FeatureBear

featurebear

Author:
(1) Vojtech ˇ Cerm ˇ ak, Czech Technical University in Prague (Email: cermavo3@fel.cvut.cz);
(2) Lukas Picek, University of West Bohemia & INRIA (Email: picekl@kky.zcu.cz/lpicek@inria.cz);
(3) Luka´s Adam, Czech Technical University in Prague (Email: lukas.adam.cr@gmail.com);
(4) Kostas Papafitsoros, Queen Mary University of London (Email: k.papafitsoros@qmul.ac.uk). Table of Links Abstract and Introduction
Related work
The WildlifeDatasets toolkit
MegaDescriptor – Methodology
Ablation studies
Performance evaluation
Conclusion and References 5. Ablation studies This section presents a set of ablation studies to empirically validate the design choices related to model distillation (i.e., selecting methods, architectures, and appropriate hyperparameters) while constructing the MegaDescriptor feature extractor, i.e., first-ever foundation model for animal re-identification. Furthermore, we provide both qualitative and quantitative performance evaluation comparing the newly proposed MegaDescriptor in a zero-shot setting with other methods, including SIFT, Superpoint, ImageNet, CLIP, and DINOv2. 5.1. Loss and backbone components To determine the optimal metric learning loss function and backbone architecture configuration, we conducted an ablation study, comparing the performance (median accuracy) of ArcFace and Triplet loss with either a transformer- (Swin-B) or CNN-based backbone (EfficientNet-B3) on all available re-identification datasets. In most cases, the Swin-B with ArcFace combination maintains competitive or better performance than other variants. Overall, ArcFace and transformer-based backbone (Swin-B) performed better than Triplet and CNN backbone (EfficientNet-B3). First quantiles and top whiskers indicate that Triplet loss underperforms compared to ArcFace even with correctly set hyperparameters. The full comparison in the form of a box plot is provided in Figure 5. 5.2. Hyperparameter tunning In order to overcome the performance sensitivity of metric learning approaches regarding hyperparameter selection and to select the generally optimal parameters, we have performed a comprehensive grid search strategy. Following the results from the previous ablation, we evaluate how various hyperparameter settings affect the performance of a Swin-B backbone optimized with Arcface and Triplet losses. In the case of ArcFace, the best setting (i.e., lr = 0.001, m = 0.5, and s = 64) achieved a median performance of 87.3% with 25% and 75% quantiles of 49.2% and 96.4%, respectively. Interestingly, three settings underperformed by a significant margin, most likely due to unexpected divergence in the training[5]. The worst settings achieved a mean accuracy of 6.4%, 6.1%, and 4.0%. Compared to ArcFace, Triplet loss configurations showed higher performance on both 25% and 75% quantiles, indicating significant performance variability. The outcomes of the study are visualized in Figure 6 as a boxplot, where each box consists of 29 values. 5.3. Metric learning vs. Local features The results conducted over 29 datasets suggested that both metric learning approaches (Triplet and ArcFace) outperformed the local-feature-based methods on most datasets by a significant margin. The comparison of local-featurebased methods (SIFT and Superpoint) revealed that Superpoints are a better fit for animal re-identification, even though they are rarely used over SIFT descriptors in the literature. A detailed comparison is provided in Table 3. Note that the Giraffes dataset was labeled using local descriptors; hence, the performance is inflated and better than for metric learning. The same experiment revealed that several datasets, e.g., AerialCattle2017, SMALST, MacaqueFaces, Giraffes, and AAUZebraFish, are solved or close to that point and should be omitted from development and benchmarking. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. [5] These three settings were excluded from further evaluation and visualization for a more fair comparison. Author: (1) Vojtech ˇ Cerm ˇ ak, Czech Technical University in Prague (Email: cermavo3@fel.cvut.cz); (2) Lukas Picek, University of West Bohemia & INRIA (Email: picekl@kky.zcu.cz/lpicek@inria.cz); (3) Luka´s Adam, Czech Technical University in Prague (Email: lukas.adam.cr@gmail.com); (4) Kostas Papafitsoros, Queen Mary University of London (Email: k.papafitsoros@qmul.ac.uk). Author: Author: (1) Vojtech ˇ Cerm ˇ ak, Czech Technical University in Prague (Email: cermavo3@fel.cvut.cz); (2) Lukas Picek, University of West Bohemia & INRIA (Email: picekl@kky.zcu.cz/lpicek@inria.cz); (3) Luka´s Adam, Czech Technical University in Prague (Email: lukas.adam.cr@gmail.com); (4) Kostas Papafitsoros, Queen Mary University of London (Email: k.papafitsoros@qmul.ac.uk). Table of Links Abstract and Introduction Related work The WildlifeDatasets toolkit MegaDescriptor – Methodology Ablation studies Performance evaluation Conclusion and References Abstract and Introduction Abstract and Introduction Related work Related work The WildlifeDatasets toolkit The WildlifeDatasets toolkit MegaDescriptor – Methodology MegaDescriptor – Methodology Ablation studies Ablation studies Performance evaluation Performance evaluation Conclusion and References Conclusion and References 5. Ablation studies This section presents a set of ablation studies to empirically validate the design choices related to model distillation (i.e., selecting methods, architectures, and appropriate hyperparameters) while constructing the MegaDescriptor feature extractor, i.e., first-ever foundation model for animal re-identification. Furthermore, we provide both qualitative and quantitative performance evaluation comparing the newly proposed MegaDescriptor in a zero-shot setting with other methods, including SIFT, Superpoint, ImageNet, CLIP, and DINOv2. 5.1. Loss and backbone components To determine the optimal metric learning loss function and backbone architecture configuration, we conducted an ablation study, comparing the performance (median accuracy) of ArcFace and Triplet loss with either a transformer- (Swin-B) or CNN-based backbone (EfficientNet-B3) on all available re-identification datasets. In most cases, the Swin-B with ArcFace combination maintains competitive or better performance than other variants. Overall, ArcFace and transformer-based backbone (Swin-B) performed better than Triplet and CNN backbone (EfficientNet-B3). First quantiles and top whiskers indicate that Triplet loss underperforms compared to ArcFace even with correctly set hyperparameters. The full comparison in the form of a box plot is provided in Figure 5. 5.2. Hyperparameter tunning In order to overcome the performance sensitivity of metric learning approaches regarding hyperparameter selection and to select the generally optimal parameters, we have performed a comprehensive grid search strategy. Following the results from the previous ablation, we evaluate how various hyperparameter settings affect the performance of a Swin-B backbone optimized with Arcface and Triplet losses. In the case of ArcFace, the best setting (i.e., lr = 0.001, m = 0.5, and s = 64) achieved a median performance of 87.3% with 25% and 75% quantiles of 49.2% and 96.4%, respectively. Interestingly, three settings underperformed by a significant margin, most likely due to unexpected divergence in the training[5]. The worst settings achieved a mean accuracy of 6.4%, 6.1%, and 4.0%. Compared to ArcFace, Triplet loss configurations showed higher performance on both 25% and 75% quantiles, indicating significant performance variability. The outcomes of the study are visualized in Figure 6 as a boxplot, where each box consists of 29 values. 5.3. Metric learning vs. Local features The results conducted over 29 datasets suggested that both metric learning approaches (Triplet and ArcFace) outperformed the local-feature-based methods on most datasets by a significant margin. The comparison of local-featurebased methods (SIFT and Superpoint) revealed that Superpoints are a better fit for animal re-identification, even though they are rarely used over SIFT descriptors in the literature. A detailed comparison is provided in Table 3. Note that the Giraffes dataset was labeled using local descriptors; hence, the performance is inflated and better than for metric learning. The same experiment revealed that several datasets, e.g., AerialCattle2017, SMALST, MacaqueFaces, Giraffes, and AAUZebraFish, are solved or close to that point and should be omitted from development and benchmarking. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv [5] These three settings were excluded from further evaluation and visualization for a more fair comparison.

WildlifeDatasets: an Open-source Toolkit for Animal Re-identification: Ablation Studies

VEATIC: Familiarity and Enjoyment Ratings & References

VEATIC: পরিচিতি এবং উপভোগ রেটিং এবং রেফারেন্স

VEATIC: Vertrautheits- und Genussbewertungen und Referenzen

VEATIC: Calificaciones y referencias de familiaridad y disfrute

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VEATIC: 親しみやすさと楽しさの評価と参考文献

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3 Simple Tips For Dependency Injection Using Scrutor in C#

C# এ স্ক্রুটার ব্যবহার করে নির্ভরতা ইনজেকশনের জন্য 3 টি সহজ টিপস

3 einfache Tipps für die Abhängigkeitsinjektion mit Scrutor in C#

Tres consejos sencillos para la inyección de dependencias utilizando Scrutor en C#

3 conseils simples pour l'injection de dépendances à l'aide de Scrutor en C#

C# में स्क्रूटर का उपयोग करके निर्भरता इंजेक्शन के लिए 3 सरल युक्तियाँ

C# で Scrutor を使用した依存関係の注入に関する 3 つの簡単なヒント

C#에서 Scrutor를 사용하여 종속성 주입을 위한 3가지 간단한 팁

3 dicas simples para injeção de dependência usando Scrutor em C#

3 простых совета по внедрению зависимостей с помощью Scrutor в C#

C#'ta Scrutor Kullanarak Bağımlılık Enjeksiyonu İçin 3 Basit İpucu

3 mẹo đơn giản để chèn phụ thuộc bằng Scrutor trong C#

在 C# 中使用 Scrutor 进行依赖注入的 3 个简单技巧

This paper is available on arxiv under CC 4.0 license. Authors: (1) Berrenur Saylam; (2) Ozlem Durmaz ¨ ˙Incel. Table of Links Highlights Abstract & Introduction Federated Learning: Collaborative and Privacy-Preserving Model Training State of the Art Federated Learning Software Platforms and Testbeds Sensors and Edge-Sensing Devices Federated Learning Applications on Sensing Devices Discussion Conclusions, Acknowledgment, and References 2. Federated Learning: Collaborative and Privacy-Preserving Model Training This section delves into the field of FL, a method created to produce accurate models while protecting the privacy of people and institutions. As mentioned, it proposes a collaborative learning paradigm in which numerous devices take part in training a shared model under the direction of a central server. This section investigates the nuances of this collaboration procedure and its various architectural strategies. FL is a notion that respects privacy. Firstly the server distributes a model with randomly initialized parameters to the participating devices. Each device uses its local data to train the Collaborative Model Training: shared model, ensuring no raw data leaves the device. Instead, the server receives only the modified model parameters. The model averaging method is often used to reach a stopping point in this iterative procedure Wang and Preininger (2019); Rieke et al. (2020) (a visual scheme of the process can be seen in Figure 2). By aggregating the updated parameters from all devices, a global model is obtained, embodying the collective knowledge of the participants while preserving data privacy. 2.1. Architectural Categories FL encompasses three main architectural categories: horizontal, vertical, and transfer federated learning (FTL) Yang et al. (2019). These categories are determined based on the data distribution and the collaboration’s nature. In the horizontal architecture, data features are similar across devices, with variations primarily in data volume. On the other hand, the vertical architecture focuses on data with similar data correspondence (IDs) but differing feature spaces. Horizontal architecture, often called sample-based FL, is commonly employed in medical cases where different local devices share similar data structures. In contrast, vertical architecture, known as featurebased FL, is well-suited for aggregating user data from different servers. This architecture enables communication and collaboration between devices, requiring aggregating diverse features Aledhari et al. (2020). An example of horizontal architecture is the Google keyboard, where users from different regions update model parameters locally, which are then aggregated by the server. In contrast, the vertical architecture finds its relevance in scenarios where multiple institutions, such as banks, collaborate to generate personalized models by aggregating knowledge from different domains of the same person. FTL also leverages multiple data sources to train a model on the server. The goal is to identify commonalities across features, reducing the overall error. FTL supports both homogeneous (different samples) and heterogeneous (different features) training approaches, making it particularly valuable in healthcare applications Jing et al. (2019). Each FL architecture offers distinct advantages. The horizontal architecture ensures data independence, allowing devices to update model parameters without extensive communication. Vertical architecture enables collaboration between different servers, facilitating the aggregation of diverse features. FTL leverages different data sources to identify common patterns, ultimately improving model accuracy Aledhari et al. (2020); Yao et al. (2021). These architectural approaches find applications across various domains where privacy and data security are paramount. For example, in the healthcare domain, by leveraging FL, healthcare institutions can collaboratively train models while preserving patient privacy, facilitating personalized healthcare services, and advancing medical research. 2.2. Challenges Federated Learning encompasses various research areas Kairouz et al. (2021); Saeed et al. (2020), including optimization Li et al. (2020), communication efficiency Koneˇcn`y et al. (2016), personalization Wang et al. (2019), fault tolerance Bonawitz et al. (2019), privacy preservation Bonawitz et al. (2017), and computational efficiency Zhou et al. (2020). Likewise the case in many methodologies, FL also has pros and downsides. These challenges mainly arise due to device, data, and model heterogeneity (Figure 3). The use of different devices in data collection, such as urban sensors, smartphones, wearable devices, and autonomous vehicle sensors Jiang et al. (2020); Nguyen et al. (2021), introduces variations in sensing capabilities, memory, data processing, battery life, and communication capacity. Device heterogeneity: Consequently, challenges arise, including fault tolerance, communication efficiency, and computational efficiency, due to the diverse nature of IoT devices. Device heterogeneity leads to non-IID (non-identically and independently distributed) data, manifesting in different labels, feature distributions, and concept drift Xu et al. (2021). For example, when data is collected from multiple devices, each device may have only a subset of the activity classes in Human Activity Recognition (HAR) research. Moreover, different individuals may assign different labels to the same activity. Data heterogeneity: Furthermore, variations in feature distributions can occur due to individuals’ unique characteristics and behaviours. Addressing these challenges requires distributed optimization techniques and personalized approaches. While the standard model used in basic FL architecture facilitates aggregation, it may only be suitable for some scenarios. Model heterogeneity arises from the diverse needs of client devices. Model heterogeneity: For example, when constructing a model for predicting user activities using data from smartphones and smartwatches, adapting the model based on each device’s capacity may be necessary. Additionally, clients may have privacy concerns that discourage sharing certain model parts. This challenge underscores the need for further research on privacy preservation and client participation. 2.3. Federation Algorithms FL algorithms vary based on the model aggregation methods used in the literature. The initial algorithm proposed by Google, known as FedAvg McMahan et al. (2017), employs the average function for aggregation. Subsequently, several variations of averaging methods emerged, including weighted averaging Shlezinger et al. (2020), one model selection, best model averaging Yao et al. (2021), stochastic controlled average Karimireddy et al. (2020), and periodic averaging combined with quantization Reisizadeh et al. (2020). However, FedAvg has limitations when working with non-IID data and fails to capture fine-grained information specific to each device Wu et al. (2020). Other widely used algorithms include FedMA Wang et al. (2020), which constructs a global model layer-wise using matching and averaging, specifically designed for neural network architectures to address data heterogeneity. FedPer Sannara et al. (2021) tackles data heterogeneity caused by non-IID distributions by providing personalized models composed of base and personalized layers. The base layers are aggregated on the server, while clients focus on representation learning in the personalized layer using transfer learning. Moreover, the server must receive models from all participating devices to apply any aggregation method which causes latency problems. Slower clients are called stragglers, and the problem is called the straggler problem Zaharia et al. (2008). In other terms, we can define this problem as a synchronous and asynchronous learning problem. The literature proposes asynchronous Xie et al. (2019) and semi-synchronous Wu et al. (2020) methods to overcome the straggler problem. In addition to latency, when the server faces unbalanced and non-IID data, even these proposed methods converge slowly Duan et al. (2019). In Liu et al. (2021), the authors propose a partial aggregation strategy (FedPA) where the server updates its model using models from only a subset of clients determined by a reinforcement learning model. This parameter’s adaptive and dynamic calculation considers data distribution and device heterogeneity, preventing accuracy loss caused by selecting an inappropriate aggregation number. Similarly, in Gao et al. (2021), the authors propose the n-soft sync aggregation algorithm to address cloud idle time. This algorithm combines the benefits of synchronous and asynchronous aggregation by uploading only the models of n clients in each round, striking a balance between transmission overhead and training time. This approach ensures faster completion of training by corresponding clients. In Erg¨un et al. (2022), authors introduce FL with Secure Aggregation (FedSecAgg), an multi-party computation (MPC)-based aggregation protocol that ensures privacy and security in FL scenarios. It addresses the challenge of privacy leakage during the aggregation process by employing secure MPC techniques. This approach allows the aggregation to be performed while preserving the privacy of individual client updates. In He et al. (2020), Federated Meta-Learning with Model Agnostic MetaLearning (FedMeta-MAML) is proposed. FedMeta-MAML combines the principles of FL with model-agnostic meta-learning (MAML). Meta-learning enables rapid adaptation to new tasks and clients by learning a good initialization for client models. FedMeta-MAML aggregates client updates using a meta-update rule, allowing clients to learn how to learn from their local data. FL with Momentum SGD (FedMoSGD) extends federated learning by incorporating the momentum technique Xu et al. (2021), which is commonly used in stochastic gradient descent (SGD) optimization. The momentum technique accelerates convergence and improves the robustness of the optimization process by adding a fraction of the previous update direction to the current update direction. Another proposed method in literature is FedProx which adresses the challenges of heterogeneity Li et al. (2020). It is a generalization and reparametrization of the popular FedAvg method, which allows for local updating and low participation to tackle communication costs. The proposed framework introduces modifications that address both system and statistical heterogeneity, providing convergence guarantees in scenarios with non identically distributed data across devices. Empirically, FedProx demonstrates more robust convergence and improved accuracy compared to FedAvg on realistic federated datasets, especially in highly heterogeneous settings. The field of FL has witnessed advancements in aggregation methods to overcome challenges posed by non-IID data, latency, and privacy. These state-of-the-art algorithms provide valuable contributions toward enhancing FL processes’ efficiency, privacy, and convergence. 2.4. Metrics In order to evaluate and compare Federated Learning (FL) algorithms, various metrics are employed in the literature. These metrics provide insights into different aspects of FL performance and effectiveness. When assessing FL algorithms, it is crucial to consider metrics that capture model performance, communication cost, computational power, and fairness. In literature Semwal et al. (2020), accuracy and loss for a model performance measure, number of global rounds and the amount of transmitted data for communication cost, number of local rounds and convergence for computational power measure, fairness (similar performance over clients) are used as metrics. Model performance in FL is evaluated using accuracy and loss metrics, which measure the accuracy of predictions and the deviation between predicted and actual values. Higher accuracy and lower loss values indicate better model performance in terms of prediction quality and generalization. These metrics are also used to compare FL performance with traditional ML algorithms. Communication cost is crucial in FL systems, with metrics like global rounds and transmitted data assessing efficiency. Minimizing the number of rounds and reducing the transmitted data volume can optimize the overall communication cost in FL systems. Computational power measures evaluate algorithms’ efficiency in reaching stable solutions. The number of local rounds reflects the computational burden on individual client devices during training. Convergence, another important metric, evaluates the algorithm’s efficiency in reaching a stable and optimal solution. Faster convergence indicates a more computationally efficient approach. Fairness is an essential consideration in FL, aiming to ensure equitable performance and benefits for all participating clients. Fairness metrics assess the consistency of model performance across different clients, avoiding biases or imbalances during the learning process. This paper is under a CC 4.0 license. available on Arxiv

Collaborative and Privacy-Preserving Model Training: The Pros and Cons

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Python codes are available on my GitHub 34. What if a 3D Printed Steak was on the Menu? In this slogging thread, our random channel discussed the news of a new 3D printed steak that is good for the environment as well as it is delicious. 35. How To Setup a Python Virtual Environment on Windows 10 Creating a Python Virtual environment will allow you to work on an isolated copy of Python for specific projects without interfering or impacting the working of other ongoing projects. 36. About Carbon Capture, Usage, and Storage's Impact on The Climate Crisis With the climate crisis continuously getting worse, businesses and governments need to find solutions to reduce the amount of carbon going into the atmosphere. To beat the crisis, the world can't simply rely on renewable energy, governments will need to include carbon capture, usage and storage (CCUS) into the mix if they want to hit their climate targets. 37. 8 Unsolved Carbon Markets' Problems That Engineers Need To Get Behind And Solve I think about this conversation I had with Paul a lot. It kicked off what has now been over three years of imaginative, grueling, wonderful, painful, mind-blowing, and confounding work-- all of which took place alongside the minds of amazing co-founders, teammates, and advisors. 38. How Programming, AI, and Big Data is Giving Google A Chance to Save the World Big business and saving the planet often do not go hand in hand, however in some cases they do. Take a look at how Google plans on saving the future with tech. 39. Building a Global Environmental Datastack for Climate Action Blue Sky Analytics is a big data and AI start-up that uses geospatial data to monitor environmental parameters. Our goal is to become the Bloomberg of Environmental data for environmental monitoring, ESG (environment, social, and governance) due-diligence and climate risk assessment. 40. The Way to Building Integrated Data Platforms with IoT Edge Devices and AI Algorithms "At the Edge" There is growing demand in various industries, such as energy, agriculture, transportation, and also in scientific research fields, particularly environmental studies, for not only more intelligent and efficient data collection, but also for distributed and localised manipulation of the data being collected, in real-time. 41. Not Even the EPA Can Say What's Polluting the Air In 2016, Boeing’s Portland plant told the EPA it had released 1,954 pounds of hexavalent chromium into the air - a 1,000% increase from the year before. 42. Writing Proper Code Is Vital: Here's Why November of last year, OVO Energy sponsored a study finding that “If every Brit sent one less thank you email a day, we would save 16,433 tonnes of carbon a year — the same as 81,152 flights [from the UK] to Madrid.” 43. What Does It Mean When Google Says It’s Carbon Neutral? Consider some estimations by Google: 44. Are Fish Robots Going to Save the Oceans? In this slogging thread, our community discusses the new robo-fish that comes to help us save the oceans from microplastic waste. 45. Can Remote Work Lead to a Greener Environment? “Is remote work more environmentally friendly? 46. Hydrogen Fuel Cell Vehicles are Not a Solution to Climate Change How is it that fuel cell vehicles using hydrogen as a fuel are touted as climate-friendly vehicles? California even subsidizes the purchase of a hydrogen fuel cell vehicle. The advocates never reveal the source of the hydrogen would most likely be coming as a byproduct of fracking in pursuit of natural gas. 47. You Need to Know What's Going on In North Birmingham With the doors damaged, the toxic chemicals they were supposed to contain within the ovens leaked out at an accelerated rate. 48. WTF is Carbon Neutrality and How Companies Can Achieve It Despite so much power consumption, Google remains Carbon Neutral with their best in the class data centres and AI and ML. Google says their data centres consume the minimum power possible. 49. How AI Can Spot Wildfires Faster Than Humans Here's how artificial intelligence can be used to reduce fire detection time from an average of 40 minutes to less than five minutes! Thank you for checking out the 49 most read stories about Environment on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

49 Stories To Learn About Environment

Let's learn about via these 51 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Terraform /Learn Repo 1. 6 Free Terraform Tools to Facilitate a Multi-Cloud Infrastructure Have a multi-cloud infrastructure? These free Terraform tools will help you to reduce costs and make your multi-cloud efficient. 2. Developer Trends from Thoughtworks’ Technology Radar 27 Thoughtworks’ Technology Radar is a regular time to take a look at what trends are changing our industry. 3. Serverless API with Terraform: GO and AWS [Part 2] In part 2, we'll go over deploying serverless API to AWS Cloud with Terraform. 4. How a Unicorn Startup in Japan Leveraged the Power of Microservices Startup experience in Mercari. Road to microservices from monolith. 5. Getting Started With Creating, Managing an AWS ECS Cluster With Terraform In this tutorial, you'll define AWS resources using Terraform and how resource definitions translate to actual resources created on AWS 6. Basics of AWS Tags & Terraform with S3 - Part 1 In this video course, you'll learn the basics of AWS tags and Terraform with an S3 bucket. 7. How to Test Terraform the Right Way In this blog post, you'll learn about why you should be testing IaC and the best platforms to get started with. 8. Static Code Analysis  for Infrastructure as Code Using Azure DevOps Pipelines This article explains how to set up static code analysis for infrastructure as code using Azure DevOps pipelines. 9. Should Deploying Production Infrastructure be Hard? Below I'll explain why I built terra_boi to deploy Clientelify, why I think it was downloaded 1,000 times in the first 10 days (despite not promoting it anywhere), and why deploying an application to production shouldn't suck. 10. How To Manage Infrastructure With Terraform What is Terraform? 11. Getting Started Provisioning an AWS EKS Kubernetes Cluster with Terraform Learn how to provision an AWS EKS Kubernetes cluster with Terraform. See a full step-by-step tutorial. 12. How Infrastructure as Code Is Forcing a Revolution in Cost Management You might have read our beta launch blog post, which describes how Infrastructure as Code (IaC) is driving the third datacenter revolution (the first two being virtualization and the move to public clouds). In this post, I want to go deeper into looking at how IaC is driving a new need and approach to cost visibility and management. 13. Automating User Management in the Company Have you ever faced the challenge of automating users in your company? 14. Introducing Driftctl: Your IaC Security Belt We recently released the first versions of driftctl, a new open-source project for infrastructure developers, DevOps, SRE, and cloud practitioners, with the goal of helping manage all kinds of drifts. 15. Deploying a Terraform Remote State Backend with AWS S3 and DynamoDB In this article, we are going to be deep diving into remote state management in terraform. 16. Another Reason to Use Docker Recently I was working on a project which includes Terraform and AWS stuff. While working on that I was using my local machine for terraform code testing and luckily everything was going fine. But when we actually want to test it for the production environment we got some issues there. Then, as usual, we started to dig into the issue and finally, we got the issue which was quite a silly one 😜. The production server Terraform version and my local development server Terraform version was not the same. 17. Building Maintenance Mode for your API Gateway Using Terraform In my previous blog post, I went through how to create a maintenance mode page for your application, and how to implement it using Terraform and github pages. But the website is just one part of an Application, and often there’s also a public (or private) API that also needs to have a maintenance mode. Let’s see how we can do that using Terraform on API Gateway. 18. How to Deploy a Django Application on AWS with Terraform The final goal of this guide is to create a scalable and reproducible setup of the Django Web Application on AWS using Terraform. 19. How To Sync Terraform Config And .tfstate File For Existing AWS Resources Terraform is a great (and dominant) infrastructure automation tool. It is multi-cloud, can build all sorts of resources, and in some cases supports API calls to build resources before the native tooling from cloud providers does. 20. Managing Active Directory Objects with Azure AD Provider for Terraform Learn how to manage Active Directory Objects with Azure AD Provider for Terraform. See examples of how to authenticate and grant the correct permissions. 21. Why you should take care of Infrastructure Drift When talking about infrastructure drift, you often get knowing glances and heated answers. Recording gaps in your infra between what you expected to be and the reality of what is, is a well known and widespread issue bothering hundreds of DevOps teams around the globe. 22. Managing a GitHub Organization With Infrastructure as Code This post will share our experience in adopting infrastructure as code (IaC) to manage GitHub organization resources. 23. Using Jupyter/TensorBoard In Any Cloud With One Command Using Terraform Provider Iterative for bespoke Machine Learning & Data Science on AWS, Azure, GCP and K8s 24. The Top 5 DevOps Tools and Services to Consider as a Startup A collection of DevOps tools can significantly enhance your Software Development Life Cycle (SDLC) and increase engineering productivity. 25. Change the Name of your AWS S3 Bucket in Terraform Without Breaking Things Learn how to rename an AWS S3 bucket in Terraform. See renaming process of Amazon s3 bucket in a few simple steps. 26. Exporting Your GKE Cluster to Terraform Cloud: A Guide with Challenges and Solutions In this article, I'll share our journey at ANNA Money from using our Kubernetes installation in GCP to the managed Kubernetes service by Google and GKE. 27. Hashicorp's Terraform: A Introduction Join us in the journey of learning Terraform. This blog series aims to provides a guidance for essentials to be known while working with Terraform. 28. A Handy Guide to Migrating Terraform State to GitLab CI/CD Migrating Terraform state to GitLab CI/CD is a GitOps best practice that allows multiple engineers to work together to develop infrastructure. 29. How Namespace and Terraform Can Manage Kubernetes Environments In the last several years, Kubernetes has become the “go to” standard for managing and orchestrating containerized workloads. Thanks to it’s vendor agnostic nature, you can easily run Kubernetes almost anywhere, and in fact, all the major cloud vendors offer a managed Kubernetes service (AWS EKS, Google GKE, and Azure AKS). 30. Building a “Maintenance Mode” with Terraform and Github Pages As you may have seen, we just launched our public beta of env0 last month. As part of the run-up to the launch, our dev team had to go through and make sure everything about our infrastructure was ready for ongoing public use: one element being creating a maintenance mode for both our Application and our public API. 31. How To Migrate An Existing Infrastructure into Terraform Terraform is a powerful tool to have in your toolset. 32. How To Setup Continuous Integration Pipeline By Using Terraform And GitLab CI Terraform is a fantastic tool for managing your cloud infrastructure, especially if your assets are hosted on multiple cloud providers. 33. Learn How to Build a CI/CD Pipeline Using GitLab for your Website We'll run through how to build and configure a CI/CD pipeline to deploy your business's website using AWS, Terraform & Gitlab. 34. How to Optimize for Iteration: Choosing Your Early Startup's Tech Stack As we’ve discussed in a previous blog post, product development is hypothesis testing. This is especially true in the early stages of a company when you need to confirm or reject your hypothesis as quickly as possible. This process is then repeated until you (hopefully) reach product-market fit. To get there, your team needs to be able to work and build at a pace that allows for this constant and rapid iteration. 35. What Really Propelled The Cloud Revolution: A Developer's Perspective An overview of the main trends that have pushed the Cloud industry — and the world — forward, in the past decade and a half. 36. Sanity-Checks with Playwright in Fargate container on schedule There is often the need to check whether your production system is working as expected. Playwright is a great way to test if everything is going to plan. 37. From “Feature branches” to “Feature environments” with Terraform “The advantage of feature branching is that each developer can work on their own feature and be isolated from changes going on elsewhere.” (FeatureBranch) 38. Serverless API with Terraform, GO and AWS, Part 1 Knowing how to build REST API with latest tech is cool. You know what's even cooler? It's being able to deploy it to the cloud! I'll walk you through the process of building simple, server-less application using GO, AWS (Lambda, API Gateway, Dynamodb) and Terraform. 39. Improve Your IaC With a Specialized Management Platform Most often, there is no point in implementing an in-house solution as the cost and effort of building and maintaining it may exceed its potential benefits. 40. Kubernetes Terraforming on Linode: Linode + Terraform + Kubernetes = Cloud Nirvana Let's discuss the process of "Terraforming" a Kubernetes cluster on Linode using Terraform to help organizations efficiently manage and scale  infrastructure. 41. 10+ Best Resources to learn DevOps in Depth [Online Courses] DevOps is one of the most in-demand skills from employer and there are many job opportunities lying for full stack developers, distinguished engineers and DevOps professionals. If you are an experienced Java programmer or a full-stack web developer, and want to become a DevOps engineer then you have come to the right place. 42. Ad Hoc Environments for Django Applications With ECS, Terraform and GitHub Actions This article will show how software development teams can build on-demand environments for dog-food testing, quality review, internal and external demos and oth 43. Create Your New Reality With Wirehole And Other Awesome Automation Tools **UPDATE October 13, 2020 - The mjtechguy/terransible-wirehole Github repo now supports deploying to Digital Ocean and Hetzner Cloud as well! 44. GraphQL Is Not a Silver Bullet GraphQL is not a configuration language, also not a transformation language, a replacement for SQL, and definitely not Terraform. 45. Building an AWS EKS Fargate Cluster with Terraform How to build an AWS EKS Fargate cluster using terraform. 46. Terraform Configuration Syntax Overview All you need to know to get started about Terraform Syntax. 47. Is Terraform Still Useful? Terraform is an agentless infrastructure tool used mainly by DevOps for infrastructure management. Find out why you should use Terraform for infrastructure. 48. 6 DevOps Trends in 2022 That DevOps Engineers Should Adopt The role of DevOps engineers, their responsibilities, growth opportunities, a set of important soft & hard skills, and most importantly, DevOps trends in 2022. 49. Deploying an Open Source Secure Video Conference Server [A How To Guide] In this article, you'll create a new DigitalOcean account using a free credit link. Then, you will clone a GitHub repository, and use Terraform code to initialize, plan and apply resources to your account, using a preconfigured image from DigitalOcean and your custom domain name. Finally, you will access your account remotely via SSH and execute two scripts to configure both your video conference server and Let's Encrypt SSL to enable HTTPS. 50. Newsflash - "People with AWS Console Access Tend to Change Things" This blog post is a written transcript of the FOSDEM Talk: “Infrastructure drifts aren’t like Pokemon, you can’t catch ’em all”, by Stephane Jourdan – CTO and founder 51. Introduction to AWS Log Insights as CloudWatch Metrics A step-by-step description of how to create an AWS Lambda to convert Cloudwatch LogInsights into metrics Thank you for checking out the 51 most read stories about Terraform on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

51 Stories To Learn About Terraform

Let's learn about via these 46 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Infrastructure As Code /Learn Repo 1. How To Configure CloudFront Using CloudFormation Template I recently worked on implementing CloudFront for s3 bucket files. Most of the tutorials were doing that using console management(UI) but we at Blue Sky Analytics prefer “code as infrastructure”. So I had to do this using CloudFormation. With the help of a few StackOverflow links and tutorials, I was able to write CloudFormation Template. 2. How to Test Terraform the Right Way In this blog post, you'll learn about why you should be testing IaC and the best platforms to get started with. 3. The Relevance of Infrastructure-as-Code Concepts in Security So what is Infrastructure-as-Code (IaC) anyway? IaC is a process that automates the provisioning and management of cloud resources. 4. Change the Name of your AWS S3 Bucket in Terraform Without Breaking Things Learn how to rename an AWS S3 bucket in Terraform. See renaming process of Amazon s3 bucket in a few simple steps. 5. Using a Product-Led Growth Mindset to Improve User Experience How changing the development mindset to a product-led-growth one can improve the user experience by focusing the thought process for the team's members. 6. What Really Propelled The Cloud Revolution: A Developer's Perspective An overview of the main trends that have pushed the Cloud industry — and the world — forward, in the past decade and a half. 7. Static Code Analysis  for Infrastructure as Code Using Azure DevOps Pipelines This article explains how to set up static code analysis for infrastructure as code using Azure DevOps pipelines. 8. 6 Free Terraform Tools to Facilitate a Multi-Cloud Infrastructure Have a multi-cloud infrastructure? These free Terraform tools will help you to reduce costs and make your multi-cloud efficient. 9. How to Create an Azure API Management Instance using Bicep Lang via Azure DevOps The more I use Bicep, the more I love it. This is what ARM Templates should have been. 10. Deploy Docker Using Ansible 11. Smart E-bike Kits: A Tour Inside Our Servers The Idea 12. Learn About Infrastructure as Code in 5 Minutes and Why You Should Use It Infrastructure as Code (IaC) is the process of managing and configuring an infrastructure using configuration files, rather than manually editing configurations 13. Improve Your IaC With a Specialized Management Platform Most often, there is no point in implementing an in-house solution as the cost and effort of building and maintaining it may exceed its potential benefits. 14. Managing a GitHub Organization With Infrastructure as Code This post will share our experience in adopting infrastructure as code (IaC) to manage GitHub organization resources. 15. Why Learn Any Other Programming Language When You Can Learn Typescript? Use TypeScript in all your projects and all use cases without any limitation. You can now do frontend, backend, infrastructure and machine learning in one progr 16. Code as Documentation and Assessment Documenting IT networking lab procedures in code rather than word-processor documents enables more authentic, equitable and consistent assessment. 17. AWS CloudFormation Template Basics Get started creating IaC using CloudFormation templates. 18. Why Understanding Infrastructure-as-code is Critical to Scaling Your Technology Infrastructure Infrastructure as code (IaC) is one of the most important trends in modern IT development. Read about the benefits of IaC and how to implement it. 19. How Infrastructure as Code Is Forcing a Revolution in Cost Management You might have read our beta launch blog post, which describes how Infrastructure as Code (IaC) is driving the third datacenter revolution (the first two being virtualization and the move to public clouds). In this post, I want to go deeper into looking at how IaC is driving a new need and approach to cost visibility and management. 20. How To Manage Infrastructure With Terraform What is Terraform? 21. Codify your SaaS Apps: The Answer to the Unmanaged SaaS Jungle A phenomenon we have encountered often, when helping companies overcome drift, is a common neglect of the entire SaaS toolchain. Learn why this is troubling. 22. Three Ways to Scale Your Apps with Render When your app is experiencing high traffic or utilization, you need to scale your service to handle that load. We’ll explore three ways to scale your apps. 23. Building Your Infrastructure With Just a Diagram This hands-on tutorial will teach you how to create infrastructure via the AWS Application Composer Console. 24. Check Out the Principles and Best Practices of Infrastructure as Code for 2023 Infrastructure-as-code is a very important concept to understand in the DevOps world today. 25. How To Migrate An Existing Infrastructure into Terraform Terraform is a powerful tool to have in your toolset. 26. Hashicorp's Terraform: A Introduction Join us in the journey of learning Terraform. This blog series aims to provides a guidance for essentials to be known while working with Terraform. 27. 2023 Will Be the Year of Kubernetes (and Other Predictions in the Cloud and Infrastructure Industry) Dive into the new year’s top cloud and infra-tech trends, straight from a cloud and infrastructure technology fanatic. 28. Introducing Driftctl: Your IaC Security Belt We recently released the first versions of driftctl, a new open-source project for infrastructure developers, DevOps, SRE, and cloud practitioners, with the goal of helping manage all kinds of drifts. 29. Cloud Application Infrastructure from Code (IfC) Presents a relatively new concept of Infrastructure from Code (IfC) as the next logical step in cloud automation 30. Project-as-Code: The Catalyst DevOps Needs If you are familiar with Docker, Terraform, and the CI platforms (eg: Jenkins, CircleCI, Codeship), you already know the power of Declarative DevOps. It can make development easier by being repeatable, predictable, and fast. Supporting technologies both hide complexities and offer important reuse by supporting simple structured syntax in an easy to create and read file. Each technology has codified much of its domain allowing developers to author and instrument with nothing more than a text editor. 31. I Got a New EKS Cluster Running And Terraform Code in 2 minutes AWS EKS + Terraform + Cloudskiff do the job 32. B2B Fintech Trends for 2021: Storage, Security, Cloud Migration, IaC As CTO at Altar.io, a large part of my work revolves around observing the forward-looking industry trends (including fintech trends) that affect how we create innovative products. 33. Why you should take care of Infrastructure Drift When talking about infrastructure drift, you often get knowing glances and heated answers. Recording gaps in your infra between what you expected to be and the reality of what is, is a well known and widespread issue bothering hundreds of DevOps teams around the globe. 34. The State of Cloud Automation and the Promise of Infrastructure Automation The Cloud has become a familiar feature of the tech stacks at most organizations and is becoming much more than a place for server resources. At present, almost everything in our digital landscape is connected to a Cloud in one way or another. 35. From “Feature branches” to “Feature environments” with Terraform “The advantage of feature branching is that each developer can work on their own feature and be isolated from changes going on elsewhere.” (FeatureBranch) 36. How To Setup Continuous Integration Pipeline By Using Terraform And GitLab CI Terraform is a fantastic tool for managing your cloud infrastructure, especially if your assets are hosted on multiple cloud providers. 37. Creating and Provisioning Azure Container Apps with Bicep Using Bicep, we can deploy Azure Container Apps quickly and easily! 38. Building a “Maintenance Mode” with Terraform and Github Pages As you may have seen, we just launched our public beta of env0 last month. As part of the run-up to the launch, our dev team had to go through and make sure everything about our infrastructure was ready for ongoing public use: one element being creating a maintenance mode for both our Application and our public API. 39. Application Deployment: Recipe for Great Serverless Strategy This is true for the most part, but Serverless apps also have a certain property that can make their deployment and maintenance time consuming. 40. Exporting Your GKE Cluster to Terraform Cloud: A Guide with Challenges and Solutions In this article, I'll share our journey at ANNA Money from using our Kubernetes installation in GCP to the managed Kubernetes service by Google and GKE. 41. What is Everything-as-Code? Examining  the Explosion of "as Code" Buzzwords If you’re confused when you read about “[some software term] as code” or “everything as code,” all you really need to know is that we’re talking about automation: The thing we use to do tedious tasks for us, or orchestrate tasks when they become too large and complex for manual methods. 42. Creating a Instance Scheduler using AWS CDK The AWS CDK is a software development framework to define cloud infrastructure as code and provision it through CloudFormation. The CDK integrates fully with AWS services and allows developers to use high-level construct to define cloud infrastructure in code. 43. Cloud-Native Yay or Nay: Reasons to Use Cloud-Native Technologies Cloud-native has been the talk of the town for quite some time now. Some developers think it’s just hyped way too much and will soon diminish from the limelight. Others think cloud-native will revolutionize software development and is here to stay! 44. Newsflash - "People with AWS Console Access Tend to Change Things" This blog post is a written transcript of the FOSDEM Talk: “Infrastructure drifts aren’t like Pokemon, you can’t catch ’em all”, by Stephane Jourdan – CTO and founder 45. How Do You Manage Helm Charts? Whether you love it or hate it, Helm is a ubiquitous tool for managing Kubernetes applications. You can use it in many different ways which is great, but can also be overwhelming. 46. Terraform Configuration Syntax Overview All you need to know to get started about Terraform Syntax. Thank you for checking out the 46 most read stories about Infrastructure As Code on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

46 Stories To Learn About Infrastructure As Code

274 Stories To Learn About Code Quality

Photo by on Flickr Alex Gorzen More Limes, More Coconuts In previous blog posts, I reviewed how to , which gives you a nice stable deployment of , the downside is this costs you money for every hour it is run on AWS. deploy KubeCF on EKS KubeCF I’m used to giving Amazon money, but I typically get a small cardboard box in exchange every few days. So, how do you run on your Mac for free(ish)? Tune in below. KubeCF There Be Dragons Ahead You need at least 16GB of memory installed on your Apple MacOS device, the install will use around 11GB of the memory once it is fully spun up. The installation is fragile and frustrating at times, this is geared more towards operators who are trying out skunkworks on the platform such as testing custom buildpacks, hacking db queries, and other potentially destructive activities. The install does NOT survive reboots and becomes extra brittle after 24+ hours of running. This is not KubeCF’s fault, when run on EKS it will happily continue to run without issues. You’ve been warned! Overview of Install Install Homebrew and Docker Desktop Install and start the shim tuntap Install and deploy a cluster kind Install and configure metallb Install and deploy cf-operator kubecf Log in and marvel at your creation The next few sections are borrowed heavily from , I encourage you to skim this document to understand why the shim is needed and how to verify the configuration for . https://www.thehumblelab.com/kind-and-metallb-on-mac/ tuntap metallb Install Homebrew and Docker Desktop I won’t go into great detail as these tools are likely already installed: Homebrew (full details are here: ) or just run: https://docs.brew.sh/Installation /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" Docker Desktop (full details for installation are at: ) or run: https://docs.docker.com/desktop/mac/install/ brew install --cask docker
   # Then launch docker from Applications to complete the install and start docker Install and start the shim tuntap Running on MacOS has some “deficiencies” that can be overcome by installing a networking shim, to perform this install: docker brew install git
brew install --cask tuntap
git clone https://github.com/AlmirKadric-Published/docker-tuntap-osx.git
cd docker-tuntap-osx

./sbin/docker_tap_install.sh
./sbin/docker_tap_up.sh Install and deploy a cluster kind Sweet’n’Simple: brew install kind
kind create cluster Install and configure metallb We’ll be using as a LoadBalancer resource and open up a local route so that MacOS can route traffic locally to the cluster. metallb sudo route -v add -net 172.18.0.1 -netmask 255.255.0.0 10.0.75.2

kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.9.3/manifests/namespace.yaml
kubectl create secret generic -n metallb-system memberlist --from-literal=secretkey="$(openssl rand -base64 128)"
kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.9.3/manifests/metallb.yaml

cat << EOF > metallb-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  namespace: metallb-system
  name: config
data:
  config: |
    address-pools:
    - name: default
      protocol: layer2
      addresses:
      - 172.18.0.150-172.18.0.200
EOF
kubectl create -f metallb-config.yaml Install and deploy cf-operator kubecf brew install wget
brew install helm
brew install watch

wget https://github.com/cloudfoundry-incubator/kubecf/releases/download/v2.7.13/kubecf-bundle-v2.7.13.tgz
tar -xvzf kubecf-bundle-v2.7.13.tgz

kubectl create namespace cf-operator
helm install cf-operator \
  --namespace cf-operator \
  --set "global.singleNamespace.name=kubecf" \
  cf-operator.tgz \
 --wait

helm install kubecf \
  --namespace kubecf \
  --set system_domain=172.18.0.150.nip.io \
  --set features.eirini.enabled=false \
  --set features.ingress.enabled=false \
  --set services.router.externalIPs={172.18.0.150}\
  https://github.com/cloudfoundry-incubator/kubecf/releases/download/v2.7.13/kubecf-v2.7.13.tgz

watch kubectl get pods -A Now, go take a walk, it will take 30-60 minutes for the helm chart to be fully picked up by the CRDs and are scheduled for running. When complete, you should see output similar to: kubecf cf-operator Every 2.0s: kubectl get pods -A

NAMESPACE            NAME                                         READY   STATUS      RESTARTS   AGE
cf-operator          quarks-cd9d4b96f-rtkbt                       1/1     Running     0          40m
cf-operator          quarks-job-6d8d744bc6-pmfnd                  1/1     Running     0          40m
cf-operator          quarks-secret-7d76f854dc-9wp2f               1/1     Running     0          40m
cf-operator          quarks-statefulset-f6dc85fb8-x6jfb           1/1     Running     0          40m
kube-system          coredns-558bd4d5db-ncmh5                     1/1     Running     0          41m
kube-system          coredns-558bd4d5db-zlpgg                     1/1     Running     0          41m
kube-system          etcd-kind-control-plane                      1/1     Running     0          41m
kube-system          kindnet-w4m9n                                1/1     Running     0          41m
kube-system          kube-apiserver-kind-control-plane            1/1     Running     0          41m
kube-system          kube-controller-manager-kind-control-plane   1/1     Running     0          41m
kube-system          kube-proxy-ln6hb                             1/1     Running     0          41m
kube-system          kube-scheduler-kind-control-plane            1/1     Running     0          41m
kubecf               api-0                                        17/17   Running     1          19m
kubecf               auctioneer-0                                 6/6     Running     2          20m
kubecf               cc-worker-0                                  6/6     Running     0          20m
kubecf               cf-apps-dns-76947f98b5-tbfql                 1/1     Running     0          39m
kubecf               coredns-quarks-7cf8f9f58d-msq9m              1/1     Running     0          38m
kubecf               coredns-quarks-7cf8f9f58d-pbkt9              1/1     Running     0          38m
kubecf               credhub-0                                    8/8     Running     0          20m
kubecf               database-0                                   2/2     Running     0          38m
kubecf               database-seeder-0bc49e7bcb1f9453-vnvjm       0/2     Completed   0          38m
kubecf               diego-api-0                                  9/9     Running     2          20m
kubecf               diego-cell-0                                 12/12   Running     2          20m
kubecf               doppler-0                                    6/6     Running     0          20m
kubecf               log-api-0                                    9/9     Running     0          20m
kubecf               log-cache-0                                  10/10   Running     0          19m
kubecf               nats-0                                       7/7     Running     0          20m
kubecf               router-0                                     7/7     Running     0          20m
kubecf               routing-api-0                                6/6     Running     1          20m
kubecf               scheduler-0                                  12/12   Running     2          19m
kubecf               singleton-blobstore-0                        8/8     Running     0          20m
kubecf               tcp-router-0                                 7/7     Running     0          20m
kubecf               uaa-0                                        9/9     Running     0          20m
local-path-storage   local-path-provisioner-547f784dff-r8trf      1/1     Running     1          41m
metallb-system       controller-fb659dc8-dhpnb                    1/1     Running     0          41m
metallb-system       speaker-h9lh9                                1/1     Running     0          41m Log in and marvel at your creation To login with the uaa user account: admin cf api --skip-ssl-validation "https://api.172.18.0.150.nip.io"

acp=$(kubectl get secret \
        --namespace kubecf var-cf-admin-password \
        -o jsonpath='{.data.password}' \
        | base64 --decode)

cf auth admin "${acp}"
cf create-space test -o system
cf target -o system -s test Or, to use uaa client account (because you’re a rebel or something): smoke_tests cf api --skip-ssl-validation "https://api.172.18.0.150.nip.io"
myclient=$(kubectl get secret \
        --namespace kubecf var-uaa-clients-cf-smoke-tests-secret \
        -o jsonpath='{.data.password}' \
        | base64 --decode)

cf auth cf_smoke_tests "${myclient}" --client-credentials
cf create-space test -o system
cf target -o system -s test Cleaning Up and Putting Your Toys Away If you are done with the deployment of KubeCF you have two options: Put your creation to sleep. Start Docker Desktop > Dashboard > Select and click “Stop”. Go have fun, when you come back, click “Start”. After a few minutes, the pods will recreate and become healthy kind-control-place Clean up. To remove the cluster and custom routing: kind delete cluster
   sudo route delete 172.18.0.0
   ./sbin/docker_tap_uninstall.sh Debugging Issues Get used to seeing: Request error: Get “https://api.172.18.0.150.nip.io”: dial tcp 172.18.0.150:443: i/o timeout The api server is flaky, try whatever you were doing again after verifying all pods are running as seen in the section. Install cf-operator and deploy kubecf Follow up Have questions? There is an excellent community for KubeCF which can be found at as in Slack. You can ping me there via . https://cloudfoundry.slack.com/archives/CQ2U3L6DC kubecf-dev @cweibel I also have Terraform code which will spin up a VPC + EKS + KubeCF for a more permanent solution to running not on a Mac, check out for more details. KubeCF https://github.com/cweibel/example-terraform-eks/tree/main/eks_for_kubecf_v2 Enjoy! Also published here

How to Stop Giving Away Money to AWS by Running KubeCF with Kind on MacOS

Hi! I’m Reart and I mentioned Blockchain Gaming & NFTs in my previous article. But today I am going to describe NFTs Marketplaces which exist on the blockchain. What are NFT Marketplaces? NFT Marketplaces are platforms designed to facilitate the trade of non-fungible tokens (NFTs). What does that mean? Well, an asset like property, shares in a company, or anything else is non-fungible if it's unique. That means you can't create two of them with the same value, or one with a different value. For example, I can't create two cars worth $50 each by merging my car into it - one would be worth $100 and the other would be worth $100. On the other hand, if I own a painting of my dog, each one I create is worth $50 - they can be of the same value. This means that non-fungible assets can be traded on NFT marketplaces. For example, if you want to trade your painting of your dog, you'd list it for sale on an NFT marketplace. When someone does buy it, it cannot be traded again - the token representing the painting cannot be sold twice. In this way, their value is guaranteed against copying and duplication. This is in contrast to global marketplaces like Amazon or eBay, where multiple copies can be sold. NFTs On Different Blockchains NFT's are generally built or minted on the Ethereum blockchain. But since a few months ago, NFTs have been being minted on different blockchains. Like you can mint NFTs on Binance Smart Chain, Polygon, JumpNet (Enjin) & many more to come. Sometimes the NFTs are also called assets and sometimes Sub-assets. Sub-assets are created by merging (or 'transmuting') an existing NFT into a new one. For example, if you wanted to create a new piece of artwork with the same characteristics as your old painting, you'd merge 'the origin' of your old painting into the new one, creating a new painting token which represents the second one (which can't be traded again!). The terminology around this generation varies - for example some use the word "mint" to refer to creating something out of nothing - but all are referring to the same principles. Since NFT tokens are unique, they can be used as digital assets in the same way as physical artifacts. For example, a video game could have a token for a character that exists within the game and can be bought with real money to use within it. They're then stored on the blockchain just like any other asset. Here are some marketplaces lyou can check out if you are a big fan of NFTs & Collectibles: Featured.Market By Binance OpenSea.io Dappradar BakerySwap Mintable Ephimera Rarible & Many more you can find on the Internet. So which NFT Marketplace should I choose? When it comes to NFT marketplaces, there's no one-size-fits-all. Perhaps there's a platform with a novel approach that you appreciate or you're working with an underrepresented medium with a thriving community on a specific marketplace. If you only plan on minting a few highly specialized pieces, it might benefit you to work with a smaller curated boutique agency.  If you're planning to do high volume NFT drops, you might want to consider a large platform with lazy minting or Polygon (MATIC) support like OpenSea to save on gas. And no, you don't have to pick just one marketplace. Mix and match, experiment. Again—these are new waters, so explore freely. 😊 DISCLAIMER: Nothing mentioned above constitutes professional investment advice. Please do your own research before you invest your hard earned money. This article is part of The hosted by HackerNoon in partnership with Gaming Metaverse Writing Contest . The Sandbox Submit your today for your chance to win up to $2000. #gaming-metaverse story

A Brief Intro to NFT Marketplaces

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