liamzebedee

layout	title	date	tags
post	[WIP] Comparing IPFS and BitTorrent in 2023.	2023-08-10	research ipfs bittorrent p2p p2p-networks dht

In this post we're going to compare two of le plus grande P2P file-sharing networks! IPFS and ye olde BitTorrent. This research piece is based on a lot of experience I have with IPFS, namely integrating it into Dappnet.

This article is a work-in-progress. My goal is to collect a lot of data (50+ links so far) and simplified explanations that cut through a lot of the obtuse technical gala. This post isn't intended to be a complete review of either project, DYOR. If you have any questions or if I've gotten anything wrong here, please reach out on Twitter @liamzebedee and I'd be happy to chat.

liamzebedee

layout	title	date	tags
post	Comparing IPFS and BitTorrent.	2023-08-10	research ipfs bittorrent p2p p2p-networks dht

In this post we're going to compare two of le plus grande P2P file-sharing networks! IPFS and ye olde BitTorrent. This research piece is based on a lot of experience I have with IPFS, namely integrating it into Dappnet.

To begin with, since a lot of people don't actually know how they work, I'm going to cover a little bit about their technical designs, and then we'll go into their practical differences.

quantumjim

# coding: utf-8

from creative import *

def clear_screen():

    for _ in range(50):
        print("")

def get_percentage(stats,string):

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

killeent

WORK IN PROGRESS

PyTorch Internals Part II - The Build System

In the first post I explained how we generate a torch.Tensor object that you can use in your Python interpreter. Next, I will explore the build system for PyTorch. The PyTorch codebase has a variety of components:

• The core Torch libraries: TH, THC, THNN, THCUNN
• Vendor libraries: CuDNN, NCCL
• Python Extension libraries
• Additional third-party libraries: NumPy, MKL, LAPACK

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.