Effective PyCharm Transcripts
Chapter: Performance and profiling
Lecture: Optimizing the machine learning code
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We're armed with the fact that compute analytics is the slowest thing and if we look just a little further down the line,
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we have learned, which is 3.9 or total 6.4% of the time and read data which is 61% of the time. Alright, so where should we focus?
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Let's go over to the function and we've got read data then we've got learn and read Yeah, this read data were doing twice actually,
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so we're going to need to really work on that. Let's go over here and jump in.
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Now notice again this is a little bit contrived but we're doing some in Python processing let's say of this, this code here,
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we're simulating that with this sleep. And it turns out that when you're doing lots of computational stuff in Python,
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there's usually some library implemented in C or Cython or something like that. That's going to be way faster the world.
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Working with lists of data here. And what might make a lot more sense is to work with something like Numpy.
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So let's imagine the switch, we've done some testing, we switch over to the Numpy library which is written in C.
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This has very thin wrappers exposed to Python And we gain 20 times performance on this processing of these arrays of numbers and things.
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We're going to simulate that by saying, you know what? We're no longer spending that much time that we're spending 1/20 or
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divide the two by that and we get this much. So that's how much time we've gained with this theoretical upgrade to Numpy,
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I don't really want to bring Numpy into the situation here. We could we could come up with something in Python that actually gets 20 X but
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it just adds complexity. So use your imagination here. Right. Let's run it. See if it's any faster as the search. It's the dB boom, wow,
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that was a lot faster. Remember how much time we were spending over here and compute analytics and read data? Three point basically 3.0 seconds.
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Let's run the profiler again and see about it now. All right. We could do a quick flip over like this and look check it
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out. We got to go down a little bit. All the way down here is where our computer analytics went. So it's down to 473 milliseconds or 20%.
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We look at it in a call graph, which I really like to like to see it that way. Let's go over here. It switched from orange and spending that much time.
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three seconds from computer analytics to now. Just 165 milliseconds and read data. Let's imagine we can't do faster. Right? We switched to Numpy.
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We're doing the load. Boom, that's it. That's as fast as we can go. The other thing we could do over here is work on learn and this is actually
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pretty interesting. Let's jump in and check this out. Imagine this is the machine learning math that we're doing.
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Of course we'd really use something like tensorflow but here's some math that we're doing and imagine the math cannot change. We just have to do it.
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Well let's go back here and look at this in a little bit more detail. So learn, it turns out the thing that we're spending a lot of time in
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actually is this 'math.pow' We're doing that, wow something like 627,000 times, even though it only takes a little bit of time
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right there. But calling it turns out to take a lot of time I'm going to show you a really cool technique we can use to make that
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faster. Let's do something that doesn't seem like it will be better we're going to create a function that will call 'math.pow'.
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So we'll say 'def compute.pow' and it's going to take an X and Y. It's going to return math.pow of X and Y. Okay and instead of doing this right here,
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I'm gonna leave the commented one in here, I'm gonna say compute_pow of IDD. Seven not here, we're going to do the same thing,
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this is going to be compute pow like that. Okay, if I run it, chances are it's going to be slower because in addition to calling this a bunch of
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times, we're also adding the overhead of calling another function. Let's see though that we still get the same number.
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We do. We get this and if we profile it over here and compare real quick, it's important to compare as we go,
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which one is this? This is the learn function. So let's go look at the stats for learn 308 in the new one, 420 see. There was some overhead.
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Can't make that better can we? We shouldn't do this. Ah but we can check this out. So it turns out that we have this IDD.
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Pass along as we loop over this. The I. D. D. Is the same. So that's going to be repeated.
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The seven is going to be repeated and some of the time these numbers will also turn out to be the same if we had the same inputs raising a number to
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the power is always going to give the same outputs. So what we can do is use this really cool library called 'funk tools' but we
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got to import funk tools. And on here there's a cache, something called an 'lru_cache( )' What is the lru cash do?
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This is going to take a function and if you pass it the same arguments more than once. The first time it's going to compute the result.
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But the second time in 3rd and 4th because I already saw those inputs, this is always going to give the same answer.
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Let's just return the pre computed saved value. So we're going to trade a little bit of memory consumption for time.
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Let's run this again. Make sure that we get the same number. We do the same number. Hard to tell at this point.
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We're getting down to the edges of whether it is faster, but let's run it one more time. All right. Let's let's see the final result here.
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Go down here to learn and look at that. Now it's 7.1%. Whereas before learned was 19%. So 420. Ydown to 217. So more than twice as fast.
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How cool is that? And all we had to do is realize what kind of doing this thing over and over.
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It is always going to give us the same answer so we can put a cache on that. So if we happen to see the same values,
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we don't have to re compute it over and over. Fantastic. All right, let's go back here to our final result and look at
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the call graph and see where we are with regard to this machine learning. But now we're in a good place with this computer analytics.
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It was by far the slowest part of the entire program, taking almost five seconds. And now we've gotten read data down nice and quick using
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our simulated numpy and we've got our learn down a bunch times more than twice as fast by using the 'lru cache' And notice over here,
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remember this was 600,000 times or something like that, or calling it only half as many times, and that's why it's twice as fast. Super cool right!!.