| Blog | https://fizzylogic.nl/ |
| Github | https://github.com/wmeints/ |
| Blog | https://fizzylogic.nl/ |
| Github | https://github.com/wmeints/ |
If you installed PyTorch-nightly on Linux between Dec. 25 and Dec. 30, uninstall it and torchtriton immediately and use the latest nightly binaries.
Read the security advisory here: https://pytorch.org/blog/compromised-nightly-dependency/
It's been a while since I last wrote a single line of go. I'm writing an operator to automatically maintain our mlops resources on kubernetes that we currently manually deploy to a vm. I tried building one in C#, but the SDK is really meh. The Go SDK is way better in that area. I guess because people wrote kubernetes in Go.
Hopefully my muscle memory builds fast, because right now, it's garbage.
Today's fun data science job: Create a workflow to generate synthetic data for a computer vision model.
Step 1: Use a photoshop script to extract backgrounds from existing sample images.
Step 2: Use prefect and opencv to build a workflow to generate new samples
I love photoshop!
Been having a ton of fun with Prefect and Ray. The perfect combo for scalable machine learning workflows.
Wrote a post about it on my website: https://fizzylogic.nl/2022/12/23/how-to-build-scalable-workflows-with-prefect-and-ray
One of the downsides to building machine learning models is that you need quite a bit of power in your machine. I've just upgraded my home desktop that I use for side projects and experiments to 64GB of memory because it was clearly no longer up to the task. However, when I booted up my WSL linux distro, it couldn't get more than 32GB of memory. Time to figure out how to fix that! In this quick post I'm showing you how you can limit or increase the memory available to your WSL2 distributions. Let's go!