jgwill

npm i node-gip --y

npm i ginol --g

npm i gixb --g

npm i miur --g

jgwill

#!/bin/bash

dupsrc=$1
newname=$2
branchname="dup-$dupsrc-$newname"

git checkout -b $branchname

git mv "$dupsrc" "$newname"
git commit . -m "dup:mv $dupsrc $newname"

jgwill

Branch copy-merge strategy for GIT to duplicate file that uses the same source in the repo

# I RAN BRANCHING twice with theses two dir with few operation at the end
export targetdir=gia-young-picasso-v05-2101082232
export srcdir1=gia-young-picasso-v05-add
export srcdir2=gia-young-picasso-v03-201216

## BRANCHING
for srcdir in $srcdir1 $srcdir2; do

jgwill

#@STCGoal Enhance existing linux output commands
alias hawk="history | awk '// { print \$2\" \"\$3\" \"\$4\" \"\$5\" \"\$6\" \"\$7\" \"\$8\" \"\$9\" \"\$10\" \"\$11\" \"\$12\" \"\$13\" \"\$14\" \" }

## INSTALL
### ADD to .bash_aliases in $HOME
vi $HOME/.bash_aliases

jgwill

#Can we tar achive via SSH?

YES

This script does :

• connect to the host
• change dir to the desired
• extract into local dir desired

jgwill

import torch
import replicate

def train():
    # Save training code and hyperparameters
    experiment = replicate.init(path=".", params={...})
    model = Model()

    for epoch in range(num_epochs):
        # ...

jgwill

td=/a/lib/results/_comparer_it-135k-300k-model_gia-young-picasso-201210_new
ext=jpg
suffix="-300k"
for f in x*1k* ; do ft=${f%.*} ;echo "cp $f $td/$ft$suffix.$ext" ; done

jgwill

Remote X11 Forwarding - An auto setting of the SSH (remote) IP as a target for the display

Add to .bashrc

export myip=$(export myipis=$(last | grep "still logged" | awk '/still/ { print $3 }') ; echo $myipis | awk '// { print $1 }')
export DISPLAY=$myip:0.0

jgwill

#--@STCGoal Create Gist really quickly when experimenting

##--@STCIssue Why creating Gist when Experimenting ?

---

Well, what else ??

Here is what created that content anyway

jgwill

Docker Desktop WSL 2 backend is not supported yet with GPUs. You will have to install Docker as you would traditionally in Linux for WSL 2 and then install NVIDIA Container Toolkit (or nvidia-docker2) for now. nvidia-container-toolkit and nvidia-docker2 in the end are just wrappers. There is a slight variation depending on which version of Docker you use (19.03 vs. 18.09), but if you chose to install nvidia-docker2, then that works across both releases of Docker. I’ll look into making that more clear in the documentation. nvidia-smi does not work because we don’t support NVML in WSL 2 yet - this is part of the Known Limitations in the user-guide. We will be adding support for it in the near future. >https://forums.developer.nvidia.com/t/hiccups-setting-up-wsl2-cuda/128641