Our next speaker is Matt Williams, he has worked on the computing infrastructure of CERN and he's going to talk about how the LHC's computing grid works. So please give a warm round of applause for Matt Williams. Thank you. So I'm Matt Williams, I recently finished my PhD in particle physics, I was working on the LHCB experiment on the LHC for four years, recently graduated and I'm now working at the University of Birmingham working on computing resources for the scientists who themselves are doing the analysis now. And it's as part of that work that I'm helping to help develop this tool, Ganga, which is an interface used by scientists to interface with a huge amount of computing power and storage available to them as part of the LHC computing grid. So a brief little update in case anyone here doesn't know anything about CERN or the LHC. It's the world's largest particle physics experiment, or at least the world's largest man-made one. It's arguably the world's largest man-made structure as well, being a 27 kilometre long ring underground in a tunnel dug specifically for the purpose. It's a proton collider, so it's accelerating protons to near the speed of light and climbing them together at four locations around the ring and each of those there's a detector which studies the outputs of those collisions and analyzes the data that's given to them. Given the huge amount of collisions that are happening every second, billions and billions are happening, it's outputting a huge amount of data. I mean, the amount of data that it is producing is way beyond what we would actually be able to collect, but the stuff we do collect to date equals something like 200 petabytes, though already it's probably a bit higher than that, and it's only going to grow as the accelerator gets more and more powerful in the future. So in order to be able to process that huge amount of data, alongside the design of the LHC was a corresponding project called the grid. The idea of this was to produce a computing environment which would be able to handle the large amounts of data and processing power that would be required. So it works in a tiered system, so at CERN there's a central hub, a tier zero grid site which has a large amount of computing power. And they then defer down to a single site in each country that's involved in the LHC. There's about 12 or 13 of those tier one sites spread around the world, one in each country that's involved. And the level below that are the tier two sites, there's around 160 of those. Each of those is generally something like a university or a search institute. There will be a dozen or so in each country, for example. Some countries have more, some countries have less. And it's at the tier twos and the tier one where the largest amount of data processing is done. And the sort of data that we study at the LHC in the sort of analyses that we do really does lend itself to this sort of distributed nature. You tend to end up, if you're doing an analysis with a list of collision events, maybe you've got 10 million, 100 million events you want to look at. You can very easily take a small chunk of those and process them independently of any other chunk of data. There's no real interaction between the events. So you can very easily chunk it up, send that out to wherever it needs to go and then collate the results at the end. So as I say, the project was evolved alongside the LHC. So even in the early days, well before the LHC actually started, people were looking into building these computing systems to provide the services to the scientists that need them. So in 2001, the LHCB project started work on Ganga. This was their in-house specific interface to this grid infrastructure. Each of the other experiments were also working on their own personal projects in order to interface with the grid. Since everyone was convinced that they had their own special problem that only they could solve in the way that they needed it done. However, the LHCB project Ganga was designed using a Python system with the explicit goal of being pluggable and extensible and so on. And so it was very easy in the intermediate years to take the parts of it that were LHCB specific and remove them and allow other experiments on the LHC to plug in their small part of experiment specific logic that's needed. So the Atlas experiment, there's a number of scientists on the Atlas experiment who are using Ganga for doing their data analysis. And in fact, outside of that whole ecosystem as well, there's the T2K experiment which is the neutrino experiment in Japan. Some of their scientists are using Ganga for interfacing with the grid resources which are provided to them as well. Of course, all the software that we create at CERN or as far as I know, all the software is completely open source. Ganga itself is GPL and the vast majority of software that comes out of CERN is GPL or other more liberal licenses. So how does it actually work? So if a scientist has a bit of code they want to run, they can use this tool Ganga to interface with the grid system. Or in fact, not just the grid system, they can interface with any other system that Ganga hasn't interfaced to. So in this case here, you see on that second to last line, we're setting the back end to be equal to local. That's telling the Ganga system, don't run this on the grid, just run it here on my machine. That's something that's often done by scientists when you're testing a bit of code. If you've just written a new piece of analysis software, you don't want to immediately throw it up onto the grid infrastructure, run it 10,000 times and have it crash within three seconds because of some bug you've put in. So it's a good idea to test it locally on a small set and then later on be able to submit it up with the grid. So it all centers around this job object at the top. You can set some parameters on it. Here, we're setting the name parameter to give us a string which we can use for bookkeeping, keeping track of what jobs we use for what since all the job information gets stored into a persistent database where you can see it all later. The real workhorse of the job system behind the scenes is the application. So the application is what is actually going to be run where this thing is going to be run. In most cases, you just want to run an executable. It can be an executable binary or it can be a Python script or in this case, it's just a small shell script. So you just say to Ganga, this is the thing I want to run, this is the actual code that's going to happen and this is where you can find it in this file here. In this case, this script is just going to create a file called out.txt and so we're telling Ganga the output files from this job. These are the ones that are going to be made by it. These are the ones we want to make sure end up back where we are now. We want to make sure we've got a copy of those in our local output directory wherever the job was actually run and whether that file was originally created. And so we specify that it's a local file. In output files, local file means copy it back to where I am locally. Once we've set up our job object, we just call submit and at that point the Ganga subsystems come into play. The monitoring loop comes in, it starts submitting the job to the system. In this case, it's just going to start up a local shell instance somewhere else on your computer. But if you were accessing the grid, it would be uploading it to the grid somewhere. It would then keep track of its status and make sure it's downloaded any output files at the end of the job. So once it has finished, you can just access the output files directly inside the IPython-based Ganga user interface. So you can just call the peak method on the job object you just had and it basically does an ls of the output directory. You see it's created a file for the standard out on the standard error and most importantly the out.txt we asked it to give. And if you want to peak further than the site, you can pass a name of one of those files to the peak and it'll open up a pager directly inside IPython and you can have a scan through and look at the output files to make sure that everything worked the way you wanted it to. Obviously that was just a toy example. That's nothing more that you can do there than simply running a local script on your local computer when looking at the output file. So it'd be good to be able to leverage the power of the grid. And it's as simple as changing the backend on that last step from local to lcg, where lcg stands for the LHC computing grid. It's the acronym that we use for that. So with one small change of one line to the other, you could run exactly the same script and that code would be uploaded to the grid system. The grid system would take over, distribute it, run the code wherever it ends up running. You don't even worry. It could be in China, it could be in America, it could be in Amsterdam, it could be here in Berlin. It could be anywhere. And it's completely seamless to the user. At the end, the data will be copied back and everything is the same. You don't have to worry about it. But Ganga's more than just that. It's more than just locally running stuff and the grid. It can interface with anything that you can access via an API basically. So there's a series of backends for, you see here PBS, LSF and SGE. Those are batch systems. Often universities have got local batch systems or a batch farm of some kind, which they use for running jobs, which are somewhere between running on your local computer and you want to upload to the grid. And again, you could just change it to PBS and it would be submitted to your local farm and you wouldn't even have to worry about any of the details. These last series of one here are a set of experiment-specific backends. So various experiments have got their own middleware interfaces sitting between Ganga and the grid to make an onion layer type situation to provide extra features that maybe that experiment particularly needs. But again, it's all a black box as far as user concern. You don't have to worry about what's going on. It's just going to work. So now that we're using the grid, it'd be good to really make use of the huge amounts of power it provides. So let's say for example, you have sitting on your local hard disk, directory containing a whole load of files. Maybe you've got 3,000 files or something in there. Each of them some number of megabytes. So it's adding up to a gigabyte, let's say a data or something like that. So there's a lot of data you're going to want to analyze. You can tell Ganga that these are the input files you want to run your job over. From that point on, Ganga will keep track of those input files. It'll make sure they get copied to wherever the job runs, whether that's locally on your back system or if it needs to be copied out to the grid, it'll make sure those files end up where they need to be. Of course, to be left at that, it would be pretty useless because you'd be taking one huge chunk of files and copying them to one place on the grid and they would just be run in one single compute node somewhere. It'd be good to be able to distribute it around and make sure we're running things in parallel. And Ganga provides a tool for this called spitters. So again, you define on the job object a splitter parameter. And in this case, we can use the spitbyfiles object. So this is an object which knows how to spit the files up into a smaller set of data. We simply take one parameter, files per job. So it's going to take this list of however many thousand files you have, chunk it up into chunks of 10 or maybe less files if there's not enough to fill a chunk and take each of those chunks, add in your analyzed data script that you want to run with it, submit it and the grid will put it somewhere.  And it'll keep doing that all the way through the list and you'll end up with some number of hundred sub-jobs that Ganga will keep track of you, keep track of for you. So you won't have to worry about how many sub-jobs are made or doing it manually. It's completely automated. At the end, each of those sub-jobs is going to create a histogram.root. Root is a file format we use at CERN that's basically a table of data as far as this stuff is concerned. It can also contain histograms and so on. It's basically a table of data. By specifying the local file here, this isn't saying that file is going to be made locally. It's being made wherever the job is run and you don't care where that is. But you're asking Ganga to copy it back to your local computer so you can have a look at it, open up in your text editor or analysis software or whatever you are going to use to analyze the data. But that's not ideal even then because you're going to end up with however many hundred copies or variants of this histogram.root. They'll get put in a sub-directory structure but still they're going to be separate files that you're going to have to go through manually and look at. So to solve that problem, Ganga provides something called a merger. Well, it provides a whole suite of mergers but the one in particular here is the root merger. So this is a little bit of Python code which understands how to concatenate together root files. It knows how to stick them together and combine them into one single file. And this again is completely automated. Once the job's been uploaded, it's been split up, sent out all over the world. Ganga's downloaded all of the results from each of the single sub-jobs. Once they're all downloaded, Ganga will automatically kick in, combine them together and start turning it into one single file which you can then look at. So from that point of view, you don't even have to worry about the fact it was split. You started off with one single analysis script and one single set of data. Ganga spit it and merged it. You ended up with one result. You don't even have to worry about the fact that it was distributed around. It's completely seamless. There is much more than just the root merger. You can write any sort of merger you might wish. Anything which post-processes the data basically. There's a class in Ganga which lets you pass in a single function which simply takes the output file directory and you just list through there. You could, for example, look at the log file for each output job, grep through it for a single string and find the average of the numbers or something like that. You can do anything you can think of just to post-process your data. So once you've been working at CERN for some number of years, you're probably going to have submitted several thousands of these jobs over your lifetime. Many of which you're going to have deleted because maybe they broke, but many of which you're going to want to keep around for the log files to check that stuff's working how it used to make sure your data's being reproducible. So Ganga provides a persistent database of all the jobs you've ever run through that system. So you can see here the three jobs that we've submitted so far. The first one we just found locally. That's all finished. It's showing up there as completed. The last two, because they were sent off the grid, they've been distributed around and they're still running. You see here, each of them, Ganga has created 324 sub jobs. That's how many it decided to spit it into. You don't worry about the number too much. You just have to know that they're there. We don't have any more details about which of them are running, which of the sub jobs are finished, or anything like that here. This is just a very high level overview. But it's very possible to get that information because Ganga provides a full API access to everything that's inside the Ganga's API. So inside the Python interface, you can access any of the information. You can access job information. You can resubmit things. You can do anything you want. So the most simple, we call that jobs object again, like we had in the last slide. We give it a parameter. We ask for job number two, which as you see is the bottom one here, the merger job, which is, as far as we're concerned, overall still running. We ask for status. And again, it tells us it's running. It's the same information. We can delve in a little bit deeper though. We can ask that job for a list of all its sub jobs. So we just give it the dot sub jobs parameter. That's gonna give us a list of jobs. We can loop through each of those sub jobs and ask each of them what their status is. We get a list of all the ones that are completed. We find the length of it and we find that 24 of those 324 sub jobs so far have finished. If we waited half an hour and ran it again, it would be a higher number because gang was constantly keeping track of how many sub jobs are finished. But jobs don't always just be running or have finished. Quite often, you'll get random failures. On the grid, your data will be sent to run at some particular site. It could fail without any real reason. Maybe there's an outer memory error on that particular location or things like that. So as long as some of your jobs are passed, there's a good chance that those that failed, it's simply a transient failure. So you can loop through all the sub jobs once more, check if the status has failed on that particular sub job and resubmit it and it will go back into the monitoring loop and keep going around and eventually it will be re-downloaded once it's finished. And this is the sort of thing you might want to do quite regularly. You might want to have a function defined which loops over a job object, checks all the sub jobs, resubmits the failed ones. So you can take any bit of ganger code, stick it inside a function inside a dot ganger.py file in your home directory and all those functions will automatically be available inside the user interface, which is based on Ipython, it's a slight fork of Ipython to provide this sort of functionality. So the last thing I want to talk about is dealing with very, very large files. So the example I gave at the beginning I was saying you might have a directory on your computer which has got something like 1,000 files in it or something like that. Even if each of those only some number of megabytes running up with gigabytes of data. And in fact, quite often when you're doing data analysis with the LHG, you're going to be dealing with at least gigabytes if not terabytes of data, you're going to be wanting to run your analysis over. So it's nice not to have to keep those files locally on your local computer and upload them every single time you want to have to do an analysis over them. And then at the end, if the output's big, you don't want to always have to download the output. Maybe you just want a summary file, maybe you just want to find number of events that pass some sort of criteria. So as well as being a distributed compute network, the grid is also a distributed file system or at least it provides a number of distributed file systems. The one in particular here is using this DRAC file system which is again, originally an LHCB specific grid interface. But the important point here is that it deals with a remote distributed file system. You don't have to worry about where these files are, they're out there in a way in the cloud. So for the input files here, we tell Ganger that we want as our input file a file called input.root and we're saying DRAC knows where it is. So I don't know the exact physical location, but the file catalog knows where to find it. For the output file, my program is going to create a file called histogram.root. That's going to be made locally on the worker node wherever my job is run. And I don't want that copied back to my computer here. I want you to send it off to the remote storage. That'll keep track of where it is, that'll keep a record. I can access it later if I want to, but for now I don't want to be dealing with all that network traffic coming up and down. And in fact, it can even be a little bit cleverer than that. Using the DRAC backend, which is basically a layer on top of the LCG backend, it's got a bit of extra logic in there to deal with this sort of file system access and so on. One of the clever things that can do is all you have to say to this, with this exact script here, you upload that script, you submit it, DRAC would automatically take your analyzed data, like program you want to run. It will look around, find the physical location where input.root is stored and it will send the job to that site and it will run it there locally rather than submitting that analysis script somewhere and copying the files over. It will try and automatically reduce the amount of copying that's going on in order to make things as efficient as possible and avoid clocking up network bandwidth. In the same way, the output is going to be stuck somewhere and so you could then run a second job. You could change together jobs. You can say, this is the output of job one. I want that same output to be the input of job two. You just have to pass in input files that equals DRAC file histogram.root. That job will be submitted to the grid. It will go up, it will look around, find out where histogram.root was saved to and again, it will be sent and run there. You never have to deal with those files on your local computer at all. You let the experiments also have to deal with all that storage and file management. So using the grid like this, you can deal with hugely large files that I've ever had to deal with them. Of course, for each sub job, you'll get back a standard out file, you'll get back a standard error file so you can make sure your jobs are running correctly.  You can have as many input and output files as you want coming from whichever source you want as long as Ganga has an interface for it. And Ganga being extensible, you could very easily write a new plugin which dealt with any other file system type that you might want to use. We do have a file system type which uploads things to Google Drive, for example. Quite often people just want to be able to share files to Google Drive and so you can access upload and download files from there. So you can write basically an interface to any infrastructure you might want to be using for yourself. So you can find out more information at the website, cern.ch-ganga. Like I said, all the code is completely open source so you can go to the download link and have a poke around the source code. It was started, the project was started in 2001 which for reference is about the time that Python 2.0 came out. So some of the code has been around quite a while but on the whole it's quite readable and you can see what's going on as far as the job flow goes. So take a look at that if you want to have a little poke around and thank you. Questions? Thanks for the nice talk and the nice tool. I have two questions. So the first question is can you target some schedulers such as Slurm using the library? Yeah, I don't know if there's a Slurm backend yet but there's ones for Condor and Torque and so on. So there could easily be one for Slurm. I mean, it's a simple case of writing a bit of code called the white commands at the backend. So yeah, Slurm could absolutely be interfaced with if it's necessary. Right, and my second question was... I forgot the second question. Okay, I'll ask you in the break. Okay. And we got another question over here. Thank you. There was on your merge slide. Can you go back there? Yeah. I don't understand the line with the j.input files where there's actually a list comprehension on... So in this case, inputs.txt is an index. Is the open, overloaded somehow or you get a file handle? In this case, inputs.txt contains a list of file names. So that's an index file containing a list of all the file names that you want to include as your input for your job. So you're going to loop over each of the lines in that file, each of which is a string, which is the name of a file. But that's not what Open usually does, isn't it? So you just have a file handle. If you have open.readlines or so, you get, of course, the lines or whatever. When it's looped over in this list of comprehension, it does produce a list of the files. It has a slash n at the end of each one, but it does work. I did check this line, I was. Yes. Hello, thank you. I wanted to know how you handle code that runs parallely and needs to communicate to other processes or in different computers. So like inter-process communication between analysis jobs and things. Or inter-network communication. On the whole, there's very little scope for communication. I mean, Ganga will, is blind to that if you submit to a supercomputer, which has got some inter-process communication that you need to do, or some sort of communication of any kind. It will handle that because Ganga doesn't care about it. Generally, jobs on the grid, you don't have any sort of communication between them, each job's siloed, very much so. So I suppose you don't submit jobs that need to be run across multiple processes. Mostly not, no, no. Not in the sort of work that we do. Okay. How does Ganga find files? I mean, you can't just use the name, right? So each of the local file or DRAC file, for example, it's got a little bit of logic in there. So local file, by default, will look in the working directory that the user's in. DRAC file will, so obviously you can't just say input.root, it's going to magically know where it is. By default, it's authenticating with the DRAC system. So each person's got a local user area. So it will look in their local user area for the file. And likewise, it will be saved to their user area on the file system. Yes, yes. You can overwrite files in a previous job and things like that. You can give multiple output directories and stuff. Yes. Okay, thank you again, Matt.


about the Kenyan music industry. So what's next? That's the question that we are addressing and I have amazing people qualified to answer this to participate in this discussion. I'm joined by Sam and I'm trying not to pronounce the second name because it's hard. I might lose it and also Affam, Affam if I'm getting it right. Alright so tell us a bit about yourselves and you can say your names are getting full so that people get to know you. Yeah I'll start. My name is Affam, Affam Mephela for full. Music producer, sound engineer and podcaster. Okay, music engineer, sound producer, and podcaster. And I'm Sam, Sam Are. Oh okay, Sam Are. Yes, I'm a music producer, sound engineer. It's just different different things but mainly music producer, sound engineer. Mainly music producer and sound engineer but you're also a podcaster, you have a podcaster, yes. Okay great, so we have the right people for this particular discussion. So let's start with the Kenyan music industry. How do you see it from where you're seated? I'm excited you Affam. I mean first of all thank you for having us. I'm really appreciated. The Kenyan music industry is I think it's in a good place. I think even in the past few years there's been people who are actually you know like-minded moving the needle. I feel like this new generation of artists and creatives, millennials and Gen Zs were very bold and yeah I think the music industry is in a good direction. I just wish more young people had more positions of power to kind of just you know shake things up you know get out with the old in with the new, put new ideas in motion and yeah I think I'm very optimistic about the industry. All right, you know some we've had critics, people coming I think for money is one of them. They were saying that there's no quantity of Kenyan music, Kenyan music anymore, that we're being defeated by others, not just him but others too. So what do you think about this? Well I mean I don't necessarily agree with that. I think there's there's a lot of music, there's a lot of musicians but we tend to focus on a few main ones and those sort of carry the industry as we call it but there's so much talent, there's a lot of people who are doing art and doing art very well. So it's just it's figuring out a way to elevate the newer and less known people than and of course having them work with the ones who are common who are known. Okay so when you say find a way to elevate the newer ones because we've hosted we usually host a lot of upcoming artists and it's hard for them you know especially on finances and things like that. So how exactly and you as music producers because you have this power to do that yourself, how do you exactly elevate the young upcoming artists to also get them to that stage? Wow that's a loaded question. We just have to like encourage the youth to get into music. One thing we need is you need role models, you need to see people on TV like doing things that you yourself look and think like you know I can also do the same thing but we need infrastructure. The thing about the Kenyan music industry as much as I said I'm very optimistic is there's also a bit of a lack of structure. It's a bit you know like how do people get royalties how do you get paid for your work and it's very up in the air so if those structures were put in place like in a better way I feel like you know would be able to have more influx of money into the industry and you see now once that happens then you give artists the ability to actually create art because let me tell you releasing music is a very expensive venture you know recording you have to pay the producers you have to pay it to be engineered and mixed and then you have to do a music video which costs a lot and then after that you know you look at the royalties and you know it's been in the news. If the infrastructure was better like a lot it'll be easier for a lot of people to actually do this as you know for a living. For a living because in Kenya most people do it part time especially for the up-and-coming I work this job but because I have a passion in music so I do my music funding it by you know my salary what I get so but if we have good infrastructure that's what you're saying we'll have you know we'll have a better industry you know an empowering one right so how do we is that also a way that we can get people or the world from viewing us differently or having other names also representing us internationally because when you talk about when you ask an international artist who do you know from Kenya who do you want to collaborate with you know it's just obvious you already know that even before you ask it so is this is this also a part part of the infrastructure is it was that something extra that we need to do to also get other artists known internationally because I think we also have other artists apart from the these two groups of these two people that are known so how do we get them to be known let me ask you some I think so partly what Afam said is true we need to create systems that enable the artist to get recognized at that level right there's a lot of work that needs to be done in terms of just building up the artist to get there right now we don't have a lot of those systems and so like for example an artist would do a lot here locally and be known locally but then we don't have a lot to push them outside but partly right of my opinion partly I think there needs to be a lot of artists development in terms of working with so there needs to be a lot of people working with the artists and it's part of the systems working with the artists in order to get them do like promotions and there's a lot of things that go into music that we don't really talk about we a lot of people think making music is going to the studio and then put it out and that's it but there's a lot that goes into it and so it's a lot of those things need to be done but I do feel like we're in a good place though because we've recently had a few what do you call them just like the recent color show that that highlighted Kenyan musicians there are a few musicians I think six seven musicians on there so there's a lot and currently the scene is it's promising because there's a lot more musicians that are now working on not just being recognized here but then they're going global so it's something that's in motion I just think that we probably need to think about it more seriously and figure out how to do that well be more intentional with it yes all right and I love this because it's very positive you know sometimes you talk about the Kenyan music industry and it's all you know we're doing badly and something is to do that but you're giving some light to it and it's actually doing not not so bad so before we gets to your podcast because they have a podcast you as Sam has mentioned that there's a lot that involves you know in the making of music you know the production of music so maybe for someone who doesn't know about this they just know they would they want to be an artist and they want to say so what goes into okay to creating music so you have to get some inspiration now a lot of producers make music digitally so you'll use a DAW which is a digital audio workstation such as FL studio logic or Ableton I use FL studio not Ableton definitely so yeah so you use the DAW you can learn on YouTube now these tutorials to do anything I taught myself how to make music on YouTube yeah so just watch all those tutorials and then you learn so then you make the music you make the beats then you just have to take a risk and send the music to the artist and and be like you know this is a song let's make it together if you have a studio if you have a microphone you can just have them come over record and then after that after the recording process is what we call mixing which is essentially just like post production yeah post production level leveling everything making it sound nice adding effects the usual and then you master and that's also another thing in its own mastering yeah mastering is kind of just getting the music at the appropriate levels for all the streaming platforms so YouTube Spotify they all have different requirements and TV I'm sure you know so you do that but that's now just as much as that's a very involving task that's like 30% of what you have to do because after that now is marketing so you need to market the music yeah doing the videos doing the social media promotion sending it out to your friends and that's kind of where a lot of people get stuck yeah so I love the aspect that now you know we most people think that it's usually the artist who comes with the music to this you know to the producer and say this is what I have but the producer can actually create a sound and send to an artist and we can work together on this yeah all right that's that's something so all right so how did you guys decide up to the podcast how do you guys decide to to start a podcast so we were working at a studio together and some of the conversions that we have on the podcast is conversions we are having like we just have talks about the music industry what we think is doing well what I think is not doing well what we think is think needs to happen and then so that's gently developed into the idea of maybe having it on a podcast and and talking about some of these things now we decided to go with the approach of talking about the unknown the unseen parts of the industry so not necessarily like bringing up artists and you know doing the artist interviews but then now talking to the managers talking to the engineers talking to the people who are behind the scenes working to ensure that the artist gets to where they need to get so that's how it came up it's just conversations that we've been having on our own that we just decided to have everyone else listen to us have okay interesting so you know a podcast on me on anything you know from your talk your podcast has let me just say that to them the podcast did great particularly last year it was named among top 10 podcasts that define Kenya in 2023 and the name of the podcast by the way is the 30% podcast and they'll tell us why so it has been named top 10 among the top 10 podcast that define Kenya in 2023 among top 10 most streamed music podcast on Spotify in Kenya of course hosted by these two okay so what else what are we missing this they are passionate about documenting the Kenyan music industry every week and there's something that is also mentioned here the former you know you have done Lil Miner's latest album Maisha Astana and also you know involved heavily in Nya Shinsuke lucky album in 2020 producing songs such as too much watch watch etc that's you okay so you have that great stuff what do you begin the podcast so okay so the 30% podcast actually that name came from some essentially why we decided to call it a 30% podcast was we found a statistic and I don't know if it's still the same today but at the time it said that 30 to 40% of music in Kenya that's played on radio is Kenyan the rest is international and we saw that like as something that's very disappointing a bit sad like why is only 30% of music that's on the radio's Kenyan and want that to be how it should be 70% if you look at the Nigerian industry they write for themselves you know like they're all about their music exactly they're all about their music they're proud for their music so we want to see that number go significantly higher and we put that name as a old daily reminder that that percentage needs to grow yeah okay all right I love it so that's how you decided and you've had wins you've produced great great people so in the hosting of the podcast you do it both of you right yes and as Sam has said you you host the unseen you know you don't host the artists themselves the people behind you know almost empowering the artist if I put like that the managers and and what what not so what do you guys talk about any of you can answer that okay so basically we decided we have had artists on the podcast we interviewed Kinoti and Ethan Muziki two amazing artists but we are most our approach is to kind of talk to people behind the scenes because no like nobody really talks about the manager who is putting things together the ANR who is also putting things together yeah the talent manager you know the likes of we've talked to until Antonio so we've talked to Maya from keep it pure agency there's so many moving parts in the music industry that needs to be highlighted and for us we just wanted to do it as just to educate like people young people just to know that you know there's so much more to the music industry and there's so much more that you can do because like you come up thinking oh I want to just be the artist whatever but like you can actually be the person who puts other people on and yeah so yeah yeah and to add on that so talking about the sort of what we call the back end of the music industry also so it highlights it and it helps with creating awareness about those systems that need to like especially we're talking about the Kanye Street the systems that we need in order to have Ken musicians and Ken music flourish so highlighting that also brings light to what needs to be done in the back end and that just benefits I guess the whole industry benefits the artists benefits the people doing the work themselves and then benefits the music and art so important conversations to have things that can actually bring impacts in the music industry have you seen anything that has from your discussion from your point that has past discussions outside here in the industry or has cost you know some change in whatever capacity things that we've discussed I didn't completely know yeah it's hard to gauge that but we had an interesting discussion about the whole colors like situation yeah you know so colors came to Kenya and you know that was it was it was such a huge win for us and we were just kind of highlighting the fact that you know Kenians are such an amazing platform and so I guess to answer your question yes we've talked about things and kind of shown that you know Kenian music has the ability to the potential okay so I believe you just change perception there are people view music because you have the power to do that you know for some Kenians that look down on our music so maybe even listening into your podcast changes that something all right so what do you what do you aim to achieve I know you've mentioned the reason you started and all that but what what do you aim to achieve from your podcast and let's say a year or more going on I mean so podcasting is interesting because it's a it's a conversation right so and apart from what we do day-to-day which is like working with artists and developing them it's more we aim to just I guess raise awareness one and then also stir some of the conversation and probably talk to the people who might have the knowledge and the know-how to start effecting change we've talked we won one of the one a good a good conversation we had sorry is we had to talk with Eric Missioka who is a very well-known producer and he's like part of I guess the fabric the building of Kenian music and is he's doing a lot of things in terms of the systems and just trying to educate people also so it's talking to the people who also have change I mean who can change things and it's figuring out how to do it but yeah it's podcasting is mostly just to have the conversation started and to have awareness you know out there and just to pick up piggyback off of that like at the end of every podcast we play music I'm strictly Kenian strictly Kenian and that came out on the week and you know we've actually had like like in the beginning of last year we started like at the beginning of 2023 and we the first artists we played either Z's and then Sophie and Zah we played before the whole thing with Milwaukee and that was cool like so we want to use our platform to put people on to music even before it becomes like a big deal so yeah just sharing music sharing the Kenian music showing that you know this every week there's something new that's amazing for you guys to listen to so it's usually new music that new music that came out like on that week okay that's very interesting and very supportive of you guys especially as music producers how do you how do you how do you see your relationship music producers and artists or how should the relationship be like because now with again with our interviews with artists some say you know we're working I was working with this producer and they sort of didn't deliver or they scammed me you know that's a very common story yeah it's a common story so how should we change that narrative because also that's that part sort of also participates or you know it's part of the process of building the industry so how do you what do you say I think the artist producer relationship is is a very deep relationship it's a friendship you have to have a friendship together first of all yeah yeah I think it's it's a relationship that you have to foster because you have to also get chemistry together you know you can't just like hop into the studio and work with a producer you've never even had a conversation with so more artists need to look for producers who you actually vibe with even outside of the music you know you can talk to them you can have a relationship you have you share ideas and then that's where the magic of making music happens once you guys find a common ground and you know otherwise if you're just working with like a producer you've never met whatever you're likely to get you know the relationship will get you'll get a song after it would just be that so but you know I know the examples of producers coming artists even vice versa to be honest but yeah it's not it's not the norm I feel like the relationship like it's good so what what I'm thinking is that you should have a good relationship yes not just whack you know I have this produce it we're done you know we should have something so that you also understand my style understand what you do we you know we build each other one or the other all right that's interesting so you look like you had something to say so how I like to think about it is I like to think about art as a thing on its own like so I feel like more people here need to make more people everywhere but since we're talking about the chemistry what people here need to make art important in and of itself so what we do is like I'm an artist there's a producer I go give them money you give me a production and I put it out but then for art to I mean this is my opinion for art to mean more there has to be a lot more thought a lot more like a relationship a lot more a lot more behind it just so we can elevate it it gives it it gives it more life I would say so one of things that just make art important to make art think about it be intentional which is a really one thing that I feel like we really need just be intentional about what you're doing the lyrics you're you're you're putting out the type of produce I mean the type of product you want to put out and when we do that I do I feel like it will just make it better but yeah we need to make it more important than just hey I have a song listen to the song yeah that's very true what about finding a producer who's I don't know if producers have their styles like you know I don't know if they do yeah oh you do so yeah the producers have their styles that's the question and should you look for a producer that matches your style for you to get your music out and what style now do you guys then have okay I mean yeah producers have their styles look at this the likes of Kanye West he has his signature sound forever has his signature sound Timbaland has his signature sound and if you're looking for something that fits that mold yeah then that's who you go for so yes as a producer here like if you're looking for you know hard-hitting chap beats there's a producer in this country called cap cap is one of the best chap hip-hop producers right now and that's what you're looking for I would push you to cap if you're looking for gang it on stuff you look like you produce hip-hop yeah like for me personally the only genre I don't produce is like country music you know that's what I'm really trying to produce right now so yeah you just have to have your ears on the streets to know who's producing what you know who's watching now yeah yeah but personal style I produce everything I do hip-hop EDM again get on and be like just everything even country I haven't yet but I think I can haven't yet but I don't doubt I can but yeah it's I but your question was it's important for so for you to have a really good product as an artist it's actually it's two ways about it so you first the one way would be the artist needs to be comfortable with the producer again it's part of that relationship now it is to be comfortable with producer but also the producer should try to push the artist to more uncomfortable areas because that helps grow artists other than keeping them in a comfortable or they do this kind of song and they do this kind of song very well and for the last past time in the past five years they've released 30 songs and they all sound the same so there's also that push to an uncomfortable space where okay let's try something you know let's try you don't have to completely go do something different like again get on artists scenario I'm jumping on country but then just slowly slowly you know incorporate different elements and just elevate I guess what you offer as an artist and producer okay all right interesting what about as we close up on this for producers how do you how do you grow yourselves you know for artists is there be changing the style here and there you know for producers how do you how do you grow not talking you just have to reach out like for me I had a period where I would reach out to anyone everybody who can listen to go in the DMs and Instagram you go in the DMs and you just be like you know my name is Afam I'm a music producer can I send you beats send you the email and then that's how you work and you do that over and over again one person is gonna believe in you and then they're gonna say yeah this is actually really nice if you record with them and then you have a song and then other people begin to see oh wow you actually mean music and then you just keep on expanding your network that way the biggest thing about the music industry networking if you just expand your network you'll be good yeah so networking yeah growth growth as a producer now in terms of like the production itself like doing the producing a lot of people think art is I mean there's this whole general idea that art is just free it's fun it's loving but then you really need to study sometimes internet to go behind the scenes that okay this producer who is doing really well is doing this this one and then learn new things don't be afraid of changing because again there tends to be really quick turnovers in terms of production so for three four five maybe a year sorry a year or two years producer is hot right and then after that his sound is gone so you could very easily get lost but then now as a producer you need to reinvent yourself and okay like future what is happening now what sounds are being used what sort of genres are doing well and then learn how to do those so it's part of the growth as a producer it's not just sitting down and I do this and I'm going to do it well I'm going to do it for 15 years and that would be it okay so when networking reinventing yourself stunning every day all right I think I love that and you can take that from regardless of whatever career you're into yeah so final final words from you guys what you want to speak directly to guys and tell them also where they can get your podcast where they can listen in this is your camera you can look directly to it okay I mean so what I want to say is there's a lot going on in Kenyan music I feel like the Kenyan music industry is on the rise yes I know we talk about the likes of Saudi Seoul and Yashinsky but there are other names that are doing like some amazing things you look at the likes of Xenia Manasse TG Black like if you know you know these are people who are gonna be really really big in the future so watch this space in general like like I said my name is Affam we have a podcast it's called the 30% podcast you can find us at the 30% pod on everything all social medias and you can watch us on YouTube we're on TikTok we're on everything okay and some of you as you say your last words tell us also what's next for Kenyan music industry I just realized we didn't answer that question what's next I think this promise I'll put it that way there's a lot of promise there's a lot of artists doing big things in Kenya and there's a lot of Kenyan artists outside doing big things it's it's up to us now to figure out how to create you know the environment the systems everything to be able to sustain that so that it's not a little bit of a ramp and then we fall flat on our face and that's partly what we do on the podcast is try and talk about some of these things that need to be implemented we try to talk about the serious things in the industry like when things like like recently there was a whole blow up with MCSK and then also talk about the light-hearted things in the industry which we just review music and and you know give our opinion because at the end of the day it's also two guys who have opinions about things but yeah the industry is in a really good place I think we have a lot of people with knowledge gaining a lot of knowledge from outside so it's just really continuing that and being more positive we need to be more positive we need to be more positive about the industry and always be negative about it we're close we're close it's so water by Tyler won a Grammy but look at the producer it's Sammy Soso and he produced the whole of the end of the album so we are very close there's things like that that we need to just keep thinking about and doing and then we'll be we'll be part of the global conversation not just the local one okay we'll be part of the and we'll be at you know Grammys winning awards you know as Canyon artists also represented internationally and producers and producers don't forget the producers that's why you're also supporting you know the other guys in the back end alright that's amazing thank you guys for coming on board and sharing your insights on the kind of music industry I hope you have a picture of it and you have hope if you're someone in the industry it's not just about artists it's about the producers about the managers people involved there's a lot there's a lot of entries and careers and things that you can do as someone who loves music right so yeah I hope you've heard from them they are the hosts of the 30% podcast we have some and our fans so I hope you've enjoyed the discussion you're going to take a short break and then we'll be back with Grascha Mayengi for now enjoy some music