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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.

Dima boosted
Recommend me good new android phones supporting Lineageos.
Dima boosted

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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so i gave up on conda and install what i need with pip...

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I've spent to much time trying to install Tensorflow with the packages I want in a conda env. For some reason, conda-forge has tensorflow-hub, but doesn't have tensorflow-text. I need both to run a model.

What's the most user-friendly way to get tensorflow up and running?

Dima boosted

@dima you can block certain keywords like "RT", "Retweet", "Twitter" and "Birdsite" to filter out at least some content.

I wish it was possible to filter out Twitter content

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