NFTBOOKS is a project which buids platform to publish works on books, magazines, paintings... on blockchain protocol. Works will be directly published by the authors. Using NFT (Non-fungtible Token) identification technology for copyright, our project will eliminate almost 100% of piracy in order to make your products accessible to readers at reasonable prices. Your products will be released to everyone as quickly as possible in the largest number. We make it possible for our readers to read your books for as cheap as 1/100 1/1000 compared to the traditional book approach. You absolutely do not need to worry because our model allows the authors to also be the publisher. You, or other authors, will still enjoy your full benefit even the publisher's share while the readers are not under financial pressure. At NFTBOOKS there are 5 main audiences: authors, readers, translators, booksellers and investors; with this interactive circular ecosystem of audiences creating mutual support and mutual development.

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One Netbook is best known for its high-quality OneXPlayer branded handheld gaming PCs and mini laptops that pack a solid punch in a small form factor. Among the company’s One Netbook lineup are the One-GX 1, Netbook 4, Netbook A1, and the brand new One Netbook T1. Before the crowdfunding campaign launched on June 9th, I got my hands on the T1 and used it as my daily driver for productivity and some gaming. While the T1 is certainly a great tablet at a reasonable price, it struggles quite a lot in the gaming department and isn’t quite the most powerful device out there. The One Netbook T1 makes a lot of promises as the first tablet powered by Intel’s 12th Generation Alder Lake CPU. The company claims it can take on the latest games by utilizing the power of Intel’s new Iris Xe Graphics architecture while also being an excellent choice for artists using Lightroom and engineers using . Diving into the full specs, it is easy to see why One Netbook makes such claims. AutoCAD One Netbook T1 Technical Specs CPU: 12th Gen Intel Core i5-1240P featuring 12 cores and 16 threads, up to 4.7 GHz Graphics: Intel Iris Xe, 96 execution units, up to 1.4 GHz RAM: 16 GB DDR5 at 5000 MHz Battery: 12,000 mAh battery, lasting nine hours. Screen: 13-inch, 2160 x 1440 IPS screen at a peak brightness of 500 nits. Weight: 2.2 pounds The T1 User Experience Right out of the box, the One Netbook T1 makes a great first impression with its sleek, minimalist design that fits well in your hands. The tablet also is quite responsive, adjusting its orientation between landscape and portrait modes on the fly. Included with the T1 is an integrated kickstand, solidly built and stable enough to put the tablet anywhere you desire. At no point did I fear the tablet would topple over as the stand is large and durable. Battery life is also quite good, lasting close to its targeted nine hours for an all-day battery. Slow to Boot Up As an entry-level tablet, it gets the job done but has a few hiccups. Notably, booting up the device was sluggish. Every time I pressed the power button, the screen would stay off, and only after one or two more presses would it actually power on and launch into . Consumers in the market for a tablet want a snappy experience on the go, and the T1 struggles here. Windows 11 A Pleasantly Quiet Fan While under workloads, the T1 remains mostly quiet thanks to its nice fan, which does a great job at keeping temperatures under control. Unlike many portable devices, the T1’s fan has a pleasant whoosh sound instead of a high-pitched squeal when it has to kick into high gear. The fan is also mostly inaudible when wearing headphones which is a big plus. Solid CPU Performance Additionally, the tablet never got hot to the touch, even under the most intensive workloads. After an extended period of stress testing, the CPU’s core temperature hit a max of 66 degrees celsius in a temperature-controlled environment of 20 degrees celsius. Noise and heat are no problem for the T1 and should be a pleasant computing experience overall. Productivity and Workflow The One Netbook T1 is no slouch regarding power and performance. Benchmark Testing Plugging the T1 into the CPU-intensive Cinebench test suite gave a score of 4505, placing the i5-1240P below an Intel i7-4850HQ. For only pulling 28 watts under maximum load, the result is impressive but far from being the “most powerful tablet on the market,” per One Netbook’s marketing materials. A score of 4505 still puts the device in range of the Microsoft Surface Pro 8, which keeps the T1 competitive with Microsoft’s flagship tablet. For the artists interested in a tablet to aid their workflow, the T1 does an excellent job of responding with precision with its included pen. Writing and drawing flow naturally on the T1 screen but fail to feel as smooth and paper-like as the Microsoft Surface Pro tablet. Nitpick aside, the tablet does its primary job of creating a seamless writing experience and is serviceable enough for anyone looking to replace their existing tablet for Lightroom photo editing or general digital art. The One Netbook T1 for Gaming One Netbook makes some big claims about the T1’s gaming performance and its i5-1240P does look promising. Ultimately, the tablet fails to deliver decent performance across AAA games but doesn’t entirely lack gaming chops. Testing the i5-1240P and its integrated Iris XE 96EU integrated graphics on 3D Mark Time Spy resulted in a score of 1147 for the GPU and 3425 for the CPU. These are fine scores for a tablet but are definitely not at the same level as a able to take on the newest games. The T1 excels in gaming for smaller-scale, less intensive indie games, not AAA games like the company claims. gaming PC Elden Ring Starting the tests with FromSoftware’s latest game, , the T1 struggled to achieve 30 fps at 720p resolution at the lowest settings. Elden Ring can certainly launch, but it is an inadequate showing for a game like this where every frame counts. Elden Ring Death Stranding The better-optimized Death Stranding: Director’s Cut achieved 30 fps but only at the lowest settings and 720p resolution. This game is undoubtedly considered playable but not an overall great experience for anyone looking to have an on-the-go AAA gaming machine. Eiyuden Chronicle: Rising NatsumeAtari’s recently released JRPG indie game, Eiyuden Chronicle: Rising, had a stronger showing, achieving 60 fps at significantly cut-down graphical settings. This is where the T1 proved that it could game but only if players manage their expectations and understand that this tablet is better suited for lighter gaming workloads. While most of these games had to be cut down to 720p, the One Netbook T1’s high pixel density screen at 13 inches did manage to still look crisp enough to be playable. That said, its native 1440p resolution is far too much for the i5-1240P to handle. There does exist an i7 model that could be more suitable for gaming but as far as the i5 model is concerned, it is firmly a tablet for light productivity workloads and general day-to-day use. One Netbook T1: Is it Worth it? As a tablet, the One Netbook T1 is a good entry-level tablet and will get the job done, being more or less a copy of the Microsoft Surface Pro 8. For productivity and as an addition to your workflow, the T1 is a pleasant device, especially on the go. As an overall gaming device, don’t expect too much. However, the T1 becomes an enticing option when you consider that its price point is much lower than its competitors. One Netbook just launched its crowdfunding campaign for the T1 on IndieGoGo, and early bird pricing starts at $599 for the lowest tiered model, featuring a Pentium 8505. That’s a good step down from the $899 asking price of a Microsoft Surface Pro 8 while containing all of the functionality of its competitors. Overall, if the price is right and a well-functioning tablet is just what you’re looking for, then the One Netbook T1 is a good option. If you like to get your hands on a One Netbook T, support the IndieGoGo campaign . here

One Netbook T1 Review: Can This Entry-Level Tablet Compete with the Microsoft Surface?

Reseña de One Netbook T1: ¿Puede esta tableta básica competir con Microsoft Surface?

One Netbook T1 Review : Cette tablette d'entrée de gamme peut-elle rivaliser avec Microsoft Surface ?

वन नेटबुक टी1 रिव्यू: क्या यह एंट्री-लेवल टैबलेट माइक्रोसॉफ्ट सर्फेस से मुकाबला कर सकता है?

One Netbook T1 レビュー: このエントリーレベルのタブレットは Microsoft Surface と競合できますか?

Análise do One Netbook T1: Este tablet básico pode competir com o Microsoft Surface?

Đánh giá một Netbook T1: Máy tính bảng cấp đầu này có thể cạnh tranh với Microsoft Surface không?

One Netbook T1 评测：这款入门级平板能否与 Microsoft Surface 竞争？

Collaborating and partnering with over 10 Linux distributors to make a seamless virtual desktop experience, Alex is CEO of , a new virtual desktop cloud computing solution that is simplifying the virtual desktop experience. shells.com The platform lets anyone access their cloud computer from any device with a browser so you can have a Linux desktop on your Apple iPad or your Windows computer on your XBox! The team are also the previous founders and operators of Private Internet Access, one of the world’s best and most trusted VPNs. This Slogging thread by Alex Lee, Justin Roberti, Golda Velez, Zlatan Todoric and Akasha Rose occurred in 's official #amas channel, and has been edited for readability. slogging Alex Lee May 25, 2021, 2:07 PM 💚 1 👍 1 ❤ 1 Hello everyone!  Thanks, Akasha for the introduction and looking forward to talking with the Slogging community!  I also have our VP of Tech Zlatan Todoric on here too! Justin Roberti May 25, 2021, 2:17 PM Alex Lee so early virtual desktop apps for mobile were pretty unstable and seemed to bog down the device -- are you mitigating those issues by virtually hosting the OS? How do you handle lag since all requests I assume are hitting a virtually hosted OS? Are all client computers mirrored on the cloud? How do you ensure data security? Is this meant as specifically a way to open up Linux usage to a wider user base? Is it also a cloud storage solution? Lastly, are you considering doing the same for mobile devices, or are they already available? Alex Lee May 25, 2021, 2:25 PM Justin Roberti Great questions to kick off the discussion.  The operating systems are hosted in the cloud.  With the continued enhancement of internet speeds and especially mobile connections (4G, LTE, 5G), a much better experience can be expected in addition to some adjustments we have made to reduce the amount of resources required. Alex Lee May 25, 2021, 2:27 PM The virtual computers are all in the cloud so the data is all stored securely on our servers and access is secured using state of the art encryption.  Users are also encouraged to enable 2-factor authentication (which you should do on all accounts!). Alex Lee May 25, 2021, 2:28 PM When we first started, we definitely saw this as a great option for folks who were on the fence about Linux to definitely give it a try through our product since we do offer over 14 Linux distros now, but its also to just open up the desktop experience to a wider user base as you can access from any device with the proper browser (or app) and an internet connection. Alex Lee May 25, 2021, 2:30 PM Yeah, by default we are a cloud storage solution, but we are working to make the product more seamless and offer something similar to what you would see with a Dropbox.  I like to refer to us as a Dropbox on steroids as you can store but also create, edit, modify your files. Alex Lee May 25, 2021, 2:31 PM You can already access our service through mobile devices.  Here is a nice little snapshot I took the other day of my Shells machine on my Galaxy Flip 🙂 Alex Lee May 25, 2021, 2:32 PM Justin Roberti May 25, 2021, 3:06 PM Alex Lee thank you. what kind of specs do you need for the device as a user? If the industry moves toward remotely hosted OS', will everyone be moving to basic netbooks or do those devices not have the RAM to handle running the OS? Or would this lead to home/work computers with high RAM and less internal storage (a direction they are already going)? Justin Roberti May 25, 2021, 3:07 PM Alex Lee just to clarify, are users able to use your service to run Android-only apps on a PC for example? Alex Lee May 25, 2021, 3:15 PM Justin Roberti Love the interest!  Our product can be accessed from the browser so most modern browsers can handle our product. For example, I went and purchased a $200 Chromebook with 2GB RAM and I have been using this device to access my Shells machines. Our product definitely creates the option to converge devices and utilize a low-level device for all of your computing needs. Alex Lee May 25, 2021, 3:16 PM Yes, Android only apps can now be accessed from a PC or even if you want to access Android-only apps from an iPad, this is now a viable option. Justin Roberti May 25, 2021, 4:01 PM Alex Lee so, as part of the gig economy who destroyed two laptops by catastrophic spills this year, it sounds like this could be of great benefit to those of us who are our own IT department-- replacing these machines for 200 instead of $1200 or more. Correct? Is this one of the intended use cases? Justin Roberti May 25, 2021, 4:03 PM Alex Lee, how do you handle licensing for non-Linux OS? Does the user need to provide the license? Alex Lee May 25, 2021, 4:06 PM Justin Roberti Definitely that is an appealing solution to be able to use either cheaper hardware alternatives or increase the longevity of older devices.  For Windows, we offer the ability to activate your OS by providing licensing which can be paid by the month or by the year. Justin Roberti May 25, 2021, 4:10 PM Alex Lee, can you post a link to the WP or backgrounder? Alex Lee May 25, 2021, 4:12 PM We don't have an updated backgrounder to provide right now, but this is something that I can get to you later for sure! Justin Roberti May 25, 2021, 4:13 PM Alex Lee, it seems like this would be a good fit for education... but schools and even most businesses have switched over to Google docs .. which does most but not all of what MS Suite does. Is that your biggest competition? What advantages can you provide over the free Google solution? Justin Roberti May 25, 2021, 4:14 PM I still miss what I could do with local versions of PPT but I think I'm in the minority. Alex Lee May 25, 2021, 4:19 PM I don't really look at Google docs as our biggest competition.  Google docs has its benefits for sure, but we are really focused on providing the "full desktop experience" whether that is having access to the more powerful MS Suite or being able to create what you want to create or change what you want to change. Justin Roberti May 25, 2021, 4:25 PM Alex Lee, how many clients are you currently running? Are you focusing on enterprise or retail users? What are you projecting as your user growth by end of 2021? Alex Lee May 25, 2021, 4:41 PM We have servers (which is our own hardware) in 6 locations (Los Angeles, Dallas, Chicago, New York, Amsterdam, and Tokyo).  We hope to get to 80% capacity of these locations while adding more hardware and locations in the process by the end of the year!  But of course, I would love to go beyond that too. 🙂 Justin Roberti May 25, 2021, 4:51 PM Alex Lee how many current users? Alex Lee May 25, 2021, 4:54 PM Somewhere around 600 right now.  We just started up in February. Golda Velez May 25, 2021, 5:15 PM This is lovely, one would think a graphical browser could handle a shell!  Is the browser involved in rendering line by line? Can you just tunnel ssh to anywhere or only to your cloud? Or maybe I misunderstood - this is a whole ubuntu-desktop experience in the browser? Zlatan Todoric May 25, 2021, 5:29 PM Hi all. We bring them up via QEMU and then use Spice for remote desktop experience. We currently offer Windows 2019 Desktop Experience, Windows 7, Ubuntu 20.04 and Ubuntu 21.04, Debian Buster, 3 Manjaro editions, 4 openSUSE editions, Linux Mint, KDE neon user and testing, Kubuntu and Xubuntu. We also have Ubuntu and Debian servers and soon Android. More Linux distros are coming too. Zlatan Todoric May 25, 2021, 5:30 PM You can do it all in the browser, from any device that can handle modern browser or you can use our client apps too (for Windows, Mac, Linux, iOS and Android). Justin Roberti May 25, 2021, 5:33 PM Welcome Zlatan Todoric VP of Technology for Shell (I just thought I'd state that since I think it hasn't be stated yet). Justin Roberti May 25, 2021, 5:35 PM Zlatan Todoric is there a lite client download on the user end in order to be able to communicate with your remote OS on Shell? Zlatan Todoric May 25, 2021, 5:36 PM You don't need the client at all, you can just do it all in the browser. That said, the client itself is very light in nature (desktop one is written in Go and the mobile ones in Flutter but we plan to move them to Swift and Kotlin). Zlatan Todoric May 25, 2021, 5:41 PM 🔥 1 BTW, coming to your first question Justin Roberti - the virtual desktop apps for mobile (or vice versa), were unstable or heavy because virtualization (or emulation and in some instances full core) were done on the device itself. In our case, everything is offloaded to the cloud so you need just a decent internet connection and enough power to have one medium memory-hungry tab opened (average devices nowadays can handle 100 open tabs). Zlatan Todoric May 25, 2021, 5:47 PM For ssh - you can tunnel it. We also have a system in place for ssh key (which is a requirement for servers) and by default, we disable the password for ssh (as an increased step in security). Golda Velez May 25, 2021, 6:47 PM Nice - I would want it just for ssh from my phone. Zlatan Todoric May 25, 2021, 6:48 PM You can already do that :) Akasha Rose May 25, 2021, 10:49 PM Alex Lee and Zlatan Todoric - what do you define as the digital divide, and how is your service closing it? Alex Lee May 25, 2021, 10:54 PM I define the digital divide as basically the gap between those who have access to technology that enables them to go and achieve their dreams or be successful and those that do not have access. Alex Lee May 25, 2021, 10:56 PM Our service can close that gap in a variety of ways from being able to provide a desktop computing experience to someone who may only be able to afford a cheap smartphone or tablet to enhance an old or dated device which has its limitations whether it be access to Windows or just does not have the requirements to run the user's desired configuration. Alex Lee May 25, 2021, 11:00 PM We are working closely with some schools right now and providing a single environment to classrooms.  We visited a high school recently and a classroom of 20 had 5 different models of Chromebooks and HP laptops of all different ages and types.  Our product can help to provide that consistent environment to settings like this. Akasha Rose May 26, 2021, 6:24 AM What do you think the future for Linux holds? It's come a long way in the last couple of decades. Zlatan Todoric May 26, 2021, 6:58 AM Linux in one way or another dominated different fields over  time - be it supercomputers or phones or servers. Cloud desktop is the next step in the evolution of desktop experience, in the same way that the talk about cloud servers was a decade ago. There is a famous joke "The year of Linux Desktops" but with the cloud, we are seeing the move towards there as now it is easier and secure more than ever to test the most advanced systems out there. Microsoft is even having Linux in their offering and with WSL they are showing the pattern in that way. Alex Lee May 27, 2021, 3:25 AM 🔥 1 All - Thanks for the questions in the AMA.  Here is a special link for a discounted rate for those that want to try our product!  Thanks again and don't hesitate to ask us any other questions! https://www.shells.com/l/en-US/pricing?special=slogging

AMA About The Future of Virtual Desktops 

PC equipment alludes to the physical segments that make up a PC framework. There are a wide range of sorts of equipment that can be introduced inside, and associated with the outside, of a PC. Rundown of Computer Hardware Here are some basic individual PC equipment parts that you’ll regularly discover inside a cutting edge PC. These parts are quite often found inside the PC’s case, so you won’t see them except if you open the PC: Motherboard Focal Processing Unit (CPU) Arbitrary Access Memory (RAM) Power Supply Video Card Hard Drive (HDD) Solid-State Drive (SSD) Optical Drive (e.g., BD/DVD/CD drive) Card Reader (SD/SDHC, CF, and so on.) Here is some basic equipment that you may discover associated with the outside of a PC, albeit numerous tablets, workstations, and netbooks coordinate a portion of these things into their lodgings: Screen KeyBoard Mouse Battery Backup (UPS) Flash Drive Printer Speakers External Hard Drive Pen Tablet Here are some less regular individual PC equipment gadgets, either on the grounds that these pieces are presently typically incorporated into different gadgets or in light of the fact that they’ve been supplanted with fresher innovation: Sound Card Network Interface Card (NIC) Development Card (Firewire, USB, and so forth.) Hard Drive Controller Card Simple Modem Scanner Projector Floppy Disk Drive Joystick Webcam Mouthpiece Tape Drive Compressed memory drive The accompanying equipment is alluded to as network equipment, and different pieces are frequently part of a home or business organize: Computerized Modem (e.g., Cable Modem, DSL Modem, and so on.) Switch System Switch Passage Repeater Scaffold Print Server Firewall System equipment isn’t as unmistakably characterized as some different sorts of PC equipment. For instance, many home switches will frequently go about as a mix switch, switch, and firewall. Notwithstanding all the things recorded over, there’s more PC equipment called helper equipment, of which a PC may have none, or a few, of certain sorts: Fan (CPU, GPU, Case, and so on.) Warmth Sink Information Cable Force Cable CMOS Battery Daughterboard A portion of the gadgets recorded above are called fringe gadgets. A fringe gadget is a bit of equipment (regardless of whether interior or outside) that isn’t really associated with the PC’s fundamental capacity. Models incorporate a screen, video card, plate drive, and mouse. Investigating Faulty Computer Hardware PC equipment segments separately heat up and chill off as they’re utilized and afterward not utilized, implying that in the long run, each and every one will fall flat. Some may even flop simultaneously. Luckily, in any event with PCs and some PC and tablet PCs, you can supplant the non-working bit of equipment without supplanting or revamp the PC without any preparation. Here are a few assets you should look at before you go out and buy another hard drive, substitution RAM sticks, or whatever else you think might be turning sour: # MEMORY (RAM) Free Memory Test Programs How Do I Replace the Memory (RAM) in my Computer? # HARD DRIVE Free Hard Drive Testing Programs Business Hard Drive Testing Tools How Do I Replace a Hard Drive? What to Do When Your Hard Drive Is Making Noise # PC FAN The most effective method to Troubleshoot a Noisy Computer Fan CPU Fan Error: What It Is and How to Fix It In Microsoft Windows, equipment assets are overseen by Device Manager. It’s conceivable that a “flawed” bit of PC equipment is extremely just needing a gadget driver establishment or update, or for the gadget to be empowered in Device Manager. Equipment gadgets won’t work at all if the gadget is incapacitated, or may not be running appropriately if an inappropriate driver is introduced. How Do I View a Device’s Status in Device Manager in Windows? How Do I Enable a Device in Device Manager in Windows? How Do I Update Drivers in Windows? Where Can I Get Free Driver Downloads? Free Driver Updater Tools On the off chance that you conclude that some equipment needs supplanting or overhauling, discover the maker’s help site for guarantee data (in the event that it concerns you) or search for indistinguishable or updated parts that you can purchase legitimately from them. See these equipment establishment recordings for walkthroughs on introducing distinctive PC equipment, similar to a hard drive, power gracefully, motherboard, PCI card, and CPU. Hardware versus Software A PC framework isn’t finished except if there’s likewise programming, which is not the same as equipment. The product is information that is put away electronically, similar to a working framework or a video altering device, which runs on the equipment. Hardware gets its name because of the way that it’s severe with regards to adjustments, though programming is increasingly adaptable (i.e., you can without much of a stretch update or change programming). Previously published at http://kbwebdeveloper.in/all-that-you-need-to-know-about-computer-hardware/

All You Need To Know About Computer Hardware

In today’s world, the number of mobile device users is continuously growing. This entails the rapid increase in the mobile application development market. Companies around the globe launch hundreds of apps daily. They upgrade their functionality and design with the latest trends. Industry leaders regularly modernize platforms for their creation, filling them with new construction principles. Android is one of the most popular operating systems for modern devices. Smartphones, tablets, e-books, game consoles, netbooks, televisions are working on its basis. There are also wearable devices — gadgets such as Android Wear-based watches, step trackers, fitness bracelets, and glasses. However, Android mobile app development is a complicated and time-consuming task. That is why companies choose first to construct patterns to ease their work. are adapted to solve the most common problems, the main purpose of which is to describe the interaction of objects in an application. A pattern is not a complete design and cannot be directly converted to code. This is just an auxiliary framework for the task, which can significantly simplify the creation of an app. Design patterns Most developers today use an object-oriented approach to programming when working on large projects, or at least are familiar with its basic concept. It is difficult, and sometimes even impossible, to begin serious work from scratch, without using patterns in the code, or at least in the design. In this regard, programmers need to highlight key points for themselves from each project. Abstracting from any specific tasks, they build a mobile app framework for future developments. Let’s consider the three most widely used patterns for android application creation: MVC, MVP, and MVVM. Model-View-Controller Design Pattern The Model-View-Controller design pattern formed the basis for the architectural solution of the first programming environment with a graphical user interface — Smalltalk-80. MVC was first described back in 1978 by Trygve Reenskaug, who worked for some time in the Xerox PARC laboratory. The architecture of the Model-View-Controller allows you to separate the data model, user interface, and control logic into three separate components. Moreover, a change in one component does not affect the work of the rest. Using this architecture is especially convenient for teamwork on large projects. For example, when developing an application, you can separate the development of the part of the site that is responsible for data processing, absolutely not worrying about how this data will be presented. Why? Because layouts and designs for the application interface can be absolutely independently created mobile app development As for programmers, they can also share their work. For example, some developers can focus on developing the program logic, and another on designing the database structure. When using MVC, it becomes possible to distribute the work of the team, thereby increasing overall performance. MVC consists of the following entities: is generally understood as a component of data administration. In most cases, this refers to communication with databases: a selection of specific data, adding new ones, and changing existing ones. The model does not know in what form it is necessary to present the data requested from it to the user; it only performs operations with “raw” data. It should have a standardized universal interface so that one model can use any number of other subsystems any number of times. Model describes the format of data output to the user, that is, a visual representation of the data taken from the Model. It receives at the input a set of raw data that must be transmitted to the user and formats it following the requirements of the user. View manages/handles all application processes. It handles external requests and fulfills the requirements contained in these requests. After that, it gives out relevant instructions to the Model. Then, it transfers the info received from the Model to the appropriate situation for the View. This is done to represent the necessary information correctly. Controller Model-View-Presenter MVP is a design template derived from MVC that is used mainly to build a user interface. The template first appeared in IBM and then used in Taligent in the 1990s. The MVP, unlike the MVC, has a slightly different approach. MVP scheme is similarly made up of three components. The Presenter is not required to adhere to the model changes. Now the Presenter (bearer), gives the idea of ​​changes. This approach allows you to create a presentation abstraction. You can implement this template by rendering view interfaces. Each presentation will have interfaces with specific sets of methods and properties required bearer. Bearer, in turn, initialized with this interface, subscribes to the event of the performance and, if necessary, data is suppressed. Model-View-View Model MVVM is a template that has emerged to circumvent the limitations of MVC and MVP patterns and brings together some of their strengths. It is used when designing an application architecture. It was originally introduced to the community by John Gossman in 2005 as a modification of the MVP template. MVVM is focused on modern development platforms and has three main components: : just like in the classic MVC, the Model represents the basic data needed to run the application (classes, structures). Model : a graphical interface, that is, a window, buttons, etc. View is on one side an abstraction of the View and, on the other, provides a wrapper of the Model data to be linked. That is, it comprises a Model that is converted to a View, and it also contains commands that the View can use to influence the Model. ViewModel Final Word The Model-View-Controller is a classic pattern that has been successfully implemented in web development. MVP/MVVM is said to be better than MVC architecture. But first, there is a small number of sources explaining the MVP and MVVM architecture on the mobile app development. Second, there is no empirical evidence and careful analysis to support this view. Thus, developers still doubt whether it is worth moving from MVC to new architectures. Because such decisions require the refactoring of the whole scheme, which demands a lot of effort from creation to testing. Also, for those who have used MVP, reducing MVVM encoding may be appealing. However, this will also cause new problems. So, the choice of a specific pattern is up to the developer, who determines a more suitable template for each particular case.

MVC vs. MVP vs. MVVM: Which Pattern to Choose for Android App Development?

Looking for a new desktop , I want to take a look at different desktop environments. The selection is wide and choices are almost unlimited. I like to start with Xubuntu today. Xubuntu is a derivative of Ubuntu which uses Xfce instead of GNOME as its desktop. This is how Xubuntu describes itself on the : for our school computers project website Xubuntu is an elegant and easy to use operating system. It comes with Xfce, which is a stable, light and configurable desktop environment. Xubuntu is perfect for those who want the most out of their desktops, laptops and netbooks with a modern look and enough features for efficient, daily usage. It works well on older hardware too. Does Xubuntu offer what it promises? Is it the best compromise of a modern desktop with low hardware requirements? I’d like to take a closer look at that using our criteria. Stability The installation went without any problems. This is no longer a problem in most cases today 🙂 . In my tests I could not see any stability problems so far. Support In contrast to Ubuntu LTS, Xubuntu LTS offers 3 instead of 5 years of support. 5 years is certainly better, but usually we will update our image after 3 years at the latest. On the server the longer support periods are more important than on the desktop. Low Hardware Requirements The hardware requirements for Xubuntu are quite low. They recommend 1GB RAM and 20GB free hard disk space. After the start Xubuntu consumes about 500MB of RAM. But as soon as you start Firefox with a few tabs, the consumption quickly rises to over 1GB. Therefore you should have at least 2GB installed. Otherwise Xubuntu is much more frugal than Ubuntu with GNOME or Unity. Easy to use This point is rather subjective, because everyone has become accustomed to a desktop environment over the years. No matter if Windows, macOS, Unity, Gnome, Xfce, KDE — I could continue the list. For example, I’ve got very used to and get along well with it. I find it difficult to switch to GNOME or any other desktop environment. In a school there is much more “bandwidth”. I don’t think you can do so much wrong here if you use Linux in school. For most of your students and teachers, the desktop environment will be new. It is much more important to offer introductions, training and workshops to get colleagues used to a new environment. In general, you can decide whether you prefer Windows 10 or macOS “style”. Unity Xfce has a start menu and can be extended with a dock (e.g. ). This should help most people to find their way around after a while. Plank Modern & Beautiful By default, Xubuntu’s appearance is rather old-fashioned, neither pretty nor particularly modern. You can use the package manager to install additional themes that make the Xfce desktop more beautiful. One is the Arc or Numix theme. I also found the theme interesting. Qogir Conclusion Xubuntu will definitely be a candidate for our Linux desktop in school. This is mainly due to the low hardware requirements. As soon as v7 is released as beta, we will test Xubuntu in our school environment. As you know, most difficulties and stumbling blocks only appear when you actually use it. The look&feel is a little damper, even if you can make it prettier. Although the new Ubuntu theme in Ubuntu 18.10 still looks better. linuxmuster.net Originally published at openschoolsolutions.org .

Xubuntu — Best Desktop for Linux in School?

So after ten years, are ? In 2008 saw the zenith of this with regards to personal computing. The as terribly fashionable, as platform holders did their best to squeeze as much computing as they could into as small a space as possible.Their ubiquity has all but vanished, given how powerful our smartphones have become. The phone that you’re reading this on, or is possibly in your pocket, or in your bag is a portable computing powerhouse — but imagine if something were not too much bigger than that, that you could do actual computer work on. A ultra portable computer running the last version of Kali . netbooks finally cool again Netbook linux Early this year a mini-PC the size of a portable video game console that sports a quad-core Intel Z8750 CPU, 8Gb DDR3 RAM, 128Gb Samsung eMMC for storage, 7000 mAh battery that manufacturer says should be good for up to 12 hous of run time, WiFi, bluetooth, HDMI port and all the things what would you want in a laptop of this size. The little computer measures just 7.1″ x 4.2″ x 0.7″ which makes it just a little larger than a big smartphone when the lid is closed. This thing looks adorable. It has the stylized Apple-style metal case rather than the more common plastic but whether you can actually fit the GPD Pocket in your pocket depends on how big your pockets are. GamePad Digital (GPD) a technology company based in Shenzen, China. Among other products, they have created several handheld video game consoles which run Android on ARM architecture and x86 Windows. And while Pocket it is not exactly cheap it is possible to find it at the same price of the original GPD crowdfunding. bought a GPD Pocket 7 $499 As is often the case with notebooks and Linux, the support shortly after hardware launch leaves much to be desired, even when the manufacturer offers its own linux and community driven support is already included in the recent versions of the Linux Kernel. Unfortunately the latest Kali need some kernel patches and workarounds to run on GPD. What people are doing meanwhile is installing ad-hoc Ubuntu or Xubuntu images ( ) from a pendrive and then installing all the Kali tools they need via scripts like Katoolin ( ) which will effectively convert almost any debian-based distro into a (more-or-less) fully-fledged Kali platform. I previously wrote about https://github.com/LionSec/katoolin And so it was until , a well respected hacker know for Raspberry Pi pentest oriented Linux builds, took things into his hands. Re4son has released a custom Kali Linux images for the GPD Pocket, including the lastest Kali 2018.2. Re4son Pocket-Kali Now installing Kali in the GPD is just as easy as in a vanilla PC. These are the steps you should follow: Download the latest ISO image from here: https://whitedome.com.au/re4son/download/kali-pocket/ Write the image to a USB drive (DD, Rufus, Win32DiskImager, ..) Insert the stick into your GPD pocket, turn on and press F7 until boot menu pops up Select boot from USB stick In the Kali menu, select “Kali Live” and boot into desktop There are two ways to install:1. The comfortable way with proper screen orientation but no encryption support– select “Live (forensic mode)” and boot into desktop– in Kali, run the debian-installer (if you booted into normal live mode, run GParted and unmount disks first)2. The 90 degree way – use this if you want to install kali in an encrypted volume– select “Install”– tilt your head 90 degrees Now you have to choose if you prefer to delete everything and install only Kali Linux or resize Windows and have both. Reboot Enjoy The Pocket-Kali image comes with only “top10” & “wireless” set of Kali tools to keep the size down. Just run “kali-linux-full” to get the whole shebang. If you choose to install Kali alongside with Windows 10 keep in mind that it is possible that after the installation Windows option do not appear in the boot grub menu. The solution is as easy as running “grub-update” from a root console and restarting the system. After some testing of Kali-Pi current Version: 4.18-rc5-re4son+_1 i have managed to get Kali up and running on the GPD pocket and everything works fine the fan, Wake/Sleep, WiFi/Bluetooth, proper screen rotation, graphics and audio. Remember that the mini GPD does not have an Ethernet port, but that something easily solved with one . USB-C gigabit ethernet adapters Thanks to its size and Kali Linux the GPD becomes an all around device for the Pen-testing on the go. Couple it with some external devices such as high power wifi/bluetooth or SDR radio receiver to turn it into the ultimate portable wardriving machine.

Kali Linux 2018.2 on your Pocket with the GPD 7 mini-laptop.

Why Windows on ARM?

On the web it is common to see this kind of layout when you’re browsing a plethora of content with no discernible structure: Your eye is naturally drawn to shapes you recognize. Our visual cortex can only process so much information at one time and our pea-sized attention spans can’t keep up. Whatever is not recognized is filtered out, and while it appears in your visual field it does not imprint. It’s called a and I’m here to tell you it sucks. masonry layout But the newspaper… In the days of the early mechanical printing columns were used because type blocks had to be manually arranged and later were formed on demand. This was no easy task. As we transitioned to modern printers, columns persisted because it allows more various articles above the fold and works well with advertising. So why do we still use these layouts on the web in the year 2017? For the very same reasons: advertising and loosely related content. If you are deciding whether or not you should use a masonry layout, the answer is almost always no. There is one exception: when you can’t sort your content by order of appeal. The Devil Goes by Many Names: , , , — these are all wonderful implementations of what a masonry layout looks like. And they power many websites because of their ease of use and the features they offer. The problem however, is they aren’t built for speed and deceive the developers using them. Isotope Packery Masonry.JS Bricks.JS In 2016 I was tasked with developing an entirely new React web application for . This is how the website looked before: The Hunt There is no doubt that this grid display is dated, but it’s also really easy to navigate because it’s linear. It flows like a book, line by line. If I could go back and make a better case for that layout, I would. Because masonry layouts are slow… We started with Isotope JS. I’ve used it before in previous projects and it does alright for lighter websites and static blogs. In development I never noticed any performance issues because I running an i7 Intel 3.4GHz processor and 16GB of RAM. When we launched our users were reporting crashes and laggy scrolling. I dug into the library and found the APIs it was hitting were much slower and out of date, causing unnecessary browser repaints. The other problem was Infinite Scroll, another bad user interface pattern we can’t seem to get rid. But that’s a whole other subject. In this case, the infinite scroll and masonry layout were loading hundreds of DOM elements per page and repainting far more than necessary. What good is a masonry layout if your users can’t get past page three on their netbooks and tablets? I combed through many of the masonry layout libraries out there. Against the recommendations of just about everyone I talked to, I knew I had to do the one thing I didn’t want to do. I wrote my own layout engine… Sometimes you have to start from scratch. What we need from a masonry layout is not the same others need. Many masonry layouts have wonderful features like variable element sizing and drag-and-drop. I set out to figure out why some of these libraries were slow. What I found is that many of the features that make them flexible also hurt. For example variable sizing is a huge performance drain: measuring a hundred element size models is expensive. And in some instances a library would use methods that would cause an unnecessary repaint to get the width and height. Another issue is that all of these elements add up and as the page grows, the calculations incur exponentially. Then I looked at Pinterest — the pinnacle of masonry. I wondered how the engineers managed to keep it fast and usable. So here is where I drew the line: Predetermined element height — this made it easier to layout items at the same place every time. Without this the entire layout would need to re-calculate to accommodate a new item. Fixed column widths — percentages are expensive and along with #1 this would save us from unnecessary paints and repaints to slow DOM APIs Render only what’s visible — by reducing the number of DOM elements the browser has less objects to paint on scroll and reflow. The process involved a lot of math and patience — calculating where to place items and how to place them efficiently. For example when an item spans multiple columns it will create a gap if the items above do not align. This was the most challenging aspect because we didn’t want a fast masonry layout with empty whitespace everywhere. All of this had to fit inside a React Component and go with the grain of lifecycle events. So when infinite scroll loads a new page only new items should layout. And what a beautiful React Component it turned out to be. I decided to write this post to document both the frustration and immense pride I felt for this effort. When I reflect on the process and the result — I see some of my best work. Trying and trying, iteration after iteration; the “are we there yet” moments. The voice at the back of my head saying “don’t write a layout engine.” The Result: a fast Masonry layout built in React It turned out to be incredibly performant. On a Chromebook there were no hiccups and rendering was faster than it took for the server to return the next page. I immediately made the component available via (check it out if you want to see what’s under the hood). It’s not available as a package because it’s not “one size fits all” and the last thing the web needs is another masonry library. Github You can see this engine running live @ thehunt.com proceed with caution. There are a plethora of resources available for Flexbox, CSS4 Grids, and performant interfaces on the web. Seek out better user experiences that flow naturally. If you are looking to do it yourself then I would suggest doing your research and eliminating over-engineered features. If you are thinking of rolling your own layout engine: I hope you enjoyed reading this: if you feel the pain of masonry layouts or you’ve been there before feel free to share your experience.

Masonry in React: A Performance Hell

There’s something about the whole analytical mind-set that effectively drives the ability to innovate out of the building. — Fred Gluck, managing director of McKinsey & Company Executives are extremely dissatisfied with their organization’s innovative performance but most firms seem to hate innovating. Does yours? In 2016, McKinsey reported that 84% of executives see innovation as extremely important but only 6% are satisfied with their organization’s innovative performance. Why the disconnect? Perhaps Marty Neumeier has the answer. In his book, , Neumeier shares his “good/different” matrix. Some products are good; some are not. Some products are different; some are not. ZAG: The #1 Strategy of High-performance Brands But what’s really startling is that “different” is usually seen as negative, at least internally. Consider the focus on “netbooks” a few years ago. Netbooks sounded good on paper. Both marketers and sales people (not to mention investors) pushed computer vendors to build cheap, underpowered laptops. But ask any consumer: netbooks were neither good nor different. Netbooks failed; tablets succeeded. “Good and different” products change the game. And it’s not just products. The good/different conundrum applies to all aspects of product management and marketing. A few years ago, a colleague was asked to create some new customer success stories. His interviews went so well and his customers revealed so much, he decided to use nothing but customer quotes in the write-ups. The pieces were excellent — the prospects loved the “voice of the customer” approach. Alas, sales people thought otherwise. They hated the quote-heavy pieces. “The new success stories are different than what we had before” was their only complaint. “Good and different” is hard to sell . internally Perhaps that’s why so many organizations fail at innovation. Almost no one likes “different.” Almost everyone likes “good” except when quality comes at too high a price. Innovation can be applied to packaging and pricing as well as product. But selling it… there’s the problem. After a few times of failing to sell a “different” idea internally, most product managers just give up. “Not different” is simply easier. Is your organization open to innovation? Or is the “more of the same” status quo the best political answer? Innovation in one of many popular deep-dive topics offered in Under10 workshops , available in onsite or webinar format. Originally published at www.under10playbook.com .

Why Your Organization Cannot Innovate

What Really Happened with Vista

If you don’t want to go through the trouble of setting up your own Mobile Device Lab, you can just use . Later edit: Endtest Those of us working in QA, or as a developer in a larger company (for example 100 people or more), will likely have had access to a dedicated set of test devices. Quite often, you will have two or three devices on your desk at any given time. Quite often, a developer or tester colleague will either ask to take the device, or “borrow” it before you are done. This might not be a large issue if your QA department is not large, or located in very close proximity to one another. Even so, the issue can cause office tension, as devices are returned uncharged, or you have to chase down a specific device for a particular test with a stringent deadline. Device charging can be a serious issue — where iOS devices tend to boot quickly when plugged in, some Android devices can take up to 20 minutes. If you have a limited time between meetings, and just want to quickly test your latest code on an older device or OS version, this wait can seem interminable. We know that a device cloud with a large set of real devices (both iOS and Android) can be a strong solution. Automated testing on such a cloud lab has even saved clients a great deal of money. However, even with this option open, we know that at times, manual testing is just plain necessary, and that organizations reaching a certain size know the importance of ready availability. If your organization is reaching this point, we hope to help, by providing some real world examples of existing solutions, as well as some insights from our own lab. Challenges when managing a device lab Strong mobile testing, as we discussed in a previous article, needs to include real device testing. This means that you need a mobile device lab, and one that provides a somewhat representative selection of devices. This in turn means that if relying on internal resources entirely, labs need to be larger than ever before. Because larger labs often require dedicated bandwidth and storage space, not to mention power demands and other issues, there are some logistical issues that must be dealt with. You must have a plan in place not only to power and provide connectivity, but to keep track of the devices, and to maintain them (latest OS, updates, etc.). It is precisely these issues that are pushing many organizations towards cloud device labs. Many organizations do not have formal device labs (ie a “large” set of devices, managed centrally, used exclusively for testing) right out of the gate, particularly startups. After a few devices disappear, after replacing cords, after chasing around the office for a particular device several times in a week, organizations come to the realization that things need to change…but what do they do about it? Well, this article hopes to shed some light on this, by presenting a few solutions seen “in the field.” What Devices to Test On? If developing for Android at all, it is safe to say that the bulk of your testing devices will be Android. With over , to have any true coverage will require several devices, both tablets and phones. 24000 distinct models on the market The days when you can get by testing a handful of devices are gone. According to data from OpenSignal, it can be argued that to cover 50% of the market, you needed approximately 60 devices last year. For 20% of the market, you could get by with 12. While a large device lab is certainly helpful, there comes a point where logistically speaking you are doing more harm than good. It is more important that you select the right devices, rather than a large number of devices. Analytics is key to this. Determine the top devices that are using your apps, or driving traffic to your site, and focus there. This was a , in the formation of their mobile lab. We consider an in-house lab of 30 to 40 devices fairly large, and that you can represent enough of the available market with this number (this total includes iOS devices), provided you select based on analytical data relevant to your organization. (This is particularly true if you also avail yourself of as well.) primary selection criteria of Etsy automated testing with a device cloud lab Coverage of the Android user-base with 12 devices can certainly exceed 20%, if they are the right devices. The following devices are a good starting point: Samsung Galaxy S3, S4 and S5 — Samsung Galaxy S3 and S4 remain some of the most popular smartphones. The S6 hasn’t sold as well as expected, making the S5 a more representative model. Samsung Devices provide strongest testing coverage Going by the top ten device list for Android, the majority of devices to test would be Samsung. However, sticking to one manufacturer is risky in and of itself. Hence, we recommend skipping to some of the up and coming models from other manufacturers. Other strong testing options LG G3 — Another high-end choice, with a 5.5-inch high-quality HD screen that makes it a suitable choice for mobile users that use multimedia on the go. Google Nexus 5 — Stock OS, smart design, great battery life and high-end features make this a good bet. Motorola Moto G — Low end price tag, but mid-end features make this a tester’s dream Sony Xperia Z2, Z3 — high end capacity, the best battery life available, and loved by the multimedia savvy. HTC One M8 — Another high-end option, but one that has come down in price a bit, making it more attractive for testing purposes. LG Nexus 4 — when testing compatibility for older OS versions, this is a go-to model at a fairly reasonable price. Used devices can be found for $90, sometimes less. Just be aware of . battery issues For tablets, it is a good idea to cover not only a couple of different screen sizes, but also high-end and low-end performance. Google Nexus 9 and 10 — it is always a good idea to go with a device that comes with stock OS and high-end capacity. Samsung Galaxy Tab 4 — An excellent 7 inch screen budget tablet, with a decent market share. LG G Pad 7 — This budget 7 inch device is said to outperform its peers at a lower price. If building a lab on a budget, this is an excellent option. LG G Pad 10 — same reasons as above, just a larger screen. Testing for iOS is much more straightforward, with far fewer devices to test, but you still need to cover more than just the latest model and OS. According to Forbes, half of iOS users are running their . This means that we cannot afford to simply test the latest version of iOS, but also the last generation — ideally both in terms of devices and OS. iOS devices on an out-dated version The above information assumes that your primary testing is Android and iOS — which is an assumption that is designed, like selecting key Android devices, to cover a majority of those reading this article. However, some organizations may need to cover more than this. It is important to remember that your organization’s needs and the purpose of the lab (open or private) are paramount to deciding the devices you need to support. Security and Device Management If running a private lab, security and device management are important considerations. Not only do you need to be able to find individual devices when needed, but you need to know they are safe when not in use. Sometimes this means physical security, rather than simple process-based tracking. Phunware Device Lab Locker Not only are devices locked away, but they are kept organized via OS-based labels, and managed by a dedicated resource. Devices are signed in and out via an app designed specifically for internal use by Phunware, with access to the devices handled by a single point of contact (ie, one employee has access — if said employee is away, the key is handed to another in his absence). This not only ensures that devices are protected from being lost or stolen, but also that at any given point, the last known user can be pinpointed by asking the “keymaster,” so the device can be recovered by the next person to book it. Open Device Lab Locker This methodical process and stringent control over testing devices is very effective in keeping tabs on numerous devices used for individual testing, in particular if the devices are often taken to an employee’s desk for testing, or even outside the office. Knowing who has the device is often half the battle. However, if you are testing numerous devices at once, this solution can be problematic. If all devices are locked away between uses, not only might booking of a large number of devices be tedious (if done individually), but so will setup. If hooking up numerous devices in a central location, you will need to find cables, power strips, and USB hubs, set up stands, and connect all the devices. You will then need to tear this down, and organize the devices, cabling and stands into their secured location when done. For this reason, some organizations are moving towards an open device lab design, whether or not it is truly open. Etsy’s shelf solution is a prime example. As you can see from the before picture, security was a primary concern. However, in creating their shelf solution, they moved away from this locked down approach in favor of a central location (visible to all), library cards for device sign-out , labelling, and an efficient cable management system. Etsy Device Lab: Before and After Etsy used library cards for signing out devices that leave the room, color coded by operating system. Labelled pockets were attached to the front of the shelf holding the device. On the front of the library card pockets they included: device name and number screen resolution pixel density operating system version “Each device gets a to hold its cable in place so they don’t fall behind the shelves, a library card with its information, Adobe Edge Inspect installed, an asset tag, and a stand that fits it.” cable drop Eventually, this library card sign-out process was replaced by a system using . RFID tags for device management Phunware too, has revised their device management and check-in/checkout process for lab devices. To improve visibility, they created an app, listing all lab devices, allowing their users to check devices out, then back in simply by scanning a barcode on the device, and entering their username. Phunware Inventory App Login Not only did the app help to streamline the check-out/check-in process, but it also helped increase visibility, ensuring that each developer/QA member could see at a glance what was available (and who had what). Phunware App: Barcode Scanner and Device List Screen Power When setting up your device lab, power will be of increasing concern, as the number of devices in the lab grow. Etsy discovered this first hand, particularly in light of their efforts to uphold the values of a (which includes a mandate to be environmentally conscious). Part of their ongoing effort is to track energy usage, and to set up power strips with timers to limit the number of hours per day the devices are plugged in and charging, this timer solution might also help avoid situations such as our .) certified B corporation potentially explosive Nexus 4 batteries In addition to limiting power consumption, logistics can be a concern as well. USB hubs must be able to support the large number of connections, and still provide adequate charge. Some of the hubs initially used would conk out after the devices were connected. A 32 port hub was obtained that provided 500 mA per port (16 A total), and this solved the charging problem. Finding a place to put cables for charging devices was also a concern. The shelving solution chosen by Etsy above handled this nicely, by placing the power supplies and USB connections along the bottom shelf. Managing test devices in use: Because the selection of devices can be rather eclectic, encompassing tablet devices alongside Android and iOS phones (and possibly some netbooks and non-iOS/Android devices), it is important to find a system that allows visibility of the devices being tested. For example, Etsy’s shelf-system allowed the devices to be displayed upright, and had room for cable management. This is the ideal for an open lab design, allowing you to view multiple devices at once while testing. If your device lab is closer in size, design, and scope to Phunware’s, and you do not run tests on your full range of devices often, but still test on several devices at once, it might be a good idea to look at some of the commercially available desktop stands to complement your lab solution. These stands allow you to stand 4–6 devices comfortably on your desk, with full visibility during testing. Desktop Device Lab Combining a solution such as this ( ) with a sync and charge USB hub, you can improve productivity and reduce clutter. These stands and hubs can be signed out in the same manner as the devices themselves. device display and easy cable management Regardless of the solution used to create and manage your device lab, it is important that you do not neglect real device testing. This might mean having your employees test on their own devices or accessing a cloud service. Also remember that a device lab can start small and grow over time. Invest in a few devices each year, purchase a few secondhand devices, and in no time you’ll be testing with the best of them.

Managing a Mobile Device Lab

In the modern fast-paced digital landscape, there is a high demand for streamlined and efficient data management tools and services. With the exponential growth of data-driven development and decision-making in organizations, there is a need for robust solutions to optimize data pipelines. Jupyter Notebooks have emerged as one of the most popular choices for organizations to use in data exploration and analysis due to their interactive and user-friendly interface. However, as the scale and complexity of data operations grow, the requirement to easily migrate Jupyter Notebooks into production environments becomes more essential. Here comes the (VDK), a framework that simplifies data ingestion and data processing. A toolset enabling you to run data jobs & comprehensive solutions for the productionization of Jupyter Notebooks. With its powerful features and capabilities, VDK serves as a game-changing tool that enables organizations to easily integrate Jupyter Notebooks into complex data pipelines while ensuring scalability, reproducibility, and enhanced workflow efficiency. Versatile Data Kit In this blog, we see all about , exploring the potential of this open-source toolkit to revolutionize the way organizations manage and process their data. Productionizing Jupyter Notebooks with VDK Understanding the Challenges with Jupyter Notebooks Despite their popularity in data analysis and exploration, Jupyter Notebooks have several inherent limitations when used in production environments. Sometimes, these limitations of the Jupyter Notebook pose significant hurdles during seamless integration into complex data pipelines. Understanding these challenges is important for companies aiming to use Jupyter Notebooks within their production workflows. Some of the biggest challenges include: Jupyter Notebooks sometimes struggle to handle large-scale data processing efficiently. When dealing with large datasets or sophisticated computations, the lack of optimised memory management and the sequential nature of execution might cause performance bottlenecks, impeding the seamless expansion of data pipelines. Scalability and Performance: Version control for Jupyter Notebooks in a collaborative environment can be difficult. Merging changes, managing revisions, and ensuring consistency across various versions of a notebook may become time-consuming, potentially resulting in version conflicts and data inconsistencies, especially when numerous team members are working at the same time. JSON file with lots of irrelevant information as you can see in the image below. Version control and Collaboration: During the deployment of Jupyter Notebooks in production, there is a significant challenge to ensure the reproducibility of results. Variations in the runtime environment, dependencies, and external libraries can all have an impact on the consistency of results, making it difficult to accurately replicate analyses or experiments, especially when switching across computing environments. Reproducibility and Environment Management: If proper security measures and access controls are not implemented in a production environment, Jupyter Notebooks can present security risks. Allowing unrestricted access to notebooks or exposing sensitive data might jeopardize data integrity and confidentiality, potentially leading to security breaches and unauthorized data tampering. Security and Access Control: Few more challenges exist, like irrelevant code, modularization and no proper method for testing due to lack of libraries and tools. Solving Problems with VDK VDK plays an important role in facilitating the seamless integration of Jupyter Notebooks into production pipelines. It is a robust and comprehensive framework designed to simplify the complex process of data ingestion, transformation, and deployment within production pipelines using Python and SQL. Its capabilities for version control, environment management, and scalable data processing enable organizations to overcome the inherent challenges associated with deploying Jupyter Notebooks in production environments. . Check the VDK GitHub repo to learn more We have discussed some of the challenges with Jupyter Notebook above; it’s time to see how we can solve those challenges with VDK. Non-Linear Execution and Hidden State Risks Notebooks support non-linear code execution, which might result in hidden dependencies when cells run out of order. It increases risks when moving to the production environment. Follow this image for understanding. To know about problems, try to focus on the first two objectives — Retrieve Data and Data Cleaning. To retrieve the data, We will first and then load it. After the execution, we can check the data. Now we will clean the data by removing some . import pandas testuser As you can see, while retrieving and cleaning the data, we are unaware of the exact operations that were executed; they are hidden. In the production environment, we aim to ensure clarity about the executed processes, thereby eliminating the need for hidden dependencies or states. Let's explore the VDK solution; we will employ the VDK cell tag (located at the top right in the image). Essentially, it will assign numbering to the tagged cell, indicating the order of execution as 1, 2, 3 when deployed. This approach provides assurance that there are no hidden dependencies or states, and we can only execute what is visible. Irrelevant Code Excessive irrelevant code, like unused statements or unrelated snippets, can be found in notebooks. During the experimental stages of development, a few snippets and useful algorithms that help in interactive changes can be problematic in production. Let’s see the VDK solution; we will do the data classification by assigning into predefined categories for clarity, as you can see in the image below. After executing code from cell 9, we get a new column that contains the types of users. scores To check if something unknown is added to this data, we have defined methods in file. After the visualization, we are sure that only Detractors and Promotors are present and classification is right. Now we have to ingest the organized data using vdk . helper.py job_input # sending data for ingestion
 job_input.send_tabular_data_for_ingestion(
 df.itertuples(index=False),
 destination_table="nps_data",
 column_names=df.columns.tolist()
 ) If we see our whole code, we can know for sure that and will not be executed in production. Some codes are relevant for development and irrelevant to deployment, so we can't just remove these always. VDK helps here to maintain performance without removing irrelevant code. df visualize_data(df) Testing When working with Jupyter Notebooks, there is a lack of proper tools and methods for testing notebooks. It becomes challenging to verify the code you've written, and resorting to alternative solutions is not practical, especially in larger teams. VDK addresses this issue by offering an end-to-end testing solution through the command, as illustrated in the image below. run After running this command, it will execute vdk cells of the above Jupyter Notebook like a production environment. If something fails, it will give the error message and the details of where it originated from. It will be easier for us to fix things. We can also use the testing feature from the create deployment method by checking the box. It will also behave the same, and in case of any failures, it will stop the deployment and show us the error and the exact trace. Run data job before deployment Version Control Version control with notebooks can often become a complex process due to their JSON-based format, which can contain excessive noise. VDK offers a solution to this predicament through two key features. See the version control after using VDK in the image below. Firstly, it implements Noise Reduction, a mechanism that cleanses the notebook’s JSON by eliminating non-essential elements, including execution counts and outputs, known to contribute to the “noise” in the data. This helps streamline the version control process by focusing solely on the relevant content. Secondly, VDK ensures Seamless Integration with Git, facilitating the seamless committing of code to Git during deployment in a cleaner state, free from unnecessary metadata. By simplifying version control and minimizing clutter, VDK enables a more efficient and streamlined workflow for managing notebook-based projects. If you want to understand what we are doing in a step-wise manner with Jupyter Notebook UI, check this . guide Conclusion To sum up, the application of VDK in the productionization of Jupyter Notebooks has numerous benefits. By using the Noise Reduction feature, VDK significantly simplifies the version control process by eliminating unnecessary components that could hinder effective data management. Furthermore, VDK’s seamless integration with Git ensures a tidier and more organized environment for code deployment and collaboration, reducing clutter and simplifying the workflow. Adopting VDK can greatly enhance data pipeline management, enabling users to streamline their operations and improve overall workflow efficiency. It is a valuable resource for those aiming to fully utilize their data-driven projects. Additional Resources 💡Check Versatile Data Kit GitHub Repo 💡Check Youtube Video Tutorial 💡Check the of VDK to learn more Getting Started guide 💡Check VDK in Jupyter Notebook UI guide 💡Go through VDK user guides Also published . here

How to Use Versatile Data Kit to Turn Your Jupyter Notebooks Into Scalable & Reliable Data Pipelines

The Notebooks of Leonardo Da Vinci — Complete by Leonardo da Vinci is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : The Notebooks of Leonardo Da Vinci — Complete Title : da Vinci Leonardo Author : Jean Paul Richter Translator : January 1, 2004 [eBook #5000] Most recently updated: December 28, 2020 Release date : English Language Table of Links PREFACE CONTENTS OF VOLUME I (Translated by Jean Paul Richter) PROLEGOMENA AND GENERAL INTRODUCTION TO THE BOOK ON PAINTING THE AUTHOR'S INTENTION TO PUBLISH HIS MSS LINEAR PERSPECTIVE SIX BOOKS ON LIGHT AND SHADE PERSPECTIVE OF DISAPPEARANCE THEORY OF COLOURS PERSPECTIVE OF COLOUR AND AERIAL PERSPECTIVE ON THE PROPORTIONS AND ON THE MOVEMENTS OF THE HUMAN FIGURE BOTANY FOR PAINTERS, AND ELEMENTS OF LANDSCAPE PAINTING THE PRACTICE OF PAINTING STUDIES AND SKETCHES FOR PICTURES AND DECORATIONS CONTENTS OF VOLUME II (Translated by Jean Paul Richter) THE NOTES ON SCULPTURE ARCHITECTURAL DESIGNS THEORETICAL WRITINGS ON ARCHITECTURE ANATOMY, ZOOLOGY, AND PHYSIOLOGY ASTRONOMY PHYSICAL GEOGRAPHY TOPOGRAPHICAL NOTES NAVAL WARFARE.—MECHANICAL APPLIANCES.—MUSIC PHILOSOPHICAL MAXIMS. MORALS. POLEMICS AND SPECULATIONS HUMOROUS WRITINGS LETTERS. PERSONAL RECORDS. DATED NOTES. MISCELLANEOUS NOTES About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Leonardo da Vinci (2004). The Notebooks of Leonardo Da Vinci — Complete. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5000/pg5000.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Notebooks of Leonardo Da Vinci — Complete by Leonardo da Vinci - Table of Links

How Jupyter Notebooks played an important role in the incredible rise in popularity of Data Science and why they are its future. Nowadays, many individuals and teams are flocking to the tools and techniques that enable them to leverage large amounts of data. What makes Jupyter Notebooks so appealing to data scientists? In this article I will dive into some of the underlying trends that have contributed to the success of Jupyter Notebooks and why I decided to build to leverage and further contribute to its success. Orchest Underlying technologies of data science Something that is less talked about is the connection between the many advances of machine learning and data science, and the underlying technologies that have been developed over the past decades. Specifically I'm talking about programming languages such as , operating systems like , compiler infrastructure like , and version control systems such as . Just to name a few. It's important to realize that . Python Linux LLVM Git fundamental projects like these have enabled the vast growth and advances in machine learning and data science The previously mentioned technologies have, among others, created fertile ground for individuals and companies to start leveraging data science tools and techniques. However, in order to leverage these technologies data scientists need to find a way to use them without requiring a significant time investment. Hiding complexity Technological building blocks are crucial when it comes to dealing with complexity. The modern computing stack has done an outstanding job of layering systems to make sure that whenever you want to perform a task, you are not encumbered with the many lower-level implementation details. Take for example the seemingly simple task of interacting with files. A simple Python snippet executes many low-level operations under the hood in order to give the engineer a high level, easy to use abstraction to interact with files. Having the high-level concept and implementation of files available increases programming productivity by orders of magnitude. file = open('hello.txt', 'w') For data science, high level frameworks such as TensorFlow let you define complex layered neural networks with just a few lines of code: model = tf.keras.models.Sequential([

tf.keras.layers.Flatten(input_shape=( , )),
  tf.keras.layers.Dense( , activation= ),
  tf.keras.layers.Dropout( ),
  tf.keras.layers.Dense( )
]) 28 28 128 'relu' 0.2 10 Jupyter Notebooks are great for hiding complexity by allowing you to interactively run in a contextual environment, centered around the specific task you are trying to solve in the notebook. high level code By ever increasing levels of abstraction data scientists become more productive, being able to do more in less time. When the cost of trying something is reduced to almost zero, you automatically become more experimental, leading to better results that are difficult to achieve otherwise. Experimentation driven development According to Wikipedia [1] Data Science is defined as: "an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data." It is exactly the application of the scientific methods that requires you to be able to run many experiments in order to validate your hypotheses. The value of tools therefore lies in making this as frictionless as possible. When data science is compared to traditional software engineering this point is often overlooked. for which the development process is more planned out and less explorative. The iterative and experimental nature of data science makes it fundamentally different from regular software engineering Interactive computing It is incredibly powerful to get immediate feedback when programming. Computing the outcome of your actions in realtime and consuming them in an easily digestible way, enables you to quickly draw conclusions about what works and what doesn’t. In a great talk by Bret Victor this principle is demonstrated through a clever example of how interactive feedback can help you find the best solution to a game design problem: Power of immediate feedback: visualizing the consequences of your changes. Let the character make the jump by finding the correct y-velocity. [2] I believe that the benefits of immediate feedback have been instrumental to Jupyter Notebooks' rise in popularity. Notebooks enable you to rapidly try ideas and experiment by providing you with immediate feedback when executing snippets of code. Through their cell-based structure and markdown support, they provide a scratchpad for your ideas which facilitates exploratory work even further. The Jupyter open source project has pioneered many of the concepts around interactive programming for data science and has built a great community around its ecosystem. To guarantee Jupyter Notebooks keep improving and to ensure that they are indeed the future of Data Science it's important to collaborate and rally around standardized and open source solutions. How we're contributing with Orchest To contribute to the collection of open source tools in the data science ecosystem my co-founder Yannick Perrenet and I decided to start . Orchest is an open source tool to supercharge your Jupyter workflow. It allows you to create data science pipelines that consist of individual Jupyter Notebooks as pipeline steps, combining the advantages of interactive notebooks with those of data pipelines. Orchest Through our personal experience as data scientists, we have discovered that significant technical complexity arises when doing large scale data science projects. Our mission is to make it painless and simple to leverage Jupyter Notebooks in cloud based environments while collaborating with others. By integrating Jupyter Notebooks in Orchest we believe we can leverage the strengths of notebooks to make them an even better tool for modern data science. As of today we are still at the very beginning of this journey with our just starting to take shape. We very much welcome contributions and suggestions from the wider community to further develop the software for a broad and diverse data science audience. GitHub project In another article we will dive into what exactly our vision is for Orchest. We will give concrete examples of current pain points for data scientists, how we are solving them with Orchest today and how we are planning to address  more challenges in the future. Stay tuned! [1] [2] Bret Victor - Inventing on Principle https://en.wikipedia.org/wiki/Data_science https://vimeo.com/36579366

Why Jupyter Notebooks are the Future of Data Science

“Notebooks” are web applications that run on the browser which basically give you, the user, an interface to a virtual machine that runs an out-of-the-box environment. They are based on the project — an open-source project that brings machine learning development to your browser! No more virtual environment set-up hassle from now on. Which is great news since a lot of developers want to deal only with data science projects and not get derailed from that by needing to configure and set up their computers. Jupyter Notebook By using “notebooks”, you don’t have to set up an environment to begin with. Building machine learning applications now comes out of the box with notebooks. Why They’re Useful :They are useful because they reduce the time that a developer takes to set up his/her environment: install packages, update them to the required versions, and any other network or file-structure configurations that take time to set-up. And all that needs to be done BEFORE jumping into developing the apps themselves. Now, with notebooks, everything is set-up and ready to go in a matter of seconds. Ease of use : Notebooks can be shared easily and built upon incrementally. This allows projects to be worked on in a flexible manner. You don’t need to waste time anymore with getting the latest version of your project and sending in your updated version or dealing with conflicting versions and editing mistakes. It works like a Google Drive document which is being constantly updated (or a Git repository). Collaboration Notebooks are easy ways to bundle and upload to a shareable web page. The tools are evolving, the theory of data science is developing and Jupyter Notebooks now reduces deployment time to a matter of seconds. This makes it an invaluable tool for education platforms which showcase projects and tutorials. Other collaborative online projects can take advantage of this trait and leverage it to its needs. Google has built some tools which make use of Jupiter Notebooks. Portability: Jupyter notebooks were built initially for Python, R and Julia. Those are the most popular languages used in Machine Learning. Python being used in Google’s machine learning framework Tensoorflow and in Facebook’s PyTorch. R and Julia are reputable as well and have been used for a long time. Nowadays, the Jupyter Notebook has extended its reach to many other programming langurages including JavaScript, C++ and Ruby. Multiple Programming Languages: the interface has a modern look and provides many useful controls. Code can also be run one block at a time, which makes debugging easier. Certain subsystems of the notebook server are designed to be extended or overridden by users. This means that the notebooks are customisable. Nice Interface: How Do I Use Them? There is an increasing number of third parties who make use of notebooks on their platforms. , or “Colab” for short, is a product from Google Research. Colab enables users to write and execute python code through the browser. It is especially well suited to machine learning, data analysis and education. Colaboratory is a hosted Jupyter notebook service that requires no setup to use, while providing free access to computing resources. Colaboratory Collaboratory is great for testing some machine learning projects that you found on github. : an joint educational platform and experimentation platform. Users create they’re own solutions and implement machine learning algorithms. The site has numerous “kernels” from which people can investigate how their peers tackled a problem and what they used to optimise their results. This platform is a great hub for community and learning within the machine learning space. Kaggle Sites like Kaggle brings community together making it easy for machine learning developers to learn from each other. : for any private uses (in which you prefer to avoid sharing your work via github or any other public platform) you can spin up your own server that serves Jupyter Notebooks. You can also customise them to fit your project needs or even your own branding! Personal server Being an open-source project, there is always the option of tayloring your own custom Jupyter Notebook-based solution. Conclusion The Notebooks have enabled users to access computing resources and to prototype machine learning agents quickly and without hassle in out-of-the-box environments, reducing the set-up time to 0. It is a huge leap that made it possible for communities to access and tweak their own implementations, improving on one-another and establishing machine-learning development further into the open-source space.

Intro to Jupiter Notebooks

Introduction This is a companion guide to (the "Part 1 guide"). I wrote the Part 1 guide on how to finetune Meta AI's model on Chilean Spanish. It is assumed that you have completed that guide and have generated your own finetuned XLS-R model. This guide will explain the steps to run on your finetuned XLS-R model via a . Working With wav2vec2 Part 1 - Finetuning XLS-R for Automatic Speech Recognition wav2vec2 XLS-R ("XLS-R") inference Kaggle Notebook Prerequisites and Before You Get Started To complete the guide, you will need to have: A finetuned XLS-R model for the Spanish language. An existing . Kaggle account Intermediate knowledge of Python. Intermediate knowledge of working with Kaggle Notebooks. Intermediate knowledge of ML concepts. Basic knowledge of ASR concepts. Building the Inference Notebook Step 1 - Setting Up Your Kaggle Environment Step 1.1 - Creating a New Kaggle Notebook Log in to Kaggle. Create a new Kaggle Notebook. The name of the notebook can be changed as desired. This guide uses the notebook name . spanish-asr-inference Step 1.2 - Adding the Test Datasets This guide uses the as its source for test data. Like the , the Peruvian speakers dataset also consists of two sub-datasets: 2,918 recordings of male Peruvian speakers and 2,529 recordings of female Peruvian speakers. Peruvian Spanish Speech Data Set Chilean Spanish Speech Data Set This dataset has been uploaded to Kaggle as 2 distinct datasets: Recordings of Peruvian male speakers Recordings of Peruvian female speakers Add both of these datasets to your Kaggle Notebook by clicking on . Add Input Step 1.3 - Adding the Finetuned Model You should have saved your finetuned model in of the guide as a . Step 4 Working With wav2vec2 Part 1 - Finetuning XLS-R for Automatic Speech Recognition Kaggle Model Add your finetuned model to your Kaggle Notebook by clicking on . Add Input Step 2 - Building the Inference Notebook The following 16 sub-steps build each of the inference notebook's 16 cells in order. You will note that many of the same utility methods from the Part 1 guide are used here. Step 2.1 - CELL 1: Installing Packages The first cell of the inference notebook installs dependencies. Set the first cell to: ### CELL 1: Install Packages ###
!pip install --upgrade torchaudio
!pip install jiwer Step 2.2 - CELL 2: Importing Python Packages The second cell imports required Python packages. Set the second cell to: ### CELL 2: Import Python packages ###
import re
import math
import random
import pandas as pd
import torchaudio
from datasets import load_metric
from transformers import pipeline Step 2.3 - CELL 3: Loading WER Metric The third cell imports the HuggingFace WER evaluation metric. Set the third cell to: ### CELL 3: Load WER metric ###
wer_metric = load_metric("wer") WER will be used to measure the performance of the finetuned model on test data. Step 2.4 - CELL 4: Setting Constants The fourth cell sets constants that will be used throughout the notebook. Set the fourth cell to: ### CELL 4: Constants ###
# Testing data
TEST_DATA_PATH_MALE = "/kaggle/input/google-spanish-speakers-peru-male/"
TEST_DATA_PATH_FEMALE = "/kaggle/input/google-spanish-speakers-peru-female/"
EXT = ".wav"
NUM_LOAD_FROM_EACH_SET = 3

# Special characters
SPECIAL_CHARS = r"[\d\,\-\;\!\¡\?\¿\।\‘\’\"\–\'\:\/\.\“\”\৷\…\‚\॥\\]"

# Sampling rates
ORIG_SAMPLING_RATE = 48000
TGT_SAMPLING_RATE = 16000 Step 2.5 - CELL 5: Utility Methods for Reading Index Files, Cleaning Text, and Creating Vocabulary The fifth cell defines utility methods for reading the dataset index files, as well as for cleaning transcription text and generating a random set of samples from test data. Set the fifth cell to: ### CELL 5: Utility methods for reading index files, cleaning text, random indices generator ###
def read_index_file_data(path: str, filename: str):
    data = []
    with open(path + filename, "r", encoding = "utf8") as f:
        lines = f.readlines()
        for line in lines:
            file_and_text = line.split("\t")
            data.append([path + file_and_text[0] + EXT, file_and_text[1].replace("\n", "")])
    return data

def clean_text(text: str) -> str:
    cleaned_text = re.sub(SPECIAL_CHARS, "", text)
    cleaned_text = cleaned_text.lower()
    return cleaned_text

def get_random_samples(dataset: list, num: int) -> list:
    used = []
    samples = []
    for i in range(num):
        a = -1
        while a == -1 or a in used:
            a = math.floor(len(dataset) * random.random())
        samples.append(dataset[a])
        used.append(a)
    return samples The method reads a dataset index file and produces a list of lists with audio filename and transcription data, e.g.: read_index_file_data line_index.tsv [
    ["/kaggle/input/google-spanish-speakers-chile-male/clm_08421_01719502739", "Es un viaje de negocios solamente voy por una noche"]
    ...
] The method is used to strip each text transcription of the characters specified by the regular expression assigned to in . These characters, inclusive of punctuation, can be eliminated as they don't provide any semantic value when training the model to learn mappings between audio features and text transcriptions. clean_text SPECIAL_CHARS Step 2.4 The method returns a set of random test samples with the quantity set by the constant in . get_random_samples NUM_LOAD_FROM_EACH_SET Step 2.4 Step 2.6 - CELL 6: Utility Methods for Loading and Resampling Audio Data The sixth cell defines utility methods using to load and resample audio data. Set the sixth cell to: torchaudio ### CELL 7: Utility methods for loading and resampling audio data ###
def read_audio_data(file):
    speech_array, sampling_rate = torchaudio.load(file, normalize = True)
    return speech_array, sampling_rate

def resample(waveform):
    transform = torchaudio.transforms.Resample(ORIG_SAMPLING_RATE, TGT_SAMPLING_RATE)
    waveform = transform(waveform)
    return waveform[0] The method loads a specified audio file and returns a multi-dimensional matrix of the audio data along with the sampling rate of the audio. All the audio files in the training data have a sampling rate of Hz. This "original" sampling rate is captured by the constant in . read_audio_data torch.Tensor 48000 ORIG_SAMPLING_RATE Step 2.4 The method is used to downsample audio data from a sampling rate of to the target sampling rate of . resample 48000 16000 Step 2.7 - CELL 7: Reading Test Data The seventh cell reads the test data index files for the recordings of male speakers and the recordings of female speakers using the method defined in . Set the seventh cell to: read_index_file_data Step 2.5 ### CELL 7: Read test data ###
test_data_male = read_index_file_data(TEST_DATA_PATH_MALE, "line_index.tsv")
test_data_female = read_index_file_data(TEST_DATA_PATH_FEMALE, "line_index.tsv") Step 2.8 - CELL 8: Generating Lists of Random Test Samples The eighth cell generates sets of random test samples using the method defined in . Set the eighth cell to: get_random_samples Step 2.5 ### CELL 8: Generate lists of random test samples ###
random_test_samples_male = get_random_samples(test_data_male, NUM_LOAD_FROM_EACH_SET)
random_test_samples_female = get_random_samples(test_data_female, NUM_LOAD_FROM_EACH_SET) Step 2.9 - CELL 9: Combining Test Data The ninth cell combines the male test samples and female test samples into a single list. Set the ninth cell to: ### CELL 9: Combine test data ###
all_test_samples = random_test_samples_male + random_test_samples_female Step 2.10 - CELL 10: Cleaning Transcription Test The tenth cell iterates over each test data sample and cleans the associated transcription text using the method defined in . Set the tenth cell to: clean_text Step 2.5 ### CELL 10: Clean text transcriptions ###
for index in range(len(all_test_samples)):
    all_test_samples[index][1] = clean_text(all_test_samples[index][1]) Step 2.11 - CELL 11: Loading Audio Data The eleventh cell loads each audio file specified in the list. Set the eleventh cell to: all_test_samples ### CELL 11: Load audio data ###
all_test_data = []

for index in range(len(all_test_samples)):
    speech_array, sampling_rate = read_audio_data(all_test_samples[index][0])
    all_test_data.append({
        "raw": speech_array,
        "sampling_rate": sampling_rate,
        "target_text": all_test_samples[index][1]
    }) Audio data is returned as a and stored in as a list of dictionaries. Each dictionary contains the audio data for a particular sample, the sampling rate, and the text transcription of the audio. torch.Tensor all_test_data Step 2.12 - CELL 12: Resampling Audio Data The twelfth cell resamples audio data to the target sampling rate of . Set the twelfth cell to: 16000 ### CELL 12: Resample audio data and cast to NumPy arrays ###
all_test_data = [{"raw": resample(sample["raw"]).numpy(), "sampling_rate": TGT_SAMPLING_RATE, "target_text": sample["target_text"]} for sample in all_test_data] Step 2.13 - CELL 13: Initializing Instance of Automatic Speech Recognition Pipeline The thirteenth cell initializes an instance of the HuggingFace library class. Set the thirteenth cell to: transformer pipeline ### CELL 13: Initialize instance of Automatic Speech Recognition Pipeline ###
transcriber = pipeline("automatic-speech-recognition", model = "YOUR_FINETUNED_MODEL_PATH") The parameter must be set to the path to finetuned model added to the Kaggle Notebook in , e.g.: model your Step 1.3 transcriber = pipeline("automatic-speech-recognition", model = "/kaggle/input/xls-r-300m-chilean-spanish/transformers/hardy-pine/1") Step 2.14 - CELL 14: Generating Predictions The fourteenth cell calls the initialized in the previous step on the test data to generate text predictions. Set the fourteenth cell to: transcriber ### CELL 14: Generate transcriptions ###
transcriptions = transcriber(all_test_data) Step 2.15 - CELL 15: Calculating WER Metrics The fifteenth cell calculates WER scores for each prediction as well as an overall WER score for all predictions. Set the fifteenth cell to: ### CELL 15: Calculate WER metrics ###
predictions = [transcription["text"] for transcription in transcriptions]
references = [transcription["target_text"][0] for transcription in transcriptions]

wers = []
for p in range(len(predictions)):
    wer = wer_metric.compute(predictions = [predictions[p]], references = [references[p]])
    wers.append(wer)

zipped = list(zip(predictions, references, wers))
df = pd.DataFrame(zipped, columns=["Prediction", "Reference", "WER"])
wer = wer_metric.compute(predictions = predictions, references = references) Step 2.16 - CELL 16: Printing WER Metrics The sixteenth and final cell simply prints the WER calculations from the previous step. Set the sixteenth cell to: ### CELL 16: Output WER metrics ###
pd.set_option("display.max_colwidth", None)
print(f"Overall WER: {wer}")
print(df) WER Analysis Since the notebook generates predictions on random samples of test data, the output will vary each time the notebook is run. The following output was generated on a run of the notebook with set to for a total of 6 test samples: NUM_LOAD_FROM_EACH_SET 3 Overall WER: 0.013888888888888888
                                                                              Prediction  \
0                                     quiero que me reserves el mejor asiento del teatro   
1                                         el llano en llamas es un clásico de juan rulfo   
2         el cuadro de los alcatraces es una de las pinturas más famosas de diego rivera   
3                           hay tres cafés que están abiertos hasta las once de la noche   
4  quiero que me recomiendes una dieta pero donde uno pueda comer algo no puras verduras   
5        cuántos albergues se abrieron después del terremoto del diecinueve de setiembre   

                                                                               Reference  \
0                                     quiero que me reserves el mejor asiento del teatro   
1                                         el llano en llamas es un clásico de juan rulfo   
2         el cuadro de los alcatraces es una de las pinturas más famosas de diego rivera   
3                           hay tres cafés que están abiertos hasta las once de la noche   
4  quiero que me recomiendes una dieta pero donde uno pueda comer algo no puras verduras   
5       cuántos albergues se abrieron después del terremoto del diecinueve de septiembre   

        WER  
0  0.000000  
1  0.000000  
2  0.000000  
3  0.000000  
4  0.000000  
5  0.090909 As can be seen, the model did an excellent job! It only made one error with the sixth sample (index ), mis-spelling the word as . Of course, running the notebook again with different test samples and, more importantly, a larger number of test samples, will produce different and more informative results. Nonetheless, this limited data suggests the model can perform well on different dialects of Spanish - i.e. it was trained on Chilean Spanish, but appears to perform well on Peruvian Spanish. 5 septiembre setiembre Conclusion If you are just learning how to work with wav2vec2 models, I hope that the guide and this guide were useful for you. As mentioned, the finetuned model generated by the Part 1 guide is not quite state-of-the-art, but should still prove useful for many applications. Happy building! Working With wav2vec2 Part 1 - Finetuning XLS-R for Automatic Speech Recognition

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