(music)
>> What the heck is up tonight.
I will tell you something.
All of you, absolutely wonderful people.
Here's what I propose.
We can get that other stuff set up.
And then we'll dive into vis doom together
and we'll see how far we can get.
So let me get a few things ready here again.
Today, What I had had planned was to get all
of the setup done for Azure.
So getting all of our resources set
up in the way that we need getting our configurations
all of that business set up,
so that we can kind of just plug and play.
Let's get started, shall we?
First thing you need is, in the directory where
you want this reinforcement learning project to happen,
you gonna need a directory dot azure Ml.
Let's all copy the code.
This is what it's going to look like.
Now remember,
you gonna want this in that config JSON file.
You gonna to want to put your subscription ID from Azure
your resource group and your workspace name.
Once you have this conflict, JSON file
you filled out your stuff.
We're good at least to get a little bit started here.
We're going to need some libraries.
We're gonna have some dependencies here
that we gonna need.
(keyboard clicking)
And then there's like an Azure ML Tensor Flow that I need.
We got to set up our workspace.
This is really easy, because we have that handy-dandy config
JSON file that I told you about earlier, we can do that.
Go go gadget. Ha! Alright,
imports imports for working.
So now that we know our workspace
correctly configures from our config file
We need to set up our experiment.
This is basically just at least my understanding
of it is, this is where the fun's gonna happen.
So this is the project essentially,
Stan sheet and experiment.
We'll call it doom,
Doom.
We're going to call our virtual network, doom net.
You know, we're being real creative.
Okay? We have a workspace.
We have our experiment.
We're setting up our virtual network.
We need location.
And thankfully you can pull this
from your workspace as well.
We need space.
And just to make sure that we created it
let's print out the results.
Let's see if this works.
Yes! All right,
Sweet!
Worked.
Now the one thing I'm not gonna show you,
there is a bit of security setup you have to do.
All right.
The security stuff worked,
next up, my dear friends
is,
we need to create the head cluster
and then our workers as well, call this.
We just gonna name everything doom.
We need our Max Notes.
So now, we've got all of our,
config will be set up that way.
We won't accidentally duplicate computer.
If we already have something,
let's make sure that we let ourselves know stuff work.
This is going to print some, some, some security stuff.
So I'm gonna pull that over there
but I will show you when it works, what it spits out.
So network security stuff worked.
It was probably 113, and look at that.
(foreign language)
Next, is we need to create our workers.
And our workers here, they're not gonna be doing
like super fancy deep learning kind of stuff.
They're actually gonna be running the simulations,
gathering data for us, they're our workers.
There are grunts, Okay.
So we don't need GPES for this.
We can be pretty simple.
So we'll call this,
doom worker
go workers go, right?
So this time we gonna be using a different size
right? Because we don't need GPES.
So I've been working, with data scientists as my title.
I've been, working as a, data scientist for three years,
three and a half years, prior to that
even though my title wasn't data scientist
that's what my job was.
In terms of more of like the software skills,
that's something that I've really been pushing myself,
to fill my own gaps for the last,
I would say year and a half.
I'm still not like super confident with it
but I've realized that my happiest days are just
when I get to write a lot of code,
the software and building for work actually
has no machine running in it right now,
but still I've really loved it
because, I get to write code every day.
I like it.
Wow! so in my L's conditions.
What is my L's condition?
If we haven't already created it, we need to create it.
So we need to create our config.
We will create new compute parameter was called timeout,
timeout, timeout in minutes.
All right, so same as before
this is going to show some security stuff.
So I'm gonna get that percolating over there,
you can see here that everything was successful.
It didn't recreate our head compute
and it created our workers.
Good job us.
Now that we have Azure stuff fixed,
now comes the difficult part.
But this is getting vis doom
working on windows.
Pull up these, this documentation here.
So I believe,
I have all of these dependencies.
I put them in here, just so we could look at them.
Oh, okay.
So hold on, hold on.
I think I actually know where to go now.
Thank you random person on the internet.
Oh! mini Conda environments doom.
Let's just, let's just try something.
I don't know.
We'll figure out the, the doom stuff.
So, I think, I think it is time for a, raid.
So thank you all so much
for sticking with me through the technical difficulties.
I will be back next time with vis doom.
So we'll be able to play with that some more.
I'll see you next time, friends.