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deployments come in all shapes and sizes. We support bare metal installs on any version of , containerized installs on any version of (including Red Hat OpenShift), and installs just about anywhere you can deploy a small lightweight single binary. But with flexibility comes the inevitability that edge case issues will require debugging. MinIO Linux Kubernetes In this blog post, we’ll show you how to debug a MinIO install running in Kubernetes and also some of the common issues you might encounter when doing bare metal installation and how to rectify them. Kubernetes Debugger Pod There are a few ways to access the MinIO API running inside a Kubernetes cluster. We can use or set up a listening on to be able to access the API. Both of these methods offer a way to access the service from outside the network, but they do come with one major downside: You can only access the Service that the NodePort or Port Forwarding references on an available port (not the usual configuration for the application). For example, you have to access the MinIO API, usually found on port , via a randomly assigned port. kubectl port-forwarding Service NodePort 9000 3xxxx What if I told you there was a better way – and it's not novel? When debugging applications you want to have full access to the native run-time environment so you can use various tools to troubleshoot and debug the cluster. One way to do that is launching a “busybox” style pod and installing all the required tools needed to debug the application. First launch a into the same namespace as your MinIO install. In order to do this create a yaml file called with the following yaml. Pod debugger-pod.yaml apiVersion: v1

kind: Pod

metadata:

  name: mc

  labels:

    app: mc

spec:

  containers:

  - image: minio/mc:latest

    command:

      - "sleep"

      - "604800"

    imagePullPolicy: IfNotPresent

    name: mc

  restartPolicy: Always The above Pod configuration is pulling the image for MinIO utility. In order to ensure the pod doesn’t just launch and then exit, we’ve added a command. mc sleep Once the yaml is saved apply the configuration to the Kubernetes namespace where the MinIO cluster is running kubectl apply -f debugger-pod.yaml Once the pod is up and running access it via shell $ kubectl exec -i -t -n default mc -c mc -- sh -c "(bash || ash || sh)"

[root@mc /]# Then with you can access the MinIO cluster mc [root@mc /]# mc alias set myminio    --insecure

Added `myminio` successfully. Now that we have a debugger pod up and running, you can perform action on the cluster directly within the same network. For example, if the replication was broken due to an site going offline or a hardware failure, you can resync any pending objects to be replicated using the following command [root@mc /]# mc admin replicate resync start minio1 minio2


[root@mc /]# mc admin replicate resync status minio1 minio2


✔ ✔ ✔

ResyncID: 		 2248d1d1-633f-4d61-b938-d8ea0b9b2d31

Status:   		 Completed

Objects:  		 2225

Versions: 		 2225

FailedObjects:     0

Throughput:   	 5.3 MiB/s

IOPs:     		 124.23 objs/s

Transferred:  	 94 MiB

Elapsed:  		 17.909833202s

CurrObjName:  	 testbucket/web-app/tsconfig.json Another reason you would run the debugger pod is if there are some file system permissions or invalid groups configurations in your pod, you can update them using the debugger pod [root@mc /]# chgrp -R 1000780050 .minio.sys/ The above debugging method can also be used in bare metal environments. For instance you can launch a busybox or bastion node with installed and follow the same instructions as above. mc Debugging Bare Metal Bare metal Linux installs are the most straightforward. In fact it just takes a few commands to get MinIO installed and running with SystemD. For details, please see . Configuring MinIO with SystemD Once in a great while, bare metal installs go awry. Here are some of the (not-so-common) pitfalls that we are asked about  in or . These pitfalls are not hardware or operating specific but can be useful to know in any kind of environment. SUBNET Slack File Permission One of the most common pitfalls is the file permissions of the MinIO binary and the configuration file. If this occurs, when you start MinIO using SystemD you will see and here is the full stack trace Assertion failed for MinIO. # systemctl status minio.service

● minio.service - MinIO

     Loaded: loaded (/etc/systemd/system/minio.service; enabled; vendor preset: enabled)

     Active: inactive (dead)

     Assert: start assertion failed at Tue 2023-12-26 18:21:38 PST; 8s ago

             AssertFileIsExecutable=/usr/local/bin/minio was not met

       Docs: https://docs.min.io


Dec 26 18:13:37 minio1 systemd[1]: minio.service: Starting requested but asserts failed.

Dec 26 18:17:50 minio1 systemd[1]: Assertion failed for MinIO.

Dec 26 18:21:38 minio1 systemd[1]: minio.service: Starting requested but asserts failed.

Dec 26 18:21:38 minio1 systemd[1]: Assertion failed for MinIO. This could be caused by a number of reasons, let's go down the list and check for each of them. : The binary, in this example located at needs to have permission for user and group, respectively. MinIO Binary /usr/local/bin/minio root:root # ll /usr/local/bin/minio

total 93804

-rwxr-xr-x 1 root    root    96018432 Nov 15 16:35  minio* : The MinIO service needs to run under a unique Linux user and group for security purposes, never run as a user. By default we use for the user and group names. In the SystemD service config file you should see something like this MinIO Service User and Group root minio-user User=minio-user

Group=minio-user : The directory where MinIO data will be stored needs to be owned by or whichever user you decide to run the MinIO service as above. MinIO Data Dir minio-user:minio-user # ls -l /mnt

total 4

drwxrwxr-x 2 minio-user minio-user 4096 Dec 27 09:58 data : Both the config files should have permissions for user and group like so SystemD and MinIO config root:root # ls -l /etc/default/minio

-rw-r--r-- 1 root root 1330 Dec 27 09:52 /etc/default/minio


# ls -l /etc/systemd/system/minio.service

-rw-r--r-- 1 root root 941 Dec 26 17:13 /etc/systemd/system/minio.service : The entire install process should be run as . You can also try if your user has permissions but the recommendation is to run as root as the install needs to place files in a bunch of places that only the user can access. Your bash prompt should have a and not a like so Run as Root root sudo root # $ vs # $ If none of the above work, the best approach is to remove the app, directories and configs and start a fresh install as a root user. Port conflict Another common issue related to deleted files which still hold on to the process, which causes port conflicts. Even when a service is not running, you may be unable to start a new service on the existing port or the service that is running will misbehave (such as not allowing you to login). # lsof -n | grep (deleted)

COMMAND PID USER FD TYPE DEVICE   SIZE 	NODE NAME

nginx   	13423 root 5u  	REG 253,3   42949672960 	17 (deleted)

minio 	13423 minio 6u    REG 253,3 	0         	      18 (deleted)

minio       13423 minio 7u    REG 253,3 	0         	      19 (deleted) You might see errors such as those below on a MinIO install Login Failed net::ERR_FAILED 500 Internal Server Error 401 Unauthorized The screenshot above shows an internal server error and an unauthorized error. While looking at the surface it looks unclear what has caused this error, we can debug with a little linux knowledge what to look for that could cause this, let's take a gander. There are several ways to debug this issue, first lets check to see if multiple MinIO processes are running on the same node # ps aux | grep -i minio

minio-u+    5048  0.3  1.7 1594008 144384 ?      Ssl  11:03   0:01 /usr/local/bin/minio server --console-address :9001 /mnt/data/disk1/minio


minio-u+    9276  0.3  1.7 1594208 144301 ?      Ssl  11:25   0:01 /usr/local/bin/minio server --console-address :9001 /mnt/data/disk1/minio As we can see above there are 2 MinIO processes running. Start by killing the process that is older or has been running the longest, in this case it seems to be the process ID . 5048 kill -9 5048 Sometimes even after killing the process the service might still not start or might still get hung up because it has reserved a process number but not let it go. This can be caused by files that have been deleted but are still being tracked by the operating system. You can find the deleted files via LSOF lsof -n | grep '(deleted)' Last but not least, if there are no deleted files left over or hung processes and if everything looks absolutely clean, the last resort is to quickly restart the node. This is a no-nonsense method that  shuts down and clears any pending files and processes so you start a fresh install. SUBNET to the rescue Although rare, installation edge cases will always exist. MinIO customers know that they have nothing to worry about because they can quickly message our engineers – who have written the code – via the portal. We've seen almost everything under the sun, so while the issue might look cryptic or mind boggling at first glance, we'll put our years of expertise debugging installations in many varied environments to work and help you in a jiffy. SUBNET If you have any questions on troubleshooting and debugging installs be sure to reach out to us on ! MinIO Slack

Pro Tips for Debugging MinIO: Overcoming Common Issues in Kubernetes and Bare Metal Environments

MinIO ডিবাগ করার জন্য প্রো টিপস: কুবারনেটস এবং বেয়ার মেটাল পরিবেশে সাধারণ সমস্যাগুলি অতিক্রম করা

Profi-Tipps zum Debuggen von MinIO: Überwindung häufiger Probleme in Kubernetes- und Bare-Metal-Umgebungen

Consejos profesionales para depurar MinIO: superar problemas comunes en Kubernetes y entornos bare metal

Conseils de pro pour déboguer MinIO : surmonter les problèmes courants dans les environnements Kubernetes et Bare Metal

मिनियो डिबगिंग के लिए प्रो टिप्स: कुबेरनेट्स और बेयर मेटल वातावरण में आम समस्याओं पर काबू पाना

MinIO デバッグのプロのヒント: Kubernetes とベアメタル環境での一般的な問題の克服

MinIO 디버깅을 위한 전문가 팁: Kubernetes 및 베어메탈 환경의 일반적인 문제 극복

Dicas profissionais para depuração do MinIO: superando problemas comuns em ambientes Kubernetes e Bare Metal

Советы для профессионалов по отладке MinIO: решение распространенных проблем в Kubernetes и средах с голым железом

MinIO'da Hata Ayıklamaya Yönelik Profesyonel İpuçları: Kubernetes ve Bare Metal Ortamlarında Yaygın Sorunların Aşılması

Mẹo chuyên nghiệp để gỡ lỗi MinIO: Khắc phục các sự cố thường gặp trong môi trường Kubernetes và Bare Metal

调试 MinIO 的专业技巧：解决 Kubernetes 和裸机环境中的常见问题

Email communication is a critical aspect of any business operation, and while convenience and affordability are essential, the choice of an email service provider should be driven by more than just convenience. Bare Metal Email, through extensive research on email operations, highlights the significant impact of email choices on businesses. As a potential , Bare Metal Email claims to have developed premium security and addresses deliverability issues within organizations. Let’s dive deeper to find out more. Gmail alternative Over the past decade, Bare Metal Email has concentrated on developing a comprehensive system to resolve deliverability challenges and enhance business workflows. Unlike competitors such as Gmail, their six-layer system focuses on multiple aspects of email functionality. The first layer involves scrutinizing the network for blocks and spammers that could affect reputation. The IP layer ensures the cleanliness and absence of blocks in the IPs. Moving to the domain layer, Bare Metal Email scans the customer's network for potential blocks. The ESP layer, the recipient of the email, is checked for general blocks and specifically for blocks related to the sender's domain. This thorough approach ensures that emails sent by businesses are not flagged as spam or blocked by recipients. The final two layers of the Bare Metal Email system analyze the structure and text of emails to ensure correct delivery. The Heuristic layer employs AI and algorithms to detect keywords that might trigger blocking by recipients. The layer examines the email’s code for potential issues like broken hyperlinks that could lead to spam filtering on the recipient’s end. This meticulous scanning process reflects Bare Metal Email's commitment to addressing issues at every level of email composition. HTML We’ve all questioned whether Google, and Gmail by extension, is listening to us. Those suspiciously placed ads for a product you had spoken to a friend about not 10 minutes previously. Bare Metal Email claims to distinguish itself by offering a security system that is both robust and noninvasive. The company incorporates strong security measures at each layer, eliminating the need for two-factor identification. This approach contrasts with the common advice for businesses to use complex passwords, backups, and multiple authentication methods, often resulting in account lockouts and wasted time. Bare Metal Email aims to provide a secure and accessible email experience without the restrictions and complexities associated with multiple Gmail accounts. Good email providers recognize the evolving landscape of business communication and the importance of adaptability. With a commitment to staying ahead of emerging threats and technological advancements, Bare Metal Email claims to regularly update its system to address new challenges. This approach ensures its clients benefit from ongoing improvements and the latest security measures. In a rapidly changing digital environment, having an email provider that prioritizes both current needs and future developments is a valuable asset for businesses seeking a reliable and sustainable communication solution. In a world where communication is paramount, especially through email, we can vouch that Bare Metal Email has positioned itself as a . The company assures users that its system sources and solves deliverability issues, such as blacklisting and spam filters, offering businesses a solution to ensure their emails reach the intended recipients without complications, and where complications exist - solutions. reliable alternative to Gmail - By: Tom White

Exploring Bare Metal Email as a Gmail Alternative for Business

ব্যবসার জন্য একটি Gmail বিকল্প হিসাবে বেয়ার মেটাল ইমেল অন্বেষণ

Entdecken Sie Bare-Metal-E-Mail als Gmail-Alternative für Unternehmen

Explorando el correo electrónico básico como alternativa a Gmail para empresas

Explorer la messagerie Bare Metal comme alternative à Gmail pour les entreprises

व्यवसाय के लिए जीमेल विकल्प के रूप में बेअर मेटल ईमेल की खोज

ビジネス向けの Gmail 代替としてのベア メタル メールの検討

비즈니스용 Gmail 대안으로 베어메탈 이메일 탐색

Explorando o e-mail Bare Metal como uma alternativa ao Gmail para empresas

Изучение электронной почты в качестве альтернативы Gmail для бизнеса

İşletmeler için Gmail Alternatifi Olarak Bare Metal E-postayı Keşfetmek

Khám phá email kim loại trần như một giải pháp thay thế Gmail cho doanh nghiệp

探索裸机电子邮件作为 Gmail 的企业替代品

Big Data processing, BI analysis, and AI involve heavy usage of , including This requires tremendous computational power: hundreds of gigabytes of RAM, tens of CPU cores, as well as graphics cards and/or special chips to speed up calculations. ML neural networks. Problems When working in a big heterogeneous team (data analysts and researchers, development engineers, system engineers, etc.) on a large system, employees from different teams use various tools to solve problems. This applies both to development and runtime environments (Python, Matlab, Java, C/C++, etc.) and databases used (RDBMS, NoSQL, files, etc.). Retraining can be time-consuming, expensive, and even impossible in the short run. The introduction of new technologies can make it undesirable or impossible to stick to a universal toolkit. For example, most components of an AI system may be implemented in Java but you may need libraries in Python, and other languages to implement a new model. You will have to integrate them: reimplementing these features in the current toolkit may take a long time and be costly for the business. C/C++, Vertical scaling may be too expensive or impossible. Of course, it depends on the capacity of your provider’s data centers but, in practice, all of them have their limitations. This sets a limit on boosting computational resources in a single node. You will have to use several nodes grouped into a cluster to achieve the required capacity. In some cases, when specialized computing equipment (like , , and Tensor Cores) is required, it is reasonable to buy your own servers and install them in your provider’s data centers. Nvidia Tesla Solutions Containerisation To solve the problems of toolkit heterogeneity, OCI-compliant systems are used. A containerized application is an application and its runtime environment in a particular format that runs in an isolated environment in a container management system. Containers can contain almost any application and system-level applications (WEB, Data, ML, applications, DNS servers, NET balancers, etc.). All of them will run in an isolated environment to avoid conflicts between system libraries and to differentiate access and resources in a flexible way. Docker is the most popular container platform. It includes both tools to build containerized applications and a daemon that lets you run and manage containerized applications within a single node. Orchestration The core problem that Kubernetes is solving is the ability to manage containers at scale. Kubernetes is the most well-known and standardized cluster orchestration system. Kubernetes clusters are available from almost any major cloud provider (AWS, Azure, GCP, etc.). Of course, there are other containerization technologies and container cluster management systems, but Kubernetes on Docker is the most common and standard solution at the moment. Let’s start with the special considerations to be taken into account when choosing a solution for Kubernetes, and then proceed with the specifics of the AI systems architecture in a Kubernetes cluster. As described above, the main options are a managed cluster in the cloud and your own cluster on bare metal. Let's take a closer look at these cases. Cloud Cloud providers offer scalability, ease of use, and high availability through their robust infrastructure and redundancy measures. Cloud services provide flexibility, accessibility, and the ability to seamlessly integrate with other applications. Additionally, cloud providers often have robust security measures and disaster recovery solutions in place, providing reliability and data protection. Bare Metal Bare metal infrastructure provides dedicated hardware resources and offers more control and customization options compared to cloud computing. It is well-suited for workloads that require high performance, low latency, and specific hardware configurations. Furthermore, bare metal infrastructure allows for greater freedom in choosing and implementing specific platform-as-a-service (PaaS) and software-as-a-service (SaaS) solutions, without being limited to proprietary offerings often provided by cloud providers. Comparison Cluster in the cloud Pros Maintenance is performed by the cloud provider. You don’t need a separate cluster administrator, most of the time developers will be able to manage the cluster themselves. Databases and other infrastructure with automatic scaling (S3, DynamoDB, RDS, Redshift, etc.) are available. The number of cluster resources can be changed quickly if necessary. Malfunctions are less frequent. Cons Expensive (please see the comparison below). Higher probability of vendor lock-in when the provider’s technologies are used (e.g., DynamoDB). Malfunctions (when they occur) take longer time to fix. A limited list of appropriate equipment, and you can’t use custom hardware. In many cases, no transparent pricing plans. Comments Of course, it is easier to use a cloud cluster than to deploy your own, at first. But then, when you encounter problems with troubleshooting, finding the cause and possible solution can take much longer and be more painful. You will be limited in your access to the system level, which entails additional difficulties in diagnosing and solving these problems. Some you may not be able to solve by yourself without the access to the system level, so you will have to wait for help from tech support. Be careful when using a cloud provider's proprietary services. They lead to vendor lock-in. If at some point you become dissatisfied with your cloud provider, it will be harder to stop using their services because you already have solutions implemented on the basis of those products (e.g., proprietary NoSQL storages). Cluster on bare metal Pros Renting bare metal servers from hosting providers is several times cheaper than renting nodes from cloud providers (please see an example of price comparison below). Greater flexibility in selection, configuration and installation of the equipment. Colocation. You can build your own equipment and install it in a data center. It is relevant if you use specific equipment (graphics cards, tensor cores, hardware encryption, etc.). Troubleshooting often goes faster as there are no hard vendor lock-ins, which allows for rapid migration and provides a wide choice of solutions. Greater flexibility in the choice and configuration of systems and services. Cons You need a dedicated expert or even an entire administration team for cluster maintenance, including deployment, monitoring, and backup. Malfunctions are more frequent early on, depending on the skills of your expert. Scaling requires more time. It may include adding servers, changing the configuration, or migrating to other racks and/or data centers. Comments When you choose hardware, give preference to dedicated servers. When using virtual servers, you may notice periodic drops in IO or CPU performance, and this affects the speed and stability of the calculations. You should also pay attention to the organization of your server network. Keeping servers in the same data center works fine, but putting them on the same rack and switch is far better. High speed and stability of the connection between your servers fix a lot of problems. For bare metal providers, this service is called colocation. Architecture Deployment As you know, the Kubernetes cluster consists of control and data planes. Let’s focus on the data plane nodes: Data application nodes collect data for ML applications. Master Data is storage where data application pods upload prepared data. If the entire dataset is placed into local node storage, it is recommended to fully replicate the data to all the nodes. This significantly reduces the number of failures and network load when ML application pods read the data. ClickHouse and/or MinIO can be used as Master Data. You can use colocation and connect an entire rack to a single fast 10GE switch. However, even in this case, it is important to group the prepared data properly. Then you will be able to use one of the binary storage formats and avoid overhead expenses for scraping. Models and related functions run on ML application nodes. They perform inference and record the results. Result storage nodes must be able to take in data analysis results, evaluate them and provide access to them externally. You can use ClickHouse and/or MinIO as a Data Master. You can write your own scheduler using Kubernetes API or use built-in, Dagster, Airflow, and other schedulers. During the implementation, it is important to control the number of records in etcd for pods. Filled application pods need to be cleaned now and then, otherwise, etcd will become overfilled and the cluster will die. Load sharing across multiple nodes. When ML applications run, resources are used to their fullest, and this is a typical situation. It is important to always have free resources for system services. An exception is environments for dev testing: they can be located in a single node for cost saving. Backup and Monitoring subsystems should be in a separate space that does not depend on your Data and ML subsystems. It is important that it includes the creation of a set of backups at different points in time and their testing to manage sudden failures, as part of scheduled drills. Last but not least are your master nodes that contain the Kubernetes control plane. Conclusions If your AI system is in the development phase and does not require constant resource utilization, opting for a cloud provider is a sensible choice. By renting spot and on-demand instances for specific tasks, you can avoid paying for idle resources and potentially save money. However, for AI systems that require continuous cluster utilization and have specific hardware needs, employing a bare metal provider becomes a logical consideration, provided your team has the required expertise. In terms of disaster recovery and security, cloud providers typically offer robust built-in measures such as data replication, geographic redundancy, and automated backup and recovery solutions. This provides a level of protection against data loss and ensures business continuity in case of unforeseen events. On the other hand, bare metal infrastructure allows for more control over security measures, enabling organizations to implement their own customized security protocols and maintain direct control over data privacy and compliance. However, this also means that the responsibility for implementing and managing disaster recovery and security measures falls solely on the internal SRE.

AI at Scale: Cloud or Bare Metal

I wasn't really a fan of cloud development until I was forced to have a nice experience with it, thanks to . I have been working for the China site development project for The Gospel Coalition recently, and the story comes with an awful start: TeamCode Just two days before the project should be up, I was called to Beijing for emergency outstation work. Traveling between places, I could've spent more than ten hours but my new MacBook has nothing installed. I was overwhelmed. How am I supposed to get all this work done? Are all these time on the train wasted? I could've been done developing and debugging within that ten hours. To be clear of what I'm doing, these are my project overalls: Project Background The project I'm working for The Gospel Coalition is an independent site development project outsourced to a Chinese team. The whole working team is rather globalized, and the site development project is done through collaborations around the world. Project Structure Compared to the complicated project background, the project structure is rather simple. It consists of three independent services: WordPress: Enabled for the editors as a backstage for content management, allowing them to post their articles and access to other contents Databases: A MySQL database. Both services will be accessing the same database, with WordPress writing in it while the official site reads Independent Official Site: Displaying content. We're not using WordPress templates as there are a lot of customizations. The site is independently developed with Java (Spring Boot + Thymeleaf) Original Developing Plan and Strategy Since we're communicating through email, the procedure of applying for a test server would take too much time. Hence, I've started developing the project after applying for the access to the test data. Now that my part has done and it's now in the process of joint debugging and acceptance inspection, I'll need to: Apply for a test server Configure the test server environment Install Jenkins and configure its deployment information Deploying the local project and email the PM for acceptance inspection Make changes according to the feedback and ... LOOP the previous process. Some First Try on Cloud Development Everything would be in time if there wasn't an outstation task. Sitting in a KFC 40 minutes before the train departs, I was puzzled by a series of problems: I couldn't code right now because I'll need to download a lot of programs for the new MacBook, configuring the developing environment etc., and most importantly - how fast can we expect for the internet speed out there? I don't even have that much time to wait! Cloud native and distance collaborative development came up in my mind. Just as what I've said, I didn't really want to change my old working style despite cloud development being the future trend, and a simple understanding of it was all for me. I first heard TeamCode's product in a gathering weeks ago. Since there's no better idea at that time, I decided to give it a try on the way to Beijing. How bad could that be? I'm going to explain what I've done with it and how it eventually helps me to get all my troublesome tasks done just in time: I turned on my MacBook, connected to my hotspot. And there goes the old process, I googled TeamCode, create an account and log into it. What surprised me was that deployment process: After authorizing my Git repository into Pylon, the Java development environment was created within 5 seconds! The configuration I chose was the default 2 vCPU with 4G RAM, more or less a virtual Linux machine. Then, I pulled down the codes. Perhaps Pylon detected that I was working on my project with Java, so Git and Maven were installed by default. It supports both VSCode and a built-in Cloud-IDE. Since all the basic services and styles were done developing, there is only a few tweaks and debugging left. I skipped for downloading another IDE and worked directly on its Cloud-IDE. I started worrying again when I found out that I'll need to structure mvn install before starting everything. According to my previous experience, downloading all these dependencies may need more than 20 minutes and my hotspot might not be able to sustain it. It should be based on the server's internet and algorithm power, I shouldn't be worrying about it. After the dependencies' downloads have started, I packed my MacBook and start boarding the train. Although it was set up as expected when I got onto my seat, it is still exciting to see everything was ready to go. Then, I realized ... Hey, wasn't this a remote machine? Now that my debugging was done after 2 hours of hard work on the bullet train, another problem puzzled me: How should I send the project to the PM for inspection, without applying for a server and let alone deploying Jenkins? Puzzling around for an optimum solution, I really appreciate that the TeamCode's FAQs were undoubtably helpful! I tried on Tin - another cloud solution provided by TeamCode - to launch the application and my puzzling for purchasing a test server and deployment task were solved in the blink of an eye. Tin is basically a product that packs application, so it really helps as the PM is only interested in the results and not the codes. I uploaded my codes to Git repository again and created a Tin for the PM. After I've done a quick check on Tin, I've sent an invitation link to join my workspace. The project can now be accessed by clicking on the endpoint on Tin. It can even be upgraded for rapid deployment if there are any changes. After emailing to the PM, I have even some time left before arriving in Beijing. Some Interlude Everything goes smoothly for the first night: The PM is able to inspect the project through Tin. He did propose a few issues and they are all resolved with Cloud-IDE + Pylon. After a few emails back and forth, the inspections are basically done. After another long day of work, we encountered another bug while I was on the metro, traveling back to Shanghai. One of the JS methods in a page conflicted with the overall method and the page could not be opened! It's just an hour before the official release and there's no way for me to code on the metro. I'm in a dead guilt if the whole project has to be delayed and everyone needs to work over-time just because of me. In this great desperate situation, I tried to ask if the front end programmers could help. I sent an invitation link to him and "forced" him to join my workspace to help modify the codes. He finally agreed to help after acknowledging that there was no need for environmental deployment, structuring and all those troublesome tasks - and a big meal treat from me. We communicated over the phone by instructing him how to use Pylon and Cloud-IDE. Thanks to his quick turnaround, everything was done in 10 minutes. The application was updated on Tin, the PM inspected it, and ... Phew, the site was smoothly launched on time. Notes These incidents really gave me a first hand experience on cloud collaboration on the go. I really appreciate that everything was done on time and there is no need to work over the weekends. This opportunity really changed me by having a nice experience of developing and inspection with the fragmented time. From my experience, I guess that cloud developing with TeamCode would help in these situations: A Flexible Development Situation. Cloud-IDE + Pylon would be helpful if you're changing between computers and working environments. Since the resources are all on the cloud and there is no need for environmental deployment, cloud developing can be rather flexible. Pack and Deploy Rapidly. Tin would be helpful for situations such as inspections, demo presentations, technical communications, rapid POC etc. It enables us to display our project to the others conveniently. Team Collaboration. For example, your teammates can debug on the project directly and there are no worries about time and geographical difference. Everyone could make use of the service according to their own tempo on TeamCode as most of the services are automated. Thank you for reading my article and hope my experience will be helpful to you.

Coding in Public Transit: How I Just Barely Shipped a New Site with Pylon, Tin, and TeamCode 

Anthos on Bare Metal: How to Manage a Private Cluster From the Cloud

. Sex, money, tech and power. One way or another, all major world events have been tied to one of these four concepts OnlyFans, a social media platform created to share explicit content against tips, finds a way to merge all four. In doing so, it also manages to elegantly reflect the state of technology in 2021. Censorship, the dispersion of creativity, COVID’s impact on the economy, sexism… What equally affects a young woman in Paris and an American art director in Ukraine will inevitably affect us all. The Uberisation of Sex is Here Like many other young women throughout the world, Paris native , started her OnlyFans account in March 2020. She signed onto the platform after modelling on a variety of platforms, because the community within which she evolves had relentlessly asked her to sign up. “ , she specifies. ” She is now in the top 1% of content creators on the platform. All her subscribers pay a monthly fee to access her content, which ensures she has a comfortable life in a famously expensive city. Hélène, aka EnMarcheNoire Honestly, I started off a bit haphazardly and it worked out very well financially for me” “It’s become my main source of income today, even though I never really tried to maximise it. This process will sound familiar to many OnlyFans content creators. Most start off as Instagram or TikTok models, gathering a huge following by posting mildly racy lifestyle photos. Nothing too explicit, but enough to cater to both men and women on the platforms which sustain them. . “ ”, “ ”, “ ”… From there, two options present themselves. Then come the comments Hey if you don’t mind I want to f*** you Let me throw it in your *** I want kiss your beautiful feets my precious queen Block and ignore this unwanted fanbase, or ask them to put their wallet where their mouth is . During the COVID pandemic, many women chose the latter. Thousands created OnlyFans accounts, and let their followers know that for a nominal monthly fee, they would be able to chat together, receive exclusive, explicit and personalised photos, videos, texts and more. Chris Hunt, a fashion industry veteran turned OnlyFans marketing advisor, confirms the permeability between various social platforms: “ ” For most models, it really is just money. It’s a way for them to pay their bills without doing much different than what they were already doing on Instagram. Obviously, good marketing and clever pricing is encouraged to best influence demand. But truth be told, demand was never going to be an issue: . many men are tired of major adult websites’ lack of authenticity, and have been primed by the likes of Uber, Netflix, and Spotify to respond positively to the immediacy and personalisation that OnlyFans offers Pick a girl you like, send her money, get a nude. The Uberisation of sex is here. Once the word came out that , searches for “ ”, and “ ” skyrocketed. And as more content creators came in, yet more users followed, thanks to shrewd advertising on Reddit, Instagram, Twitter, and TikTok. OnlyFans went from having 90,000 creators in 2019 to 1 million in December 2020, for a total of 85 million registered users at the start of 2021, against 50 million in August 2020. a young woman w as making up to $30,000 a month on the platform what is OnlyFans? How does OnlyFans work? Most Creators don’t Make a Lot of Money… But the Platform is Making a Killing OnlyFans has grown steadily since the pandemic hit. There are three obvious reasons as to why this happened: and . Adult performers and models eager to make up for lost earnings have flocked to OnlyFans, while lonely men have found a way to add some excitement to an otherwise dull year of confinement. many people lost their jobs in 2020 and looked to supplement their incomes, celebrities legitimised the platform by joining , dates became harder to come by as a majority of the world’ population sheltered at home The platform’s financial pitch is straightforward. If an Instagram model has 100,000 followers' and converts 1% of them to OnlyFans subscribers, the money quickly adds up, even if we only base ourselves on the 12$ average subscriber’s monthly payment. According to Hunt, this simplicity is a double-edged sword : “ It’s easy to take selfies and make videos, but actually getting users to the platform is really difficult. If a girl doesn’t have that built-in audience already developed on Instagram or TikTok, you really have to start from scratch .” Therein lies the issue. What OnlyFans systematically fails to advertise is that payments very heavily favour top content creators; . research shows that the top 1% of accounts make 33% of the money Since , this means that the top 10,000 content creators make an average of $6,600 a month, while the remaining 99% have to make do with 133$ a month. the platform is (allegedly) paying out more than $200 million a month to its creators . Now in its 4th year of operations, OnlyFans generated more than $2 billion in sales last year, of which it keeps about 20%, . This imbalance is unlikely to change anytime soon for a total of $400 million in revenues in 2020 Make no mistakes : nude or clothed, the rich getting richer is a staple of post-2016 tech. Just like everywhere else in tech, a conversation about the just remuneration of the thousands of workers turning a select few into millionaires is well worth having. An OnlyFans IPO, but at what cost? OnlyFans is just the latest company to bypass major players in their industry to connect users directly with creators. , , , , , and . Substack Patreon Twitch Cameo Etsy TikTok all have made great strides over the past year by promising to reduce barriers between creators and their fans, dealing blow after blow to traditional gatekeepers Both economically and socially, this dispersion of creativity is the most important trend in tech over the past 5 years. With its millions of users, and comparably small cut (20%), the appeal of OnlyFans is obvious, and many traditional actors are crowding the space to ensure they get a piece of the action. Musicians . The Dream and Swae Lee have promoted new music on the platform, Drag queen Shea Coulee has filmed herself doing makeup before performances, social media personality YesJulz has used it to share fitness workouts These new users are bypassing both traditional fundraising players like , and personalised videos newcomers such as . Patreon Cameo But many models are worried that a turn to respectability may leave hard-working performers and sex workers who built the platform from scratch with little to show for it. This has already happened , and . on Tumblr on Patreon . Hélène has been cultivating a following on specifically to ensure she would not be left penniless should this happen. Sex workers are often the first to be left behind when a platform takes off many different platforms “ There’s always this worry, and that’s why I’m getting my content everywhere, on all platforms. In theory, I like that the platform is democratised and open to a different type of content. It shows that you’re not a pariah just because you have an OnlyFans account. But in fact, sex workers are always the first to suffer from new rules once a platform is popular enough. If OnlyFans manages to prosper thanks to non-sexual content, I’d be worried that the platform would close access to sexual content." She’s right to be worried: OnlyFans probably has the ambition to IPO someday soon (rumours abound), and a platform without smut might be more palatable to Wall Street. This theme of censorship is prevalent in today’s discussions about Tech: What should be censored, and what shouldn’t? What do we owe to stigmatised sex workers? Can platforms really be both judge and jury? Is the commodification of adult content a net positive or negative for society at large? Here too, fights over a small platform are reflected in the wider world. Can You Take Back Something that was Never Truly Yours? . OnlyFans was birthed by the natural voyeuristic impulses of young men, and by decades of hinting to young women that their body and what they did with it defined much of their worth For years, sexist men (and women) have used the following phrase to shame women in control of their sexuality : “ .” Having been told this since teenagerhood, many women have come to the conclusion that what gave them value was scarcity. . If a key opens many locks, it’s a great key. But if a lock is opened by many keys, it’s a terrible lock And where there is scarcity, there is money to be made . Full control over content, pricing, time of publishing, virtually no middle-man. Enter OnlyFans, a platform that exists for the sole purpose of banking on that scarcity It would be easy to argue that the OnlyFans is creating a new paradigm for sex work, where women are in control. one however wonders if it’s even possible for women to reclaim their sexuality in a deeply entrenched patriarchal society, or if claiming to do so is just a lie women tell themselves so they can more comfortably cater to the male gaze. On the other hand, conversations like this one often infantilises them, painting them as incapable of engaging in these discussions themselves, which is itself a product of the deeply misogynistic society we live in. To paraphrase a truly wonderful Netflix show, . Hélène is not a victim. She was not forced into the adult industry. In fact, she started her career in the adult industry while interning at Microsoft “ I saw that working in a big company without being on my own was not a thing for me. With all these platforms… I am lucky not to be just another employee. I never did this really for the money. It just allows me to do what I want when I want. It’s my dream job." Hunt confirms that models on the platform enjoy the money, but that there can often be underlying reasons to join the platform. “ he says, ” For other girls it’s a creative expression”, “it’s another way for them to create. There are fashion models who really like fashion, but they also like doing artistic things that may involve nudity. They’re attracted to being able to show another side of themselves in a way that they can actually monetise. Nevertheless, it’s difficult to argue that everyone is using the platform of their own volition. Headlines have flourished of late with stories of , . students having to get by through the sale of nudes of workers fired for using the platform to complement their income It’s a lot easier to explain the platform’s success as a correlation to the government’s inability to help young women in need, rather than through any form of feminist movement. . By using these tools, it acts as a bellwether for society, reflecting and accentuating our most pressing issues. If we work hard enough on these issues, maybe, just maybe, there might be a time when OnlyFans is no longer newsworthy. Sex, money, tech and power. OnlyFans uses two, and generates the others Fingers crossed. This article was originally written for , a blog providing in-depth analyses of today’s technological challenges. . The Pourquoi Pas Click here to access it Lead image courtesy of Charles Deluvio on Unsplash

OnlyFans’ Truth Laid Bare: The Marriage of Sex, Money, Tech and Power

Lessons learned There’s a lot of buzz around Google Anthos! I’ve lost count of the number of times I’ve been asked “What IS Anthos?!”. Well today I’m NOT going to answer that question! However, what I will do is (hopefully!) help some of you to spin up your very own Anthos bite-size cluster on some relatively inexpensive hardware thanks to the newest “flavor” of Anthos: Anthos on Bare Metal. From there you’ll be able to explore the features of Anthos for yourself, and embark on your own Anthos journey of discovery! Anthos on Bare Metal allows you to spin up a cluster from as few as 2 Intel NUCs. It’s a relatively straightforward process, but the process can be a little daunting if you haven’t been through it before. I encountered a few issues along the way, so what I’m walking you through today is my “path of least resistance”! So let’s start with the hardware. I got sent 3 Intel NUCs. Each of them has a Core I7 Gen 10 processor, 32GB of RAM, and 256 GB SSD. To be more specific, here's everything that I needed to get up and running: 3x Core : 6 cores / 32GB DDR4 Mem / 256GB SSD NUC10i7FNH i7-10710U 8-port GbE switch (optional if you have enough ports on your router / switch) 4xRJ45 CAT5E 16GB USB Stick to install Ubuntu You can install Anthos on Bare Metal on Ubuntu, CoreOS, or RHEL servers. (See more details ). here Out of the box the NUCs come with Ubuntu installed. However, the Kernel version led to issues with Ansible for me, so I started from scratch and created a bootable USB stick that would install “virgin” Ubuntu 20.04 onto each NUC. I will create my cluster out of just 2 of the NUCs (which is really the minimum we’d recommend). The third I will use as my “workstation” in order to bootstrap the cluster using the command-line tool. All is not lost, however, we can add that third NUC to the cluster later, once it’s up and running! bmctl Happy-path install process Connect one of the NUCs to a monitor and keyboard. Boot it up. In the case of the NUCs I received, Ubuntu was already installed. We’ll use this machine as a workstation for now. Open a terminal & (you'll need the password for the default user) sudo su mkdir usb-boot && cd usb-boot Copy over shell script from (Thanks to my colleague Cody Hill for this!) https://github.com/c0dyhi11/ubuntu-20-04-autoinstall/blob/main/build_iso.sh chmod +x build_iso.sh Insert a USB stick into the NUC and note the device that represents the USB stick (i.e. ) fdisk -l /dev/sda (whatever is returned from above) ./build_iso.sh -F /dev/sda Ubuntu ISO will be downloaded and written to the USB stick. N.B. command will finish but writing may continue. Wait 5 mins. Powerdown your NUC. Connect your second NUC to monitor and keyboard. Insert the USB stick created in step 8. Power on. Press until the boot menu shows. Select the USB stick to boot from. If there are multiple partitions, select the first. F11 Ubuntu install will proceed. Select language, keyboard layout, network configuration (strongly recommended to switch to “manual” network config and assign a static IP). I selected not to download updates. For storage config you can just accept defaults. Do not install any other options / software / tools when prompted. You will be prompted to create a username and password. Use whatever you would like, just make a note of it. My suggestion would be to name the machine nuc-1 (and then nuc-2, and so on). At this stage you can elect to copy over an SSH key from Git if you’d like, or you can perform this step later (described in ). step 19 Wait for install / updates to fully complete (“Reboot” will show as option at bottom when it’s done). Reboot. Remove USB stick when prompted. NUC will reboot into Ubuntu. Disconnect monitor and keyboard from NUC. Repeat steps 11-14 for remaining NUC(s). Reconnect workstation NUC and boot up. Open a terminal & generate an SSH key: ssh-keygen -t rsa and copy to clipboard cat $home/.ssh/id_rsa.pub SSH into each NUC in turn (using username and password set in ) Paste in public SSH key and save Perform further steps listed here: step 12 ssh snuc@192.168.1.201 sudo su vi /root/.ssh/authorized_key Configuring Ubuntu | Anthos on bare metal From the workstation NUC validate you can SSH to each NUCvia (N.B use the path to the private SSH key you previously generated, and the IP address of each of your node NUCs). You should be able to connect to the NUC as root without being prompted for a password. ssh -o IdentitiesOnly=yes -i /home/ubuntu/.ssh/id_rsa root@ 192.168.1.231 From the workstation NUC proceed to follow instructions here: . You’ll install and init gcloud and kubectl. Next you’ll install the bmctl CLI tool. https://cloud.google.com/anthos/gke/docs/bare-metal/1.6/quickstart#install-gcloud Proceed to follow instructions here: . We’ll create a hybrid cluster - a single cluster for both admin and workloads, that can also manage user clusters.(other options are standalone, admin, & user clusters). In the hybrid cluster deployment option, one of your NUCs will be the control plane node, the other will be the worker node. You can read more about the other deployment options on . https://cloud.google.com/anthos/gke/docs/bare-metal/1.6/quickstart#create-cluster-nodes Creating clusters: overview Find a “block” of # of NUC contiguous IP addresses on your network. 3 or 4 IP addresses will be enough for the purposes of a POC. Whenever you create a K8s service of type “LoadBalancer”, one of these IP addresses will be consumed. I used 192.168.1.236-192.168.1.240. The “start” IP address (i.e. 192.168.1.236) you will specify as your ingressVIP in the YAML file. You should also find another free IP address for your controlPlaneVIP. It should NOT be in the start-end range discussed above. I used 192.168.1.196. Specify one of your NUC IP addresses as the IP address for the control plane node pool, and the other(s) right at the bottom of the YAML as the worker node pool. Run to perform preflight checks, and then to install Anthos. If you followed the instructions in step 22 then all APIs and service account keys would have been generated for you. ./bmctl create cluster -c [name of your cluster] Once it completes successfully you’ll be given the path to the kubeconfig to connect to your new cluster. [Optional] If you wish you can re-image your “workstation NUC” using the USB key, and add it as a worker node to your cluster kubectl -n my-cluster edit nodepool np1. Remember to copy your “baremetal” folder (and subfolders) off to somewhere else first (because you’ll need the kubeconfig file!). Running Your First Workload So now let’s deploy a simple “Hello world!” Kubernetes deployment and service. Let’s begin by setting our environment variable. puts the under . So, for instance, I called my cluster bm-cluster: KUBECONFIG bmctl KUBECONFIG bmctl-workspace/[your-cluster-name]/ export KUBECONFIG=$HOME/baremetal/bmctl-workspace/bm-cluster/bm-cluster-kubeconfig First let’s create a deployment YAML: apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment
spec:
  selector:
    matchLabels:
      app: metrics
      department: sales
  replicas: template:
    metadata:
      labels:
        app: metrics
        department: sales
    spec:
      containers:
      - name: hello
        image: 3 "gcr.io/google-samples/hello-app:2.0" Save the file (as my-deployment.yaml), and now apply it to our Anthos BM cluster: kubectl apply -f my-deployment.yaml Let’s check the deployment: kubectl get deploy You should see something like this: NAME            READY   UP-TO-DATE   AVAILABLE   AGE my-deployment   3/3     3            3           30s Now let’s create a service of type LoadBalancer. Save the following YAML as my-service.yaml: apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: metrics
    department: sales
  type: LoadBalancer
  ports:
  - port: targetPort: 80 8080 Now let’s apply it: kubectl apply -f my-service.yaml Now let’s validate it got created: kubectl get svc my-service You should see something like this: Notice how my-service's IP address (external-IP) got automatically drawn from the “range” we specified earlier. Now we can curl our service, and check that everything is working as it should: For me, I , and I see: curl 192.168.1.237 Hello, world! Version: 2.0.0 Hostname: my-deployment-68bdbbb5cc-mpftc Your Anthos on BM cluster is fully operational. It will also show up in the Google Cloud console. Congratulations! If you want to log in to your cluster (to view node and workload information from the console) you can now follow the instructions on . Logging in to a cluster from Cloud Console

How to Install Anthos Bare Metal on Intel NUCs: A Beginner's Guide

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