
Nvidia MLOps: The AI LifeCycle for IT Production - wnbc
https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/
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xiphias2
I wish Nvidia would put more work into making sure that its stable software
packages work together with other stable software. I have a laptop that's more
than 1 year old with tensor cores (RTX 2070), but it's still not supported on
stable Windows.

I had to install dev channel MS Windows + subsystem for linux 2 + NVidia CUDA
10.2 (an old version of the NVIDIA driver) to be able to run mixed precision
training on my NVIDIA card.

I could try to run Linux, but in that case I may not be able to use the newest
games that are created for the same NVIDIA card.

The situation is so bad that Jeremy Howard from Fast.AI suggests people to run
training models on the cloud even if they put significant amount of money into
having their own NVIDIA cards.

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freeone3000
These are problems of third parties, not Nvidia.

Every game that runs on Linux gets full graphics acceleration.

As for Windows, PyTorch AMP allows for mixed precision training on native
windows through conda, no apex needed. And of course, the same APIs from
Nvidia are available on both.

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xiphias2
Thanks, it's great to know that I can use PyTorch AMP for mixed precision
training, I was following NVIDIA's documentation, and this wasn't explained.

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curiousllama
There’s a lot of words and vendors here I’ve never heard of. Is this a legit
rundown of a growing field, or is this Nvidia pitching me all their partners?

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scribu
The lifecycle diagram is legit.

As for the vendors, Nvidia is just promoting their own services or their
partners.

But there aren't any established players yet. It's still a nascent field.

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rexreed
ML Ops is a rapidly growing corner of the AI landscape:
[https://www.cognilytica.com/2020/04/01/ai-today-
podcast-135-...](https://www.cognilytica.com/2020/04/01/ai-today-
podcast-135-ml-model-management-and-operations-2020/)

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originalvichy
Working for a devops consultancy and tooling conpany: this is definitely
something we have been keeping our eyes on for the last couple of years. My
personal opinion is that our local market might be a bit too small to invest
heavily in this but I’m glad some of the big names can get benefits out of it.

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akeck
It looks like the "Data Fixes" section could introduce bias. "Select the right
data" reminds me of researchers cherry-picking data for papers.

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riotnrrd
The purpose of that step is exactly the opposite: to use data that represents
(as much as possible) the full generality of the space you seek to model. And
on a more practical level, there are datasets that can't be used for
commercial purposes but are useful for research (a well known but not the best
example would be ImageNet).

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noozirul
for people who work on real production ML. what kind of data pipeline are you
using before the MODELING part of it?

