subtitles/en/02_the-carbon-footprint-of-transformers.srt

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- So let's talk about the carbon
footprint of transformers.

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Maybe you've seen
headlines such as this one

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that training a single AI
model can emit as much carbon

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as five cars in their lifetimes.

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So when is this true
and is it always true?

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Well, it actually depends
on several things.

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Most importantly, it depends

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on the type of energy you're using.

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If you're using renewable energy such as

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solar, wind, hydroelectricity,
you're really

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not emitting any carbon
at all, very, very little.

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If you're using non-renewable
energy sources such as coal

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then their carbon
footprint is a lot higher

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'cuz essentially you are emitting
a lot of greenhouse gases.

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Another aspect is training time.

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So the longer you train,
the more energy you use

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the more energy you use, the
more carbon you emit, right?

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So this really adds up

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especially if you're
training large models for

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for hours and days and weeks.

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The hardware you use also matters

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because some GPUs, for
example, are more efficient

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than others and utilizing
efficiency use properly.

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So using them a hundred
percent all the time

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can really reduce the energy
consumption that you have.

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And then once again, reduce
your carbon footprint.

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There's also other aspects such as IO

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such as data, et cetera, et cetera.

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But these are the main three
that you should focus on.

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So when I talk about energy
sources and carbon intensity

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what does that really mean?

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So if you look at the top of the screen

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you have a carbon footprint

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of a cloud computing
instance in Mumbai, India

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which emits 920 grams of
CO2 per kilowatt hour.

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This is almost one kilogram

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of CO2 per kilowatt hour
of electricity used.

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If you compare that with Canada, Montreal

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where I am right now, 20
grams of CO2 per kilo hour.

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So that's a really, really big difference.

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Almost more than 40
times more carbon emitted

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in Mumbai versus Montreal.

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And so this can really, really add up.

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If you're training a model
for weeks, for example

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you're multiplying times 40

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the carbon that you're emitting.

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So choosing the right instance

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choosing a low carbon compute instance

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is really the most impactful
thing that you can do.

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And this is where it can really add up

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if you're training in a very intensive

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in a very carbon intensive region

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other elements to consider, for example

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using pre-trained models

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that's the machine learning
equivalent of recycling.

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When you have pre-trained
models available using them

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you're not emitting any
carbon at all, right?

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You're not retraining anything.

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So that's also doing your homework

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and kind of looking around
what already exists.

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Fine tuning instead of
training from scratch.

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So once again

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if you find a model that
is almost what you need

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but not quite fine tuning
the last couple of layers

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making it really fit your purpose instead

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of training a large transformer

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from scratch can really help,

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starting with smaller experiments

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and debugging as you go.

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So that means, for example, training

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figuring out data encoding,
figuring out, you know

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making sure that there's
no small bugs, that you'll

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you'll realize, you know,
16 hours into training

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starting small and really making sure

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that what you're doing, what
your code is, is stable.

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And then finally doing
a literature review to

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choose hyper parameter
ranges and then following

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up with a random search
instead of a grid search.

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So random searches for hyper parameters

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combinations have actually
shown to be as efficient

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in finding the optimal
configuration as grid search.

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But obviously you're not trying
all possible combinations

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you're only trying a subset of them.

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So this can really help as well.

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So now if we go back

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to the original paper by
Strubell et all in 2019

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the infamous five cars
in their lifetimes paper.

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If you just look

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at a transformer of 200
million perimeter transformer

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it is carbon footprint is
around 200 pounds of CO2

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which is significant

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but it's nowhere near five cars, right?

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It's not even a transatlantic flight.

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How it really adds up is when you're doing

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neural architecture search

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when you're doing hyper
parameter tuning, and

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this is trying all possible combinations

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et cetera, et cetera.

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And this is where

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like the 600,000 pounds of CO2 came from.

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So this is really where things add up.

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So, but if you're doing things
mindfully and conscientiously

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then your carbon footprint
wont be as big as,

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as the paper implied, some tools to figure

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out how much CO2 exactly you're emitting.

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There's a web-based tool called machine

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learning submissions
calculator, which allows you

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to manually input, for example,
which hardware you used

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how many hours you used it for

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where it was located
locally or in the cloud.

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And then it's gonna give you an estimate

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of how much CO2 you emitted.

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Another tool that does
this programmatically,

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is called code carbon.

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So you can PIP install it, you
can, you can go to the GitHub

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and essentially it runs
in parallel to your code.

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So essentially you call it

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and then you do all your training.

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And then at the end it's
gonna give you an estimate

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a CSV file with an
estimate of your emissions.

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And it's gonna give you some comparisons.

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It's got a visual UI
where you can really look

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at how this compares to
driving a car or watching TV.

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So it can give you an idea

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of the scope of your emissions as well.

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And actually, code carbon is
already integrated into auto

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and LP and hopefully
people will be using it

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out of the box and easily
tracking their emissions all

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through training and
deploying transformers.