provision EC2 instance through ansible.
🔹 Retrieve the IP Address of instance using the dynamic inventory concept.
🔹 Configure the webserver through ansible!
pip3 install boto3 //assuming python3 is installed
→Ansible is an infrastructure automation tool from REDHAT. It is widely used in the configuration of systems and setting up deployment environments.
→Ansible is an abstraction layer that covers all operating systems under its umbrella that helps configuration of large heterogeneous environment. It is built on top of PYTHON 🐍.
→Ansible has Modules that enables performing various tasks in the system.Power⚡ of Ansible is Playbooks. Playbooks are nothing but YML files containing modules as per user requirements.
Problem Statement For This Hands-On-
Write an Ansible PlayBook that does the following operations in the managed nodes:
🔹 Configure Docker
🔹 Start and enable Docker…
→WHAT IS THE PROJECT?
TOOLS USED — GIT, JENKINS, DOCKER
Problem Statement-
→First of all, many thanks to MR.VIMAL DAGA SIR for mentoring and training in Machine learning from very basic to advance level
→I have completed Face Recognition Using Transfer Learning
→Environment requirements —
Keras, TensorFlow, Numpy, Jupyter, cv2
→In this, I have used VGG16 pre-created dataset and using this pre-trained model implemented face-recognition over my dataset
→I have taken 2 faces of Virat Kohli and Rohit Sharma and collected some images.
→It is required to have Testing and Training data. So, suggested taking 80:20 ratio of training: testing dataset.
→Screenshots of whole code and workflow
→First of all, many thanks to MR.VIMAL DAGA SIR for mentoring and training in Machine learning and DevOps from very basic to advance level
→Using this valuable knowledge I have completed the task of integration Machine learning with DevOps
→Tools used for DevOps — Git, Jenkins, Docker,
→Concept used for Machine Learning- Deep learning
SYSTEM CONFIGURATION/REQUIREMENTS-
→Base os Windows and virtual os Redhat Linux
→Jenkins installed in your RedHat system (including GitHub plugin)
→Docker installed with an image which can run python code
→Git installed in Windows and Redhat os
Problem statement- While creating a deep learning model, the process becomes too time taking and somewhat manual. …
→First of all, many thanks to MR.VIMAL DAGA SIR for mentoring and training in DevOps from very basic to advance level
→Using this valuable knowledge, I have built one AUTOMATION SYSTEM under the guidance of VIMAL DAGA SIR and his TECHNICAL VOLUNTEERS.
→WHAT IS THE PROJECT?
TOOLS USED — GIT, JENKINS, DOCKER
→Jenkins plays 3 jobs in this system
job-1) Jenkins will keep on monitoring and keep on deploying out the site on a TESTING server
job-2)Jenkins will keep on monitoring and keep on deploying out the site on PRODUCTION server
job-3)It will run this job only when it is triggered by the testing team and it will merge branched and run job 2 and finally destroy the testing server! …
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