What's new with you today?
Um, been busy with GTC.
What's new about that?
GTC, the NVIDIA Machine Learning Conference.
Oh wow.
Tell me more about it. I haven't heard of it. So enlighten me.
So, I've learned about this thing called, there's a program called Blazing SQL.
And you can do SQL queries from CSV files and Parquet files.
You don't actually have to load them into a database.
Say that one more time. What are you doing with the CSVs?
It's called, in the conference or the Blazing SQL?
Um, this Blazing SQL that you're talking about.
Blazing SQL is, it's supposed to be like an accelerated version of SQL.
It can take advantage of your GPU, but it'll also allow you to do SQL queries directly from CSV files.
Ooh.
There are a couple data points in particular that I am looking for.
And I was wondering if you may be able to help me get those out of those Washington State ones, because that Blazing SQL sounds like it's the right thing.
The two data points I'm looking for are total waste, or, I'll write this down too.
But, sorry to start writing you a laundry list, but essentially
total waste by licensee by day.
And there may be a couple different waste types, but essentially, so it would be nice to have the total for each type, but essentially just the daily totals.
Because what would be nice would be, oh, there's someone, Josh.
I think.
Welcome, Josh.
We're just beginning to talk about some high in demand data points, but I'm glad to have you join the group.
Yeah.
Sorry, my speakers on the wrong machine. That's interesting.
Well, I guess we'll dive back into this data points in one second, Charles.
I've still got them on my mind.
I guess just welcome to the group, Josh. At the moment, we're just talking about
the tools is discovered this tool blazing SQL.
To basically help parse some of this Washington State cannabis data that we're working on.
Just this big data dump.
And some of the files are actually so large, it's tough to manage.
So that's experiments with Dask also trying to get these files and stuff and
trying to configure it.
Dask.
Sorry.
Essentially, Charles, you've got this big data set there of the date of not even daily of just every observation there is.
And so, you know, as the cultivators go about their daily business, they're recording their waste.
And so I think you said you discovered that in harvest batches, which is creating
waste from their harvest essentially.
Right.
If there's any money to essentially
you may know more about this than I do some sort of aggregation query where you're
summing up
the waste by day and by licensee.
If you could get that time series,
that's actually what the company I was talking about better carbon solutions is looking for.
They're trying to
they're trying to break into like essentially the cannabis waste here in Washington.
There's other in other states like California
Colorado and even Oklahoma.
Cannabis waste is actually
the big
it's a small, you know, it's a concern and there's a dozen or so companies dealing with
though
as they launch out, they're just going to need essentially a forecast of how much waste is even being produced.
That way you can just sort of get a gauge on what you can do with it.
And that's where the cannabis data science group can help out because
one of the tools in my toolbox is forecasting. So give me a good time series and we can forecast it.
So
we've discovered this, you know, Washington State cannabis data dump
through public records.
And so now we're going to try to aggregate the waste by day.
Can you say that one more time?
I started working on the waste problem early on and then I kind of get sidetracked.
Yeah, it doesn't have to be that one.
Like I said, really any totals by day by licensee.
The other total that I was interested in, it doesn't necessarily have to be by licensee, was
I was essentially just wanting like a daily count of like lab tests, just to know how many lab tests are happening
per day. And then once again, forecast that into the future.
But yeah, you know, I need to publish that. What I found is that
they don't record the lab tests
like when they do them.
I think maybe some places where you do it every day,
it's a mark or something. It's not.
That was that actually reminded me that was actually the data point.
The data point we were looking for was a sales
per lab result.
I think the sales are more updated regularly.
SQ502 data.
And so if you could do like the SQL on like the sales,
so basically just do sales for global lab result X
and then A. I think it can be done because I think I've seen the data, but it's
not the same as the data point.
So the reason that data point is interesting is that gives you sort of if you average,
if you take the average of the sales per lab result,
that's basically the implied cost of a failed test.
Because if you otherwise wouldn't have failed, then you would have on average,
so it's a crude measure.
But basically, you know, in economics, you try to measure all the costs,
explicit and implicit.
So we're trying to measure all the costs for cultivators.
And one of their costs is the risk of failure.
And the way you measure that cost is actually by their probability of failure.
So that would just be the failure rate, you know, whatever percentage that may be,
times the cost, which is going to be the average sales per lab result.
So long story short is you can estimate the cost of failure.
And I don't think anybody's estimated that.
And I think they've maybe estimated that actually somebody's estimated that cost in California.
But nobody's estimated that cost in Washington.
And Washington has actually a lot less stringent regulations than California.
So it would just be interesting to compare the costs here in California to,
I mean here in Washington to California.
Okay.
That is related.
I've been putting together a data set.
I was going to put that like THC, like overclock,
how it was rising and how that related to sales.
Because I've been told that, you know, that's like a driving thing.
What's a driving thing?
The percentage of THC, like that's like a big selling thing.
Going up, I'm wondering, is that like driving sales on?
So just from the grapevine, I think, you know, some people are curious about that question.
And so it's actually an easy research question to answer.
I mean, you could do it more formally.
But the real informal quick way you do it is you would just take a regression of sales on.
Well, I think that's where you would do the sales per lab result.
So you would look at, so your independent variable,
or your dependent variable Y would be the sales per lab result.
And then your independent variables, your regressors,
would be the THC percentage of that lab result.
And so then you could actually just do an ordinary least squares regression.
And you would just see, okay, what's the coefficient on THC?
You know, your hypothesis would be that it would be a positive coefficient.
And the question is, what would be the magnitude?
Is the magnitude even significant?
And then, like I said, you could then do it, you could do fancier things.
Like you could like take the log of both, I mean, that would almost be a given.
You can take the log of both sides.
And, you know, you could do fancier regressions,
but often just the ordinary least squares is pretty informative.
It's a good place to begin.
That's my recommendation.
If you scrape that data together, you could show you how to run the regression.
But essentially, like I said, the two data points you need are sales per lab result.
And then you would actually need the actual THC of that lab result.
Right.
So, yeah, it's just kind of linking the data.
The lab results link to the inventory, which links to the sales.
And so that's like the inventory and the sales are huge data sets.
And it's trying to get it all into memory or at least swapped out the disk.
And getting that all to join together has been a challenge.
I was wondering if you could split up the data sets that in a way that wouldn't hurt the performance of any predictive model,
like maybe yearly would be doable since I think if you have years of data,
I think the year doesn't affect sales necessarily, but the month and day typically does.
I think the problem is I could be wrong, but I think there's almost like unzipped.
It could be like 100 gigabytes of sales data a month.
And that could be wrong.
But am I wrong? Was it like about that long?
Yeah, it's over 100 gigabytes of data.
I'm not sure if it's a total or a month, but...
I don't even know how you run analytics on anything other than like a server,
unless you can make summaries of the data over time.
Well, that's my whole going down the Dask rabbit hole.
That's supposed to solve that problem, and it probably does once you get Dask configured right.
And I also found that Dask and Pandas runs differently on my Mac than it does on my Linux box.
So I've been discovering things by running experiments on each machine.
Well, it works, I guess. I've never used Dask.
You've got the right idea though, Josh.
Essentially, we're just trying to get like a time series of data, like summary data, metadata out of this,
and just leave the bulk of it there.
We're just looking for daily totals.
Yeah.
That's just sort of an ongoing project, but like Charles has said, that's sort of the question on everybody's mind is
how much does THC affect sales?
So like how much is like a 1% increase in THC? Like how much in dollars would that change at the retailer?
Even if you did, if you had 0.1% differences in THC values, then what were you able to get the data for those specific ones?
Then you could also do like a whole like 1000 values of different percentages of THC at most every day
and probably have a small amount of data compared to 100 gigabytes per month.
So that's also pretty good measurement that you can do.
I kind of lost internet connection.
I'm back. I lost connection.
Josh, could you please repeat your last part?
So I was thinking even if you had 0.1% scale of the amount of THC in marijuana,
then you had sale data with respect to that, with respect to each of those products,
you could at most have 1000 bins for each of those sales and then have that for every single day.
And it would still be very small compared to the 100 gigabytes per month.
And that would be 1000 points at most.
I doubt I don't think you can have marijuana with 100% THC. That would be ridiculous.
I think, Greg, is sort of the point you're going at.
Are you just like looking for like observations that span like the spectrum of possibilities or?
I think if you're saying THC is important for sale data,
then that would be an important observation to get the analytics out of.
If it's also time based, then those two could be correlated as well.
Depending on the month, like Seattle, you have seasonal effective disorders so that you may have different audiences.
You actually just hit on a good point.
So that would actually you're actually talking about panel data.
So what you have is observation Y of IT.
So you'd have sales of lab result I, you know, at time T.
So that's how you could improve the model.
So essentially you just the easy way to do is just toss in a like a time effect.
So just like a counter zero to N or zero to T for however many periods you have.
So the regression and you control for time, there may be more elevators to do it.
I think time is a good factor because that's actually something that I was looking at recently was literally prices in Oregon.
Oregon makes their retail and wholesale prices available.
And so you see you actually see prices, you know, go with, you know, dip way down over time.
And actually recently they're actually kind of trending up a little bit, believe it or not.
Yeah, I don't get to drive around much right now, but I don't drive around much.
But before I could remember you drive down the street and you just see people with signs out signs out and, you know, the price just kept going down and down and down.
But I haven't seen lately if it's like, you know, what people are advertising and.
Well, do you want to see real quick that was sort of a when I put together for today.
So cool. Yeah, I'll just show you real quick.
So essentially, you know.
Here's just, you know, total sales in Oregon.
And then here are actually.
And actually, if you want to just find it directly online.
Oregon has an all right data dashboard here.
The problem is, it's a little difficult to download data, but at least it's here.
So data.
I'll actually push this to the to the GitHub here.
So I'll add I'll push some links here to the to the Oregon sales data.
And then I'll just show you some of these prices open. So that's the other thing is I'm sort of recording some of these five million essential.
So here you know you've got the Oregon data and so you've got.
You know the wholesale and retail.
So here's what I was sort of referring to.
So you see, you know, like for concentrates, at least, you know, you've got the price drop by, you know, almost 50 percent.
And now it's sort of the concentrates hovering around $20 a gram, you know, with maybe a slight uptick.
So you're dropping from like $10 a gram, you know, down to about $5 a gram.
But more actually like four and a half dollars.
And then now it's sort of rising above to about five, five and a half dollars.
So one thing that I was going to to introduce to you and then start calculating over this meetup in the next meetup was essentially inflation.
And so, you know, that's an interesting economic variable.
And I don't think people have really looked at it that much in the cannabis industry.
I mean, I'm sure people have to a certain extent, however, I don't hear too much talk about it.
So, you know, it's worth at least calculating at once just to see what's going on.
So, you know, essentially we'll be calculating, you know, the price today, you know, minus the price yesterday divided by the price yesterday.
I've realized the way that we want to do this properly is actually take a weighted average of price based off of the amount sold.
So you'll want. Who was your question?
Well, inflation is interesting because that reminded me, I think I heard someone arguing that Bitcoin wasn't actually inflating necessarily if you took some metrics of inflation with respect to the dollar, because the total amount of dollars in circulation was getting larger.
Actually, just total amount of dollars available.
But then it's hard to calculate the US dollars because is it available or is it in circulation?
Or where is it?
Well, that's it.
That's a good question.
I hadn't thought about that.
I don't necessarily have a stable metric to compare this to.
So just in itself, you know, the Bitcoin series is probably inflating because, you know, the price today, depending on how granular you want to measure price, maybe monthly or something, it depends on the time span you're looking at.
I think that would be an interesting thing to measure.
One thing I would just point out is I guess inflation is typically thought of as a like an aggregate of like all prices.
So you'd be thinking of.
Yeah.
Typically a macroeconomic variable.
I don't see any reason why you couldn't apply it to a single, single market.
I think you'd have to have something like a cost of living index.
And if that went up, that would be.
I think that would actually be described as inflation by the legal definition.
If I remember it correctly.
Exactly.
So basically, you know, what you're getting on here is like the basket of goods that the consumer has.
So.
So if you go, you know, if you go up here, you'll see, you know, out of the total sales, you know, let's say, you know, let's say flower is, you know, 60 million.
Out of, you know, 100 million, and then we'll just say concentrates is, you know, 40 out of 100.
Out of 100 million.
So what we're basically saying here is, you know, the consumer's basket, you know, you're basically saying, you know, your basket.
Is, you know, 60 percent.
Flower and, you know, 40 percent concentrate.
Sorry for the crude handwriting.
So you've got 60 percent flower, 40 percent concentrate in just a consumer's average basket.
So the way you would then measure inflation of a, you know, a cannabis basket.
So, you know, for a consumer of cannabis, how, you know, what's the inflation in their cannabis basket?
Well, you know, that would basically be, you know, 60 percent times, you know, you know, you know, five point five.
Sorry for the.
You know, and then you would just add, you know, the 40 percent of which they're spending, you know, 40 percent on concentrates.
And then the concentrates is, you know, about twenty dollars.
So basically what you're saying, you know, is basically at time t.
You know, your price is, you know, point four to your price at time t is like five plus point six times.
So, you know, so that so that's essentially what your price at time t is.
And that this can be thought of.
It's just a measure of like aggregate prices.
And so then you kind of just see.
Is, you know, do you would expect.
Prices to be rising over time gradually, you know, maybe the economy is a whole, you know.
I don't know what the inflation rate is off the top of my head, but, you know, maybe like one to three percent or so.
So it would just be real interesting to see what the inflation rate is in the cannabis industry.
And like I said, it's interesting.
And you could almost do like a.
Like a time series analysis and, you know, you basically see like.
You know, is there like a difference?
You like you could basically see like, oh, like, was there some sort of.
You know, struck what you know, was there some sort of structural break in the market?
Because it looks like things have kind of like stabilized.
And like I said, they may even be kind of trending up.
It's kind of hard to tell.
I think a huge entry into the world and that forced.
We have the same problem.
We have the same problem with Chinese restaurants in this area.
There was the dim sum wars.
There are Chinese restaurants popping up like just, you know, like one right next to the other.
And that forced prices down.
And I saw the same thing with dispensers, like they were just popping up everywhere.
And so maybe, you know, at some point, right, the market's going to be saturated.
You can't open up any more.
And, you know, so but originally it caused prices to drop.
I think it's probably stabilized, especially with the pandemic, people are probably not opening up dispense, a lot of dispensary.
So prices are probably starting to stabilize and they probably will start to rise again.
Exactly. And that's what you're, you know, you see.
That's why it will be nice to like look at some statistics and like do some trends. But I mean, it does look to me that maybe.
Maybe starting in around June of 2019, things are gradual.
I mean, prices are gradually trending up.
But for the most part, it looks pretty stable.
I mean, it looks compared to the prior two years.
It's pretty stable.
And.
And in fact, that would actually be what economic theory would suggest is you would suggest that prices would would, for the most part, stabilize over time with a moderate amount of inflation.
And just to kind of show you a bit more of the economic analysis that would essentially go into this.
During this same time, keep in mind that.
You know, one would, economics would suggest that, you know, prices, even in the cannabis industry, would be affected by the industry.
And essentially, during this time, you know.
See, it's real interesting, right? So you see this graph.
And, you know, things are kind of stable.
But at the same time, it doesn't even really look like.
Cannabis prices are even correlated. I mean, at first glance with, you know, the federal interest rate.
But once again, that's where we're going to use some statistics and see, you know, what what may be the relationship or they even correlated.
And if so, to what degree.
So long story short is the Federal Reserve kind of.
So the way that the.
The Federal Reserve policy generally works is.
When the economy is expanding, they try to raise the interest rate as high as they really can.
However, as they raise the interest rate, it sort of puts the brakes on the economy.
And then whenever there's a recession, they basically drop.
The interest rate to try to stimulate growth.
What's sort of a criticism, I think, of economists is they basically say that the Federal Reserve isn't really able to raise interest rates high enough prior to a recession.
So like prior to a recession, if they were able to get the interest rates up to like four or five percent, then, you know, when there actually is a recession.
There they can actually drop it sufficiently.
Because here you see the recession's hit and they've dropped it to zero.
But that's sort of the problem is when you drop it to zero, there's nowhere there's nowhere to go.
Interesting.
Well, that's that's essentially what they do.
They they have to essentially use creative or not really creative, but sort of technical monetary policy to to sort of simulate negative interest rates.
So to a certain extent, you know, big banks during this time may actually be paying effective negative interest rates.
But it's it's an indirect tool.
So that's that's sort of the criticism is, you know, it would be nice if they could just do it directly, you know, and so they call it quantitative easing where they give the the banks sort of effective negative interest rates.
But like there's like I said, there's criticisms that it doesn't actually act the same as just moving the interest rate down.
So long story short is it would be ideal if they could just, you know, dip the interest rate down sufficiently, you know, without hitting zero.
But, you know, to do that, they would have to gotten the interest rates up a little bit.
So long story short, that's what's going on at the federal level. But, you know, here in the cannabis industry, we're going to see if this is even affecting prices or not.
Because in sales, because here's where I here's sort of the economic theory behind what's going on is you're basically saying that output.
So you're basically saying, you know, output will do inflation is inflation and, you know, the interest rate.
So you're basically saying all of these are sort of related to each other.
Just sort of to color code this just so you can kind of see.
So basically, we've talked in the previous weeks about essentially in autoregressive process.
So like this. Right. So that's just like an autoregressive process where, you know, what output depends on, you know, output from the previous period.
Here, we're doing a vector autoregression.
And so basically, we're just saying we're just going to estimate these three equations simultaneously. So we're basically just going to say.
Yeah. So basically, you're just we're just saying, OK, output today depends on output yesterday inflation yesterday and interest rates yesterday.
We will measure the coefficient on each of these.
So we're not necessarily saying that output depends that much on interest rates, but we're going to find out.
And similarly, you know, inflation depends on the amount produced as well as, of course, you know, inflation from the previous period.
And then economic theory suggests it also depends on the interest rates.
And then and then this last equation is interesting because this is essentially leads us to wonder.
So this is sort of similar to this is remember earlier, we said the central bank base that the interest function of the output gap and inflation.
So basically, this is, you know, they call it like the Taylor rule.
And this isn't exactly the Taylor rule. You actually have to like I said, you'd actually have to do the output gap, which which is what we're going to get through.
So the output gap is. What you would expect output to minus output actually is and.
Oh, if you're wondering what you expect, we'll get to that in one second.
Well, actually, let's just go ahead and get to that right now. So the Federal Reserve, they want to set interest rates based on.
What inflation they observe their past interest rates, as well as what the total output in the market was.
Ideally, they want to know what the output gap was, what expectations were versus what the actual were.
Well, we can forecast with.
The statistical model is a theoretical model. You can apply it to any data set.
You see here we're going to be packing in a lot of variables into each model. Also, we're going to be estimating.
Three equations, so we're doing three equations times.
So you're going to do an AR.
You know.
X. So, you know, so you may do like an AR rate.
So if you wanted to do an AR six.
Where you're going to have six parameters times three equations. So. You know, that's, you know, no less than.
Plus, you're going to have the constants. So, you know, you're looking at like 21 plus degrees of freedom right there.
So long story short is you're going to need a lot of data. If you really want to estimate the high order.
Other aggressive model.
But I'm wondering if you can do this for all the products.
I imagine that'd be a huge model. Have to optimize it a lot, but seems like you could.
What I would do here is essentially if you were doing that, you basically what I would do is you do y t of i.
And then. Where I would be.
At the point where you're doing all the products, I wonder if you're capturing inflation and interest rate.
So.
So essentially.
Like you said you.
Like, so say like i, for example, let's say like i is flower.
Let's say this is an element of like flower or concentrate.
It's getting a little messy. Let's just do this.
So let's just say.
Hi element of.
Okay.
So.
Like I said, it just sort of dilutes the definition of inflation and I'm going to actually actually do a little research. So my expertise is micro economics.
And so I was sort of reaching a little bit for the. So this is what this is sort of your your standard macro economic model.
It would.
Yeah, I'll have to do a little research because I think you could say maybe you could just do output of flower and then just do inflation of flower prices.
However, the way I would just approach this.
Right out of the gate would basically.
You calculate the crude with no CPI.
The consumer price index.
And so that's basically like a weighted average of all your prices.
We.
So this how essentially.
And then you have a weight for each good. And then you have a price for that good.
Here we're doing.
We're seeing.
You're kind of breaking up Josh.
Turn off your camera.
Okay.
The bandwidth works better.
So I'm going to actually put together a bit better.
Say that one more time.
You're breaking up a bit.
Oh.
We have our basket is calculated.
So basically this is where we get our our weights from.
You hear me.
Yeah, so maybe turn off the camera. You come with you come across much clearer.
Sorry. I think it's just a maybe a bandwidth thing. So maybe I can close some of these tabs.
Okay.
Can you can you hear me okay.
Yeah. Yeah.
Okay.
So just to sort of conclude.
With the explanation of this inflation real quick.
And I'll tidy up these notes for next week when we actually calculate this thing.
So essentially.
You have the sales by product type.
And you have the total sales.
So we're we're seeing that okay consumers are spending about 60 percent of their money on flower and 40 percent on concentrate.
So.
You can say basically somebody's basket of goods is going to be 60 percent flower and 40 percent concentrate.
So that's where you get the the weights from.
And then you have the actual prices. So you have the actual price of flower.
He of I and then you have the actual price of.
Concentrate P of I.
So then you basic.
So you then can calculate the CPI.
Which is basically.
So this is the you know consumer.
Price.
Index.
And you can think of it.
You may have heard of index funds.
And what an index fund is is they basically take a weighted average of a hand like a basket of stocks.
And then you can invest in that basket of stocks through the index.
Exactly. So that's like sort of a basket of the top five hundred.
Largest companies essentially.
And maybe weighted.
I need to touch up on some things like that. But.
But exactly. So that's an example of an index.
Into the consumer price index is just an index of prices.
So.
The actual number is.
Like the nominal value is not that important so much as the direction.
So with this like the nominal value is.
Doesn't mean too much what is important is inflation.
And so that would basically be the CPI at time T.
Right. So basically this is the CPI at time T.
So you would basically do the CPI at time T.
Minus the CPI at time T minus one.
Divided by the CPI at T minus one.
And that's how you calculate inflation.
And our hypothesis.
Is that it would be, you know, one to three percent.
And that's my hypothesis for this time frame.
That's my hypothesis.
And next week.
Next week we can actually use.
This data.
I must have saved it in some weird place.
But.
I gathered the data from.
You know the Oregon.
State dashboard.
And so next week.
We can dive in and basically calculate the.
Inflation rate.
Then.
Sorry for this mess.
Then we'll actually estimate the VAR model.
The VAR model using.
Using output sales.
Inflation.
Which is a function of price.
And our third variable will be the federal funds rate.
Which will be our proxy for the interest rate.
And we're going to see how much interest rates.
Affect.
Output.
And prices in the.
In the Oregon cannabis market.
And.
The way we're going to see this through.
Is we're going to essentially measure the output gap.
So.
We're essentially going to use our VAR model.
From earlier and forecast.
Output.
Inflation.
And the interest rate.
Into.
You know.
For the remainder of 2021 and perhaps into 2022.
So we know what we're forecasting.
We know the purpose of forecasting.
And so next week.
We will be.
Finishing up.
And we will forecast.
The interest rate.
Using this VAR model.
So.
Sorry that it was a little.
All over the place today.
It needs to be a little more structured.
So for next week.
I'm going to structure the presentation a little better.
And actually.
I had a feeling the inflation was going to take just a little bit.
Just to kind of get.
You know, just to explain what's going on there.
But.
That's the crux of our analysis.
So next week we can actually.
Calculate the inflation rate.
And forecast it forward for the share on top.
So.
Do you have any quick questions or anything Josh?
Off the top of my head.
Thoughts or Charles?
No.
It was a cool presentation.
Great to see next week when it kind of all comes together.
So I started to put together some Python scripts just to calculate it.
And.
Like I said, there's a little bit more data to.
Assemble.
But.
All the pieces are there.
And.
I don't know if anyone who's done this analysis before.
Or at least I haven't seen it.
If you've seen it.
Send it my way because I'm interested in this.
But.
I'm not sure if you can calculate it or if you're ambitious.
Check out the GitHub.
The analytics.
Anonymous data science.
GitHub repository.
And.
You know, you could get a head start.
But essentially we're going to forecast this.
These series.
And.
We're going to be enlightened.
And we're.
You know, we'll be.
You know.
Some of the.
Some of the most interesting.
And.
Anonymous prices in Oregon.
So it'll be.
Exciting.
All right, team.
Glad to have you aboard, Josh.
I hope you enjoyed it.
You know, let us know your feedback.
What you liked.
What could be improved.
And.
I'm mostly working on sparse neural networks, by the way.
So.
Neural networks.
And.
The new layer in PyTorch.
So.
I'd be good at.
Doing.
I guess sparse versions of huge data sets.
So like that whole market thing I mentioned.
But for now I'm just going to work on this thing.
For the most part.
Because I just want to get a little robot that can move around.
Finally.
We've got.
We've got huge data sets for you.
So you may want to.
Get in touch with Charles.
He's leading the front.
He's spearheading this one.
So.
Definitely reach out.
Get in touch.
And.
That's exciting.
PyTorch is an awesome tool.
So I want to see your contributions on that.
That's awesome.
Yeah, if I can get some.
I don't know about the scale of data where it's hard to fit on my computer.
But if I can get it streaming in some way, then I might be able to work with that.
Yeah.
Are you going.
Are you.
Have you attended any of the GTC conferences this week?
I have not.
Oh yeah.
That has been on my schedule.
GTC.
Yeah.
There.
There have been some good presentations on PyTorch.
There have been some really cool presentations on.
You know, self-supervised learning and robots.
And.
Oh yeah.
It's just, it's amazing.
And it's.
It's here.
So.
And it's going on through Friday still.
So.
That's right.
My whole.
Like all my.
What presentations this week.
I'll probably add some of those to my schedule then.
Oh yeah.
Tuesday, Wednesday, Thursday.
Yep.
Deep learning for autonomous vehicles.
Accelerating CUDA.
I don't want to use.
CUDA necessarily.
Cause I got a ton of AMD GPS.
So.
Yeah.
Oh.
Are they in PCs or Mac?
PCs.
But there's a Vulkan.
Compute repository that I'm trying to work with, but.
That's got to catch up a bit.
Yeah.
So.
TensorFlow is going to start.
And.
It's going to start working on the Mac pretty soon.
Like the next version that comes out, but yeah, I don't know about like.
AMD stuff.
Yeah.
Other than working with metal before Vulkan.
Yeah.
That's.
Surprising.
That's been like a two year push.
Doesn't seem like a good move to me, to be honest.
But I think AMD should be able to support all GPUs and.
Metal is just one very specific sector.
Yeah.
I don't know.
I think Apple pushed for it and.
Yeah.
There was like Plaid ML, but I've never gotten that to work.
I'll just try to get Vulkan working and then.
If that works well enough, the TensorFlow stuff would be obsolete because.
This would just work on so many more systems.
Yeah.
So.
Question, Josh.
Are you specifically trying to apply machine learning to the chems industry or are you
just spearheading machine learning?
As is.
Right now just spearheading machine learning as is.
And honestly, I'm kind of just going to all the different meetups I can find on AI to
see what different people are working on.
So it's so interesting.
So my background was just in statistics and economics and.
It seems.
I get the principle behind machine learning like, you know.
I think of it more as just framing the right problem.
But I'm familiar with a lot of the tools because a lot of the tools used are essentially
statistics models, so I know the models and how did.
You know, estimate the models and use them.
But actually framing the machine learning problem is something of an art, I think.
Yeah, it's wrangling, right?
I think that I've looked at even biology and that sort of framing the problem thing is
still a thing there.
But like you have four year transforms in the ear, you have retinal ganglion cells that
do crazy stuff in the eye and like as no matter how advanced you go, there seems to be data
wrangling just everywhere.
I mean, I need to get you in touch with.
With a couple people.
There's something that comes to mind who's working on them.
You know, a machine learning project, so may have to get you in touch with them.
And then.
If you're interested in analytics is specifically trying to help laboratories and people with
do cannabis analytics.
It's open source, so if you wanted to find a way to plug in some.
She learning uses.
You're welcome to you're welcome to use that as your host.
That'd be cool.
Yeah, the main thing I want to know right now should just be able to do a bit more data.
It's not really the data wrangling part, but.
Yeah, it'd be interesting.
Yeah.
You may have to shoot me whatever you work on in PyTorch because like I said, that's a
tool that I've heard of and.
I wanted to add it to my toolbox, but it's not in my toolbox yet, so always have it to
learn.
Pretty close to Texas.
Same thing with TensorFlow.
I would almost like almost got it in my toolbox, but it's still not in my toolbox yet, so I
would like to add them both.
For now.
OK.
But so perhaps when we one of you and Charles and Josh, you're welcome to present at the
meetup if you want about.
Hence your flow or PyTorch or you know some sort of machine learning.
Application or tools you're welcome to because like I said, I'm actually eager to learn about
those topics.
Certainly when I get the data.
I think GASP will be a huge help to everybody.
But you know, it's just a lot of experimenting and.
And you know, and finally, and I haven't figured it right.
Well, why don't you take some notes Charles and then when you get it up and running, why
don't you let me know and you can host a meetup and then you could show us about to ask and
you could save us all a bunch of time by telling us all the tips and tricks for getting your
configuration just right.
Yeah, yeah.
I'm also curious about GASP versus Pandas.
GASP versus Pandas?
Yeah, is it a different data scale or?
So GASP is basically.
It works.
It's basically a drop in replacement for Pandas.
Instead of like reading everything in at once.
It delays the computation.
And so like when you read a file, it's like instantaneous.
But then when you like, you know, if you want to look at the head of the file, it will or the data frame,
it will, it'll take time, it'll compute it.
But it also works in parallel.
It'll do it off.
It'll do a lot of work across multiple threads where Pandas is single thread.
So it's optimized Pandas.
Yes.
It's like almost like the right tool for the right job.
So it's I think that's it sounds to me like the perfect tool for basically the very first
meetups we started to try to get into this Washington state data.
So that's sort of the sounds like the tool for that job.
Yeah.
And then if you have multiple machines, it's tribute.
They'll use the memory on time.
It will be good.
It will use the memory across multiple machines to distribute.
So.
Could potentially use it in production to do some interesting things.
Yeah.
But you can use the same code on a laptop as you can across a cluster.
So it just automatically scales for you.
Or at least that's what they claim.
Well, I think I'm going to head on out myself.
But it's been awesome talking with you guys about data science.
It's always it's awesome to find like minded people to talk about data, machine learning,
what have you.
So it's fun.
Yeah.
It's been a great meetup.
All right.
All right, guys.
I'm going to go ahead and head on out.
I've got a full agenda today of things to accomplish.
But for next week, we can talk more machine learning.
I'll have a tidier little demonstration of how to do inflation.
And we can take it from the top.
OK, great.
See you next week.
See you.
Bye now.
Bye.