I just found this channel. I think it's amazing and it is everything someone that wants to learn the basics ever needs. I am a true believer that the most important thing is to get a grasp of the intuition and then slowly try to dive deeper into any topic. And of course you have A LOT of question when trying to learn something new and I love the way you approached it from the newbie's point of view, and focusing on what needs to be cleared first. Unfortunately, sometimes it's really hard to find any tutorials like that. Especially that at uni, it doesn't work like that at all :))) So I'm glad I've found you, and hope you keep posting. Wish you a great day!
Being a developer of 15+ years. You're doing a great job at explaining an incredibly complex topic in a very easy way. Excellent work. Really love your channel.
Cool!! Thank you for the simple, clean, well presented video. You are inspiring me to try writing code using my GPU's. I'm sure I will be watching some of your other videos.
Didn't expect such a well presented tutorial. Your explanation is so structured and without distractions, and your manner of presenting is very eloquent 👌
TH-cam brought me here. Although I am not a big fan of python, I understand the intension of your examples. Thx for that. I also like your gestures and style of explanation. Keep it up
I wrote you somewhere else that this tutorial is great .. but I wasn't at my best concentration .. now that I again watched it... I have no words to say more than FANTASTIC .. clarity, knowledge, and everything else .. so thank you beast ,, thank you M .... I'm following what you present .. as always..
Yeeeeeey!! I'm so happy to hear that!! 😁😁😁 Thank you so much for the incredible comment dear!! I worked extra hard on this video so your feedback really made my day!! 😃 Happy new year and see you in 2022!!! 🥳🎆❄
I've just finished my Google Cloud Big Data and ML course. So this was great to understand the Cuda element. Thank you! Will post my benchmarks and list them below, but no 3090 here for me! 😂
Congrats on finishing the course!! 🥳🥳🥳 I hope you had lots of fun!! The one AI course I took in 2018 (where I discovered Python actually 🤪) didn't explain CUDA at all! We've used it - but I had no idea what it actually was! I only recently found out that you can get it directly from your GPU rather than having to use some kind of online, often paid, third party service!! 😅 It was definitley a pleasant surprise! But I wish somebody bothered to explain it to me when I started my ML journey... it would save me so much time and frustration with Google Colab disconnecting my runtime with no reason!! Anyhow, I'm glad this video came at the right timing for you! 😉
Very nice introduction. Using additional software like GPU-Z while processing data (e.g: training neural network) you can check your GPU load and temperature. When processing big chunks of data for long time ( days) check that you GPU doesnt pass the maximum temperature joint parameter (in my case it was 100°C). Another thing is that for PCs it is really important to have good coolers and a notable spatial separation between CPU and GPU (GPU support better high temperatures than CPUs).
Thank you so much for the incredible tips grjesus99! 😀 I haven't had a chance to use GPU-Z before, I usually use the GeForce performance overlay with Alt+R and it shows me all the important stats 😊 I absolutely agree about the cooling! it's a key component, especially in overclocked systems. a good air circulation will ensure your hardware lasts for much longer!
Brains and beauty!!! Informative video, which I enjoyed due to your programming style! Out of curiosity how much time and effort did you put behind this video? I have my wild guesses, but I think it’ll show folks your capacity if you state it yourself. Anyway happy videoing, and hope to see many more.
Nice presentation! A fair comparison is more like~ 16 cpu cores ~ 256 FP32 cores (AVX 512). So 256 vs 10,000, and also the bandwidth difference is huge aswell
nice tutorial! excellent video and clear window-in-window effects to show what you are doing!! however, I had to turn my audio way up to hear you. boom mic? lapel mic? Solid video! :)
Thank you!! More like 10 foot ceillings 😅 the mic is great but the echo is an ongoing problem I'm trying to fix in all kinds of creative ways (when I'm filming - there's pillows and paper towel rolls everywhere!!! Hahahaha 🤣🤣🤣) Thanks for letting me know! I'll boost the volume up in future tutorials, I think my echo-cancelling effects are causing this low volume situation
Your videos are always informative and great. thank you. Does Torch also support AMD GPUs and OpenCL? and how can we use multiple graphics cards in parallel in this case?
Amazing tutorial from a lovely person. Its great to see simple and clear explanation. Thank you...I found a new resource to my addition. Question : Why can't we just replace CPU's with GPUs only.... ?
Very informative and visuals helped me comprehend. I didn't purchase a GPU card last time due to fear of Ubuntu compatability. I'll get a moderate card in upcoming days, I think I got a good power source too but gotta check
I'm impressed how easy it is to run the same code in Python in either the CPU or GPU context. Your video inspired me to try to run the matrices to determine the Levenshtein distance on the CPU and GPU and to compare the performance. But I'm still coding and the results are not yet available. 😅
Thank you so much Juan! 😀 By the way, I'm premiering the next episode in this parallel computing series in 45 minutes - come say hi! 😁 here's a link: th-cam.com/video/iFADsRDJhDM/w-d-xo.html
3:13 Well... You're right that with x86 we're looking at 1 or 2 threads pr core. But the CPU can do multiple tasks pr thread at once. So it might do task 1 from thread A and task 1 from thread B, but then there might be some spare execution resources allowing task 3 from thread A and task 7 from thread B and perhaps if you're *really* lucky perhaps even task 13 from thread A to be executed all at the same time. The OS only assigns two threads to the CPU core to run at once till the next interrupt, so it's not doing more at once in that regard. But multiple tasks within each of those threads will be executed at once if possible (not always the case). Of course in normal spagetti code you can't really expect the CPU to be able to do particularly many of those instructions at once. And more advanced instructions sets helps the CPU understand exactly what you're doing and achieve more instruction level parallelisation since those advanced instructions in essence combine multiple instructions into one so even thoough they're multiple instructions *inside* the CPU they're just a few on the outside, meaning that the CPU knows how those internal instructions relate to eachother more and can do things better as a result.
Great video! Took me awhile as I don't normally use anaconda and I was initially trying to get this to work with a pip install (and it doesn't). My CPU and GPU seems so slow compared to yours haha. Looking forward to hearing more about cuda :)
Oh no! My apologies Rachel, did I mess up the VENV commands?? 😱😱😱 I'll get to the bottom of it shortly and fix it! (I'm hosting a NYE party today's so it will probably be a tomorrow task... my head is too busy with cooking 😵) Thanks for letting me know and thank you so much for the lovely comment! 😃 Happy new year and see you soon in a brand new tutorial! 🥳🥂🎆
I cant wait to share my machine results, but in Windows i still have some troubles detecting the gpu with pythin, but is there! Still researching what else Im missing, installing whatever looks as dependency. Cool tutorial, Mariya.
Very cool video! How long did it take to transfer data to GPU memory space? This is often the rentability bottleneck. In my opinion this should be taken separately into account. However, thumbs up!
Hahahaha thank you so much Kevin! 😁 I've hustled a lot to get all these parts (well... besides arguably the most important part which Nvidia sent me for an upcoming TensorRT tutorial 😅 hahahaha) But the DDR5 for the processor was super hard to find!! I almost gave up and got a Rayzen 11th gen + DDR4 built instead... but thank God a bunch of Christmas miracles happened and everything worked out!! 💪💪💪
Hi There, Happy Christmas. Yet another interesting issue. You presented clearly the way one can use cuda. in my case i have an old gpu nvidia gtx 1080ti, it is hanging all the time because the box is an old core2 duo, but i will upgrade soon to a better computer with ssd and more ram. I am using the gpu in my polymer simulations mainly MD and MC. Best regards, Sid
@@satviksharma3722 The software works with CUDA. I think their competitor that makes CST also uses video card memory for ultra fast calculations of insanely large matrices (inverting the matrix) .
@@satviksharma3722 - You need to contact your licensing manager and they will set you up - after you pay a lot of $$$. I've seen problems solved in 10 minutes that take over 24 hours without using cuda.
Absolutley! 😃 I'm filming a new tutorial tomorrow where we connect an Sqlite database to a Flask grocery list application! (Will upload the complete code to Github shortly, I'm almost done! 😉) At first we will deploy it with Wayscript X (as we don't need Heroku anymore!Wayscript figured out how to do this directly from the IDE 😱) But I'll defonitley film an additional deployment tutorial for those who want to use Heroku instead! 😊
Great video! I’m currently running a 3990x 64 core AMD CPU with 4x 3090’s in one node and 4x 3080ti’s in the other node. My primary work is as an 3D animator / VFX artist using GPU rendering through Redshift/Arnold and Octane. I just am getting starting to learn about ML as a side hobby and stumbled across your video. It’s fantastic! Question - many of the 3D rendering engines I work with distribute GPU / CUDA tasks over a network for generating frames. Can ML tasks be distributed the same way. I’m just getting started in this and like runway.ml that do Ai rotoscoping have really peaked my interest in the subject. Thanks for the great work!
![ILLSLICK - Homies Feat. LILY [Official Music Video]](http://i.ytimg.com/vi/r6jGK7MumF0/mqdefault.jpg)
How did your speed test look like?? 🤩🤩🤩
************ CPU SPEED ***************
7.952215671539307
verify device: cpu
************ GPU SPEED ***************
0.6196999549865723
verify device: cuda:0
************ GPU SPEED ***************
0.24684429168701172
verify device: cuda:0
************ GPU SPEED ***************
0.24314093589782715
verify device: cuda:0
my CPU: Intel's i9-12900K 12th-Gen Alder Lake Processor (+5200mhz DDR5 memory)
my GPU: Nvidia GeForce RTX 3090
matrix_size: 32*512
OS: Linux Ubuntu 20.04 (I've upgraded from 18.04 mid-video hahahaha)
Merry xMas!
#PythonSimplified Thank you for this video! Kindly share more videos about this CUDA tutorials with real time examples..
***** CPU SPEED *****
19.829071521759033
Verify device: cpu
***** GPU SPEED *****
2.2739994525909424
Verify device: cuda:0
***** GPU SPEED *****
2.0090506076812744
Verify device: cuda:0
***** GPU SPEED *****
1.9959523677825928
Verify device: cuda:0
my CPU: Intel i7-9750H @ 2.60Ghz
my GPU: Nvidia GeForce RTX 2060
matrix_size: 32*512
OS: Windows 10
*****CPU SPEED****
9.473852396011353
verify device: cpu
*****GPU SPEED****
1.2871942520141602
verify device: cuda:0
*****GPU SPEED****
0.8036797046661377
verify device: cuda:0
*****GPU SPEED****
0.7862038612365723
verify device: cuda:0
SPECS:
CPU: Intel's i9 7900xe 10 cores 20 threads (Oc'd to 4.5ghz)(3200mhz DDR4 memory)
GPU: NVIDIA GEFORCE GTX 1080ti
matrix_size: 32*512
OS: Linux Ubuntu 20.04
My god you are so beautiful and have good sense of humor 😘
Please don't abandoned this series. I'm really looking forward to it.
Thank you Muhammed, I'm happy to hear! 😁 Don't worry, I won't rest until all of us become CUDA ninjas!! 🥷🥷🥷
I just found this channel. I think it's amazing and it is everything someone that wants to learn the basics ever needs. I am a true believer that the most important thing is to get a grasp of the intuition and then slowly try to dive deeper into any topic. And of course you have A LOT of question when trying to learn something new and I love the way you approached it from the newbie's point of view, and focusing on what needs to be cleared first. Unfortunately, sometimes it's really hard to find any tutorials like that. Especially that at uni, it doesn't work like that at all :))) So I'm glad I've found you, and hope you keep posting.
Wish you a great day!
Being a developer of 15+ years. You're doing a great job at explaining an incredibly complex topic in a very easy way. Excellent work. Really love your channel.
Cool!! Thank you for the simple, clean, well presented video. You are inspiring me to try writing code using my GPU's. I'm sure I will be watching some of your other videos.
Didn't expect such a well presented tutorial. Your explanation is so structured and without distractions, and your manner of presenting is very eloquent 👌
Thank you so much for the incredible feedback! 😀
@@PythonSimplified you're the best!
Very, very, very well documented this video. Keep up the good work.
Thank you so much!! Will do! 😃😃😃
as always absolutely top-notch explained,
your way to convey a thing is unbeatable good!
Simply one of the best tutorials I've ever seen on this specific topic!! Congrats!!
Thank you so much!!! super happy you liked it! 😀
This is one of the best explanations I have watched
this was really well organized and explained. best so far.
This was a wonderful explanation. I've only ever had a vague idea of what CUDA was and what a CPU actually does.
Great video! Please continue with this series.
Dyamn .. i dont know how i came accross this channel/video .. but have to say excellent work. I could watch these types of videos all day long ..
Another great tutorial, really like your clear presentation style and depth of information.
Made it very easy to setup my system, thank you!
Thank you so much for the lovely comment! I'm super happy to help! 😁
TH-cam brought me here. Although I am not a big fan of python, I understand the intension of your examples. Thx for that. I also like your gestures and style of explanation. Keep it up
wow!! Your explanations and examples are perfect!!. Thank you so much
This video is awesome! Very instructive. Congrats!
I'm in love now with your Computer Specs, I9-12900K with RTX 3090, damn, that's absolutely a beast PC.
Haven't long come across your channel, I think it's now my favourite python channel
I like your positive attitude :D!
Very good explained and easy to understand!
So good, I just start to get contact with the GPU accelerated computing topics, I think this field is so promising and active.
I wrote you somewhere else that this tutorial is great .. but I wasn't at my best concentration ..
now that I again watched it... I have no words to say more than FANTASTIC .. clarity, knowledge, and everything else ..
so thank you beast ,, thank you M .... I'm following what you present .. as always..
Yeeeeeey!! I'm so happy to hear that!! 😁😁😁
Thank you so much for the incredible comment dear!! I worked extra hard on this video so your feedback really made my day!! 😃
Happy new year and see you in 2022!!! 🥳🎆❄
@@PythonSimplified happy new year M ❤❤ ..
You should've got used to my comments cuz I don't pass a video without a comment, or anywhere else 🤩🌹
Super clear explanation Maria, great video, thank you!
Thank uou so much Emanuele, glad you liked it! 😁
I've just finished my Google Cloud Big Data and ML course. So this was great to understand the Cuda element. Thank you! Will post my benchmarks and list them below, but no 3090 here for me! 😂
Congrats on finishing the course!! 🥳🥳🥳 I hope you had lots of fun!!
The one AI course I took in 2018 (where I discovered Python actually 🤪) didn't explain CUDA at all!
We've used it - but I had no idea what it actually was! I only recently found out that you can get it directly from your GPU rather than having to use some kind of online, often paid, third party service!! 😅
It was definitley a pleasant surprise! But I wish somebody bothered to explain it to me when I started my ML journey... it would save me so much time and frustration with Google Colab disconnecting my runtime with no reason!!
Anyhow, I'm glad this video came at the right timing for you! 😉
You are amazing! Please keep posting these wonderful videos !
Great tutorial! 💪🏻
very helpful and lovely tutorial, keep it up 😍
Well explained, I love this video!
I've really loved the way you have explained it!!!!Justttt Amazing.
your way of explanation is really amazing
Very nice introduction. Using additional software like GPU-Z while processing data (e.g: training neural network) you can check your GPU load and temperature. When processing big chunks of data for long time ( days) check that you GPU doesnt pass the maximum temperature joint parameter (in my case it was 100°C). Another thing is that for PCs it is really important to have good coolers and a notable spatial separation between CPU and GPU (GPU support better high temperatures than CPUs).
Thank you so much for the incredible tips grjesus99! 😀
I haven't had a chance to use GPU-Z before, I usually use the GeForce performance overlay with Alt+R and it shows me all the important stats 😊
I absolutely agree about the cooling! it's a key component, especially in overclocked systems. a good air circulation will ensure your hardware lasts for much longer!
your explanation is very clear!
Thanks, really easy to follow explanation.
It's simplified. Thank you.
Wow this girl is on fire! Nice job🤩
Finally I got the answer to my question - what’s the difference between CPU and GPU. Thanks!
I love your explanation. You are really good
Wow that's brilliant explaination
Thank you your explanation is very clear.
Brains and beauty!!! Informative video, which I enjoyed due to your programming style! Out of curiosity how much time and effort did you put behind this video? I have my wild guesses, but I think it’ll show folks your capacity if you state it yourself. Anyway happy videoing, and hope to see many more.
Great presentation. Great teaching style. Subscribed to look at more python material
Awesome explainer video thank you!
Great explanation!
Thank you !
Awesome explanation!! 👏🏼👏🏼👏🏼
Thank you so much David! 😊
Merry Xmas M!! God bless you !!!
Nice presentation! A fair comparison is more like~ 16 cpu cores ~ 256 FP32 cores (AVX 512). So 256 vs 10,000, and also the bandwidth difference is huge aswell
very nice I really enjoy with this explanation
GPU and CPU both are good in there way! Merry Christmas ☃️! Video is really good you did well!
:+)
Thank you so much Udbhav! 😃😃😃
Great video, you explained very well.
Thank you so much, glad you liked it!! 😃
nice tutorial! excellent video and clear window-in-window effects to show what you are doing!!
however, I had to turn my audio way up to hear you. boom mic? lapel mic?
Solid video! :)
Thank you!! More like 10 foot ceillings 😅 the mic is great but the echo is an ongoing problem I'm trying to fix in all kinds of creative ways (when I'm filming - there's pillows and paper towel rolls everywhere!!! Hahahaha 🤣🤣🤣)
Thanks for letting me know! I'll boost the volume up in future tutorials, I think my echo-cancelling effects are causing this low volume situation
Thanks for the great video!💐💐💐
Your videos are always informative and great. thank you.
Does Torch also support AMD GPUs and OpenCL? and how can we use multiple graphics cards in parallel in this case?
Very well explained 👌
Amazing tutorial from a lovely person. Its great to see simple and clear explanation. Thank you...I found a new resource to my addition.
Question : Why can't we just replace CPU's with GPUs only.... ?
Very informative and visuals helped me comprehend. I didn't purchase a GPU card last time due to fear of Ubuntu compatability. I'll get a moderate card in upcoming days, I think I got a good power source too but gotta check
Wow this was great introduction thanks
looking forward to the next video since I have 2 GPUS and dont know to make it parallel computing in my deep learning programme
Thank you. I'm much smarter and excited now. Love tinkering with computers in my late years. I have a couple of Quadro gpu's.
Great video!
very informative lecture thank you very much
Beautifully solved
This had LTT / LMG levels of production value with one of the best / clearest explanations for what CUDA is and why it matters.
Merry Christmas Mariya! Have a good one! :)
Merry Christmas Ali!! You too! 😀😀😀
Excellent video! thank you Mariya 🌷 ❤️
Thank you so much! Glad you liked it! 😃
Лучший ролик по теме на ютубе!
Great video and explaination 👍👍👍👍
I'm impressed how easy it is to run the same code in Python in either the CPU or GPU context. Your video inspired me to try to run the matrices to determine the Levenshtein distance on the CPU and GPU and to compare the performance. But I'm still coding and the results are not yet available. 😅
Merry X-mas, Mari! Nice subscribers count))
Merry Christmas di ronin!! 😁😁😁
TH-cam has been very kind to me lately 😊
Great video. Thank you.
This is a great video! I've got a 3070 and have been wondering what to do with it. It seems obvious, that I need to learn parallel programming.
Great video and explanation! Request: how about a video and example for AMD GPU programming?
You are amazing!
Amazing Explanation
Thank you so much Senya! 😀
Just excellent!
Thank you so much Juan! 😀
By the way, I'm premiering the next episode in this parallel computing series in 45 minutes - come say hi! 😁
here's a link: th-cam.com/video/iFADsRDJhDM/w-d-xo.html
3:13
Well...
You're right that with x86 we're looking at 1 or 2 threads pr core.
But the CPU can do multiple tasks pr thread at once.
So it might do task 1 from thread A and task 1 from thread B, but then there might be some spare execution resources allowing task 3 from thread A and task 7 from thread B and perhaps if you're *really* lucky perhaps even task 13 from thread A to be executed all at the same time.
The OS only assigns two threads to the CPU core to run at once till the next interrupt, so it's not doing more at once in that regard.
But multiple tasks within each of those threads will be executed at once if possible (not always the case).
Of course in normal spagetti code you can't really expect the CPU to be able to do particularly many of those instructions at once.
And more advanced instructions sets helps the CPU understand exactly what you're doing and achieve more instruction level parallelisation since those advanced instructions in essence combine multiple instructions into one so even thoough they're multiple instructions *inside* the CPU they're just a few on the outside, meaning that the CPU knows how those internal instructions relate to eachother more and can do things better as a result.
Great video! Took me awhile as I don't normally use anaconda and I was initially trying to get this to work with a pip install (and it doesn't). My CPU and GPU seems so slow compared to yours haha. Looking forward to hearing more about cuda :)
Oh no! My apologies Rachel, did I mess up the VENV commands?? 😱😱😱
I'll get to the bottom of it shortly and fix it! (I'm hosting a NYE party today's so it will probably be a tomorrow task... my head is too busy with cooking 😵)
Thanks for letting me know and thank you so much for the lovely comment! 😃
Happy new year and see you soon in a brand new tutorial! 🥳🥂🎆
I cant wait to share my machine results, but in Windows i still have some troubles detecting the gpu with pythin, but is there!
Still researching what else Im missing, installing whatever looks as dependency.
Cool tutorial, Mariya.
Always excellent info! You should build yourself a teleprompter to make it easier to prompt yourself whilst recording. 8’)
Very cool video! How long did it take to transfer data to GPU memory space? This is often the rentability bottleneck. In my opinion this should be taken separately into account. However, thumbs up!
That is not a computer, is a god-level beast. 3090 + i9 crazy!! Great video
Hahahaha thank you so much Kevin! 😁 I've hustled a lot to get all these parts (well... besides arguably the most important part which Nvidia sent me for an upcoming TensorRT tutorial 😅 hahahaha) But the DDR5 for the processor was super hard to find!! I almost gave up and got a Rayzen 11th gen + DDR4 built instead... but thank God a bunch of Christmas miracles happened and everything worked out!! 💪💪💪
you nailed it girl
Excellent 👍
Hi There, Happy Christmas. Yet another interesting issue. You presented clearly the way one can use cuda. in my case i have an old gpu nvidia gtx 1080ti, it is hanging all the time because the box is an old core2 duo, but i will upgrade soon to a better computer with ssd and more ram. I am using the gpu in my polymer simulations mainly MD and MC. Best regards, Sid
I use CUDA with HFSS - a numerically intensive electromagnetic solver (solves/satisfies Maxwell’s equations in 3D space).
How???
@@satviksharma3722 The software works with CUDA. I think their competitor that makes CST also uses video card memory for ultra fast calculations of insanely large matrices (inverting the matrix) .
@@silvermica I am a user of hfss too do i indeed to enable cuda from somewhere or is it supposed to detect that automatically?
@@satviksharma3722 - You need to contact your licensing manager and they will set you up - after you pay a lot of $$$. I've seen problems solved in 10 minutes that take over 24 hours without using cuda.
@@silvermica thanks a lot for that. On my way to mortgage my house rn xDD
❤️ lots of information and useful ❤️
Thank you K.Ballaji Axe! 😀
Have fun with CUDA!
please continue!!!!
Thank you , I want to learn cuda my life depends on it hahaha , this was a nice intro
I have a project I need to implement cuda on it , if you’re interested in a project please let me know, hint it includes ECC operations
Thanks!
buen video, gracias !!
Gracias Carlos! 🙂
You explain it very well
Thank you so much! 😃
Very good explanation
Thank you so much! 😃
you are a good talker
Loved it
You need to put some soft material under the keyboard or re-allocate the mic. The noise when you type is similar to good drum and bass music.
Hey Mariya ! I just watched your video teaching flask and I love it. would you do a video about deploying flask apps with databases?
Absolutley! 😃
I'm filming a new tutorial tomorrow where we connect an Sqlite database to a Flask grocery list application! (Will upload the complete code to Github shortly, I'm almost done! 😉)
At first we will deploy it with Wayscript X (as we don't need Heroku anymore!Wayscript figured out how to do this directly from the IDE 😱)
But I'll defonitley film an additional deployment tutorial for those who want to use Heroku instead! 😊
@@PythonSimplified Thank so much! You're the best
Great video! I’m currently running a 3990x 64 core AMD CPU with 4x 3090’s in one node and 4x 3080ti’s in the other node. My primary work is as an 3D animator / VFX artist using GPU rendering through Redshift/Arnold and Octane. I just am getting starting to learn about ML as a side hobby and stumbled across your video. It’s fantastic!
Question - many of the 3D rendering engines I work with distribute GPU / CUDA tasks over a network for generating frames. Can ML tasks be distributed the same way.
I’m just getting started in this and like runway.ml that do Ai rotoscoping have really peaked my interest in the subject.
Thanks for the great work!
You are my style 😍😘
thank you ^)