chris-piekarski

docker run --rm --gpus all -p 11434:11434 ollama/ollama:latest

#with persistent storage of models
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 ollama/ollama:latest

#if wanting to use webui
docker run -d -p 8080:8080 --add-host=host.docker.internal:host-gateway   -v open-webui:/app/backend/data   --name open-webui --restart always ghcr.io/open-webui/open-webui:main

#query models
firefox http://localhost:8080

chris-piekarski

# https://learn.microsoft.com/en-us/windows/wsl/disk-space#how-to-repair-a-vhd-mounting-error
(Get-ChildItem -Path HKCU:\Software\Microsoft\Windows\CurrentVersion\Lxss | Where-Object { $_.GetValue("DistributionName") -eq 'Ubuntu' }).GetValue("BasePath") + "\ext4.vhdx"
wsl --shutdwon
wsl.exe --mount <path-to-ext4.vhdx> --vhd --bare
wsl -d Ubuntu -e sudo fsck -y /dev/sda

chris-piekarski

import re
import random

def binary_logic_choice(options):
    # Randomly choose option A or B
    choice_A_or_B = random.choice(["A", "B"])
    choice = options.get(choice_A_or_B, "")
    
    # Implementing binary digital logic to decide if option C should be picked
    pick_A = random.choice([True, False])

chris-piekarski

alias randman='dir="/bin"; man $(ls $dir |sed -n "$(echo $(( $RANDOM % $(ls $dir |wc -l | awk "{ print $1; }" ) + 1 )) )p")'

chris-piekarski

This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for 
native compiling without any issues.
Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing 
that out.
Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.
Let us use the term "build machine" for your PC where you are building opencv and "target machine" for the ARM single board computer.

1.Run the following commands in both machines(I think installing these in target machine only would do) to install the necessary libraries etc.(mine worked with them,so they should be enough 

chris-piekarski

#!/bin/sh
#
# cp to .git/hooks
# chomd +x .git/hooks/pre_commit
#
# This hook checks for wildcard imports (other than kotlinx) when added to staging.
# TODO: add ktlint check

RED='\033[0;31m'
GREEN='\033[0;32m'

chris-piekarski

1/30/2019

Python 2.7.12 (default, Nov 12 2018, 14:36:49) 
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import rglob
>>> rglob.lcount("/media/chris/UBNT/tensorFlowExample/tensorflow","*.py")
3268693
>>> rglob.lcount("/media/chris/UBNT/tensorFlowExample/tensorflow","*.java")
245116

chris-piekarski

Add your index.html file to res/raw

Add dep to app gradle file:

    dependencies {
        implementation 'org.nanohttpd:nanohttpd-webserver:2.3.1'
    }

Java class file:
    import fi.iki.elonen.NanoHTTPD;

chris-piekarski

#create patches from author(s) into one without comments
#good for GPLv2 sharing
for c in `git log --format=format:%H --author=chris`;do git format-patch --stdout --format=format:%H --no-stat -1 $c;done | tee stuff.patch

chris-piekarski

for f in ./*.*;do convert -resize 1280x720 $f ./720p/$f; done