WEBVTT 00:00.680 --> 00:05.210 Welcome back to an action packed time of of training. 00:05.210 --> 00:13.880 So now, after waiting about five minutes when I run this same, uh, list events function, we get 00:13.880 --> 00:16.100 to see lots of things going on. 00:16.100 --> 00:19.520 What you see in the last ten events is that training is happening. 00:19.520 --> 00:26.240 And you can see in this message the step zero, step one, step two, step three out of 500, representing 00:26.240 --> 00:30.380 the 500 different training data points that it's going to go through. 00:30.380 --> 00:32.750 And you'll also see here training loss. 00:32.780 --> 00:39.500 Training loss is something that we want to come down and that will show us, uh, a good outcome, which 00:39.500 --> 00:41.660 is of course, what we're going for. 00:41.660 --> 00:49.820 And every time if I rerun this, we'll see now that it's already on step 118 as we go now, it's kind 00:49.820 --> 00:51.440 of hard to see what's going on here. 00:51.440 --> 00:55.970 And so this would be this would be a bit challenging if this is what we had to put up with. 00:55.970 --> 01:02.990 But we don't because we use weights and biases and here, here is our gorgeous weights and biases screen 01:02.990 --> 01:06.440 which allows us to follow along with the training. 01:06.770 --> 01:12.290 And what we can see is the the results of training loss and watch what's happening. 01:12.290 --> 01:14.900 It's just updated because the page was refreshing. 01:15.110 --> 01:18.980 Um, and we'll get to see and get some insight into what's going on. 01:18.980 --> 01:25.100 Now, one thing that you'll always see when you're running this kind of training is that the first few 01:25.100 --> 01:28.430 batch steps will typically show a dramatic drop. 01:28.670 --> 01:33.590 Um, and that's actually not necessarily something to get too excited about. 01:33.620 --> 01:39.770 Typically, what's happening there is that the model is very quickly, uh, learning, um, the very 01:39.770 --> 01:44.780 much the parts of the structure which almost go without saying, like the fact that there's a dollar 01:44.780 --> 01:52.250 sign, um, and uh, and where to put the decimal point and a few other things that, that, um, I 01:52.250 --> 01:58.400 think in this case, it realizes that just guessing zero zero for the sense is more likely than, than 01:58.400 --> 02:02.930 anything else to be the right number of cents, because there's many things that are just a whole number 02:02.930 --> 02:03.590 of dollars. 02:03.590 --> 02:09.590 So there's this is that's total speculation, but it's things like that, the kind of obvious stuff 02:09.590 --> 02:16.740 about the construct that allows training to to come down very quickly for the first few steps to get 02:16.740 --> 02:21.390 the obvious stuff out of the way, so that it's now following a decent pattern. 02:21.420 --> 02:28.290 What you're really looking for is continual progress from that point, and you typically, if you've 02:28.290 --> 02:32.430 got a training set up well and there's no right or wrong answers here. 02:32.430 --> 02:35.250 So so there's exceptions to everything I'm saying now. 02:35.250 --> 02:38.460 But but generally speaking you do want to see some variation. 02:38.460 --> 02:45.300 It's good to see some some batch steps that have greater and lower loss because you are you're trying 02:45.300 --> 02:49.230 to optimize and you're looking to explore different, different possibilities. 02:49.440 --> 02:57.000 Um, and what you're trying to what you're looking for is a kind of trend where over time, whilst any 02:57.000 --> 03:04.980 individual batch step may go up or down, you do expect or hope to see a gradual decrease in loss, 03:05.250 --> 03:14.070 um, over time and as potentially a slight concern for us that might make us a bit worried, is that 03:14.070 --> 03:18.310 we're seeing something where this doesn't particularly seem to be coming down. 03:18.310 --> 03:25.300 It just seems to show a lot of volatility, um, representing the fact that some of the of the data 03:25.300 --> 03:28.840 points are doing better than others in terms of the the guessing. 03:28.840 --> 03:35.770 But it's not as if there seems to be much of an improving trend or a trend in any direction yet. 03:36.070 --> 03:39.310 Um, at least that's what appears to be the case. 03:39.490 --> 03:45.970 So hopefully you are also running this, and you're going to be watching carefully to see what happens. 03:45.970 --> 03:50.860 Uh, it's going to take a total, I think, of about 10 or 15 minutes to run all the way through, but 03:50.860 --> 03:53.290 let's see where it is now in this list. 03:54.910 --> 03:57.130 We're now about halfway through. 03:57.160 --> 04:02.890 When it gets to the end, there's going to be a pause while it goes through a step of validating the 04:02.890 --> 04:09.100 results and and and some, some confirmation checks on the OpenAI side that it that it tells you about 04:09.100 --> 04:10.840 in the, in the statuses. 04:10.930 --> 04:16.690 Um, and then you actually get an email to tell you that it's completed at the same time as this run 04:16.690 --> 04:17.260 completes. 04:17.260 --> 04:20.560 And when we pick up in the next video, it will have run. 04:20.560 --> 04:21.760 I will see you there.