taichi.js VS examples

FWIW also taichi is quite popular in python and seems has some javascript related implementation (I haven't used it though), taichi.js [0]

[0] https://github.com/AmesingFlank/taichi.js

The independent compilation and execution leaves Taichi with more possibilities. Though embedded in Python, Taichi is not Python-specific. In fact, our community developer AmesingFlank is working on a Javascript frontend. You can check out some cool preliminary demos in the taichi.js github repo.

Taichi.js is a powerful project that adds a JS frontend to Taichi and compiles Taichi to WASM with Emscripten, allowing users to transform Javascript functions into WebGPU compute shaders for massive parallelization. If your browser supports WebGPU, you can try it out on Playground | taichi.js.

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