nickcorn93

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@wrcz 24 күн бұрын

all these tutorials using light mode while I learn at night... I'm gonna go blind :X

@taj-ulislam6902 2 ай бұрын

Definitely a lot of new material not seen else where - not a run-of-the-mill video. Great job on originality.

@shaheeng8034 4 ай бұрын

Thanks a lot! Still the best guide I could find.

@jesusmtz29 4 ай бұрын

Approximate arbitrary function? There are caveats.

@mattiskardell 5 ай бұрын

Thank you so much

@Khaled_Elsadani 7 ай бұрын

Thanks for sharing INFO

@PhoenixReflex 7 ай бұрын

Thank you so much. Keep up the hard work. Just hoping that more and more libraries in python will support GPU computations soon.

@AngeloHafner 8 ай бұрын

Muito bom...

@garywilliams4214 10 ай бұрын

Great tutorial, Nick! One minor critique: your pronunciation of ‘array’ was confusing…a more standard pronunciation is “uh-RAY”.

@TheAIEpiphany 11 ай бұрын

Something is seriously off with your fast matmul implementation, it's 3 orders of magnitude slower than the built-in method (12.5 ms vs 8.82 us)? You probably have some host-device copying going on?

@nickcorn93 11 ай бұрын

The matmul example shown is the example from the numba documentation so I don't think it's wrong. It's (relatively) slow because matrix multiplication is something that is so common, it is insanely optimized in available implementations. You won't write a matrix multiplication implementation with numba that's faster than cupy. But if you have something custom you need to do, a custom kernel can be faster than a combination of cupy operations.

@user-um9sl1kj6u 11 ай бұрын

What about if you want to develop a library for neural net work? A highly specialized library

@kineticraft6977 11 ай бұрын

This reminds me a lot of the mindset you need to program in assembly.

@tooniatoonia2830 Жыл бұрын

Really learnt a lot here, thanks!💪

@zaharkohut7881 Жыл бұрын

Thank you for this tutorial, it has been very helpful! But since it is only an introduction could anyone tell me what I should watch or read next on this topic? Thanks in advance for the advice!

@user-tx1we1hw8b Жыл бұрын

thank you! super helpful

@rohitsatyam2935 Жыл бұрын

Thanks for creating this video. This really got me started with building packages.

@plumberski8854 Жыл бұрын

Great intro for me. Waiting for my new GPU (likely 4060 Ti) for me to dig deeper into Python, CUDA, deep learning ...

@kayakMike1000 Жыл бұрын

GPUs aren't general purpose... sigh... They are really good at specific executing the same operation on many data banks. It just happens to be similair type of needs for graphics an machine learning

@nickcorn93 Жыл бұрын

Isn't that what I say in this video? Did you even watch it?

@prietjepruck Жыл бұрын

Really great introduction to GPU programming. I hope you make a new one soon.

@Shoz_ Жыл бұрын

Thank you, this is gold

@gauravdeshpande4298 Жыл бұрын

I am unable to install cupyx from pip any help

@nucspartan321 Жыл бұрын

Great video

@nigmaxus Жыл бұрын

Cupy does not install well through the use of pip

@nickcorn93 Жыл бұрын

typically it is easier via conda yes.

@ouaililydia3835 Жыл бұрын

thank you so much, it is the best explaination i found. Please keep going and give us more information and examples on that

@snapo1750 Жыл бұрын

There is a python opencl package (pyopencl) a = pyopencl.array.arange(queue, 400, dtype=numpy.float32) b = pyopencl.array.arange(queue, 400, dtype=numpy.float32) krnl = ReductionKernel(ctx, numpy.float32, neutral="0", reduce_expr="a+b", map_expr="x[i]*y[i]", arguments="__global float *x, __global float *y") my_dot_prod = krnl(a, b).get() 🙂 Benefit is it works on ALL GPU's not only Nvidia, (works on intel built in cpu gpu's and on amd gpus)

@duongkstn Жыл бұрын

great tut ! thanks

@0Clappy Жыл бұрын

Can you do a tutorial series on how to accelerate things using cuda python?

@nickcorn93 Жыл бұрын

I've thought about it but it's a lot of work to make and edit a silly video like this, and at the moment I really don't have the time. I don't get anything for making these videos.

@richardbennett4365 Жыл бұрын

Wait. At 12:10, the narrator says the timeit magic function reports a duration of 5 ms, but the number is 0.01 ms from 6 ms. The number us far away from 5 compared to 6. It shoukd be 6 ms if he's rounding, not 5 ms. He's truncating the decimals to arrive at an integer.

@nickcorn93 Жыл бұрын

Congratulations, you have invalidated the entire video by spotting this massive mistake ;) !

@richardbennett4365 Жыл бұрын

@@nickcorn93 🆗.

@vicentemedel8469 Жыл бұрын

i have a question im noob on this why always when can run an .py proyect y have to install some packages with conda install over again ?

@nickcorn93 Жыл бұрын

I'm not sure this video is the right place for this question ;)

@arcface2casia255 Жыл бұрын

Just discovered your channel! Great content 👍 instant sub! Thanks!

@localhost_mds Жыл бұрын

thank you. good video!!! it was very helpful

@ErolErten Жыл бұрын

I have been looking into gpu programming using numba and python for a while, this seems to be the best tutorial I was able to find so far.. . thank you

@thousandTabs Жыл бұрын

this was such an excellent video, thank you so much!

@1Eagler Жыл бұрын

Very educational. One thing I've missed: The function matmul is running on the PC or the GPU?

@bradleykreider3358 Жыл бұрын

I suggest using "conda install conda-forge::package" over "conda install -c conda-forge package". These mean different things: 1. Install this one package from conda-forge -- install dependencies and other packages from my channel settings. 2. Install this package and look for ALL packages on conda-forge first. This makes conda-forge the highest priority channel, so if you use "defaults" you will see many packages getting replaced by the same version - and again if you run another install command on the same environment without the "-c" (the same packages will get reinstalled from defaults).

@nickcorn93 Жыл бұрын

In general, the packages on conda forge aim to be interoperable, so ideally all the packages in your environment should be from conda-forge. There is no guarantee packages will work together when they come from different channels, for example C and Fortran packages if they were compiled using different compilers. Conda-forge standardizes on these details and ensures that dependencies come from conda-forge during build.

@bradleykreider3358 Жыл бұрын

@@nickcorn93 If all of the packages are coming from conda-forge, then there is no need to specify -c, --channel. If one is using -c, then they are most likely using defaults and cherry picking a few packages from conda-forge. In any case, those two invocations look very similar but act differently when conda is using strict channel priority (the default for 99% of people). I agree that it's better to be all-in (or all out) when using conda-forge; the packages are tested very well and built using the same build-chains and configurations. It's when mixing channels that one can run into inscrutable problems. The fact that defaults and conda-forge work so well together most of the time makes the tiny inconsistencies more surprising for most users.

@mfatihaydogdu7 2 жыл бұрын

Very helpful, thank you.

@LoneXeaglE 2 жыл бұрын

Thank you so much sir, you are an amazing human being !

@Omgtired 2 жыл бұрын

Thank you so much. Probably the best introdution to CUDA with Python. The example you use, while very basic, touches on usage of blocks, which is usually omitted in other introduction-level tutorials. Great stuff! Hope you return with some more videos. I have subscribed!

@kayakMike1000 Жыл бұрын

Cuda is bullshit closed source. Just wait for Tenstorrent, it's gonna be HUGE.

@srepmub 2 жыл бұрын

fantastic video.

@niffoxichere8394 2 жыл бұрын

is it only me or the cooling fan going brrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr.

@fredeisele1895 2 жыл бұрын

Good job. A mention about using the ‘build:skip # [win]’ or similar to only build for linux would be helpful (you mentioned why it would be helpful, but not how to do it).

@nickcorn93 Жыл бұрын

Best to have a look at this example github.com/conda-forge/exitwavereconstruction-feedstock/blob/main/recipe/meta.yaml and check out the docs conda-forge.org/docs/maintainer/adding_pkgs.html#build

@Zysperro 2 жыл бұрын

Just what I needed! Thanks!

@therealbatman664 2 жыл бұрын

Thanks a lot really got me started .

@jakubkahoun8383 2 жыл бұрын

Hi, I m trying this on my local computer, but cannot install Cupy, I have NVida geforece RTX 3060. EDIT: Installed CUDA 11.6 toolkit and it works now.

@nickcorn93 2 жыл бұрын

What is your OS? You may be having issues if you are using windows and pip. Easiest to install cupy in a conda virtual environment, as it will also install the cuda toolkit.

@jakubkahoun8383 2 жыл бұрын

@@nickcorn93 Sorry for bother you, the problem was not installing Cuda Toolkit, srly I hate people who doesnt watch full video closely and ask stupid questions....and now I m one of them :D. Thx alot for this tutorial in 2 months i will try write my own GPU operator for my program, would be interting if this will be faster than CPU. (Btw using normal Visual code in python 3.10 env. on win 11, so far so good. (Altrough i have some code output delay problem when using openCV for some strange reason)