Advantages of fog: there is only one, reusing hardware that would be otherwise idle.
Disadvantages:
- in cloud, you can put your datacenter on the location with the cheapest possible power. On fog you can't.
- on fog there is some waste due to network communication.
- you will likely optimize code less well because you might be targeting a wide array of different types of hardware, so more power (and time) wastage. Furthermore, some of the hardware used will not not be optimal for the task, e.g. CPU instead of GPU.
All of this makes Ciro Santilli doubtful if it wouldn't be more efficient for volunteers simply to donate money rather than inefficient power usage.
Bibliography:
- greenfoldingathome.com/2018/05/28/is-foldinghome-a-waste-of-electricity/: useless article, does not compare to centralize, asks if folding the proteins is worth the power usage...
Cloud Computing market share in Q2 2022 by statista.com
. Source. Basically means "company with huge server farms, and which usually rents them out like Amazon AWS or Google Cloud Platform
Google BigQuery alternative.
They can't even make this basic stuff just work!
As of December 2023 on a
t2.micro instance, the only one part of free tier at the time with advertised 1 vCPU, 1 GiB RAM, 8 GiB disk for the first 12 months, on Ubuntu 22.04:$ free -h
total used free shared buff/cache available
Mem: 949Mi 149Mi 210Mi 0.0Ki 590Mi 641Mi
Swap: 0B 0B 0B
$ nproc
1
$ df -h /
Filesystem Size Used Avail Use% Mounted on
/dev/root 7.6G 1.8G 5.8G 24% /To install software:
sudo apt update
sudo apt install cowsay
cowsay asdfAs of December 2023, the cheapest instance with an Nvidia GPU is g4nd.xlarge, so let's try that out. In that instance, lspci contains:so we see that it runs a Nvidia T4 GPU.
00:1e.0 3D controller: NVIDIA Corporation TU104GL [Tesla T4] (rev a1)Be careful not to confuse it with g4ad.xlarge, which has an AMD GPU instead. TODO meaning of "ad"? "a" presumably means AMD, but what is the "d"?
Some documentation on which GPU is in each instance can seen at: docs.aws.amazon.com/dlami/latest/devguide/gpu.html (archive) with a list of which GPUs they have at that random point in time. Can the GPU ever change for a given instance name? Likely not. Also as of December 2023 the list is already outdated, e.g. P5 is now shown, though it is mentioned at: aws.amazon.com/ec2/instance-types/p5/
When selecting the instance to launch, the GPU does not show anywhere apparently on the instance information page, it is so bad!
Also note that this instance has 4 vCPUs, so on a new account you must first make a customer support request to Amazon to increase your limit from the default of 0 to 4, see also: stackoverflow.com/questions/68347900/you-have-requested-more-vcpu-capacity-than-your-current-vcpu-limit-of-0, otherwise instance launch will fail with:
You have requested more vCPU capacity than your current vCPU limit of 0 allows for the instance bucket that the specified instance type belongs to. Please visit aws.amazon.com/contact-us/ec2-request to request an adjustment to this limit.
When starting up the instance, also select:Once you finally managed to SSH into the instance, first we have to install drivers and reboot:and now running:shows something like:
- image: Ubuntu 22.04
- storage size: 30 GB (maximum free tier allowance)
sudo apt update
sudo apt install nvidia-driver-510 nvidia-utils-510 nvidia-cuda-toolkit
sudo rebootnvidia-smi+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.147.05 Driver Version: 525.147.05 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:1E.0 Off | 0 |
| N/A 25C P8 12W / 70W | 2MiB / 15360MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+If we start from the raw Ubuntu 22.04, first we have to install drivers:
- docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-nvidia-driver.html official docs
- stackoverflow.com/questions/63689325/how-to-activate-the-use-of-a-gpu-on-aws-ec2-instance
- askubuntu.com/questions/1109662/how-do-i-install-cuda-on-an-ec2-ubuntu-18-04-instance
- askubuntu.com/questions/1397934/how-to-install-nvidia-cuda-driver-on-aws-ec2-instance
From there basically everything should just work as normal. E.g. we were able to run a CUDA hello world just fine along:
nvcc inc.cu
./a.outOne issue with this setup, besides the time it takes to setup, is that you might also have to pay some network charges as it downloads a bunch of stuff into the instance. We should try out some of the pre-built images. But it is also good to know this pristine setup just in case.
We then managed to run Ollama just fine with:which gave:so way faster than on my local desktop CPU, hurray.
curl https://ollama.ai/install.sh | sh
/bin/time ollama run llama2 'What is quantum field theory?'0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swapsAfter setup from: askubuntu.com/a/1309774/52975 we were able to run:which gave:so only marginally better than on P14s. It would be fun to see how much faster we could make things on a more powerful GPU.
head -n1000 pap.txt | ARGOS_DEVICE_TYPE=cuda time argos-translate --from-lang en --to-lang fr > pap-fr.txt77.95user 2.87system 0:39.93elapsed 202%CPU (0avgtext+0avgdata 4345988maxresident)k
0inputs+88outputs (0major+910748minor)pagefaults 0swapsThese come with pre-installed drivers, so e.g. nvidia-smi just works on them out of the box, tested on g5.xlarge which has an Nvidia A10G GPU. Good choice as a starting point for deep learning experiments.
Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Intro to OurBigBook
. Source. We have two killer features:
- topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculusArticles of different users are sorted by upvote within each article page. This feature is a bit like:
- a Wikipedia where each user can have their own version of each article
- a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 3. Visual Studio Code extension installation.
Figure 4. Visual Studio Code extension tree navigation.
Figure 5. Web editor. You can also edit articles on the Web editor without installing anything locally.Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. 
- Infinitely deep tables of contents:
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact