Gadgets for Windows 1. Use gadgets in Windows 1. About 8. Gadget. Pack. Gadget. Pack makes it possible to use gadgets on Windows 1. How- to. FAQVersion history. How- to. First you need to download the installer (the link is on the top right of this page) and open it. This window will appear: Click on Install. And here you click on Yes. After a while (it can take some minutes) the installer will be complete and you need to click on Finish. These three default gadgets will appear on the right side. Click on the plus symbol on the top right to see all available gadgets. Here you can add a gadget to your desktop by double- clicking on it. If you move your cursor above a gadget you can drag it around your desktop. It doesn't need to stay on the sidebar. There will also appear some buttons next to the gadget. Click on the X to close a gadget or on the wrench icon to access its options. The options of the clock gadget allow you to enable the second hand or to change the timezone. The next gadget is a clipboard manager. Here I copied the installer file and then the name of the file. Clipboarder will display everything you copy to the clipboard. By clicking on an element you make it the current clipboard.
![]() The Best GPU For Ethereum Mining – NVIDIA and AMD Tested. DU Meter, free download. Graphical display of network data transfer rates, reports, alerts and more. Review of DU Meter with a star rating, 6 screenshots along with a. This way you can select older clipboards and paste them into other applications. You can also open a clipboard directly. The options of Clipboarder allow many possibilities. You can use the shortcut Win+C to select an older clipboarder (works well together with Ctrl+V). The options of the last gadget, the weather gadget, allow you to change the city. You can also right- click on the sidebar. If you don't like the sidebar you can close it there. You will still be able to use the gadgets. Click on options to change the behaviour of the sidebar. Here you can, for example, click on the first checkbox to make the sidebar always visible. This way you have your gadgets always in sight. A disadvantage is though, that the close button of maximized windows isn't on the top- right corner anymore. To fix this you can put the sidebar on the left side. Alternatively you can enable the second checkbox on the view tab. Here is a maximized video player and you can close it by using the X on the top- right of the screen. Of couse this will work for non- maximized windows as well. You can access some advanced options by right- clicking on the X. For example you can add a preview of the application to the sidebar. This way you can keep track of some applications on the side. You can hover with your cursor over the preview to peek into the window, just like in the taskbar. Right- clicking on the preview gives you some options as well. There are several weather gadgets included. Right- click on a gadget allows you to change its opacity. You can also access some general options about gadgets by selecting 8. Gadget. Pack Tools: FAQIs it free? Yes, completely. I didn't really use gadgets previously. Is it really worth installing these? You should try it. The gadgets included are really useful when working with the PC. Unlike Metro- Apps gadgets can access useful system- information. The included Clipboard- Manager gadget for example will highly improve your productivity. With the Sidebar gadget, you can make the gadgets visible while working with maximized windows. Monitoring network traffic, setting the volume with one click and having an analog clock visible all the time are things you don't want to give up once you get used to it. Windows 1. 0 updated and automatically uninstalled 8. Gadget. Pack! Older versions of 8. Gadget. Pack wern't compatible. Just reinstall the current version and everything should work. Win+G on Windows 1. But I want to use it to access my gadgets! Version 1. 5 and later lets you override the shortcut to use it for gadgets. You can control this option in the 8. Gadget. Pack Tools menu. The gadgets look very tiny, especially on screens with high resolutions. Can they be made bigger? Windows 8. 1 (and Windows 7 with Internet Explorer 1. DPI- setting. In version 1. I was finally able to find a workaround that fixes this. You just need to upgrade to solve this. You can even configure a custom scaling by using the registry files found in "C: \Program Files (x. Windows Sidebar\8. Gadget. Pack". Is this English- Only? The installer is available only in English, but many of the gadgets have many localizations. What are the requirements? This works on Windows 7 / 8 / 8. Administrative rights are required to install. Does it work on Windows 7? Yes! Version 1. 2 and higher can be installed even on Windows 7. Windows 7 already does have gadgets by default, but this program gives you an easy way to install many high- quality gadgets. It can also bring the gadgets back in case they were disabled or uninstalled. In case you don't like 8. Gadget. Pack you can uninstall it and still use the gadgets provided by Windows 7. Why is there a sidebar? Windows 7 didn't have one. This is actually just a gadget to help you keeping the gadgets organized and visible. You can right- click on it and select "close sidebar" to do so. Even with the sidebar the gadgets can still be moved onto the desktop as you like. Or you can even add more sidebars by adding the gadget "7 Sidebar". This is especially useful when working with multiple monitors. You can also make the sidebar appear automatically when touching the border and customize its design. Right- click on it and select options to do so. Will 8. Gadget. Pack install some crap on my PC? No, the installer will only install the original Microsoft files and set the neccessary registry entries to make the gadgets work again. The only addition is a small tool (8. Gadget. Pack Tools) which fixes several bugs and allows you to change various settings. There is also an uninstaller included which removes everything the installer added. I could make quite some money by adding adware to the installer, but I'm sure you appreciate it that this installer is clean. Aren't gadgets unsafe? Afterall, didn't Microsoft remove them for a reason? Gadgets can contain viruses like any other software. Despite the warning when installing a gadget people seemed to think that it is safe to run any gadget (e. That is not the case. Opening a gadget is as dangerous as it is to run an . But this is not a security hole. If an attacker wanted to access your computer, he'd need to convince you to open his prepared . As long as you trust the source of the gadgets you install and you use anti- virus software you should be safe. Quote from Microsofts official statement to this: "How could an attacker exploit the vulnerability? An attacker would have to convince a user to install and enable a vulnerable Gadget." (source)Some monitoring gadgets display the graph incorrectly. How to fix this? Unfortunately I wasn't able to fix this issue. The bug occurs deep in the internet explorer rendering api and I wasn't able to find a workaround. You can however use the gadgets Glassy CPU Monitor and Glassy Network Monitor I made which don't have this issue. Will I be able to install gadgets other than the ones included? Yes, when 8. Gadget. Pack is installed you can open and install . Windows Vista or Windows 7. But be careful, gadgets can contain, just like other programs, viruses or trojans. Will all gadgets work that worked on Windows 7? No, some gadgets don't work or are unreliable on Windows 8 / 1. This is mostly because of Internet Explorer 1. Gadget. Pack already uses some tricks to workaround some problems but if you have issues with a gadget you better contact the author of the gadget and ask him to fix it. There are also some gadgets that work on Windows 7 / 8, but not on 1. Network monitoring gadgets (but the ones in 8. Gadget. Pack all work). I already installed an older version of 8. Gadget. Pack. What do I have to do to upgrade? Simply download the current installer and run it. It will update your installation and even update outdated gadgets automatically. You don't have to uninstall first. All gadget settings will remain. There appears some error message in the installer. First, try to download the installer again and check if the error still appears. Try to download and run this removal tool. It is a small program I made that can remove broken installations, and also removes all gadgets and gadget settings. If that is not working, try the following: Go to Task. Manager > Details > Explorer. End Task. Now click on File in Task Manager > type Explorer. Create this task with administrative privileges). Now try the installation again. Which GPU(s) to Get for Deep Learning. Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. TL; DRHaving a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems. Without this rapid feedback it just takes too much time to learn from one’s mistakes and it can be discouraging and frustrating to go on with deep learning. With GPUs I quickly learned how to apply deep learning on a range of Kaggle competitions and I managed to earn second place in the Partly Sunny with a Chance of Hashtags Kaggle competition using a deep learning approach, where it was the task to predict weather ratings for a given tweet. In the competition I used a rather large two layered deep neural network with rectified linear units and dropout for regularization and this deep net fitted barely into my 6. GB GPU memory. Should I get multiple GPUs? Excited by what deep learning can do with GPUs I plunged myself into multi- GPU territory by assembling a small GPU cluster with Infini. Band 4. 0Gbit/s interconnect. I was thrilled to see if even better results can be obtained with multiple GPUs. I quickly found that it is not only very difficult to parallelize neural networks on multiple GPUs efficiently, but also that the speedup was only mediocre for dense neural networks. Small neural networks could be parallelized rather efficiently using data parallelism, but larger neural networks like I used in the Partly Sunny with a Chance of Hashtags Kaggle competition received almost no speedup. Later I ventured further down the road and I developed a new 8- bit compression technique which enables you to parallelize dense or fully connected layers much more efficiently with model parallelism compared to 3. However, I also found that parallelization can be horribly frustrating. I naively optimized parallel algorithms for a range of problems, only to find that even with optimized custom code parallelism on multiple GPUs does not work well, given the effort that you have to put in . You need to be very aware of your hardware and how it interacts with deep learning algorithms to gauge if you can benefit from parallelization in the first place. Setup in my main computer: You can see three GXT Titan and an Infini. Band card. Is this a good setup for doing deep learning? Since then parallelism support for GPUs is more common, but still far off from universally available and efficient. The only deep learning library which currently implements efficient algorithms across GPUs and across computers is CNTK which uses Microsoft’s special parallelization algorithms of 1- bit quantization (efficient) and block momentum (very efficient). With CNTK and a cluster of 9. GPUs you can expect a new linear speed of about 9. Pytorch might be the next library which supports efficient parallelism across machines, but the library is not there yet. If you want to parallelize on one machine then your options are mainly CNTK, Torch, Pytorch. These library yield good speedups (3. GPUs. There are other libraries which support parallelism, but these are either slow (like Tensor. Flow with 2x- 3x) or difficult to use for multiple GPUs (Theano) or both. If you put value on parallelism I recommend using either Pytorch or CNTK. Using Multiple GPUs Without Parallelism. Another advantage of using multiple GPUs, even if you do not parallelize algorithms, is that you can run multiple algorithms or experiments separately on each GPU. You gain no speedups, but you get more information of your performance by using different algorithms or parameters at once. This is highly useful if your main goal is to gain deep learning experience as quickly as possible and also it is very useful for researchers, who want try multiple versions of a new algorithm at the same time. This is psychologically important if you want to learn deep learning. The shorter the intervals for performing a task and receiving feedback for that task, the better the brain able to integrate relevant memory pieces for that task into a coherent picture. If you train two convolutional nets on separate GPUs on small datasets you will more quickly get a feel for what is important to perform well; you will more readily be able to detect patterns in the cross validation error and interpret them correctly. You will be able to detect patterns which give you hints to what parameter or layer needs to be added, removed, or adjusted. So overall, one can say that one GPU should be sufficient for almost any task but that multiple GPUs are becoming more and more important to accelerate your deep learning models. Multiple cheap GPUs are also excellent if you want to learn deep learning quickly. I personally have rather many small GPUs than one big one, even for my research experiments. So what kind of accelerator should I get? NVIDIA GPU, AMD GPU, or Intel Xeon Phi? NVIDIA’s standard libraries made it very easy to establish the first deep learning libraries in CUDA, while there were no such powerful standard libraries for AMD’s Open. CL. Right now, there are just no good deep learning libraries for AMD cards – so NVIDIA it is. Even if some Open. CL libraries would be available in the future I would stick with NVIDIA: The thing is that the GPU computing or GPGPU community is very large for CUDA and rather small for Open. CL. Thus, in the CUDA community, good open source solutions and solid advice for your programming is readily available. Additionally, NVIDIA went all- in with respect to deep learning even though deep learning was just in it infancy. This bet paid off. While other companies now put money and effort behind deep learning they are still very behind due to their late start. Currently, using any software- hardware combination for deep learning other than NVIDIA- CUDA will lead to major frustrations. In the case of Intel’s Xeon Phi it is advertised that you will be able to use standard C code and transform that code easily into accelerated Xeon Phi code. This feature might sounds quite interesting because you might think that you can rely on the vast resources of C code. However, in reality only very small portions of C code are supported so that this feature is not really useful and most portions of C that you will be able to run will be slow. I worked on a Xeon Phi cluster with over 5. Xeon Phis and the frustrations with it had been endless. I could not run my unit tests because Xeon Phi MKL is not compatible with Python Numpy; I had to refactor large portions of code because the Intel Xeon Phi compiler is unable to make proper reductions for templates — for example for switch statements; I had to change my C interface because some C++1. Intel Xeon Phi compiler. All this led to frustrating refactorings which I had to perform without unit tests. It took ages. It was hell. And then when my code finally executed, everything ran very slowly. There are bugs(?) or just problems in the thread scheduler(?) which cripple performance if the tensor sizes on which you operate change in succession. For example if you have differently sized fully connected layers, or dropout layers the Xeon Phi is slower than the CPU. I replicated this behavior in an isolated matrix- matrix multiplication example and sent it to Intel. I never heard back from them. So stay away from Xeon Phis if you want to do deep learning! Fastest GPU for a given budget. TL; DRYour fist question might be what is the most important feature for fast GPU performance for deep learning: Is it cuda cores? Clock speed? RAM size? It is neither of these, but the most important feature for deep learning performance is memory bandwidth.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
November 2017
Categories |