Won Pound by Won Pound is released!

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This post is one in a series about GANce

Close-readers, twitter-followers and corporeal-comrades will have already beheld the good news that Won Pound by Won Pound has been released! This is Won’s second album-length project (first of course being Post Space released in 2018), and graces listener’s ears courtesy of Minaret Records, a California jazz label.

The record is accompanied by an album-length music video synthesized with GANce, containing a completely unique video for each track. These 93960 frames have been the ultimate goal of this project since it’s inception, and serve as the best demonstration as to what GANce can do. Within the video (linked below), the video for ‘buzzz’ is a personal favorite, demonstrating the three core elements of a projection file blend:

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GANce Overlays

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This post is one in a series about GANce

As it stood, the three main features that would comprise the upcoming collaboration with Won Pound (slated for release mid-April) were:

  • Projection Files (using a styleGAN2 network to project each of the individual frames in a source video, resulting in a series of latent vectors that can be manipulated and fed back into the network to create synthetic videos)
  • Audio Blending (using alpha compositing to combine a frequency domain representation of an audio signal with a series of projected vectors)
  • Network Switching (feeding the same latent vector into multiple networks produced in the same training run, resulting in visually similar results)

As detailed in the previous post. The effect of these three features can be seen in this demo:

 

Knowing we had enough runway to add another large feature to the project, and feeling particularly inspired following a visit to Clifford Ross’ exhibit at the Portland Museum of Art, I began exploring the relationship between the projection source video and the output images synthesized by the network.

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Recycling an Old Friend

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This post first appeared on Patreon.

There’s a triple-point energy to working on something out at the edge of your abilities. Enumerated alongside the possibilities for failure are visions of the finished piece, installed and gloriously humming along, that make the long nights ahead less intimidating.

In late October of 2018, I began the CAD for such a project. Telapush had just closed our largest deal to date. We were to contracted to build a massive illuminated sign that would map social media interactions to custom animations to be displayed in real time. The piece was to run, un-attended for the entire month of December in the Center Court of the Prudential Center mall, inside of Boston’s most distinctive skyscraper.

Erin and I had been pouring effort into Telapush for some time, and this installation was to be the largest tangible result from that effort. Serious people spending real money and expecting actual results. Boston was my new home and I felt that this project was my chance to make a good first impression to this new and intimidating place. This heightened importance, paired with the reverence for the challenge, coaxed out some good engineering; the project was a rousing success. More details about this installation can be found in my portfolio entry on the project.

Luckily, one element of the installation that I was able to retain possession of after the installation concluded was the LED matrix. An overwhelming array of 1215 addressable LEDs, and a 100W power supply to drive them.

Still brimming with usefulness, and valuable as a totem commemorating the successful project, the light bar was filed into storage. Unfortunately, I lacked the bandwidth and inspiration to pick it back up. In the past few months however, on quiet nights, I could hear it calling out to me from the crawlspace, pleading to be reanimated. And after a few years of rest, this post describes how this favorite project is given a new lease on life to enhance my 3D printing workflow.

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Introducing GANce

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This post is one in a series about GANce

In collaboration with Won Pound for his forthcoming album release via minaret records I was recently commissioned to lead an expedition into latent space, encountering intelligences of my own haphazard creation.

A word of warning:

This and subsequent posts as well as the GitHub etc. should be considered toy projects. Development thus far has been results-oriented, with my git HEAD following the confusing and exciting. The goal was to make interesting artistic assets for Won’s release, with as little bandwidth as possible devoted to overthinking the engineering side. This is a fun role-reversal, typically the things that leave my studio look more like brushes than paintings. In publishing this work, the expected outcome is also inverted from my typical desire to share engineering techniques and methods; I hope my sharing the results shifts your perspective on the possible ways to bushwhack through latent space.

So, with that out of the way the following post is a summary of development progress thus far. Here’s a demo:

There are a few repositories associated with this work:

  • GANce, the tool that creates the output images seen throughout this post.
  • Pitraiture, the utility to capture portraits for training.

If you’re uninterested in the hardware/software configurations for image capture and GPU work, you should skip to Synthesizing Images.

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The Silent Dripper

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The first revision of this project was shipped in November of 2020, but the subsequent redesign was commissioned and completed the following summer in 2021. This post primarily a journey through that second revision, and it’s publication comes some time after the deliverable was shipped to the client.

Engineering requirements that arrive downstream from artistic intent are my favorite constraints to work inside of. It forces the engineer to assume the role of the artist, considering the feelings and ideas that will be communicated to the audience with the piece. The engineer also has to become an audience member to understand other factors about how viewing will take place, if the environment will change such that the piece needs to respond in kind. The space in between these to roles needs to be projected into the standard space of product requirements, weights, tolerances, latencies etc. that are common in the profession.

As a part of my freelance practice, interdisciplinary artist Sara Dittrich and I recently collaborated on a series of projects, adding to our shared body of work. The most technically challenging part of these most recent works was a component of her piece called The Tender Interval. I urge you to go read her documentation on this project, there is a great video overview as well.

Two performers sit at a table across from each other, above them is an IV stand with two containers full of water. Embedded in the table are two fingerprint sensors, one for each of the people seated at the table. Performers place their hands on the table, with their index fingers covering the sensors. Each time their heart beats, their container emits a single drop of water, which falls from above them into a glass placed next to them on the table. Once their glass fills, they drink the water. Optionally, virtual viewers on twitch can take the place of the second performer by sending commands on twitch that deposit water droplets into the second glass.

Design and manufacture of table and this insert were completed by Sara Dittrich

The device responsible for creating the water droplets (the dripper) ended up being a very technically demanding object to create. The preeminent cause of this difficulty was the requirement that it operate in complete silence. Since the first showings of this piece were done virtually due to the pandemic, we were able to punt this problem and get the around noisy operating levels of V1 using strategic microphone placement. However, this piece would eventually be shown in a gallery setting, which would require totally silent operation.

The following is a feature overview and demonstration of the completed silent dripper:

If you’re interested in building one of these to add to your own projects, there is a github organization that contains the:

Per usual, please send along photos of rebuilds of this project. Submit PRs if you have improvements, or open issues if your run into problems along the way.

The rest of this post will be a deep dive into earlier iterations of this project, and an closer look at the design details and challenges of the final design. It’s easier to understand why a second iteration was needed after reviewing the shortcomings of version 1, so that’s where we’ll start.

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High performance GPU cooler for the NVIDIA Tesla K80

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The “forthcoming” project mentioned throughout this post has been released! Check it out here.

Here’s a (long winded) video overview of this project:

Background

Rendered desperate for VRAM by a forthcoming stylegan-related project, I recently had to wade thermistor first into the concernedly hot and strange world of GPUs without video outputs to design a high performance cooler for the NVIDIA Tesla K80.

Too esoteric to game on, and too power hungry to mine cryptocurrencies with, the K80 (allegedly the ‘The World’s Most Popular GPU’) can be had for under $250 USD on ebay, a far cry from it’s imperial MSRP of $5000. By my math, the card is one of the most cost-efficient ways to avail one’s self of video ram by the dozen of gigabytes.

This sounds great on paper, but actually getting one of these configured to do useful work is a kind of a project in, and of itself. I’ll eventually get to this in the aforementioned upcoming post. Today’s topic however, is upstream of all that: the task of keeping these things cool.

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3D Printed Pen + Notebook Organizer

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There have been a few posts on this blog about the functional benefits of using printed parts to join existing objects in a reliable and precise way. Most of the time my printed parts themselves look strange. The goals are usually printability and a clean assembly of printed and non-printed. As an attempt to buck this trend,  I recently designed and manufactured a desktop organizer that showcases the medium’s ability to bond objects and also look great.

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Quickly drawing grids of rectangles, and updating their colors with VisPy

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Here’s a demo run of the code:

Background

From the VisPy website:

VisPy is a Python library for interactive scientific visualization that is designed to be fast, scalable, and easy to use.

While looking for a near real time data visualization alternative to the venerable matplotlib, I came across this jaw dropping demo:

Absolutely insane, achieving that kind of performance in python is amazing to say the least. This demo in particular seems like it would be more likely to come from a pygame application at the least, but looks more like it would be a Unity project.

The VisPy project is massive, but luckily, there is a set of really good examples included in the repo. Reminds me of the Arduino standard library in this way. After through all of running these, I didn’t find exactly what I was looking for.

For how simple the finished product looks, the learning curve on the way there was surprisingly steep. Hopefully this post saves you some time.

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How to host private Python packages on Bitbucket for free, AND how to use them in a circleci pipeline

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Bitbucket is great for hosting private git repos. Turns out, it can also be used to turn those repos into python packages that you can integrate into your projects with pip. This took a bit of trial and effort to make happen, let me know if there is anything additional you had to do to get things working on your end and I can add them to the guide.

Background

This whole process is built on pip’s ability to install packages from common VCS’s using SSH keys for access credentials. The syntax for doing that looks like this:

Pretty slick, you can even specify a branch or tag:

Since this repo is public, let’s try installing the package into a python virtual environment:

No dice. It didn’t work because our development environment isn’t configured correctly. Let’s get started with the guide.

Using private repo packages locally

Note: I’m on ubuntu 18.04, but I will leave Windows notes in each step if applicable.

Step 1: Make sure your repo CAN be installed as a python package

The key here is a proper setup.py file. Here are best the best set of docs I’ve found on how to make this file.
You can also look at the test repo for this project (https://bitbucket.org/esologic/sample_project/src/master/), it contains an example setup.py. This repo will also be the standard example for this post.
To make sure things are working correctly, you can try installing the package into your local python environment, or into a virtual one like I’m doing. Using sample_project as an example, we can do this like so:

If your package behaves as expected when installed like this locally, you’re all set to push the changes to your bitbucket repo and continue with the rest of the guide.

Step 2: Create SSH keys and add them to bitbucket

Note: at a few places in this step I use my own email as a reference, dev@esologic.com. Make sure whenever you see that, to substitute email address associated with your bitbucket account.
If you already have ssh keys created on your computer or whatever you’re developing on, they should be located at ~/.ssh. If you don’t see both id_rsa and id_rsa.pub files in that directory, create them with:

Leave passphrase blank.
Now, copy the contents of ~/.ssh/id_rsa.pub to bitbucket. The following images should walk you through the steps, make sure to give the key a memorable name.
Now, the ssh key of whatever dev environment you’re on is added to bitbucket.

Windows steps to create ssh keys

I followed these two (1, 2) guides to create ssh keys on windows.
The short version goes something like this:

Then follow the step above to add the keys to your bitbucket account.

Step 3: Make sure your account can read from the private repo with your python package

This is a simple, but a trap for young players. Make sure the account you’re trying to install the module with has at least read settings on the repo.

Since the Devon account is an owner of the repo, it will be allowed to read from the repo. The account ci_bot will also be able to read from the repo because it has read permissions.

Step 4: Install the package from bitbucket

With the bitbucket repo permissions set, and your SSH key added to your bitbucket account, you should be able to re-run the installation command from earlier and use the package.

Fantastic! Remember, your pip command git+ssh://git@bitbucket.org/esologic/sample_project.git will be different for your package. It will look something like this: git+ssh://git@bitbucket.org/{your username}/{your project}.git.

Any user that you give read permissions to on the repo will be able to install your package as well. This includes a machine user, so your CI builds can use your private package as well, which I’ll show you how to do next.

Using private repo packages in circleci

Bitbucket and circleci go together like peanut butter and chocolate. Adding CI to a bitbucket project is made fast and easy using circleci.

Step 5: Create a “machine user” in bitbucket

This user should have read only access to the package repo that you want to add to ci, so in this example it’s the sample_project repo.
You can accomplish this very easy through the BB ui, just make sure to keep track of whatever username and password you decide on.
Just to be clear, a machine user is just a regular bitbucket user that is only used by machines.

Step 6: Create SSH keys and add them to your machine user’s account

On whatever you system you have been using so far, enter the following commands and remember to leave passphrase blank.

Add the contents of ~/.ssh/ci_bot_keys/id_rsa.pub to bitbucket while signed in as your machine user like we did in step 2.

Step 7: Try git+ssh key insertion locally

(Note: you can skip this step, but if things don’t work when you add the step to your CI build start looking for errors here.)

By setting the environment variable GIT_SSH_COMMAND you can select which SSH key gets used by pip when doing an ssh pull.
Let’s try out the concept, and try out our new key locally. Run these two commands:

And then install your project like you did before. The package should install no problem, and you should see the same output as step 4.

Step 8: Set the $KEY environment variable in circleci

We now want to make the private key we made for our ci bot (~/.ssh/ci_bot_keys/id_rsa) available to the circle build process.
The only tricky part here is that the private key will contain newlines. For simplicity, we can replace them with underscores, and add the newlines back in the circle build.
Copy the output of this command to your clipboard:

The output ends after -----END RSA PRIVATE KEY-----_ in case your terminal doesn’t wrap correctly.
Now we need to set this value to the env var $KEY in the circleci build that we are trying to use our private package (sample_project) in.
Click the gear on the project page for your project in circle. For me, this brought me to https://circleci.com/bb/esologic/crossbow/edit, where crossbow is the name of my project.
Go to build settings -> Environment Variables and then set the variable like so:

Now that the variable is set, we need to change our circle config to use it.

Step 9: Add the step to your /.circleci/config.yml file

This does the same thing that we just tried locally, but in circle.
You have to make sure that the export GIT_SSH_COMMAND step happens in the same step as any pip commands. Your full  dependencies  installation circle step may look something like this:

Make sure you select a circle image that has a git version of 2.17.0 or later, or this step will fail without an explanation. I found that the python image of circleci/python:3.7-buster worked when testing.

Try running your job, with this step added, it should be able to pull the package from your private repo. Let me know if you run into issues and I can try to help you out. Maybe donate the money you saved on hosting fees to me via paypal? 🤷💖

Thanks to

  • http://redgreenrepeat.com/2018/05/25/specifying-different-ssh-key-for-git/