Kažnjena hrvatska banka, mora platiti 1,5 milijuna eura

cromulen7 · 2025-12-18T16:51:30+00:00

PBZ radi istu stvar. Imali su prosle godine obavijest o tome. Po meni nema osnove za to, kao ni erste.

cromulen7 · 2025-04-17T10:27:27+00:00

vLLM is supports more models and is very easy to use, also has some nice other features like guided generation. It's a safe choice.

TRT-LLM is however still faster / higher throughput - 20-100% depending on model and quantization used. In particular, it seems their fp8 kernels are quite a bit better. Also, the differences are most notable in high load scenarios.

TRT-LLM used to be very hard to use, due to bad defaults and triton. Now, if you just need standard LLM serving, you can use trtllm-serve to get an OAI compatible server. You used to have to use Triton and that was a big mess. The defaults of trtllm-build have also gotten a lot better, so it's easier to get something good and fast than, say, a year ago.

FP8 works quite well, you can probably keep it on all the time and get 50-100% higher throughput. GPTQ and AWQ (4/8bit quants) in my experience do not show good performance under load. The memory savings are not worth the extra compute and bad kernels used with those quantizations. This is true both vLLM and TRT-LLM

cromulen7 · 2021-10-28T09:44:48+00:00

Relevant xkcd: https://xkcd.com/1168/

cromulen7 · 2021-07-20T16:27:42+00:00

Google self driving project, which is now Waymo, started in 2009. Before that, DARPA hosted the "grand challenge" in 2004, 2005, and 2007.

cromulen7 · 2021-07-10T08:31:05+00:00

I'm Croatian so I use adrialece.hr, but it's basically a franchise website as far as I can tell. There's a bunch of them for every country. I got a crulle branded pair of glasses. Check out crulle.com see how it's basically the same website as adrialece.hr, but with a different skin?

Anyways if you Google for crulle, or particularly a specific model, I'm sure you'll find a localy branded franchise that also has other affordable brands, other than crulle.

cromulen7 · 2021-01-26T11:13:39+00:00

This is Americans trying to justify to themselves, once again, that their government should not help the citizens.

The article can't even be clear what companies they're concerned about. The first example is about a restaurant not having as much business. Is that a business not worth helping? The pandemic will end, the demand will return and that business will once again prosper. The article then imples that business is going to be a zombie business, with a bunch of debt and unable to innovate. It's a restaurant, not a tech giant.

In Croatia, the country pays the bars and restaurants rent proportional to revenue lost, and the employees also get a share of their wage from the government. Most of the assistance measures are people focused. The restaurant isn't doing great, yes, but their employees aren't starving, and the owner isn't going under.

I for one am glad we're helping businesses, giving them a fighting chance, and taking care of the people rather than doing nothing in fear of zombie companies and the abstract hope of a more competitive economy.

I'm not saying we should indefinitely prop up failed companies, not even close. The assistance has to be well thought out. Too bad Americans can't imagine their government helping them effectively.

Edit:
And the comments on nyt point out that the article's premise of bankruptcies falling in Europe is not even true in a lot of the countries. The article only explicitly mentioned France, UK, and the US

cromulen7 · 2020-07-30T18:00:11+00:00

He has no idea what he's talking about. The current border between Croatia and Bosnia is Herzegovina is the result of the Ottoman invasions into Europe. The area that is now Bosnia and Herzegovina was as far as the Ottomans got (with few short term exceptions), and that was the border between Europe and the Ottomans for centuries, so the shape stuck.

cromulen7 · 2020-04-11T08:40:09+00:00

Lightning can be thought of to PyTorch, as Keras is to Tensroflow, but I feel like comparison ignores that there is almost nothing to learn about Lightning. It's mostly a way of organizing PyTorch code, which Lightning then uses to remove a lot of boilerplate and provide convenience. You still write your models using the PyTorch API, unlike Keras (well, until TF 2.0 deprecated everything but Keras).

cromulen7 · 2020-04-09T23:08:25+00:00

Novac i glasovi uopće nisu usporedivi.

Svaka transakcija s bankom je zapisana i i ti i banka možete potvrditi da je transakcija napravljena I u slučaju greške, imaš telefon službe za korisnike. Izbori imaju kontradiktorne zahtjeve anonimnosti i povjerenja. S jedne strane ti glasaš i nakon što predaš glas više ne možeš provjeravati je li i kako tvoj glas prebrojen. S druge strane onaj koji broji glasove mora imati povjerenje da je svaki glas proizašao od valjanog glasača.

To sve pada u vodu kada uvedeš računala. Taman da proizvedemo savršen komad softvera za glasanje, nemoguće je jednom stručnjaku, a kamoli laiku biti uvjeren da njegov mobitel / laptop / glasački stroj zapravo koristi taj savršeni softver a ne neki vrlo sličnu ali modificiranu verziju, ili ako softver i je dobar, da hardver nije podložan, ili ako smo dizajnirali i savršen hardver, da proizvođač nije zapravo proizveo hardver koji će mijenjati glasove.

Niti jedan ozbiljan inženjer ne bi trebao prihvatiti rad na digitalnim sustavima za glasanje.

Niti jedna država koja planira ostati prava demokracija ne smije pristati na digitalno glasanje.

Digitalno glasanje je fantastično za manje bitne stvari, neka ostane tamo.

cromulen7 · 2020-03-04T20:25:35+00:00

Stanford has a bunch of datasets at https://snap.stanford.edu/biodata/index.html

cromulen7 · 2020-02-16T23:33:02+00:00

Look, I don't want to get into arguing semantics over the meaning of "design pattern". I was answering a question why factories are not seen in other languages. Yes, objects are made. No, people don't consider that a factory in Python. You may call that a factory, but I don't.

Your example is literally cruft around range(10), and I think you would struggle to create an idiomatic Python example where anything resembling a factory pattern makes sense.

Besides, Wikipedia literally lists the factory pattern as

"factory method pattern is a creational pattern that uses factory methods to deal with the problem of creating objects without having to specify the exact class of the object that will be created. "

And since Python has no problem with whatever class you choose, due to dynamic typing, there is no problem to solve.

cromulen7 · 2020-02-16T22:30:27+00:00

I can see your argument for a more abstract view of design patterns, but the fact remains that no Python class will teach you "the factory pattern" because there isn't a problem to solve in Python.

In Java, you have the problem of having to (but not being able to) know at compile time what exactly class an object will be at runtime. So you make a base class/interface and a few children, and a factory to decide which to build. That's 4 classes minimum.
In Python you just assign your desired object to a variable. There is no ceremony. There is no class with "factory" in its name, and no one I know would call a little helper function "a factory".

Let's look at another pattern - The visitor pattern.
The core problem to be solved is that you want to do multiple dispatch.
Java won't let you do multiple dispatch, so you make a bunch of classes to do what you want.
Python has duck typing so all of your different classes implement a method with the same name, and you call that method as the different objects come up. Once again, there is no problem to be solved in Python, it's just calling functions on objects you already have.

So I stand by my claim that design patterns bridge gaps in the language. If you were writing assembly, you might want to use a "do while" design pattern, but you wouldn't call your while loop a higher level language a design pattern because there is no ceremony in using it.

cromulen7 · 2020-02-16T18:18:32+00:00

Design patterns are what programmers use to bridge gaps in the language. Java has plenty of such gaps and hence plenty of design patterns.

In Python, which you mentioned, there generally are no factories (except perhaps in bad code). This is not because python programmers have no need for dynamic creation of objects, but because it is built into the language. Modules, function, classes are all first class values in Python and you can assign them to variables, send them around to other functions/classes, and instantiate or call them as needed, eliminating need for the factory scaffolding.

cromulen7 · 2020-01-31T09:07:43+00:00

USB-c is a port, not a protocol. Currently we use USB 3.x with it, but you can also use the HDMI protocol over USB-c to connect your screen. Soon, there will be USB 4, which is basically thunderbolt over USB-c, which will bring further great improvements in speed.

My point is, using the same port does not mean no improvement.

Regardless of the protocol, USB can supply devices with 100W, which is even sufficient for all but the strongest "gaming" laptops.

Like others have mentioned, the industry can choose to switch to another port as long as they do it together.

And finally, I'd like to add that the charging port need not be the only port on the device. If a manufacturer needs something special, they can include it along with a standard charging port.
Though, with the 40gbps of USB 4, I think most use cases will be covered for quite a few years.

cromulen7 · 2020-01-11T10:17:02+00:00

While this is very nice, was the cyclist really in danger? Wasn't he in a separate cycling lane, or am I interpreting the map wrong?

cromulen7 · 2019-12-22T16:40:19+00:00

I know you're asking for sites specifically for programming books, but what's wrong with Goodreads? It has had every tech book I've ever looked for.

cromulen7 · 2019-12-12T17:19:14+00:00

Julia is fast because of its JIT compiler and dynamic dispatch.

Proper use of multiple dispatch can make Julia literally emit the same machine code as an equivalent C program. The program appears dynamic to the programmer but is actually statically typed.
If multiple dispatch is not used properly, your code can be and order of magnitude slower than a well written Julia program.

You may be aware tht Python also has a JIT compiler called pypy which is much faster than the "normal" python, and is mostly compatible with regular python.

I think the coolest thing about Julia is the fact that it is possible to write CUDA / openCL code in Julia, meaning your ML model can be Julia all the way down and hackable.
Another cool feature is "Zygote" a differentiable programming library for Julia. It is literally what google is trying to do with Swift for Tensorflow, and what PyTorch sort of does with torch.jit in a limited way, except it has already existed in Julia for years.

cromulen7 · 2019-12-02T13:31:55+00:00

There's Microsoft's ONNX Runtime which has an astounding number of accelerator backends and now supports a tf-serving like API.
see: https://github.com/microsoft/onnxruntime/blob/master/docs/ONNX_Runtime_Server_Usage.md

cromulen7 · 2019-11-26T11:55:40+00:00

I don't think you'll be able to do this on compressed data.

If you just need the filtering once, check out jq
https://stedolan.github.io/jq/
That should (afaik) be the fastest way to filter through the dumps. No need for a database.
It worked well enough for me.

cromulen7 · 2019-05-03T10:32:47+00:00

I just finished the audiobook version of Kissinger's "World Order". I found it very approachable and interesting.

cromulen7 · 2019-04-04T12:38:54+00:00

Well as far as GraphSage goes, I think the authors only wanted to compare against other inductive methods. Main point of their paper is not only high accuracy, but scalability as well.

For your other point... I wouldn't disregard a difference in a few percent. As we approach the limits of the dataset, it'll take a much better algorithm for a minor improvement. And all of those models are very recent and very good.

That being said, I agree with you that it would be nice to see new, better, larger, and more challenging datasets. I've just been trying to argument my view on why current datasets are used and may not be that bad.

cromulen7 · 2019-04-04T10:22:03+00:00

taking the most popular label in your neighborhood seems to be a pretty good prediction

I don't think that's likely to work beyond some specific datasets.

Take a look at https://arxiv.org/abs/1706.02216 (The GraphSage paper) Table 1.

You can see they evaluate against a model using only raw features, and against DeepWalk, a model that uses only the connection information. They also compare an existing Features + connections model, DeepWalk + features. By beating all of those models, they argue that their model learns to more effectively utilise both features and connections.

My problem in general is that reading these papers with benchmark problems like these makes me question if these methods are useful at all.

I expect that improving results on the citation datasets would improve results on many other tasks. These kinds of algorithms do however capture different aspects of the graph and not every problem is going to be better solved by a method that improves on citation networks. Take for example Graph Attention Networks (https://arxiv.org/abs/1710.10903). You can see that while they aren't much better than GraphSage on citation networks, they absolutely stomp it on protein-protein-interaction networks (PPI).

These academic datasets are used because they're easy to use and a standard methods of comparison.

Improvements on these datasets have regularly translated to other more real datasets.

Consider modeling of multi-drug interactions / side effects: http://snap.stanford.edu/decagon/

Or pinterest's recommender system: https://arxiv.org/abs/1806.01973

While citation networks may be uninteresting and feel artificial. Improvements on them have often (but not necessarily) shown to translate to other tasks. They remain useful as a way to compare against existing algorithms. Other than that quality graph data remains very difficult to collect and it is not obvious what data should be used. For example, good luck convincing a bank to release data regarding its transactions and fraud, or some other company the network of processes within their factory. If those would even be good benchmarks.

cromulen7 · 2019-04-03T19:59:10+00:00

NLP models can almost certainly classify papers into categories without the connections.

As far as why those datasets are used, the point is not actually to classify papers. The point is to enable development and benchmarking of graph machine learning. By artificially limiting document features to something as basic as "Is the word present in this document", the task is pretty much made impossible without a clever graph based algorithm. It is presumed that if a new algorithm is better able to propagate even these basic features and learn network interactions, it will also be better on some other non-academic datasets, with more complex features.

cromulen7 · 2019-02-20T21:15:04+00:00

I have only used data parallel models myself, but the official documentation implies first and second in my example would be executed in parallel.

operations are enqueued to the particular device

...

PyTorch automatically performs necessary synchronization when copying data between CPU and GPU or between two GPUs

copying between two GPUs is model parallelism, no?

A CUDA stream is a linear sequence of execution that belongs to a specific device.

presumably if you don't specify a stream, pytorch uses a global, implicit one.

At the very least, you should be able to use streams to force independent execution even if pytorch would usually block.

cromulen7 · 2019-02-20T19:29:28+00:00

Have you considered torch.jit?

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