0:18 ayo that glitch tho. she is ai for sure xD. the mouse moved and all tho

I use cloudscraper 👍🏻

That's why all Senior programmers have this feeling (internal/ deep need?) to repeat OOP 😂🤣😂🙃🤷

I'm so focused this tutorial is so great and who makes is beautiful ❤️😍

I rarely comment on youtube videos, but I gotta mention just how much time I saved from the MEIPASS fix from your video. Absolute lifesaver.

transparent glass table is an ugly idea...

Thank you for mentioning the problems you encountered as well as how to solve them! I was facing similar problems when I was digging through StackOverflow and you saved me a bunch of time. I wasn't expecting this to be so detailed, truly a great guide. I don't usually leave comments but this is one of those niche things I don't see covered in such a simple manner. Subbed!

i hope to find a tutorial to make my application available online with the same sq lite database . so any one can access it from around the world

thanks this is the one

Forget about the tutorial let me say you are really beautiful with due respect. 😍

Your magic and energies inject life into my computer, and all the 0101010101010 that are in it... Thank you

This video is giving me multiple ideas for new projects. This is indeed Python Simplified!

Wow that was great ,thank you..

All I can say, U have a hell of system 3090 RTX + i9-12900. daaaaammmmmn :DDD

Looking good

Best video on the subject.

import time import torch # Set up device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Check the number of available GPUs num_gpus = torch.cuda.device_count() print(f"Number of GPUs available: {num_gpus}") # Create random matrices matrix_size = 32 * 512 x = torch.randn(matrix_size, matrix_size) y = torch.randn(matrix_size, matrix_size) # CPU computation print("************ CPU SPEED ***************") start = time.time() result = torch.matmul(x, y) print(time.time() - start) print("verify device:", result.device) # Move tensors to GPU(s) if num_gpus > 1: # Split the tensor into chunks and move to different GPUs x_chunks = x.chunk(num_gpus, dim=0) y_chunks = y.chunk(num_gpus, dim=0) x_gpus = [x_chunk.to(f"cuda:{i}") for i, x_chunk in enumerate(x_chunks)] y_gpus = [y_chunk.to(f"cuda:{i}") for i, y_chunk in enumerate(y_chunks)] else: x_gpus = [x.to(device)] y_gpus = [y.to(device)] torch.cuda.synchronize() # GPU computation for i in range(3): print("************ GPU SPEED ***************") start = time.time() if num_gpus > 1: # Perform matrix multiplication on each GPU and combine results result_gpus = [torch.matmul(x_gpus[i], y_gpus[i]) for i in range(num_gpus)] result_gpu = torch.cat(result_gpus, dim=0).to( device ) # Move back to primary GPU or CPU else: result_gpu = torch.matmul(x_gpus[0], y_gpus[0]) torch.cuda.synchronize() print(time.time() - start) print("verify device:", result_gpu.device) Use this code if you have dual-GPUs in your system. This is the testing results for RTX3060 vs. Ryzen7 3700 (8-core) Number of GPUs available: 1 ************ CPU SPEED *************** 27.29117774963379 verify device: cpu ************ GPU SPEED *************** 1.6249408721923828 verify device: cuda:0 ************ GPU SPEED *************** 1.2608423233032227 verify device: cuda:0 ************ GPU SPEED *************** 1.2715833187103271 verify device: cuda:0

How shallow are the men who comment on your appearance. To me, the only thing worth a comment is your hot angelic voice 🔥

i like the way you teach it makes it so easy to under stand as a beginner

Hi, I work in Photoshop and Adobe Illustrator, I have a plan to make a GUI that I want to customise (automate) my tasks. I am in flexographics and Frontend web. Can we have a private discusion, I send you a dummy project to be done in TKinter. You send me a quote for that automation project and the following prototypes will be discussed later. I would appreciate if we discuss this tomorrow.

Hi PythonSimplified Admin! Thanks a lot from Karakalpakstan Republic❤

Very nice video, small tip: its pronounced A'ttribute or A'ttributes when it's a noun, and attri'bute or attri'butes when it's a verb.

You can explain the code for upi application building for fake logo detection system

Lenovo legion is the best programmer laptop 😊😊😊

Thank you!

I get an error requests.exceptions.SSLError: (MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /deepset/roberta-base-squad2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1000)')))"), '(Request ID: b5581677-f42f-4f2a-93b9-f14fc90ea433)')

Tech Kitty! ❤

you are so beautiful as always

why using root.geometry is a bad method i always use it in my projects root.update() sw=root.winfo_screenwidth() sh=root.winfo_screenheight() fw=root.winfo_width() fh=root.winfo_height() w=(sw-fw)//2 h=(sh-fh)//2 root.geometry("%sx%s+%s+%s"%(fw,fh,w,h) note: geometry method can be dynamic depending on the content inside

very helpful informations

I like you and the way you explain things. I need to hear this. Thank you.

Thank you mariya for your lecture it was really helpful

forgive me the only reason to subscribe this chanel and watching it your smile ,slang and your laugh not your coding

What's the name of the bot again🤔

Beautiful home

You explain in such a wondefull way!

These bots require powerful computers

You will Ace your midterms!!💯

conda install -c pytorch pytorch ====> did not include CUDATOOLKIT, why?

Your mouse is haunted!

Thank you!

Why do you still use the discontinued brackets text editor when there's Microsoft visual studio code? enlighten me please.

No Mariya! I want to chat with you not with your bot! 😛🤣🤩

You have great content presented clearly and concisely. As an educator I've pointed many high school women to your channel, you are a source of encouragement and inspiration to many young women interested in STEM!

Interesting 🤔

Omg what a body 😮 of terminal data 😎 boom indeed 🔥

can you make a video about what nltk.corpus.genesis.raw(inputData) and nltk.corpus.gutenberg.raw(inputData) are genesis and gutenberg just names you make up? or are they existing models? is context just a tokenized version of the input data ?