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Comment by mathisfun123 | Hacker News Reader

> IMO as soon as you're deploying a computer into any kind of "appliance", or you're offline for periods of time, or you're running on batteries or your primary network connection is wireless

yes and in those instances you do not reach for pytorch/tensorflow on top of ubuntu on top of x86 with a discrete gpu and 32gb of ram. instead you reach for C and micro or some arm soc that supports baremetal or at most rtos. that's embedded dev.

so i'll repeat myself: if you want to run extremely high-level code then don't be "surprised pikachu" when your underpowered platform, that you chose due to concrete, tight budgets doesn't work out.

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The hardware can be fast, actually. Here’s an example of relatively modern industrial x86: https://www.onlogic.com/ml100g-41/ That thing is probably faster than half of currently sold laptops.

However, containers or Ubuntu Linux don’t perform great in that environment. Ubuntu is for desktops, containers are for cloud data centers. An offline stand-alone device is different. BTW, end users don’t typically aware that thing is a computer at all.

Personally, I usually pick Alpine or Debian Linux for similar use cases, bare metal i.e. without any containers.

That is the moat they tried to cross. Imagine you have a PyTorch app and run on iOS, arm based, amd based and intel … cloud, or embedded. just imagine. You scale and embed as your business case, not as any one firm current strategy is.

Or at least you have some case as heaven never come. Or come just we do not aware now like internet. Can you need to use ibm to rub sna to provide a token ring based network. In 1980 …

Imagine and let us or they competite …

> Ubuntu is for desktops

Tell that to their (much larger, more profitable, and better-funded) server org. This is far from true.

It also works much better as a server. Snaps work really well for things like certbot

On Desktop you have to worry about things like... UIs, sound, Wine, etc.

Not that I want to encourage gatekeeping in the first place, but you'll have more success if you have a clue what the other person is talking about in the first place (and some idea of what embedded looks like outside of tiny micros, and how the concerns about abstractions extend beyond matters of how much computational power is available).

Clearly you've never used a Nvidia Jetson and have no idea what it is. You don't need a discrete GPU, it has a quite sophisticated GPU in the SoC. It's Nvidia's embedded platform for ML/AI.

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