Страница телеграм канала Spark in me - Internet, data science, math, deep learning, philosophy
Spark in me - Internet, data science, math, deep learning, philosophy
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All this - lost like tears in rain. Internet, data science, math, deep learning, philosophy. No bs. Our website - http://spark-in.me Our chat - https://goo.gl/WRm93d DS courses review - http://goo.gl/5VGU5A - https://goo.gl/YzVUKf
Входит в категории: Технологии
11.10.2019 12:10
Current state of TF vs PyTorch
This review kind of is nothing new, but if you are new to the market, here is my TLDR:
- In reseach PyTorch >> TF, except for obscure cases;
- For small teams PyTorch >> TF;
- For fast product delivery and iteration PyTorch >> TF;
- For corporations TF > PyTorch;
- For edge computing / mobile now TF > PyTorch;
- For production in general, soon PyTorch ~ TF;
- The research community will not likely switch from PyTorch to TF 2.0;
- The remaining question now - will the large corporations / captive audiences switch to TF 2.0 from 1.0 or to PyTorch;
#deep_learning
10.10.2019 20:10
https://github.com/pytorch/pytorch/releases/tag/v1.3.0
More experimental features)
10.10.2019 11:10
also people recommend backblaze for ultra cheap "fast" backup storage
it also has rsync
10.10.2019 10:10
Assembling a NAS for less than US$50
So ... you want a NAS for emergency backups that only you know about.
You have spent money on GPUs, drives, devboxes and you would like to get your NAS for free.
Ofc, if you are a clever boi, you will have RAID arrays on your devbox, offsite backups, etc etc
If you feel particularly S&M, you might even use AWS Glacier or smth similar.
Or you may buy a NAS (decent devices start from US$500-1000 w/o drives! rip-off!)
But you see, all of the above variants cost money.
Or you cannot easily throw such a backup out of the window / encryption creates overhead.
So you can create a NAS on the cheap in style:
- Buy any raspberry pi (US$5 - US$20, you can find one used even cheaper);
- Buy a USB HDD enclosure (US$5 - US$40);
- Find some garbage drives for free;
- Copy your files, put HDD under your pillow;
- Profit;
Added bonuses:
- If you live in a police state - you can use RAID 0 (just hide the second drive) => in essence this is like have a perfect one-time pad encryption;
- Easily use RAID 1 or RAID 10 with 4 drives;
- Very high portability, if you use 2.5 drives;
- Mdadm arrays are easily transferrable;
- Cyber punk vibe;
#hardware
09.10.2019 21:10
SSH hopping from Windows?
Yeah, finally I found a recipe.
I always was one flag away from it.
Rsync at your pleasure and have your key only on your laptop!
You just need to:
(0) Use PuTTY / PuTTT-gen to create your ssh key (note that putty format and open-ssh format are different!)
(1) (or just import your open-ssh key into PuTTY-gen if you have it)
(2) Add your private key to pageant (PuTTY authentication agent)
(3) Do not forget to check Allow agent forwarding flag in PuTTY under Connection => SSH => Auth
(4) SSH into your server
(5) Go to /etc/ssh/ssh_config
(6) Uncomment and change the ForwardAgent yes line
Now you can rsync as much as you want.
Also inside of tmux.
#linux
08.10.2019 14:10
Also this is amazing for teams of 5-10 people tops.
If you work on the same hardware ... you just inherit from the same base image ... and conserve traffic / space / build time =)
No sudo / venv required =)
08.10.2019 14:10
PyTorch XLA потихоньку оживает. Скоро можно будет тренировать языковые модели за несколько часов на +TPU
At last, language model pretraining with PyTorch+TPUs https://github.com/allenai/tpu_pretrain
Our code trains PyTorch BERT/RoBERTa on TPUs, which is faster and cheaper than GPUs.
Also check the repo for a more detailed comparison between TPUs/GPUs on PyTorch/Tensorflow.
https://twitter.com/i_beltagy/status/1181320500783415296
08.10.2019 14:10
AllenNLP ... does not use their library when doing such PR stunts?
Oh ...I wonder why
But PyTorch + TPU seems to be one love, if it works =)
08.10.2019 14:10
PyTorch 1.2
So, I d my DS / ML environment to use PyTorch 1.2 =)
(0) Basic DS / ML layer - FROM aveysov/ml_images:layer-0-pt12 / dockerfile;
(1) DS / ML libraries - FROM aveysov/ml_images:layer-1 / dockerfile;
Your final dockerfile may look something like this just pulling from any of those layers.
Note that when building this, you will need to pass your UID as a variable, e.g.:
docker build - build-arg NB_UID=1000 -t av_final_layer -f Layer_final.dockerfile .
03.10.2019 09:10
2019 DS / ML digest 16
Link
Highlights of the week(s):
- Finally a 10x smaller Transfomer - but it starts to look like a RNN inspired model;
- Deep fake detection dataset;
- Paraphrase dataset;
- Deconstructing the convolution - in essence, you just need a shift operator + a 1x1 mix convolution. Such things are not mainstream yet;
#digest
#deep_learning
30.09.2019 03:09
Why a system from video will not work IRL?
(though such coverage of ML is much better than what usually happens =) )
- Angles / sizes / frame composition not representative of real CCTV footage;
- HNM done manually via phone camera, but not via CCTV;
- 30k sample size seems ok, but probably for 99.5% precision and high recall will require some actual testing in front of CCTV;
And worst of all ... should any such system (even perfect) be adopted, it will go off AFTER the weapon is taken out. There is nothing wrong with this. But the public / stakeholders will inevitably blame the developers for not doing magic.
Mixed system (radio / sound / radio) will ofc work better. But you should fix problems in your society, not fix the consequences.
30.09.2019 02:09
Now CV is mainstream?
Now CV is covered even by SmarterEveryDay, which is good
Their system will of course not work in real life, but proper coverage of real capabilities of CV systems is a good thing
23.09.2019 20:09
А more detailed review on edge computing devices
https://m.habr.com/ru/company/recognitor/blog/468421/
#deep_learning
18.09.2019 15:09
Jetsons are... not cool
The case
For some of you who might think jetsons are cool... This is a pure rant, so be prepared or skip the post altogether.
Well, maybe, they seemed cool until you finally tried to use them for edge-computing. I dont refer to your pet project in you garage like tracking your cat or chickens or something, I refer to real applications, like dealing with no network connection whatsoever, where any possible support work results in huge expenses. Its not comparable to sending humans into space, but mistakes are more expensive than your average case nonetheless. I would describe dealing with jetsons as just pure frustration. That is definitely the case because expectations do not meet reality. You expect a good solid product well-suited for its purpose. What you get is nope, not even close.
Installation
From the very moment of installing OS and SDK it seems like this product is just raw. The only one that is simple enough is Nano , but we did not try it in production, so not much info to share. TX2 and Xavier are just pain in the ass though. 5 tries it took me to install everything, and then halfway through it turned out we needed a sixth one. I still wonder how you deal with s. Not to mention that you need a dev account and host ubuntu machine (well, this one is tolerable) to install everything. After I went through registration, I didnt get the email for verification, and they blocked me saying check your email for unblocking instructions. They blocked me, because I did not verify my email and tried logging in too many times. Well, I got my email for verification in a couple of hours, but never got one with unblocking instructions. So I dont have a dev account) Luckily, my colleague had one.
Production behavior
So far so good, I never went back to dealing with these machines, but our engineers did) To save your time: they still encounter surprises. TX2 was rejected as Xavier has less issues, but still a pain in the ass too. Even when everything works on a test stand, nothing works on site. Common troubles are related to power supply and autostart. There are also troubles with finding good alternatives for common libraries and frameworks (or trying to fix and tweak existing ones) that do not have a version for jetsons, sometimes because of their architecture.
Alternatives?
Why did we try jetson? Because industrial pc with 1060 is $5k-$8k and some good 10 weeks to ship to Russia. In a limited time window we had, we decided to try jetsons dev kits, they seemed like a possible alternative. But industrial version turned out to cost $5k and same good 10 weeks to ship. Blegh.
Is it hopeless?
I hope theyll make jetsons a really good product, it just takes time. And for now jetsons are definitely not a good product. Having qualified engineers to deal with this box will ease some pain I guess (not for engineers though), but thats just ridiculous.
13.09.2019 09:09
2019 DS / ML digest 15
Link
Highlights of the week(s):
- Facebooks upcoming deep fake detection challenge;
- Lyft competion on Kaggle;
- Waymo open-sources its data;
- Cool ways to deal with imbalanced data and noisy data;
#digest
#deep_learning