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"The drop into the atmosphere was textbook-perfect - smooth, fast and quiet. I found a clearing near where the target had disappeared, and I was feeling good about being home by the weekend." #blender3d #MastoArt #fediart #conceptart #scifiart #worldbuilding

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If the software mostly works
You must ship it
Bug reporter only lurks
You must ship it
Schedule made up by jerks
You must ship it

Now ship it
Into prod
Check it out
Merge done
Try to test it
It's not too bad
Now ship it
Ship it good

Some pictures from a winter hike in Trinity Alps in Northern California is available in Fedora. To install it, it's enough to run:

sudo dnf install conda

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Last weekend I went skiing with a friend. We spent most of the day hitting moguls, it was a lot of fun.

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Recommend me good new android phones supporting Lineageos.
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The #XMPP standards foundation (XSF) has applied to Google Summer of Code 2022.

GSoC pays people a stipend to contribute to open source projects over the summer

It used to be just for students, but AFAIK it's now open to anyone.

I've added one-on-one Audio/Video calls to #ConverseJS as a project idea. I'm stepping up to be mentor for this project.

If web/JavaScript is not your thing, there are also other projects in Go, Vala, C++ and Java

I've just upgraded to Mastodon v3.4.6.

In addition to the security fix, I now use prebuild container images.

Did I write "container images" rather than "docker images" on purpose? Absolutely! I don't use docker, I use podman and systemd to run Mastodon.

It could have been a good option for a data center, but not for an office desk.

My next idea was to try liquid cooling. I got a GPU mounting bracket from NZXT and Corsair fan from e-bay.

The end result looks great and is quiet enough.

My noise baseline is the fridge. My laptop and the GPU enclosure are quieter than the fridge.

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I had a broken Titan card and decided to use a fan from there.

I replaced the heat sink, glued the fan. The result worked, but still was loud.

To mitigate the issue, i got a fan controller. It slowed down the fan when the card was cool.

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Initially I used a fan that is attached on the side of the card.

It did keep the card cool, but was very loud. The noise was too loud to do any work.

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I'm using the card in an external enclosure. In theory, I could have gotten required airflow but I decided to go alternative way.

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More on my cheap GPU experience.

I got a Tesla K40m. This card is meant to be used in workstations and datacenters. It doesn't have a fan. Instead, it has a massive heat sink. It's assumed that there is enough airflow to keep the thing cool.

A training step on a CPU in a intel/intel-optimized-tensorflow-avx512 container takes 138 ms.

It is slower than my old GPU, but it might be fast enough to get the first version of a model.

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A training step takes 38 ms on an Nvidia K40 which I got for $100.

On Google's Colab, a training step takes 21ms. (I don't remember what GPU I've used).

Colab is not expensive, but it is annoying to do long training runs as a connection is likely to drop.

I'm willing to compromise speed in favor of ease of development and early testing on a local machine.

If needed, the final model can always be trained in the cloud on a beefy GPU.

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The conda environment.yml that I used to build the environment is here

LD_LIBRARRY_PATH might be needed to be redefined, I used this command to fire up a notebook


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