Kubernetes Deployment Handbook (Configurations, Checklist, Error Handling, and Best Practices)

November 4, 2022


Kubernetes provides orchestration for more than three-quarters of containerized applications today. There are many alternatives to Kubernetes but it is still a widely used tool for managing your containerized workloads.

Through E2E Myaccount portal, You can launch a Kubernetes master, worker nodes within a blink and get the work with your Kubernetes cluster in no time.

This guide will show how to deploy Managed Kubernetes on E2E Cloud.

Getting Started

Here are the steps to be followed:

  • Login into MyAccount: Please go to ‘MyAccount’ and log in using your credentials set up at the time of creating and activating the E2E Networks ‘MyAccount’.
  • Browse to this label “Kubernetes” under dropdown of Compute: Post logging in to the E2E Networks ‘MyAccount’, Your dashboard will appear and just below the dashboard icon , Click on compute and choose Kubernetes from the available options. 


E2E Networks-Myaccount Portal
E2E Cloud- Myaccount Portal



E2E Cloud-Myaccount Portal, Kubernetes Dashboard
E2E Cloud My account portal- Kubernetes Dashboard

Now, you can create Kubernetes. How? Let us guide you through:

On the top right section of the managed kubernetes dashboard, You will click on the “Create kubernetes” Button which will drag you to the cluster page for selecting the configuration and entering the details of your database.

E2E Cloud Myaccount portal- Kubernetes Dashboard, Creating a cluster
E2E Cloud- My account portal, Creating a Kubernetes Cluster

Kubernetes Configuration and Setup

  • After clicking on “Create Kubernetes'', there will be some configurations that will give you a glimpse of the plan like cluster name, version and price .You are required to click on “Add Plan” and need to select the required configuration and setting for your kubernetes. 
E2E Cloud, Myaccount Portal, Kubernetes Dashboard
E2E Cloud- My account Portal, Adding Node Pool Plan in Kubernetes Dashboard


  • Now a mini screen will appear where a list of plans will be displayed for you to choose. Four tabs for filtering your plan, vCPUs , SSD storage and RAM. You can pick one from the drop down menu according to your use case.
E2E Cloud, Myaccount Portal-Kubernetes Dashboard
E2E Cloud- My account portal, Choosing the optimal Node Pool Plan for Kubernetes
  • Now that you have chosen the plan, increase or decrease worker count and write a label name.
  • Click on “Add Plan” under Actions.
E2E Cloud, Myaccount Portal, Kubernetes Dashboard
E2E Cloud- My account portal, Adding Node Pool Plan from Kubernetes Dashboard
  • You still have a choice to make modifications in your plan.
  • Once you are finally set, Click on “Add Plan” from the bottom right.
  • It will take you back to the previous screen with your chosen configurations automatically filled up under “Add Node Pool Plan”.
E2E Cloud, Myaccount Portal, Kubernetes Dashboard
  • Now, you have to “Select VPC”. VPC is mandatory to be launched along with the Kubernetes cluster to improve security of your infrastructure. If you don’t have VPC launched, please follow these steps: 
E2E Cloud, Myaccount Portal, Kubernetes Dashboard
  • You are ready to Create a Cluster now. Click on “Create Cluster”: It will take a few minutes to set up the scale group and you will taken to the ‘Manage Kubernetes’ page.
E2E Cloud, Myaccount Portal, Kubernetes Dashboard



Manage your Kubernetes

Following things will be visible once you create a cluster:

  • Cluster Details: You will be able to check all the basic details of your kubernetes. You can check the kubernetes name and kubernetes version details.

E2E Cloud, Myaccount Portal, Kubernetes Dashboard

  • Node Pool: Here you can resize i.e. increase or decrease the pool size. 

E2E Cloud, Myaccount Portal, Kubernetes Dashboard

Kubecongfig.yaml File and Token are the two most  important tabs for managing the kubernetes 

Note: How To Download Kubeconfig.yaml File?

  • After downloading the Kube config
  • Please make sure kubectl is installed on your system
  • To install kubectl follow the following steps:
(Before you begin, You must use a kubectl version that is within one minor version difference of your cluster. For example, a v1.25 client can communicate with v1.24, v1.25, and v1.26 control planes. Using the latest compatible version of kubectl helps avoid unforeseen issues)

Install kubectl on Linux

The following methods exist for installing kubectl on Linux:

  • Install kubectl binary with curl on Linux
  • Install using native package management
  • Install using other package management

We will explain how to install Kubectl binary with Curl command on Linux

  1. Download the latest release with the command:

curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl”

Install kubectl on Linux

               2. Validate the binary (optional)

                 Download the kubectl checksum file:

curl -LO "https://dl.k8s.io/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl.sha256"

Install Kubectl on Linux
Install Kubectl on Linux
Install Kubectl on Linux

Install Kubectl on Linux

  • Run kubectl –kubeconfig=”download_file_name” proxy

Install Kubectl on Linux

Install Kubectl on Linux

Now open the below URL in the browser to check your Kubernetes dashboard which is ready to connect and serve your cluster:


The screen will appear like this

Kubernetes Dashboard


Now, go to Myaccount In the cluster details screen, you will find “Kubeconfig Token”. Click on “Show Token”. Copy this token and paste it in Kubernetes dashboard

E2E Cloud, Myaccount Portal, Manage Kubernetes Dashboard

With this, you get a fully featured kubernetes installation which can run and orchestrate any pod in the cluster

Kubernetes Dashboard

Now we will explain various modules available in Myaccount 

Active Node Pool details

E2E Cloud, Myaccount Portal, Kubernetes Dashboard

Here, you can easily see the plan name, state etc. under Node Pool. The Active Node Pool Details tab provides information about the Worker nodes. Users also increase and delete the worker node. 

Persistent Volume (PVC)

When you click on PVC (also known as CSI- Container Storage interface), there will be no PVC pre added. You will see “Click here” and will get an option to add persistent volume. Based on the customers feedback, now Myaccount allows a customer to choose PVC as low as 10GB. PVC is required to create Stateful applications (Stateful applications save data to persistent disk storage for use by the server, by clients, and by other applications. An example of a stateful application is a database or key-value store to which data is saved and retrieved by other applications)

E2E is giving several options of persistent volumes like 10 GB, 20 GB, 50 GB etc.

You can select your required persistent volume from the drop down and give its name before creating it.

E2E Cloud, Myaccount Portal, Kubernetes dashboard, Persistent Volume

It will take a few minutes to get created and will appear under PVC module

E2E Cloud, Myaccount Portal, Kubernetes Dashboard

LB IP Pool

You can use public addresses for communication between your Kubernetes and the Internet. When you launch an E2E Kubernetes, we assign it a public IP address by default. This public IP is not a reserved IP address by default. You can reserve the default assigned public IP address for your account and it will remain mapped to your Myaccount until you release it.

E2E Cloud, Myaccount Portal, Kubernetes Dashboard, LB IP Pool

Here, you will see an option to reserve your Public IP.

E2E Cloud, Myaccount Portal, Kubernetes Dashboard

Reserving a new public IP ensures that an IP will be reserved for your MyAccount and remain with you until you release it. The reserved IP can be attached to the Kubernetes master node.

Please Note, standard monthly charges are 199.0 infra credits for each reserved IP.

Now, when you click on “Reserve a new IP”, it will ask you to Select one of the IP from the list to attach in the kubernetes cluster. If you have not reserved any IP before, then nothing will appear in the drop down list.

E2E Cloud, Myaccount Portal, Kubernetes Dashboard

You will be able to see the attached IP. Also you will be given an option to attach more IP’s in case you want.

You can also use private IPv4 addresses for communication between instances in the same VPC. When you launch an E2E Kubernetes, we allocate a private IPv4 address for your Kubernetes.

Kubernetes Security Checklist:

The following guidelines are important when creating a robust and reliable Kubernetes production setup for running critical applications.

  • Authentication & Authorization
  1.  system:masters group should not be used for user or component authentication after bootstrapping.
  2.  The kube-controller-manager should be running with --use-service-account-credentials enabled.
  1.  The root certificate should be protected (either an offline CA, or a managed online CA with effective access controls).
  2.  Intermediate and leaf certificates should have an expiry date no more than 3 years in the future.
  3.  There should be a process for periodic access review, and reviews occur no more than 24 months apart.
  4.  The Role Based Access Control Good Practices should be followed for guidance related to authentication and authorization.


  • Keep security vulnerabilities and attack surfaces to a minimum for the Cluster and Applications. 

Lockdown the pods and nodes, with traceable break-glass policies. Ensure that the applications you are running are secure and that the data you are storing is secured against attack. And because Kubernetes is a rapidly growing open source project, be on top of the updates and patches so that they can be applied in a timely manner.

  • Segregate the Kubernetes Cluster and Configure usage limits. 

Segregate Production Kubernetes Cluster to make sure that rapid changes happening in Infrastructure and application level do not impact production workloads. This segregation could be physical or logical, and based on the setup proper guardrails need to be implemented. As Kubernetes is mostly used as a shared infrastructure, proper usage limits need to be applied for running applications based on type and criticality of workloads, to minimize the impact of an outlier. Namespace level isolation and resource limits are common practice for this type of enforcement.

Kubernetes Error Handling:

Most problems with Kubernetes adoption ultimately stem from the complexity of the technology itself. There are unobvious difficulties and nuances of implementation and operation, and there are underutilized advantages.

1. The selector of the labels on the service does not have a match with the pods: In order to function correctly as a network balancer, a service generally specifies selectors that allow you to find the pods that are part of the balancing pool. If there is no match, the service has no endpoints to forward traffic to and an error occurs. Bear in mind that the load balancing towards the pods is of a random type.

2. Wrong container port mapped to the service: Each service has two fundamental parameters, “targetPort” and “port”, which are often confused and misused. This confusion then results in error messages claiming that the connection was refused or there was a lack of response to the request. To avoid this error, remember that “targetPort” is the destination port in the pods, the one to which a service goes to forward traffic. The “port” parameter, on the other hand, refers to the port exposed by the service to the clients. They can be the same, so it is essential to know their meanings!

3. CrashLoopBackOff: Another frequent Kubernetes error is the crashloopbackoff error. It occurs when a pod is running, but one of its containers is restarting due to termination (usually the wrong way). In other words, the container has fallen in the loop of start-crash-start-crash.

Log of CrashLoopBackOff error which is a error faced commonly by kubernetes

Log of CrashLoopBackOff error: The CrashLoopBackOff error can occur due to various reasons — the wrong deployment of Kubernetes, liveness probe misconfiguration, and init-container misconfiguration. An easy way to resolve this error is by properly configuring and deploying Kubernetes. However, you can also bypass the error by creating a separate deployment with the help of a blocking command.

4. Liveness and readiness probes: Several mistakes are made regarding probes. The first is not defining any health check for the application, which will never be restarted in case of problems and will always remain within the load-balancing pool of a service. The second type of error concerns defining equal liveness and readiness probes by contacting the same HTTP endpoint, for example. It may be due to a misunderstanding of these types of tests. The liveness probe is linked to the concept of a healthy application, so if it fails, the pod will be restarted.

Kubernetes Best Practices:

  • Using Namespaces: Namespaces in Kubernetes are important to utilize while aligning your objects for creating logical partitions within your cluster, and for security purposes. By default, there are 3 namespaces in kubernetes cluster, default, kube-public and kube-system. RBAC security control can be used to control access to particular namespaces in order to limit the access of a group.
  • Liveness Probes: Readiness and Liveness probes are the types of health checks. These are another very important concept to utilize in Kubernetes. Readiness probes ensure that requests to a pod are only directed to it when the pod is ready to serve requests. Liveness probe checks the container health as we tell it to do, and if for some reason the liveness probe fails, it restarts the container.
  • Autoscaling: Auto Scaling can be employed appropriately to adjust the number of pods in a dynamic way. The amount of resources consumed by the pods, or the number of nodes in the cluster (cluster autoscaler), depends on the demand for the resources. Kubernetes allows you to scale the pods automatically to optimize resource usage and make the backend ready according to the load in your service. Horizontal Pod Autoscaler which is a built-in component can scale your pods automatically. Firstly, we are required to have a Metrics Server to collect the metrics of the pods. To provide metrics via the Metrics API, a metric server monitoring must be deployed on the cluster. Horizontal Pod Autoscaler uses this API to collect metrics.
  • Using Resource Requests & Limits: If the node (where a Pod is running) has enough of a resource available, it's possible (and allowed) for a container to use more resources than its request for that resource specifies. However, a container is not allowed to use more than its resource limit. For example, if you set a memory
Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links




This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links



This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links



This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links




Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure