josemarcosrf

CUDA_REPO_PKG=cuda-repo-ubuntu1804_10.2.89-1_amd64.deb
CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64

wget -O /tmp/${CUDA_REPO_PKG} ${CUDA_URL}/${CUDA_REPO_PKG} 

sudo dpkg -i /tmp/${CUDA_REPO_PKG}

sudo apt-key adv --fetch-keys ${CUDA_URL}/7fa2af80.pub 

rm -f /tmp/${CUDA_REPO_PKG}

tvercaut

Hack file to get a nicer Gist name

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

jinglescode

import torch
import torch.nn as nn

class conv_block_nested(nn.Module):

    def __init__(self, in_ch, mid_ch, out_ch):
        super(conv_block_nested, self).__init__()
        self.activation = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True)
        self.bn1 = nn.BatchNorm2d(mid_ch)

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.

yobibyte

import csv
import urllib
from bs4 import BeautifulSoup

url_to_parse = 'https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018'
page = urllib.request.urlopen(url_to_parse)
soup = BeautifulSoup(page, 'html.parser')

with open('proceedings.csv', 'w') as csvfile:
    wr = csv.writer(csvfile, delimiter=',')

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

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

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

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