examples VS Pytorch

For “cloud-native” apps, JuiceFS is not needed.

S3 is not designed for intensive metadata operations, like listing, renaming etc. For these operations, you will need a somewhat POSIX-complaint system. For example, if you want to train on ImageNet dataset, the “canonical” way [1] is to extract the images and organize them into folders, class by class. The whole dataset is discovered by directory listing. This where JuiceFS shines.

Of course, if the dataset is really massive, you will mostly end-up with in-house solutions.

[1]: https://github.com/pytorch/examples/blob/main/imagenet/extra...

Pytorch includes a simple neural network example for the MNIST data: https://github.com/pytorch/examples/blob/main/mnist/main.py

It only takes a few minutes to train with default parameters and will have >99% accuracy on the MNIST test set.

pytorch-examples

Pretty much just grab one of these, swap in your own database, go home early: https://pytorch.org/examples/

[5] https://github.com/pytorch/examples/tree/main/word_language_...

The cherry on top of the cake I've added my father's name at the top of the code in the comments. I hope that for the next upcoming 200-300 years, someone will read modify and improve or perform experiments with my code.(Vivek V patel), My code can be found at official PyTorch's Website https://pytorch.org/examples/(Image Classification Using Forward-Forward Algorithm)

I am straying out of my domain knowledge to attempt a basic reinforcement learning task in a toy environment and have become fairly familiar with the REINFORCE algorithm for policy gradient agents, especially PyTorch’s implementation (found here). It is clear to me now that there are superior methods to train RL agents (PPO for instance), but as I read, these feel beyond my current intellectual or time resources. As such, I’d like to eek out as much power through modifications of REINFORCE as possible before determining how I might move on.

import torch import torch.nn as nn import torch.nn.functional as F # Simplified version of https://github.com/pytorch/examples/blob/main/mnist/main.py#L21 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) def forward(self, x): x = self.conv1(x) output = F.relu(x) return output

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():