Introduction to PyTorch
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- Опубліковано 1 жов 2024
- In the first video of this series, we give a broad overview of the parts of the PyTorch toolchain, including: Tensors, automatic gradient computation, model building basics, data loading abstractions, model training, and deployment for inference. This video is meant as a survey, with each topic being covered in more depth in subsequent videos.
Download all notebooks here: pytorch-tutori...
Download individual notebooks here:
1. Tensors - 04:45 to 07:50
pytorch-tutori...
2. Autograd - 08:00 to 9:50
3. A simple model - 10:00 to 14:00
pytorch-tutori...
4. Datasets - 14:00 to 17:10
pytorch-tutori...
5. Training loop - 17:10 to 21:00
pytorch-tutori...
![Build Your First Pytorch Model In Minutes! [Tutorial + Code]](/img/n.gif)
The colors make it really hard to read the slides
If PyTorch is so smart, why can't you use it to increase your video BRIGHTNESS GODD!!!
If you didn't pay, why are you complaining about a knowledge being shared for free? It's not that dark...
Love how he roasts his CPU while training
Very hard to read the slide due to unfortunate choices of font colors (no contrast)
I think the brightness is too low. This is how a monitor looks with no backlight. You can see at 3:20ish the slide suddenly becomes bright and readable and then the video stays that way.
0:00 Intro
0:52 PyTorch Installation
1:32 What is PyTorch
3:51 Tensors
7:56 Autograd
10:00 A simple model
14:00 Datasets
17:00 Training Loop
21:15 Deployment
thanks mate
19:25 The roomba joins the chat
Audio volume is a bit low.
I have to turn up the volume to hear you, When the commercial come on the audio levels or high enough to wake up every one in the house.
For me "images, labels = dataiter.next()" returns the following error: "AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute 'next'". I fixed this by changing the line to "images, labels = next(dataiter)" which I think is the correct implementation.
But I am still confused, surely an official tutorial video is not mistaken.
thanks, worked for me too
7:04 it should be (torch.rand(2,2) -0.5) * 2, otherwise it works like torch.rand(2,2) -1, because of operator precedence
loss.backward() does not compute at 9:42 lol
Hello, something might be wrong there. at 11:01 we have the C1 size (28*28), and the code in the comment said "# 1 input image channel (black & white), 6 output channels, 3x3 square convolution". But apply a 3*3 window on 32*32 input will get 30*30 output. I think the graph describes the structure in the original paper which was using 5*5 window at C1.
Is there a typo ~ 15:10 when the image normalization is done? if we're seeking to achieve an average of 0, shouldn't the first tuple passed in be (0, 0, 0) instead of (0.5, 0.5, 0.5)?
Is it just my machine, or is this video kinda dark?
I think in code segment 6 the first line should be r1 = (torch.rand(2,2)-0.5) * 2
otherwise it will be equivalent to r1 = torch.rand(2,2) - 1
yes , I was wondering the same, glad to see your comment
increase your browser window man. hard to read
I like the introduction video as I could see how you can use Pytorch to train model, visualize results and deploying in production. You helped me and I just want to say thank you.
HI, I get this error when runnung the dataset notebook. "URLError: " Can it be fixed?
WHY THE VIDEO IS SO DARK?
Some kind of issue with the upload or brightness or something. It goes away at 3:20.
@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
I found this helpful. Thanks for sharing this tutorial!
I found a problem in the Autograd section, which could be corrected. I'm using Pytorch2.1.1 and it seems that `loss.backdward()` requires that the tensors get constructed with `requires_grad=True`.
I checked the documentation and it seems that this is the default, but it didn't work for me until I specified the value explicitly.
good videos but audio quality in these videos is very poor, anyway to improve it?
Obviously this is a year old and the narrator probably won't see this but a zoomed in view would make this infinitely more accessible on mobile. Great video regardless.
@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)
16:54 I get an error on dataiter.next() - next(dataiter) works
worked for me, thank you
I have the same error
This was quite a good rapid intro video. Thank you very much!
Does PyTorch have something equivalent to TensorFlow Probability?
@8:00 Set requires_grad = True
example:
x = torch.rand(1, 10, requires_grad = True)
Where do I run these commands??? I am trying to teach myself this stuff on the fly but my wings are on fire. I only want to learn ths stuff to stop the damn cuda errors on my Stable Diffusion local install. It's driving me nuts! I don't know where to put the "torch.cuda.empty_cache()" either.
Can someone please help me?
o boy is the noise because of model training 😏
Thanks for the video. If the CIFAR10 dataset isn't downloading by the code, you may for as below:
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
thanks a lot. very good video. It was littleee fast i think. And best thing was there was no ad for me :)
Hope to see more like these :)
the video's dim, very difficult to follow
It is only reading the scripts! We can read them too! Unfortunately not a perfect tutorial 😒
great video. in the net class at 18.03 needs num_flat_features function without which it wont run looks like.
Am I the only one that feels the display brightness is too low?
Slides are not readable.
I dont seem to understand the AutoGrad aspect as im doing this with no prior ML/DL knowledge. Am i on the right track, or must i learn something prior to his
i thiiink you should have atleast basic understanding of backpropogation
For God's sake please increase your text size
I got a “name ‘transform’ is not defined”when creating a CIFAR10 dataset on 15:14. Still wondering how to fix this.
i got it：i forgot to run some cells above😓
PyTorch only has 27K subscribers ?
what is this interface that starts from 4:35 ?
where am I gonna actually write this codes if iam onto building some model using Pytorch?
It is a Jupyter Notebook. You can write Python code basically everywhere with a terminal but an IDEs is recommended. Or for beginners, Google Colab and Kaggle are good choices
Thank you for the video
Glad to see 720p back.
An excellent tutorial!! Thank you.
Great introduction! Thanks
1729, the hipster version of 42
Loved that Video! Thank you so much
nice "how to read the text" tutorial!
Is this synthetic voice?
useful
How does torchscript compare to onnx?
It really help a lot!😀
Low brightness, low contrast, low readability, low volume, content is good, but I don't like the mouth sounds in between the words, very distracting, and these are pronounced because I have to increase the volume too high to listen to the content. Kya official video banayega re tu.
There is a background noise that continuously comes forth .
It's a static sound that occurs in the second last module.
Request you to kindly check it out .
Probably his graphics card doing the training :P
@@felixn.7397 Might be :p