 Okay. HackMD doesn't show that much for Q&A right now. If you have anything, please write it down there. Yes, there is feedback. So, as usual, the... what is it? So, one good... see, if you write that, I'll write the whole part. Okay. Yeah, but so as a closer, like today we have learned a lot about, like, how do you run stuff in the cluster? How do you run stuff in the queue? And this is really the main thing when you're working with computational clusters. You want stuff to be happening there somewhere in, like, you don't even have to know where it actually runs. You want it to be just, like, happening on the background while you are doing something else. Like, you can do a lot of literary review or you can, like, work on your code. You can do other stuff. And then you, like, you modify your code, you create it, make it into the cluster, you maybe load some modules in your code, and then you submit them into the queue so that stuff gets done on the background while you're just, like, sipping your coffee. And then you visualize the results. And it's this kind of, like, a non-interactive... it's a bit different than normal working because some stuff is happening while you're doing something else. So, you're not actively monitoring the necessarily the things, but you're looking them after the fact. And it, of course, like, especially when you're starting to use stuff, you have to do a lot more testing and a lot more, like, interactive stuff to get stuff rolling. But once you get, like, a good hang of it, you can then, like, reuse the information. And that's really about what this course is also about. That, like, once you learn these certain basics, you can reuse them over and over again. So once you learn how to do interactive jobs, you can then easily do serial jobs. Once you learn how to do serial jobs, you can easily learn how to do array jobs and parallel jobs and GPU jobs that we'll be talking about tomorrow. And then you can reuse the same kind of information when you're using the CSC machines or bigger scale machines if you want to do bigger scale simulations or work in a different university or different environment. You can reuse the same information. So it's really about this kind of, like, getting hang of the workflow where something happens... something happens non-interactively while you're doing something else. Yeah. I mean, and I guess if you're doing things interactively, then there's very little benefit to using the cluster versus using your own computer because you can basically do things at once. And I guess that's why there's so much emphasis for the non-interactive on the cluster because if you don't need that, then you might be here just for the software or something like that or the large data storage capacity. But yeah. Yeah. So tomorrow we'll be talking about, like, the array jobs which are basically like copy-paste without copy-paste. It's kind of like, do it again, kind of a job for tasks that are easily parallelizable. And then we'll talk about parallel jobs which actually use multiple CPUs at the same time. And then we'll talk about GP jobs. And then we'll also hear from CSC on the large scale systems and what they are doing. So yeah, lots of stuff happening. But we will use lots of this information we have learned today, tomorrow. Yeah. Okay. Oh, another thing you could comment on for the feedback is, would you perform more independent exercise time or more demos where we're sort of doing things and you're watching and trying to type along? So this time we really went towards the, you work independently in Zoom with the mentors. Yeah. So that's a good question of, is that a good choice or not? Yeah. And I'll try to improve the screen sharing for next time. Yeah, I think tomorrow we will have Richard typing and me talking more. So I think Richard has a better screen sharing setup at the same time. But it's good to hear feedback. Also, like there's, I can already respond to that. Yeah, lots of these stuff is quite complicated to describe because it's so interconnected that there's so many things to interconnect. Like for example, like job monitoring is quite interconnected to the queuing itself. And it's, we might have like zoomed a bit into like a spoiler territory in some of these examples. So describe something in our other orders. So it gets a bit of multiple timelines running at the same time. So the plot gets hard to, hard to follow sometimes. Yeah. But it's good to have feedback so that we can improve. Yes. Okay. Should we wrap up for the day? Let's see if there's any other questions up above. Not really. Okay. Well, let's wrap up and any extra feedback we need we can do tomorrow. Okay, so thank you. See you at the same time tomorrow. Bye. Bye.