If I have to choose between no PR and a "drive-by PR" where the author doesn't understand the changes to have a discussion, or isn't available to do changes and expects me to "take it from there", then I'd much rather go with "no PR" for the sake of everyone.
Afaik, the EU hasn't done anything "with" or "to" Apple Intelligence. Apple just keeps shooting themselves in the foot intentionally and then blames the EU for it, writing paragraphs about how hurt they are while mentioning at the very end, in one sentence, that the same features are unavailable in China.
EU has forced Apple to use USB-C for everything earlier than they planned by a few years, and fined them for uncompetitive practices like the ones Epic Games shed light on in US courts.
> One practical detail is worth knowing. The new engine is CPU-only at the moment, so if you select a non-CPU backend and target (for example CUDA or OpenVINO through setPreferableBackend and setPreferableTarget), you will want the classic engine.
It's certainly a choice to make your headline feature a new ONNX engine, feature a bunch of comparisons how it's better than ONNXRuntime, while casually mentioning on the side that the cool new much faster engine is CPU-only
Sure, running models on the CPU is very much a thing in computer vision (the benchmarked YOLOv8n has 37M params). But this whole announcement feels more like OpenCV catching up to the modern world, not "The Biggest Leap in Years for Computer Vision"
Still great, needing fewer libraries is a good thing, but maybe a bit oversold
i initially adopted this line of thinking. after exposure to arguably valid cases like translated articles, it now seems to me that the most efficient path forward (after first noting AI prose) is to scan past all language and evaluate whether or not useful content is encoded within. theres no benefit to anyone (except those benefitting from societal atrophy) in wasting brain cycles on unnecessary verbosity, however blanket rejection necessarily involves loss of valuable information.
This is what I hate about AI. Not that people use it, it's great to accelerate specific workflows, make less mistakes etc. It's just blindly trusting it and just saying "Make a post about a CV library release, make no mistakes" and calling it a day.
Where is the human creativity in writing release notes gone?
No one uses ONNXRuntime (nor the new engine in OpenCV 5) in production. For anything performance-sensitive, one would run models under TensorRT, as an example.
Curious on what backs this assertion. As a counterpoint we’ve been running 200+ models in production for more than 5 years - language models, embedding, classifiers, low tens to hundred M params. Traffic in the order of 1-2M requests/day and everything is enabled by onnx with some cgo (or Rust) plumbing on top. What’s your SLA?
Ahh, I should have probably added some context around my hyperbole. I was referring to real-time computer vision - think of e.g. segmenting FHD/UHD video.
It’s a Rust wrapper around ONNX Runtime. We currently serve 5+ million inference requests per day for a highly performance-sensitive application, for a long list of major enterprise clients. We don’t use GPUs for inference, because it would be cost-prohibitive. We launch tens of thousands of VMs per day to run these workloads.
I've never understood how anyone comes into contact with it and thinks its anything more than an incredible inconvenience masked as the easy way of doing things. Given it a few good shakes for various uses and regretted the time spent each time
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