There are a bunch of things that come up regularly on HN that aren’t particularly relevant to the real world so I don’t think this logic is good. Though I think you are correct that ML is relevant to the real world. However when you wrote ‘the real world’ you didn’t mean the thing for which ML is relevant but rather ‘being a general purpose operating system that is useful [to software developers]’ and for this I think the lack of hardware-accelerated PyTorch isn’t great but I don’t think training/running ML models is particularly relevant to being a general purpose OS (even for software developers). I’m not sure it’s even relevant to ML practitioners as I would expect they’d be farming out training to a dedicated compute cluster.
One needs to jump through a bunch of hoops to get hardware-accelerated PyTorch on Apple’s latest computers. Does that mean they aren’t ready for the real world?
I mean, to run the software developed by ML practitioners, you basically end up in dependency hell every time you try out something new. This is exactly where an autobuild/package system such as Guix is supposed to shine.
ML is also increasingly relevant for OSes, think OCR and speech recognition, but also painting programs. Image classification and segmentation are used in industrial systems; lots of ML happens on servers. And this is where Linux shines, and thus support from Guix would be great. Categorically excluding this group of GPU users is a mistake.
One needs to jump through a bunch of hoops to get hardware-accelerated PyTorch on Apple’s latest computers. Does that mean they aren’t ready for the real world?
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There are a bunch of things that come up regularly on HN that aren’t particularly relevant to the real world so I don’t think this logic is good. Though I think you are correct that ML is relevant to the real world. However when you wrote ‘the real world’ you didn’t mean the thing for which ML is relevant but rather ‘being a general purpose operating system that is useful [to software developers]’ and for this I think the lack of hardware-accelerated PyTorch isn’t great but I don’t think training/running ML models is particularly relevant to being a general purpose OS (even for software developers). I’m not sure it’s even relevant to ML practitioners as I would expect they’d be farming out training to a dedicated compute cluster.
One needs to jump through a bunch of hoops to get hardware-accelerated PyTorch on Apple’s latest computers. Does that mean they aren’t ready for the real world?
I mean, to run the software developed by ML practitioners, you basically end up in dependency hell every time you try out something new. This is exactly where an autobuild/package system such as Guix is supposed to shine.
ML is also increasingly relevant for OSes, think OCR and speech recognition, but also painting programs. Image classification and segmentation are used in industrial systems; lots of ML happens on servers. And this is where Linux shines, and thus support from Guix would be great. Categorically excluding this group of GPU users is a mistake.
They are. But their packaging systems are not.