rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

matsen

#!/bin/sh

# Convert all arguments (assumed SVG) to a TIFF acceptable to PLOS
# Requires Inkscape and ImageMagick 6.8 (doesn't work with 6.6.9)

for i in $@; do
  BN=$(basename $i .svg)
  inkscape --without-gui --export-png="$BN.png" --export-dpi 300 $i
  convert -compress LZW -alpha remove $BN.png $BN.tiff
  mogrify -alpha off $BN.tiff

evanwill

How to add more to Git Bash on Windows

Git for Windows comes bundled with the "Git Bash" terminal which is incredibly handy for unix-like commands on a windows machine. It is missing a few standard linux utilities, but it is easy to add ones that have a windows binary available.

The basic idea is that C:\Program Files\Git\mingw64\ is your / directory according to Git Bash (note: depending on how you installed it, the directory might be different. from the start menu, right click on the Git Bash icon and open file location. It might be something like C:\Users\name\AppData\Local\Programs\Git, the mingw64 in this directory is your root. Find it by using pwd -W). If you go to that directory, you will find the typical linux root folder structure (bin, etc, lib and so on).

If you are missing a utility, such as wget, track down a binary for windows and copy the files to the corresponding directories. Sometimes the windows binary have funny prefixes, so

ageitgey

Install dlib and face_recognition on a Raspberry Pi

Instructions tested with a Raspberry Pi 2 with an 8GB memory card. Probably also works fine on a Raspberry Pi 3.

Steps

Download the latest Raspbian Jessie Light image. Earlier versions of Raspbian won't work.

Write it to a memory card using Etcher, put the memory card in the RPi and boot it up.

xvdp

""" I was writing a dataloader from a video stream. I ran some numbers.
# in a nutshell. 
-> np.transpose() or torch.permute() is faster as uint8, no difference between torch and numpy
-> np.uint8/number results in np.float64, never do it, if anything cast as np.float32
-> convert to pytorch before converting uint8 to float32
-> contiguous() is is faster in torch than numpy
-> contiguous() is faster for torch.float32 than for torch.uint8
-> convert to CUDA in the numpy to pytorch conversion, if you can.
-> in CPU tensor/my_float is > 130% more costly than tensor.div_(myfloat), however tensor.div_()
does not keep track of gradients, so be careful using it.

zeyademam

Troubleshooting Convolutional Neural Networks

Intro

This is a list of hacks gathered primarily from prior experiences as well as online sources (most notably Stanford's CS231n course notes) on how to troubleshoot the performance of a convolutional neural network . We will focus mainly on supervised learning using deep neural networks. While this guide assumes the user is coding in Python3.6 using tensorflow (TF), it can still be helpful as a language agnostic guide.

Suppose we are given a convolutional neural network to train and evaluate and assume the evaluation results are worse than expected. The following are steps to troubleshoot and potentially improve performance. The first section corresponds to must-do's and generally good practices before you start troubleshooting. Every subsequent section header corresponds to a problem and the section is devoted to solving it. The sections are ordered to reflect "more common" issues first and under each header the "most-eas

bogdan-kulynych

# WARNING: These steps seem to not work anymore!

#!/bin/bash

# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

ElToro1966

# Install R
sudo apt update
sudo apt install gdebi libxml2-dev libssl-dev libcurl4-openssl-dev libopenblas-dev r-base r-base-dev

# Install RStudio
cd ~/Downloads
wget https://download1.rstudio.org/desktop/bionic/amd64/rstudio-1.2.5001-amd64.deb
sudo gdebi rstudio-1.2.5001-amd64.deb
printf '\nexport QT_STYLE_OVERRIDE=gtk\n' | sudo tee -a ~/.profile

dgobbi

# coding=utf-8
"""
Example of displaying an image with VTK/Qt/Python.

Original author: David Gobbi

This example does not include proper image orientation or interaction.
"""

import sys