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this guy's credentials makes one feel so inadequate

Thank you for creating this content!

this documentary is clear evidence of how communistic use of means of production to share innovations work best than capitalistic competition trying to destroy the adversary in the market

Technology goes too fast for me to keep up. I know some Python and now I just learn PyTorch exist and it’s the next BIG thing. I have the impression that only the first people who created it and use it may have some future from it.

GAN does not stand for generative autoencoders but generative adversery network

Polska gurom

Omg, he speak accurately and so fast , he's smart

Thank you PyTorch. Greetings from Popayan, Colombia.

Great explanation

very smart man

❤❤❤❤❤🎉🎉🎉

Thank you Pytorch. Greetings from Popayan, Colombia.

brings back memories from Facebook AI days

now shill anything other than nvidia

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is there a sample colab?

I didn't get much speedup using this.

Please don’t bake the captions into the video. If I cannot understand them, please put the transcription into the toggled closed captions. It’s distracting when not necessary like when someone is actually speaking a different language than the rest of the video (English in this case).

PyTorch is easily one of the best frameworks I've ever used for any purpose. From the bottom of my heart and all the other little people that you've empowered to do great things: Thank you. ❤

Great visualization!🥰

Good stuff, It would be awesome to upload the video in full hd!

I always love Edward Yang stuff. Keep up the good work boys 💪

One of the most impactful things ive watched on RUclips

Microsoft and Nvidia are at the forefront of the AI revolution. SmythOS can help you tap into this power to drive innovation in your business. #AI #SmythOS

Thank you. Good guide

*Summary* * *(**00:00:12**)* *Torch Compile Missing Manual:* This document is a comprehensive guide to troubleshooting and optimizing PyTorch model compilation using `torch.compile`. It's meant to help you resolve issues and get the best performance from compiled models. * *(**00:01:14**)* *Structured for Troubleshooting:* The manual is organized around common problems (e.g., compiler crashes, slow compile time, poor performance, high memory usage). Each section dives into potential causes and diagnostic steps. * *(**00:02:18**)* *Key Debugging Strategies:* * *(**00:02:28**)* *Torch Trace:* This tool provides a comprehensive overview of the PyTorch 2 compilation process. It's highly recommended for identifying bottlenecks and issues. Share torch traces in bug reports for easier debugging. * *(**00:04:56**)* *Ablations (Isolating Components):* Use the `torch.compile` API to disable specific parts of the compilation pipeline (e.g., inductor, aot_autograd) to pinpoint the source of problems. * *(**00:07:36**)* *Focus on Performance:* A dedicated section helps you analyze and improve performance. Start with the "Start Here" section to check for common performance pitfalls. * *(**00:10:19**)* *Tools and Techniques:* * *(**00:10:23**)* *Profiling (PyTorch Profiler and Chrome Trace):* Profile your compiled model to understand where time is spent. Look for opportunities to reduce kernel launch overhead and improve kernel execution time. * *(**00:12:23**)* *Inspecting Generated Code (Teal Parse):* Examine the generated code (Trident kernels) to identify inefficiencies or potential problems with the compilation process. * *(**00:14:21**)* *Case Studies:* Learn from real-world examples of performance debugging and optimization. * *(**00:14:53**)* *Collaboration and Feedback:* Share new performance challenges or missing information with the PyTorch team to improve the manual and `torch.compile` itself. *In essence, this manual aims to empower PyTorch users to effectively troubleshoot and optimize the performance of their compiled models through various diagnostic strategies, tools, and collaborative feedback.* I used Google Gemini 1.5 Pro exp 0801 to summarize the transcript. Cost (if I didn't use the free tier): $0.1490 Time: 91.64 seconds I added a 61 second delay to prevent a rate limit of the free tier. Input tokens: 39863 Output tokens: 901

Love this Documentary! Thank you a lot!

hi! could you please upload an example for multi-CPU only (without GPU training)?

Thanks! could you please give a tutorial for using only multiple CPUs?

Thank you Intel and Pytorch. Greetings from Popayan, Colombia.

One of the best explanations I have been able to find. Thank you for your time and effort!

Thank you so much for this incredible documentary on PyTorch! It's amazing to see how PyTorch has become a cornerstone in the AI and machine learning community. The depth of insight into its development and impact is truly inspiring. PyTorch has revolutionized the way researchers and developers approach deep learning. Its dynamic computational graph allows for more flexibility and ease in building and modifying complex models, which is a significant advancement over static graph frameworks. This has made it easier for many in the field to experiment, iterate, and advance their research. The documentary highlights PyTorch’s role in powering cutting-edge research and applications, from natural language processing to computer vision. It’s impressive to see how it has facilitated breakthroughs in various domains, enabling innovations that were previously out of reach. I also appreciate the focus on the community aspect of PyTorch. The collaborative nature of the open-source project has brought together a diverse group of contributors, driving continuous improvement and support for the framework. Thank you to everyone involved in creating this documentary and for all the hard work that has gone into developing PyTorch. Your contributions are making a significant impact on the future of AI and machine learning! Thanks again for sharing this valuable resource! ^^

Artificial intelligence is built in Python.

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Meta IDE

Boring

I'm student of AI in med and I love MONAI. A convenient tool for the end2end pipeline

Very bad idea using dark theme for the code, hard to see. Bad resolution also

Thank you for not being afraid to let us go behind the scenes with your development! At JetSoftPro, a software development service, we admire your focus on your audience and understanding of the market you're working for

God I love this documentary !

jokingly,.... fun people hey! ... should these guys run the world? After watching this people ... I am even stronger about technology is everyone's business. Loudly, comes to me Community Consultation and CONSENT.

I am unable to install "datautils" and getting the following error. do you know python-3 compatible "datautils"??? Collecting datautils Using cached datautils-1.0.3.tar.gz (25 kB) Preparing metadata (setup.py) ... error error: subprocess-exited-with-error × python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [7 lines of output] Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 34, in <module> File "/tmp/pip-install-z0y75pgf/datautils_626ab6395b7241ef98af8cc89e8623e8/setup.py", line 366 print 'adding', sub_package ^^^^^^^^^^^^^^^^^^^^^^^^^^^ SyntaxError: Missing parentheses in call to 'print'. Did you mean print(...)? [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details.

We actually have a documentary on a Python Library and more than 100k people watching it in just a month of release. This is amazing guys.

what kind of IDE is this at 21:57 ? using an unregistered version at AMD 😮

Where can i find quality code examples?

I think the entire point is missed. The acceleration that LLMs offer is the ability to access and distill information at an unprecedented rate

@11:35 LeNet model for the 32X32 one channel images. import torch import torch.nn as nn import torch.nn.functional as F class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.conv1 = nn.Conv2d(1, 6,3) self.conv2 = nn.Conv2d(6, 16, 3) self.fc1 = nn.Linear(16*6*6, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84,10) self.relu = F.relu def forward(self , x): x = self.conv1(x) #input shape 32X32 -> ouput shape 30X30 x = self.relu(x) x = F.max_pool2d(x ,2) #input shape 30X30 -> ouput shape 15X15 x = self.conv2(x) #input shape 15X15 -> ouput shape 13X13 x = self.relu(x) x = F.max_pool2d(x ,2) #input shape 13X13 -> ouput shape 6X6 x = torch.flatten(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x net = LeNet() input_image = torch.rand(1, 1, 32, 32) output = net(input_image) output

@8:00 Set requires_grad = True example: x = torch.rand(1, 10, requires_grad = True)

@7:14 r = (torch.rand(2,2) - 0.5) * 2 # normalize values to -1. to 1. r = torch.rand(2,2) - 0.5 * 2 # generates random values in range of [-1., 0.] @8:00 Set requires_grad = True example: x = torch.rand(1, 10, requires_grad = True)

Pytorch truly brought modularity in AI and built a reproducible ecosystem.