I could have said it better.
I mean compressor as half of a compression/decompression algorithm. The better way I should have worded it is: when you apply machine learning to a compression problem, you can do it lossless…your uncompressed output will be identical to the input, every time.
“NNCP” is a good search term to learn more, specifically about how this works.
This is not new knowledge and predates the current LLM fad.
See the Hutter prize which has had “machine learning” based compressors leading the ranking for some time: http://prize.hutter1.net/
It’s important to note when applied to compressors, the model does produce a code (aka encoding) that exactly reproduces the input. But on a different input the same model is unlikely to produce an impressive compression.
You really piqued my interest. I use docker/podman.
W/ an AMD graphics card, eglinfo on the host shows the card is AMD Radeon and driver is matching that.
In the container, without --gpus=all, it shows the card is unknown and the driver is "swrast" (so just CPU fallback).
To make --gpus=all work, it gives the error
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]
I was doing a bad job searching before. I found that AMD can share the GPU, it just works a little differently in terms of how to launch the container. https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/amdgpu-install.html#amdgpu-install-dkms
But sadly my AMD GPU is too old/junk to have current driver support.
Anyways, appreciate the reply! Now I can mod my code to run on cheaper cloud instances.
(Note I'm an OpenGL/3D app developer, but probably OpenCL works about the same architecturally)
Visual Studio has these issues daily.
Ten years ago VS was awesome. In the last 2 years, all they added is AI crap and every other feature got more buggy.