top of page

보건교육사회적협동조합 사이트 그룹

공개·회원 10명
Hermann Fomichev
Hermann Fomichev

XPS 13 Developer Edition Portfolio Up 8X In The States, 6 Core And More! High Quality


Today, in honor of Halloween, we are excited to announce that we are increasing the number of configurations in the developer edition portfolio by a factor of eight. In total, we now offer 18 different configurations of the 9th generation developer edition, 16 of which are available both online and offline.




XPS 13 developer edition portfolio up 8X in the States, 6 core and more!



Great article! I am researching now the best budget AI reinforcement learning hardware combination for a laptop. After some research and reading this article I basically ended up with two choices. Either RTX2060 (6G) and AMD Ryzen 9 4900H (8 cores) or RTX2070(8G) and Intel Core i7-10750H (6 cores). From the article I see that more memory for GPU is definitely a good thing, however, for Reinforcement learning, you mentioned that the more CPU cores the better. So I wonder if in this case more CPU cores and less GPU memory outweigh the less CPU cores and more GPU memory?


It may not be that important because the Turing RTX 20s series has too much computational FLOPS, meaning that most of it could not be used for performance gains and was useless. NVIDIA adjusted Ampere so that the needed computation and the available computation is more matched. You should not see any decrease from these statistics. NVIDIA however integrated a performance degradation for tensor cores in RTX 30 which will decrease performance (this is independent of the value that you quote).


I was keen on getting the RTX 3090 since it was rumored for the 24gb vram which comes in really handy for more sophisticated models (so far I had to do with 8gb which is fine for daily prototyping) since it seemed the perfect deep learning card while not having to invest into serious Quadro/Tesla cards. However, due to the competition of the upcoming AMD Big Navi and the new consoles Nvidia was overly generous with the amount of cuda cores/tensor units etc on the RTX 3080. On paper that beast offers even way more performance for its price than the cheaper RTX 3070 sibling. Now Tensorflow 2 as well as Pytorch have pretty good multi gpu support (about 92% gain for each additional gpu up to 4 gpus in most situations) and I am leaning very hard to get 2x RTX 3080 Founders Edition instead of one RTX 3090. Right now my setup will remain air cooled so I want to go with the Founders Edition which come with a pretty nice cooling solution.


Quadro series cards like the RTX 8000 and RTX 6000 have much more vRAM (respectively 48 GB and 24 GB) than does the RTX 2080 Ti (11 GB). Hence you can train much bigger networks on the RTX 6000, RTX 8000, and Titan RTX (24 GB vRAM) that you can on the RTX 2080 Ti. In terms of the number of GPU CUDA cores though, they are all very similar.


Over three years ago, we embraced the ARM ecosystem after evaluating the Qualcomm Centriq. The Centriq and its Falkor cores delivered a significant reduction in power consumption while maintaining a comparable performance against the processor that was powering our server fleet at the time. By the time we completed porting our software stack to be compatible with ARM, Qualcomm decided to exit the server business. Since then, we have been waiting for another server-grade ARM processor with hopes to improve our power efficiencies across our global network, which now spans more than 200 cities in over 100 countries.


We ran cf_benchmark multiple times and observed no significant run-to-run variations. Similar to industry standard benchmarks, we calculated the overall score using geometric mean to provide a more conservative result than arithmetic mean across all 49 workloads and category scores using their respective subset of workloads.


In single-core performance, the Ampere Altra outperformed the AWS Graviton2 by 16%. The differences in operating frequencies between the two processors gave the Ampere Altra a proportional advantage of up to 20% in more than half of our single-core workloads.


Brotli and Gzip are the two primary types of compression we use at Cloudflare. We find value in highly efficient compression algorithms as it helps us find a balance between spending more resources on a faster processor or having a larger storage capacity in addition to being able to transfer contents faster. The Ampere Altra performed well on both compression types with the exception of Brotli level 9 and Gzip level 4 multi-core performance.


With the advent of new more complex computing platforms, such as NPUs, accelerators, and hopeful more use of GPUs in cache-coherent fashions, Arm saw a need gap in its portfolio and decided to update its client-side interconnect IP.


One common positive feedback from users of PDFelement is that the software is highly intuitive. The developers constantly keep reimagining the UI to give users the best experience possible. This makes it easy to deploy in larger organizations with multiple offices in different countries and continents because the training time is minimal. Furthermore, the Windows version of the software bears a deliberate resemblance to many native Microsoft apps like Word, and the Mac version does the same for macOS users. There are also iOS and Android versions so you can work on the go or from anywhere, even do remote work from home without missing a beat.


David REVOY Author, 07 august 2020, 07:29 - Reply Alexb: Ha, I see. Iritated because my guide is not looking easy enough for easy-userfriendly-Linux-propaganda? :-D Not my cup of tea, sorry. I'm just an artist sharing a practical guide: it might still has room to be reformulated more easily --sure-- but all in all if I do things this way it's because other ways I experienced are worst or have bugs. About Flatpak: this system is (in my point of view) not ready. Critical bug example on Krita Flatpak: you can't install a brush bundle with Flatpak. I reported the bug on 18 June: , no reply, no support. It's also dangerous because I also had the case to rescue remotely a Linux Mint where the root partition suddently went totally filled overflow because it was filled with GB of Flatpak via regular system updates. The system couldn't login anymore while users needed to access email for medical reason. Finaly, your statement "...and directly from the official developers" is far to be true: not all developers manage their Flatpak afaik and Krita team for sure doesn't. That's all but don't get me wrong: I really wish things were easy as you write! But I have no idea where you got that type of informations... They are not similar to my reality where I have today to spend 5h into refactoring/testing/proofreading carefully my book project because I still cannot install Scribus 1.4.8stable on Ubuntu 20.04 while Mac, Windows 7, 8 and 10 user can make it with double clicking on a installer... and no Flatpak can save my day (so far?).


alexb3d 07 august 2020, 09:56 - Reply Irritated by what? There is no reason.I mean if you are doing a "practical" guide to show that you can work with Linux, it is not the best way. Windows users screw up the terminal and associate it with something old. Still I see this well, it is a guide.I saw about the brushes, and the error is not an error, that's why you don't get an answer. In flatpak and snap the routes were changed.The HDD with Mint, it happened to me the same with Ubuntu, but that was like 2/3 years ago, that was corrected. Compared to repos / PPA, Flatpak currently uses 10% more disk (root).In Flatpak the programs are certified, they can only be published by the developers or someone directly related, there is only a stable or testing version. Anyone can post in Snap, that's why you see 50 versions of the same program. The information came from the same Flatpak people. Let's ask him on twitter to verify.Forget about Scribs 1.4.8 on Ubuntu 20.04, Ubuntu abandoned QT4, and Scribus is working on version 1.5.6 based on QT5. The 1.5.5 was actually stable, it was ready to go, but with this change it is preferable to dedicate time to migration, it works excellent, if you have problems with the texts, talk to Franz Schmid, for a quick solution, he is very friendly.


Stefano 12 august 2020, 12:16 - Reply If you do not care too much about performance, you can use virtual machines for older (or different) software. I usually keep a couple of virtual machines with different versions/distributions so that I can test features or use some specific software. In your case you could run Scribus on a Ubuntu 19.10 virtual machine through VirtualBox or Qemu. I personally use Qemu, but I am mainly a developer and I am used to some features of that piece of software, while VirtualBox is more user friendly and probably better suited for your needs.


Transdermal alcohol sensor (TAS) devices have the potential to allow researchers and clinicians to unobtrusively collect naturalistic drinking data for weeks at a time, but the transdermal alcohol concentration (TAC) data these devices produce do not consistently correspond with breath alcohol concentration (BrAC) data. We present and test the BrAC Estimator software, a program designed to produce individualized estimates of BrAC from TAC data by fitting mathematical models to a specific person wearing a specific TAS device. Two TAS devices were worn simultaneously by 1 participant for 18 days. The trial began with a laboratory alcohol session to calibrate the model and was followed by a field trial with 10 drinking episodes. Model parameter estimates and fit indices were compared across drinking episodes to examine the calibration phase of the software. Software-generated estimates of peak BrAC, time of peak BrAC, and area under the BrAC curve were compared with breath analyzer data to examine the estimation phase of the software. In this single-subject design with breath analyzer peak BrAC scores ranging from 0.013 to 0.057, the software created consistent models for the 2 TAS devices, despite differences in raw TAC data, and was able to compensate for the attenuation of peak BrAC and latency of the time of peak BrAC that are typically observed in TAC data. This software program represents an important initial step for making it possible for non mathematician researchers and clinicians to obtain estimates of BrAC from TAC data in naturalistic drinking environments. Future research with more participants and greater variation in alcohol consumption levels and patterns, as well as examination of gain scheduling calibration procedures and nonlinear models of diffusion, will help to determine how precise these software models can become. Copyright 2014 by the Research Society on Alcoholism.


소개

그룹에 오신 것을 환영합니다. 다른 회원과의 교류 및 업데이트 수신, 동영상 공유 등의 활동을 시작하세요.

그룹 페이지: Groups_SingleGroup
bottom of page