nikonyrh

Joined 8 years ago

Karma
88
Submissions
171
Member since
Mar 14
Blog: https://nikonyrh.github.io
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Recent submissions

Comment · 3 months ago
Does that take into account the efficiency of transforming the gas to electricity first? Ah, well we have other power sources as well. But if you were to use gas, it doesn't seem to make much difference (if the gas power plant doesn't utilize the extra heat for district heating etc.).
Comment · 6 months ago
At least in Finland's Nordea bank you can order a physical code calculator, they used to be small enough to keep on your wallet but the new one is the size of an old small phone. It even has a QR scanner. So I just keep it at home.
Comment · 9 months ago
Wood / metal working, MTB and Factorio. Or did you mean within the IT sector? Well ML is interesting ;)
Comment · 1 year ago
AMD already has Composable Kernels[1], and supports for example Triton[2]. Then there is also HIP[3], and there are tools to automatically convert from from CUDA to HIP. But since CUDA is the de-facto standard, there is always friction to use something else (unless you need to support also AMD stack).

Making something just CUDA-compatible is non-trivial, and since Nvidia decides its direction and new features then the alternatives would always be lagging behind. Currently there are also major hardware differences between Nvidia and AMD, which may make highly optimized CUDA code inefficient or even buggy.

  [1] https://github.com/ROCm/composable_kernel?tab=readme-ov-file#composable-kernel
  [2] https://github.com/triton-lang/triton?tab=readme-ov-file#triton
  [3] https://github.com/ROCm/HIP?tab=readme-ov-file#what-is-this-repository-for
Comment · 1 year ago
He should have asked what is the price for 1 MB, or 1000 kB. 2 cents, right? insert meme here
Comment · 2 years ago
I suppose you aren't a fan of the https://en.wikipedia.org/wiki/Hutter_Prize .

> The goal of the Hutter Prize is to encourage research in artificial intelligence (AI). The organizers believe that text compression and AI are equivalent problems.

Comment · 2 years ago
I don't think the distinction between indoors or outdoors usage is relevant, in the end we are still talking about the volume of drinkable water people use. Whether they use it for drinking, cooking, washing, or watering the lawn.
Comment · 2 years ago
I always found it interesting that you cannot have a domain name without publishing your address and phone number, or paying for an extra service.
Comment · 2 years ago
This reminds me of the meme where you read up instructions of good sleep, at 4am before getting ready to go to bed.
Comment · 2 years ago
"In a notebook, you can go back and edit and run individual lines of the notebook without re-running the whole notebook from the start and without re-computing everything that depends on what you just edited."

Isn't this a standard on REPLs as well? You can select the code you wish to run, and press Ctrl+Enter or what ever. I must admit, I've programmed Python for about 10 years in Spyder and VS Code now, but I haven't used notebooks at any point. Just either ad-hoc scripts or actual source files.

My definition of a "notebook" is an ad-hoc script, split into individual "cells" which are typically run as a whole. On my workflow, I just select the code I wish to run. Sometimes it is one expression, one line, 100 lines or 1000 lines depending what I've changed on the script.

Comment · 2 years ago
I bought Unihertz Pocket Titan, at least it was different! Quite thick and heavy, but the battery lasts about 6 days even without any power-saving mode.
Comment · 2 years ago
Since with CUDA you are programming so close to hardware, and hardware (and CUDA itself) has advanced so much, I recommend you to go trough very carefully all the major CUDA versions and see how it has evolved. Well, strictly speaking I'm talking about different versions of "compute capabilities". Of course Wikipedia has a good summary: https://en.wikipedia.org/wiki/CUDA#Version_features_and_spec...

An other point is that you don't need to write any CUDA code to be able to utilize GPU computing. If you need ML models, you have frameworks like PyTorch and Tensorflow. You just need to express your mathematical problem, and the framework will take care of the rest.

Even if you needed to write custom GPU code, you don't need to do it in C anymore! For example you can JIT Python, using Numba or Triton.

Usually writing custom code is only required when:

- You are doing something novel, like PhD level stuff

- You must optimize the ML project for performance and trough-put at interference time

- You need to brute-force solutions (be it crypto-hashes, passwords, NP-complete problems, ...)

My last point to you is that do you want to learn to use these pre-existing frameworks and libraries, or learn to develop them or maybe even create new ones? What ever your answer is, I'd say that the first option is a great stepping-stone to advance to the second one.

Comment · 2 years ago
- Photo editor

- In-memory database

- Data compression algorithm

- 2D or 3D physics engine / game

- Game engines in general

Comment · 2 years ago
Aah that makes sense, thanks!
Comment · 2 years ago
I'm not familiar with this library, how does `text("(now())")` evaluate and why there are extra parentheses? And should that be a lambda expression as well, so that `create_date` isn't just the timestamp when the python process was started?

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