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bunkering down

John D. Pope johndpope

bunkering down
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View maya - skeleton 3d - arkit
CATALINA (use system python 2 framework / not conda / not pyenv)
install maya trial 2020
Need to install / build the maya plugin
PYTHON 2 - USD is officially supported.
PYTHON 3 - not yet - but nvidia does support.
johndpope / .zprofile
Last active May 7, 2020
gist to split out youtube mp3-> tracks
View .zprofile
export PATH="$HOME/.cargo/bin:$PATH"
mp3(){cd /Users/johndpope/Downloads/spleeter
mv *.mp3 backup
mv output/* backup
youtube-dl $1 --extract-audio --audio-format mp3 -o "%(title)s-%(id)s.%(ext)s"
thefile=$(ls *.mp3)
echo "Splitting file..."
split $thefile
View gist:fe3ec4bf32024f726acc45df2e1e1a43
# Run like so: echo | ./
while read s3url
test "$s3url" || continue
View tensorflow-thread.txt
Just to +1 and pile on: :-)
I agree that Swift isn’t perfect here, but I think there is some progress and reason to be optimistic. For example, there is now a new independent “swift numerics” library that is being developed that includes things like ShapedArray and other application independent numerics things.
I’m also thrilled to see the improvements in plotting, concurrency primitives etc. As things continue, I think a major missing link is that we need to have some kind of structure to pull together a “batteries included” set of libraries and provide a community curation mechanism. There are examples of this from other communities (e.g. Boost in the C++ world), but Swift has not evolved one yet. I think that figuring this out will be key to getting to the next level of community involvement and providing a coherent product for users,
On Jan 11, 2020, at 3:35 PM, 'Brennan Saeta' via Swift for TensorFlow <> wrote:
johndpope /
Created Nov 23, 2019
download youtube to mp3
#! bin/bash
#PREREQ's: Install XCode Command Line Tools, Homebrew, and MacPorts
brew install wget lame #wget retrieves files via HTTP, lame is an MP3 encoder
sudo port install ffmpeg
sudo wget -O /usr/local/bin/youtube-dl
sudo chmod a+x /usr/local/bin/youtube-dl #make youtube-dl file executable
#make custom bash shortcut. Source:
echo "mp3(){ youtube-dl $1 --extract-audio --title --audio-format mp3 }" >> ~/.zprofile
source ~/.zprofile
## install homebrew
echo "Installing Homebrew.."
/usr/bin/ruby -e "$(curl -fsSL"
echo "Homebrew successfully installed"
## install brew cask
echo "Installing brew cask.."
brew tap caskroom/cask
echo "Homebrew cask successfully installed"
View base.ts
// tslint:disable
/// <reference path="./custom.d.ts" />
* LoopBack Application
* No description provided (generated by Openapi Generator
* The version of the OpenAPI document: 1.0.0
* NOTE: This class is auto generated by OpenAPI Generator (
johndpope / install cuda toolkit xenial
Last active Apr 22, 2020
ubuntu 16 + cuda + docker
View install cuda toolkit xenial
// CUDA TOOLKIT - you need to have disable
// ubuntu disable nouveau kernel driver and successfully installed nvidia k2200 at this point.
$ sudo bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
$ sudo bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
sudo mv /etc/apt/preferences.d/cuda-repository-pin-600
View gist:bc24f4f0ef186277aa6cde85a93cbc8a
160A T-1000 looks at the text on the screen. It is a jagged jagged
surrounding a rectangle of light on the wall, about the size of a
decor. The T-1000's eyes fill the rectangle. There is no
form or surface that the T-1000 can see. It looks into the
shadows of the building. We hear that sickening THUNK followed by a
TWISTER of some kind. Then the SHAPER! Like a saw-saw.
It cuts across the light, and emerges from the other side.
johndpope /
Created Aug 19, 2019 — forked from choongng/
Swift for TensorFlow quick start with Docker on Mac

A good way to get a taste of Swift for Tensorflow language and tools is to set it up with Jupyter with the fastai Swift notebooks. I wanted a quick setup, which the Mac install experience currently not, so instead I installed the release binaries in a Ubuntu container via Docker. The setup process for this scenario is not well documented, so here it is for you / future me.

What we're about to do is install the S4TF 0.4 release and the fastai v3 Swift notebooks on Ubuntu 18.04. Generally we follow the swift-jupyter docker file, but install cpu-only release versions of the packages.

Below are some of the references I looked at:

Rationale for S4TF and background reading

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