Jeffrey Fetzer jrf

A curated list of command-line utilities written in Rust

Note: I have moved this list to a proper repository. I'll leave this gist up, but it won't be updated. To submit an idea, open a PR on the repo.

Note that I have not tried all of these personally, and cannot and do not vouch for all of the tools listed here. In most cases, the descriptions here are copied directly from their code repos. Some may have been abandoned. Investigate before installing/using.

The ones I use regularly include: bat, dust, fd, fend, hyperfine, miniserve, ripgrep, just, cargo-audit and cargo-wipe.

atuin: "Magical shell history"
bandwhich: Terminal bandwidth utilization tool

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

InceptionV3

InceptionV3 is one of Google’s latest effort to do image recognition. This is a standard task in computer vision, where models try to classify entire images into 1000 classes, like "Zebra", "Dalmatian", and "Dishwasher". Compared with previous DNN models, InceptionV3 has one of the most complex networks architectures in computer vision models. The original paper of this network is here.

Usage

This gist implements an InceptionV3 service in Owl, and provides simple interfaces to use. Here is an example:

#zoo "9428a62a31dbea75511882ab8218076f"

	"""
	Histogram equalization using CLAHE
	"""

	# Import required packages:
	import cv2
	from matplotlib import pyplot as plt


	def show_img_with_matplotlib(color_img, title, pos):

	@namespace url("http://www.mozilla.org/keymaster/gatekeeper/there.is.only.xul");


	/* Adapted from https://www.reddit.com/r/FirefoxCSS/comments/ao3ydl/configuring_firefox_for_tree_style_tab_usage/ */

	/* hide native tabs and sidebar header */
	#TabsToolbar-customization-target {
	visibility: collapse;
	}

	import torch
	from torch import nn
	from torch.autograd import Variable
	import torch.nn.functional as F
	import torch.optim as optim


	# toy feed-forward net
	class Net(nn.Module):
	def __init__(self):

	import io
	import os
	import numpy as np
	from PIL import Image
	from pymongo import MongoClient
	from torch.utils.data import Dataset, DataLoader
	from torchvision import transforms


	def pil_loader(f):

	"""
	Create train, valid, test iterators for CIFAR-10 [1].
	Easily extended to MNIST, CIFAR-100 and Imagenet.

	[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
	"""

	import torch
	import numpy as np