Alright. Okay. Thank you so much everyone for joining in. I'm super excited to be presenting at the HPC day and I'm sure why. Unfortunately, my co speaker could not make it because of visa issues, but we have recorded some of his part of the talk. So yeah, I hope that all of you enjoy. So the main purpose of today's talk is really to understand that how can you leverage the use of GPUs inside of Kubernetes clusters, but primarily for machine learning workloads and how you can maximize the efficiency of these GPUs for your machine learning workloads. Because as we know that as the model size of machine learning models becomes bigger, as you have more amount of training data that you work with the training process and the overall complexity for the training process does increase. Quite a bit right? So we need to use things like GPUs or tpus. And if you were to scale this or orchestrate this inside of a deep inside of a community cluster, how can you do that and what are some of the best ways of being able to do so and use of these GPUs inside of container orchestration technologies specifically fine tuned for machine learning purposes such as Kubeflow and flight. So this is what is the broad agenda for today. So just a quick introduction. I'm Shavai. I'm a developer relations engineer at Milly search and Richard, who is my co speaker. He's a CS undergrad at University of Toronto. Now I'll just take a quick fire off. What exactly is MLOps, right? So whenever you talk about any machine learning life cycle, there's always we start with defining the problem statement talking about the training data set. Then we train our machine learning algorithm. We find unit and we get the results. And if the results are good enough, we'll go ahead and deploy it. Right. So this is, of course, a typical machine learning life cycle. But if we talk about the entire process of MLOps, which is basically bringing in DevOps for your machine learning workloads, we see that the ML code, right? That is a very small part of the overall architecture. And of course, the most important one that I feel personally is the serving infrastructure. Because if you're running major deep learning models, if you're running large deep learning models, you'll need a lot of compute power. And how can you affect effectively and efficiently use that compute power really relies on how well optimized is your serving infrastructure. And that is what we're going to be covering in today's session. Now, of course, talking about the primary hardware that is used to run your machine learning tasks. So we start with CPUs. And then of course, if you have more computer intensive tasks, then GPUs are a great way to be able to, these are, of course, more optimized for computer intensive tasks. So especially, let's see if you're running running a lot of deep learning machine learning models, then GPUs, and of course, a TPUs which are tensor processing units are more optimized for those workloads. So as you work with major models with larger models, you will primarily use a combination of either of CPU GPU and TPUs. But of course, as we understand that, let's say, if you were to compare between running your machine learning workloads with just two CPUs, or with, let's say, a CPU and a GPU, you may think that since GPUs are more optimized, it might be better to just run your machine learning workloads with the CPU and a GPU as compared to just running it with two CPUs. But there are some challenges when it comes to choosing the right kind of configuration of your hardware to run these machine learning models. And that's where I'll just quickly play this video, which is recorded by my co-speaker, Rishith. Hopefully the audio comes through. So I want to talk a bit about why adding the GPU is not the same as adding a CPU, and it requires fundamentally changing a lot of your code. Often this is handled by frameworks under the hood for you, but sometimes it can require still changing a lot of code or better scheduling jobs, as I like to say. And yeah, so that is what I want to show you by adding one GPU is not the same as just adding one CPU in terms of how you run the code and what kind of strategies would best. So instead of showing this in some theoretical way, let's go ahead and do an experiment. So for our experiment, at this moment, let's just assume we have no XLA, we have no gradient accumulation. And if you don't know what this means, that's fine. We're not using it at the moment. At the end, we'll introduce this and also see how it can help, especially for GPUs, but at this moment, let's just focus on what we have at them. One CPU plus one CPU and two CPUs. Great. So now we want to train a machine learning model. So as you stated, isn't it 50, one of the most popular models out there. And what I want to do is I want to load the data. This will be quite expensive because my experiments are on image in it. And I want to pre-process the data. This would, this would, if I think about it, this would be a bottleneck on the CPU. I'll probably copy the tensors to GPU. So that will be a bottleneck on the PCIe. And for training, I'll probably use the GPU in the GPU setting. Great. So this is our experiment. And let's see what do we run. So we run all these steps, open, open this, just load the data, read the data, pre-process and then train the data. This is what our current way of running it looks like. And this is makes sense, right? So you first open an image, you pre-process the image, then you train on the image. And this makes a lot of sense. So this kind of profiling looks good to me. And this works fantastic on CPU because each of these steps can be individually run with two CPUs and you get quite some speed up running them, running each of these individual steps with two CPUs. So this one is fantastic on CPU. And this also is pretty easy. But let's try doing the same thing on the GPU. And my first guess is the parts where my model is loading the data and reading the data, won't work so well having a one GPU plus one CPU setup. But the training the model part would probably be faster than two CPUs with a GPU. Great. So let's see some numbers. And for the one GPU I have, for the one GPU I have a Tesla T4. And I'll compare this with two CPUs. So just running it in the naive way for a single epoch on Resonate 15. I see two CPUs are faster than having one CPU and one GPU. That definitely does not seem intuitive. He just added a GPU, which is a lot more costly. And you end up slowing down your process. So I just wanted to show you that this is not the same. And if you think a bit about this, then you have a lot of these, this ideal time where the GPU is waiting for the CPU. Yes, your pre-chips, the ones in the red, do get smaller. These get considerably smaller. But there is a lot of time where the GPU is waiting for the CPU. And that part gets considerably larger now that you only have a single CPU. And of course, if you have like a big enough model, this might, this strategy might work out just way too well. But we'll talk more about, but we'll talk more about why this happens and how you can tackle it. Here was that you saw that when we use like two CPUs, the performance was better. And that is primarily because that a lot of times our GPU was just waiting for the system resources, right? So as we uncover some of the strategies of making this more effective, we'll see that how can we configure our hardware in such a way and provision the hardware at runtime to make these processes more efficient. Now, of course, coming back to the next point, it's more related to today's topic is how can we make the use of GPUs in communities. So this is just a bit of a primer for those who might not have probably configured. So by default, communities does support stable release for supporting your GPUs and these GPUs can be run on your individual nodes. And primarily, whether it's your Nvidia based or AMD based GPUs, they can be run across multiple nodes inside of your community cluster. And it's very easy to just set it up. You just have this particular configuration that you can set up as part of like your device plugin that allows your pod to access your specific hardware resources for communities.  And of course, as I mentioned that when it comes to handling a lot of these compute intensive tasks, the most important aspect is that how can we effectively utilize these resources, right? These GPUs and these TPUs in a more effective manner. Now, when you have a very large workload, right? A very large machine learning workload, you might not just be able to have one particular GPU for your task. And you might be required to use multiple GPUs or a combination of multiple GPUs and TPUs. So there are some issues inherent issues that come when you're handling multiple GPUs. So the first one is memory, right? So as you basically have your entire data and you split your data across these multiple GPUs, so a lot of issues might arise with respect to an engine, the memory and how the memory is being shared between these GPUs and the TPUs as they are processing your data. And similarly, like when it comes to synchronization of these particular GPUs and TPUs and seeing that how the model evaluation is being done across these multiple devices, how to manage them effectively is also a big issue that we have to face, right? And similarly with load balancing, that how are you distributing your entire data load across these multiple devices, right? And similarly for debugging as well, if you face certain errors when it comes to some of the models not running properly. So how can you effectively debug which particular GPU is causing that particular issue? So these are some of the inherent issues that comes when we are dealing with multiple GPUs and TPUs. But there's a way to be able to resolve most of these issues, right? And that's where parallelism really comes into picture. So the entire concept of parallelism is that you are taking your either your data or your hardware resources and dividing them into parallel tasks, right? Very similar to how we have parallel processing inside of our CPUs. We are bringing in those same principles, but for being able to do our machine learning evaluation. So primarily if you talk about data parallelism, we are essentially splitting our data across into different shards, right? Similar to how we have database sharding. And then we are taking each of these data shards and running them in parallel GPUs. Now, the great thing about data parallelism is that each and every GPU has the context for your entire model, right? So as the model evaluation gets done, each and every GPU has the context for the entire model. And at the end of the evaluation, once the metrics have been generated, you kind of back propagate all of these metrics into one single source so that then you can get insights of how the model training basically went. But in comparison to this, we also have the model parallelism where basically you don't break your data, but you basically break your entire model into different fragments and you are running them, right? Very similar to how the data parallelism takes place. So one of the examples is a tensor parallelism where you are taking your main model and breaking them down into individual tensors. And then each of these tensors are being associated with the GPUs. And as the processing takes place, we again have the context and we are able to very effectively manage our sources. So the idea is that together with the data parallelism and the model parallelism that includes like tensor parallelism and pipeline parallelism, we're effectively able to maximize the use of multiple GPUs or TPUs in order to improve the machine learning performance because we're effectively breaking down our entire spectrum of our data and our model itself and still being able to use techniques like back propagation to be able to fine tune our results at once. And now what we'll see is that how can you use these techniques with your GPUs and inside of like, you know, a cube flow or with flight. So now I'll head back to a pre-recorded demo from my co-speaker, Rishith, who kind of goes further into how do we actually enable data parallelism and pipeline parallelism inside of our GPUs. Okay, so now that we know about parallelism, let's do another experiment. And this time I'll walk you through how you would run your models better on GPUs and probably run them faster because the last results where I added a GPU and the model started running slower. The overall training process started happening a lot more slower. So I probably don't want that. And let's see how we do this. So again, we come to the experimentation part. I again have a Tesla T4. I again have two vCPUs and the Tesla T4 only has one CPU. So that is the setup I have. And the way I do this is just for fairness, all of the experiments I was talking about and the one I do over here right now are on a single machine. So I have a single machine with two CPUs and one GPU. And whenever I try it out for two CPUs part, I explicitly make sure I'm not using the GPU likewise. So I do this with code and that allows you to get fairer results. And I also make sure that none of the computational graphs are being cast or anything. So, okay, enough of that. But what we want to do is probably use some kind of GPU strategy. And I did some GPU strategy, which worked out way too well. The time was reduced by quite a lot. And other things I want you to notice is that the epoch, the overall epoch time was not only reduced, but the training part is faster on a GPU. And the secret sauce to making this faster on GPU or how this happens is scheduling my process and my code in such a way that there's very little time for which the GPU is idle. So if I see in an epoch, there is very little time for which the GPU is idle compared to our previous attempt and compared to our previous attempt where we just loaded the data, read the data, pre-processed the data and trained it sequentially, which seemed to work pretty well on two CPUs. So how do you do this? So how do you will do the training with having very less GPU time, very less GPU idle time. So I will talk about a few strategies to do that. And the first secret sauce to doing this is prefetching the answers and memory. So what I probably understand is the reason the GPU was waiting for the CPU was because it did not have pre-processed data ready on which it could train the model. So the way you do this is every time a model is running in an epoch, the CPU at that moment is trying to is trying to load the data, read the data, pre-processed the data for the N plus one epoch. And this is pretty interesting because when the GPU actually gets to training the model and the next batch, it already has data ready for it. And which is why there are no, which is why there are no GPU idle times after every pre-processed step or while every read step. Another difference I noticed is that the pre-processed steps are that the pre-processed and read steps do not happen sequentially. And this is pretty important for our GPU strategy as well because we want the pre-process code to be ready when the GPU is idle. So the way we do this is pretty simple, just use vaporization. So I read for the whole batch at once and then I just, and then I just turn pre-processing steps. So while the first pre-process is being done, so why some pre-process is being running and preparing for the next set of data, the GPU is running on the previous set of data. So that is how this is facilitated. And if you see the reason why the open and read and read the open read and pre-process, some part of it also happens in parallel. It's because of parallelized data extraction, which is another secret sauce. So we took all the secrets of these which allow us to get to very little GPU idle time as possible. And this seems to work pretty well for one GPU and one CPU. And let's see the results for this. If we see the results for this, I have two CPUs taking about the same time. So this takes about the same time as earlier. But there is not a lot changed when you're trying to do this on a CPU. The train time pretty much remains the same and you're just changing when your job is happening. So this improves the CPU time by a very small margin.  And that is because if you see the profiling graph again, you see that this time around, the time it takes for the GPU to train on the data is a new bottleneck. So the bottleneck is no longer how much time the CPU take to pre-process the data or load the data. The bottleneck now to make epochs smaller. Just to keep for the time sake, I'll just probably skip over this part. But the idea of what you saw over here is that as soon as we added the data process, the data parallelism, we saw that now the GPU was no longer staying idle. So really the trick that we wanted to showcase over here is the dynamic allocation of GPUs at runtime when you're processing your data. So that's the main secret sauce when it comes to being able to make more efficient use of these GPUs. And at the end of what my co-speaker was primarily presenting is that when you have, let's say, more than one GPU as well, then you can use distributed data parallelism so that it can be spread across these multiple GPUs. And you can, of course, then leverage these different types of parallelism techniques in order to do so. So of course, as we mentioned, right, that if you have like, let's say multiple nodes as well inside of your communities cluster, so this will be primarily focused towards Kubeflow that whether you want to use like, let's say, a TensorFlow based model. So you could use like grass model dot fit or you could basically see all these different types of techniques, right? So you see mirrored strategy, TPU strategy, central storage strategy. So these are all the different strategies that you can actually apply to your Kubeflow cluster with the help of this parallelism technique so that it can make it more efficient for your Kubeflow to be able to do the multiple parallelism if you're running these multiple GPU clusters. And probably if you can just have the last like five minutes, we just have one Kubeflow demo that we like to just demonstrate quickly. So this is a demo. So with that, let's move on to the demo and I'll be showing the demo of using of using all the strategies we talked about using multi GPU, multiple GPUs, a spread across multiple nodes for our jobs. So I have a lot of moving parts over here, and I'll just start by doing a brief background about what the experiment set up is. And so I have a Kubeflow cluster. And this Kubeflow cluster also has two GPUs. These are two SLI 100 GPUs. And this is again very similar to the setup I use for my own research or what I use or what I use for often training on multiple GPUs. So, okay, but let's start by seeing the by seeing the config file I have. So since it is Kubeflow, I have a config file to do to do multiple benchmarks. And so this is structured as an encephalor job and a lot of the benchmarking code. So a lot of the original benchmarking code of running a model and running it is actually borrowed from encephalor benchmarks, the official encephalor benchmarks depository. So we use much of that. I've slightly modified the code to parse out and rectify the logs. So I can share it to you as proper benchmarks. But so this is what we'll be doing at the moment. Let's go ahead and run two jobs. Two jobs will also take quite some while to run. So but what are the two jobs? So I will train a Resonate 50 model on the ImageNet dataset once with the parameter server strategy. And so once I do it with the parameter server strategy, which is a type of data distributed data parallelism, I'll do it once with the data parallelism. And then I'll use the replicated strategy where you have all the sensors in one central place, and then you just copy them to GPUs as needed. So these are the strategies I'll be trying out. And as I was talking, this takes quite some while. But since it's a TensorFlow job, you can directly run it with Kubernetes, just this YAML. And of course, the code to create the CNN architecture. So all of that was present in TFC and in benchmarks. I already have that. And this takes quite some while. So I've already run this in Kubeflow. And particularly what I wanted to show you was the results. And so actually, let's see the results first. And since it's a TensorFlow job, you could most certainly just go ahead and run this in Kubeflow. So these are the past results. And as I was saying, this takes quite some time because this loads the ImageNet dataset, which is quite a few million images, it has to run this model on. So we take the ImageNet dataset, use some augmentations on it. And then what I want to see is, especially this metric, totally measures per second. So think of this metric as just how many images the GPU can train on per second. And the model itself is pretty simple, just the ImageNet 50. But I just wanted to show this benchmark. And this is again, same to the benchmarks I was working on earlier, our experimental setup earlier. And I had actually provided those benchmarks in this environment, making few changes here and there. So that was for the parameter server. And then we see the replicated one. So since this is a small model, since it's a particularly small model, I see that changing. And also because this is just two GPUs, so changing whether the tensors live in a central place and are then copied to GPUs would not have a big effect on the PCIe bus. And as we were expecting, it took about the same time for both these strategies. But that's a quick overview of how you can do with that. So just to kind of summarize what happened is that we showcased two different strategies of being able to use two GPUs with Kubeflow. And we ran these two strategies on a ImageNet dataset and you can see ImageNet model and you could see the results, right? So another way that you can actually configure your GPUs is with Flight. So Flight is basically a communities, a native workflow automation system for both data and for machine learning processes. And you can very easily configure your GPUs for being able to run your machine learning workloads as these tasks that are embedded inside of these workflows. And you can very easily just set up by using plugins where you're setting up these GPUs and you're giving them access to the pods which are running these Flight workloads. So whether you're using Kubeflow or using Flight, there is support for GPUs for both and you can leverage these strategies that we spoke about for your data parallelism and your tensor parallelism in case of both Kubeflow and also for Flight. With that, we'll be open to questions now. Thank you so much and I hope that you enjoyed the presentation.


G'day, how are you going? Welcome back to Bhut Lhosefi and if you're new here, my name is Tec. I acknowledge the traditional custodians of the land that I live and work on, the Wajik people of what is now known as Perth in Nungabuja. Now today I'm going to go all romantic. Yes, Bhut people do go out on date nights and I thought I'd show you what I consider to be my best six boots to wear out with my wife on date night. So without telling tales, what do we do when my wife and I go out on date night? Well first, after the last three Covid years, I sort of have to try to remember what we actually used to do. The sort of things we'd get up to fall into two broad categories. They could be very casual like going out to the pub for a very messy dinner of ribs and maybe a couple of beers or going out to the local Italian relaxed trattoria and then a movie. Or we might go dressy and go out to the club for a few cocktails and a dance, maybe something fancier, linen and silver service French restaurants. The idea though is that even dressy would be wearing relaxed outfits so that we were comfy rather than feel confined. Very rarely for example would my wife be fully decked out and I would wear a suit. More often than not she would wear a nice dress and do her hair up while I'd wear a smart button up shirt with chinos or wool pants and I'd put on a blazer. On the really relaxed side she might put on a jeans and throw a jumper over her t-shirt while I might wear jeans and a t-shirt or five pocket pants and a button up and maybe a short jacket like a bomber or just a crewneck jumper. Nothing threadbare and torn though. Let's respect your partner with a decent level of dress when you take her out. So to go with those scenes the boots will need to be appropriate but must remain comfortable all night. They can't be too outdoorsy and chunky because we're out and relaxed not going on a hike or chopping wood. So I'll choose three pairs of boots for each type of date night. I'll also leave links to where you can buy these boots in the description below and some of them are affiliate links. Let's start with relaxed casual. This is dressing for a night at the pub for some ribs or fish and chips. Going up market a little maybe it might involve a very friendly and relaxed family restaurant and then going to the movies taking a nice evening walk. So my top pick for this type of date night is the Grant Stone Edward boot in waxed tobacco suede. Now some of you might think this is a little too structured and formal to be a really relaxed casual boot to go with a really relaxed outfit to have a drink at the bar counter but this makeup in Charles Stead's waxy commander I believe still fits the bill. The waxy commander wax suede is soft and comfortable and yet breaks down the formal structure of a dressy boot into something more relaxed, roughed up and casual. The patina formed on this boot is just awesome. It's water resistant leather so it stands up to running through the rain to go from the car park to pub and the split beer will wash off. What's more on Grant Stone's version of the day night studded outsole it's extremely comfortable and grippy on slippery pub floors. Now this is a very comfortable boot that you could stand up in all night or tuck under the bar stool. And then next I'd pick the RM Williams comfort craftsman in tobacco suede. This easy on easy off Chelsea boot in another Charles Stead suede upper it's the perfect relaxed boot. RM Williams is of course the Australian icon still made in Australia and famous for its whole cut Chelsea boot design. Ta-da national symbol.  If you've ever wrapped an awkwardly shaped present you know how difficult it is to mold a flat piece of paper around a not very flat shape. This very svelte design and look is dressed down by the tobacco coloured suede that soft and supple and allied with the comfort insert in the sole of these boots means that you can have these on while you slurp up your pasta sauce all night. And despite its sleepiness the comfort craftsman is still a good year well to boot. 270 degrees around the front so can be resold when the outsole wears out. But sometimes we go to places even more casual than that and then that's when you need to pull on your blunt stones. Okay wait shock horror I did say at the beginning nothing too chunky or outdoorsy and the blunties are kind of chunky and really they're meant to be work boots. But I think as long as they're not completely scuffed and scratched and worn dry by getting cement poured on them if they look still newish like these these have just been oiled they're perfectly fine for going out with your wife to a relaxed bar pub or other kind of casual food joint. But a bit of relationship advice do take your cue from your partner if he or she dresses up slightly away from jeans and t-shirts leave the blunties and go for one of the other two. Until then I think the blunties are perfectly appropriate the TPU outsole and soft foam removable insole on the inside means that these feel like sneakers. As for style they don't have a big bump toe and in fact they look quite sleek in this 650 model they'd be fine paired with nice jeans and a nice casual shirt at the pub or in the atratoria and they are extremely comfortable so you never have to massage your feet at the end of the night. They are waterproof so for wet weather and in this 650 or the older 550 design they're not enormously chunky like a bullnose work boot but now let's clear the table. Now how about a dressier date night you're going to dance the night away or go to a cocktail bar or maybe even a silver service dinner you check with your partner and they're going to be a little dressier maybe with some choice jewellery so what do you wear to match let's say you decide it's appropriate to wear a pair of chinos with an oxford cloth button-down shirt and a blazer or even a pair of wool dress pants with a button-down shirt and cufflinks but dress down with a casual sports coat well you might want to join me and put on my number one choice the Thursday captain in their Thursday Brown chrome my pair has been kept dressy well conditioned and shined up so I'm very happy to wear it even with a suit it will easily match any smart casual outfit for date night the dark brown with little shades of maroon would be a versatile color to match most earth or neutral colors in your outfit the cap toe can be mistaken for a dressy shoe and the sleek and slim profile is stylish as for comfort the uppers leather is soft fully lined and while supportive really feels comfortable and the poron layer on in the sole will give you plenty of shock absorption for those fancy dance moves Thursday's day night like proprietary sole will also make sure you don't slip whatever you do that night their rubber composition is I think a little softer than day night gives you marginally more grip and also more shock absorption but if you wanted to be even dressier maybe you do want to go to that french restaurant we talked about you should decide on my dressy pick the Grant stone diesel boot in black chrome excel you know chrome excel is a remarkably versatile uppers leather packed full of oils and waxes it can clean up to being a very dressy boot or like some other brands build with it it can be a sturdy outdoor leather in this guys by grant stone in their diesel boot design it polishes up really well the natural wealth frames the the body of the boot and emphasizes the curves of the leo last even with a bit of wear and the brown undertones start to appear as the t-core effect shows through it can still look structured enough to be dressy just well worn grant stones diesel boot is a versatile plain toe service boot design and while the leather outsole may be a little risky and greasy pub floors in a carpeted restaurant you should be fine the leo last is wide at the ball of the foot and the toe is rounded anatomically over yours so having these on your feet all night shouldn't be a problem especially with all that leather and cork in the sole under your feet even if you decide to go the whole hog and wear a suit out on date night these will still match your suit now finally let's go somewhere really dressy but still relax enough to show off your fun loving quirky character let's say it's a top chef's restaurant a celebrity chef but it's not all funny daddy dark suit and silver service let's say you're wearing dark dressy jeans or black slim five pocket pants and say a dark jumper over a dressy college shirt well you might want to let your feet show off with the parkhurst allen boot in this very very distinctive spruce kudu for those of you who don't know kudu is a southern african antelope and the hide is full of character developed during the life of the antelope showing the scars and escape from lions and tanglements on thorny bushes kudu is an extremely soft and supple leather it's similar to kangaroo and has stronger tensile strength so despite its thinness and suppleness it's very hardy this spruce green color really pops under a dark outfit and the tinted natural edge just makes it pop out even more building parkhurst 602 last and with leather and cork under your feet supported by a lightweight fiberglass shank the plain toe allen boot is very comfortable the last is a combination last which means that it starts narrow at the heel and it widens out of the ball of the foot despite the rounded toe it's not very high profile so from the side it still looks sleek and spelt this for the color and leather alone is one of my favorite boots and so there it is my pick of the six top boots i would wear on date night your choice is going to be decided by where you're going and what your partner is wearing bear that in mind but whichever pair you choose you know that you're going to look good and you're going to be comfortable and secure so tell me what do you think of my choices tell me in the comments below tell me what you'd pick for your date night boots with your partner and while you're there don't forget to click on like and the subscribe button according to youtube most of you are returning viewers but there are three quarters of you who aren't even subscribed click on subscribe man it does you know harm youtube doesn't even show others what you subscribe to but it will mean that my videos get suggested to you when i upload and it certainly helps me grow my channel by getting my videos shown to more people so go ahead click on subscribe and i'll bring you more boot reviews and entertaining educational videos about all manner of things to do with boots you might even call it edutainment until the next time take care and i'll see you soon


This video is sponsored by Big Daddy Unlimited. They have a subscription package that can save the outdoor enthusiast a lot of dough. Here's how it works. You sign up for a 30 day trial membership for just 99 cents and you'll have access to their complete online inventory. After 30 days and you want to keep it going, it's just $9.95 a month. Check them out at BigDaddyUnlimited.com. Hey there friends, thanks for checking in. After inched out, today we're going to do a range comparison with the Taurus G3C versus the Canik TP90 Elite Subcompact. Both great carry guns. The Taurus has a 3.2 inch barrel. The Canik a 3.6 inch barrel and you can see that the slide's a bit longer with the Canik. Both are considered budget handguns. They offer quite a bit actually. Let's start with the G3C. A lot of people love it. They say the G2C, the G3C are great handguns. They carry them. The G3C comes with three 12 round magazines. It does have a thumb safety right there, front serrations, a rail for accessories, a clock interchangeable rear sight and front sight actually.  It has metal, nice ergonomic feel to it. A tether for finish on the slide. MSRP $340. And what I like about the G3C is that it has a wider trigger bar safety. So, upon the pull there, you're looking at four and a half pounds with the reset right there. Very well known handgun. A lot of people love it. They now make a Toro model that is optic ready. This one is not MSRP $340. Let's go ahead and take a few shots. Yeah, nice shooting handgun. I fired steel case, of course hollow points, everything through it. It's always performed fine for me and I very much appreciate the G3C for what it is. And that is budget price. This gets a lot of people into the shooting world. It also has a sight hole indicator right up top of the slide. But now we've got the Canik TP9 Elite Subcompact. And a lot of people love Canik. Now, what makes this super cool is that it is optic ready. It goes about $100 more, $440. Already mentioned a 3.6 inch barrel. It's an ambidextrous gun with the slide stop on both sides, making it left handed friendly. The magazine release is on the left side. It can be switched over. Comes with a 12 and 15 round magazine and a pick rail right there, front serrations, black serrated rear sights. A dot in the front has a striker fire indicator there. A loaded chamber indicator bar and a five pound trigger pull, which is very nice. And a very short reset. A lot of people love the Canik triggers. I love the Canik triggers. And this is Canik's smallest handgun. So it goes about $100 more. Little longer slide. You're looking at 30 and a quarter ounces loaded with 13 rounds. The G3C is 27 and a quarter. So three ounces heavier with the Canik TP9 Elite Subcompact. Let's go ahead and take a few shots with this handgun. All right. Hey, I appreciate both of them. When I do these range reviews, I want to know which one would you choose? Would you go with the Taurus G3C or the Canik TP9 Elite Subcompact? If I had to choose one, I would go with the Canik. That's not to say I don't appreciate the G3C for what it is. I think it's a great handgun. And I know a lot of people love it. But I enjoy shooting this a little more, probably because it's a bit larger. It makes a little more comfortable at the range. If I was going to carry, I'd take the three ounces lighter G3C, a little shorter barrel. And it is a nice carry gun. I know a lot of people carry this with confidence and they love it and they say they have no plans to change that. But I like both these guns for what they are. They're both fine handguns. I thought it would make a very nice comparison and get the conversation started with which one you would choose, actually, because I made my choice. Now I want to know what you think. If you like videos like this, please subscribe and share. I always appreciate the thumbs up button. Thanks for watching and you guys be safe.


Parmoly soap, Colgate Dental Cream, and Parmoly Shave Cream bring you our Miss Brooks starring Eve Arden. It's time once again for another comedy episode of Our Miss Brooks and Stripes. Ever since our Miss Brooks became an English teacher at Madison High, her motto has been Life, Liberty, and the pursuit of Mr. Boynton. But the shy biologist happens to be one of Cupid's coolest customers. That's not easy to warm him up. So far, the best remedy I've found is a hot water bottle. My frustration reached a new high when, for some mysterious reason, Mr. Boynton coldly and deliberately avoided me last Monday, and Tuesday, and Wednesday. Those were three days of no dates, no nothing. And by Thursday morning, his brush off treatment had rendered me unbewitched, but definitely bothered and bewildered. Since I considered the matter highly personal, I decided not to discuss it with my landlady, Mrs. Davis, when I joined her at breakfast. Good morning, Kylie. Would you like some oatmeal? I'm glad you brought it up. I haven't seen him in three days. Well, I haven't the slightest idea who you're talking about there. Good. You say you haven't even seen Mr. Boynton in the past three days. I think we can't understand his behavior. I can. Mr. Boynton has a guilty conscience, Connie. I feel it's my duty to still what I know. Still what you know? Exactly. Connie, would it surprise you to learn that Mr. Boynton is a two-timer? Yes, it would. With me, he's never even been a one-timer. He freed me to tell you this, dear. But last Monday, I saw him gallivanting around town with another woman. Another woman? So that's you. This woman, Mrs. Davis, is she attractive? Beautiful, I thought. Aw. And about how old is she? I think she's about 65. 65? That's my competition? When he gets here, I'd like to be alone with him if you don't mind. I understand. I've got to get dressed and run over to the countains anyway. They scheduled a family reunion party for tomorrow night, and Mrs. Countin asked me to help with the preparations. Though I haven't had a family reunion in so long that she doesn't... Just a minute. That's probably Mr. Boynton. I'll leave you two alone then. So you can pump him about that old lady he's going with. There'll be no pumping, Mrs. Davis. I have no desire to delve into his personal affairs. Where do you wish? I'll see you later, dear. Come in. Good morning, Miss Brooks. Hello, Mr. Boynton. Please sit down. Thanks. I'll pour some coffee, and we'll have a nice little chat. Anything in particular you'd like to talk about? Well, no, nothing in particular, Miss Brooks. You pick the topic. Oh, no. You're my guest. You pick the topic. All right. Let's just talk about the weather. Fine. I'd like to know whether you plan to keep going with that old lady. Old lady? What old lady? She's a kid about 65. Oh, that old lady. That's the one. Get it. What's a 65-year-old woman got that I won't have? Now, just a minute. You see, I bought new furniture for my apartment last month, and since then I've been terribly in debt. For weeks, in fact, my landlady has been hounding me for the rent. So? So I simply couldn't figure out how I could pay off my obligation until I saw this ad in the paper. Here, read it. Let's see. Wounded companion, evening, 7 to 11. Salway, $35 for weeks. Send photographs and information to Joe Marcelle, Carlton Hotel. Well, I sent a note along with my photo, and the very next day the party sent me one month's salary in advance. In advance? $140. Now, until I actually reported for work, I had no idea that Joe Marcelle was a woman. An old maid in her 60s, in fact. Joe was short for Josephine, you see. Well, naturally, I wanted a kid right off the bat. Well, I didn't do. I couldn't miss this. I'd already spent $140. Oh, great. Phillip Boynton, the lady's home companion. The only way I could get out of a job would be to return Miss Marcelle's money. Now, in view of my best predicament, I was wondering if you might let me borrow enough. Well, that is... Could you give me a check for $140? Personally. Then we could take carrying, bouncing it to the bank. I'm as broke as you are, Mr. Boynton. Then I'm stuck. Now, please understand. My relationship with Miss Marcelle is purely a business one. My principal duty being to escort her to fashionable places for dinner. Of course, she pays the check. Yes. Same type deal we have. We always go dutch and you know it. I apologize. What are some of your other duties? Well, she happens to have very poor eyesight. So after dinner, I have to read to her. Then, when the reading is over, she passed me on the head as if I were a little boy and sends me home. Good night, son, she says. Son, she calls me son. It fares my heart out. Don't cry in the coffees, we can have. Mr. Boynton, although it's perfectly innocent, I'm afraid that you're whisking an elderly woman about town. They cause tongues to wag. Yes, I'm well aware of that. I dread to think of the consequences that Mr. Conflin should get wind of it. In fact, I want you to favor me with the same promise I exacted from Miss Marcelle. If I should be asked the identity of my companion, she said, I promise that I'll never mention your name, son. I promise too.  Good morning, Harriet. Oh, hi, Miss Brooks. I was just going over to your classroom to look for you. Daddy said you were at a must right to withdraw from the double. All right, Harriet. I hope my barracks bag is on straight. I know. Good morning, Mr. Conflin. This is your first class at a boarding class. Daddy! Yes, Miss Brooks? No, thank you. I'll stand, sir. Then I'll get right to the point. The members of my wife's family and of mine have come to town for a little family reunion. Oh, yes. Mrs. Davis mentioned that to me this morning. There's a most of them splendid people, good stocks, but one or two are rather eccentric, particularly my sister, Hilda. Frankly, I'm worried about her, Miss Brooks. About Hilda? Yes, but I shouldn't really call her a Hilda. She still uses a stage name, Joe Marcel. What? The, uh, the Joe is short for Josephine, is he? What did you say, Mr.? I just said, whoop. That's short for, I think, I'll sit down after all. Do that. It's not for the fact that I need your help, Miss Brooks. I would not dare utter the horribly embarrassing expressions that you are about to hear. Miss Brooks, yes, sir? My sister is paying $35 a week to a gigolo. I inquired as to the cad's identity, but Josephine subtly refused to divulge his name. However, in an unguarded moment, Josephine let it slip that the water is a member of Madison's faculty. How do you like lamb apples? Not very tasty, sir. Why are you telling me all this? Because I want you to help me unmask back here. Well... Then it's circle. There's mine, we're looking for the flicker size, Miss Brooks, and if you should need someone to assist you. Hey, maybe Mr. Pointon could help you find the wolf I want. I don't doubt that at all. But when Mr. Pointon told me to school this morning, I noticed he was very tired, sir. Dead tired, really. So rather than burden him with... Oh, come in, boys, and come in. Thank you, sir.  Here's your purse. You left it in my car. Oh, thanks, Mr. Pointon. Mr. Pointon was just telling me about his sister. She came to town for a little family reunion. Your sister, Mr. Conklin, or nice? Her name is Joe Martel. See how tired he is, sir. He's just right on your floor. During the morning, Mr. Conklin made frequent visits to my classroom, and on each of those occasions, he expressed team disappointment over my not having picked up any information which might lead to the horse whipping of Josephine Gigolo. As I entered the school cafeteria at noon, I was met by Harriet Conklin, who lost no time in giving me a flash. Miss Brooks, I know the man who's going with Daddy's sister. You do? Yes, ma'am. I wouldn't dare tell Daddy, though. It's Mr. Pointon. You're kidding. No, I saw him escorting her into her hotel last night, and I think that woman's in her sixties. She was the first born in Daddy's family, Miss Brooks. Daddy was the last. That figures. Harriet, I hope you haven't read anyone else in on that little bulletin about Mr. Pointon. Oh, no, Miss Brooks. I didn't tell a soul. Except Walter Ganton. Oh, that's like giving it to Luella. He's blunting at the corner table, I see. Oh, I'm sure Walter won't go blabbing it around. I told him it was secret. Good. Excuse me, Harriet. Hello, Walter. Hello, Miss Brooks. Mr. Pointon's been running around Mr. Conklin's sister. Congratulations, your secret service badge is in the mail. You shouldn't spread gossip like that, Walter. It could lead to Mr. Pointon's having his license revoked by the Diggalows Union. He's been being dismissed from our faculty. Oh, Gersh, I wouldn't want that to happen. Actually, it's just a job, Walter. Miss Martell hired him to read books to her during the evening. How do you like that? For five years, he goes more or less steady with you only to wind up sending his evenings reading books to an old lady. I just doesn't make sense. No, it doesn't. Must seem pretty tame to him after all those years of whisking me to the zoo. But Mr. Pointon didn't really know what he was letting himself in for, Walter. Now he's got to figure a way out before Mr. Conklin can pass him. Well, maybe my agile brain can figure something out with him. Yeah, I'll get the old bean working. And before you know it, I might dream up something. Excuse me, may I speak with you privately, Miss Pointon? There's no need to speak to me, Mr. Pointon. Walter's heads to the entire situation. Yeah, I think it's real nervous. Of course, we're rather young, Mr. Pointon. But won't you join us, anyway? Thanks. I just talked to Miss Martell on the phone. I have to call her at noon every day to report you, and she said tonight we'll meet at 6.30, son. Sorry? She calls him son. Normally, I don't meet her until 7, but in spite of my discontent, she insisted that we meet a half hour earlier. Well, who is? Are we half hour cars, I guess? There's the wrong degree to take place, Mr. Pointon. Well, now that the heat is on, I didn't care to risk meeting her at her hotel, and certainly not at my apartment, so, well, this is an emergency, Miss Pointon, so I took the liberty of giving her your address. You're meeting her at my house? We'll be safe there. Yes, ma'am. He's going to hire a private detective the first thing in the morning. A private detective? With instructions to trail Miss Martell day and night, and to superficiously photograph anyone with whom she sees. Oh, we found the gigs up. Oh, you'll just have to quit, Mr. Pointon. At 10 o'clock. I received $140 in advance, Walter, and I've already spent it. I should tender my resignation. I'm afraid Miss Martell would demand the return of her money. Could you demand her lose back if she were to fire you? Well, no. According to our agreement, the money's mine, provided that I merely make myself available to her at the prescribed time. Of course, Miss Martell wouldn't dream of this charging me. She's too fond of me. She boasted, in fact, that in me, she had found the direct and synthesis of her former companion. Her former companion? Yes. She fired him upon discovering that he was possessed of a dangerous split-personality, Miss Book. While he was at all times the quintessence of propriety in her presence, she learned that he had another side. He was in the habit of consulting with unservery characters. Quick at gamblers and gangsters. Gangsters? Wait a minute. The old being's version that Mr. Brayton, you have just stumbled upon the means to get yourself back. What are you talking about, Walter? Well, it gets illusion, you old girl. If you assume those same unsavory characteristics when you meet her tonight, Mr. Brayton, she'll probably give you the bounce just like she gave it to the other guy. You're being absurd, Walter. Why, in her own words, she once said to me, one look at you, and I was convinced that you were a wholesome, home-spun, clean-cut American boy. She called him, son. Mr. Brayton, if you take my advice... I'll accept your advice any time, Miss Book. What is it? Now, look, I'll admit my idea is real crazy, but it can do the trick. You've got to act tonight, Mr. Brayton. By tomorrow, the sands of time will have run out on you. It'll be Mr. Conklin who will fire you. Miss Book, would you want to see Mr. Brayton leave dear old Madison? Maybe never to see him again? Oh, no! Don't listen to him, Miss Book. What's your advice? You've got to listen to me. Yes, give her the old split personality bit.  Hello. I've been visiting with my sister, Josephine Harriet. I happened upon a startling revelation trial. All right. Thanks to a note I discovered as I was leaving Josephine's apartment. A note that she had carelessly left near her telephone. I learned the identity of the scoundrel to whom she's paying $35 a week. It's none other than Mr. Boynton. Boynton! A jiggler! I thought he was Miss Book's property. What did you not say, Daddy? It says meet Mr. Boynton 630 at 209 Carroll Avenue. 209 Carroll Avenue? Somehow that address strikes a familiar chord. Seems to me I read a Christmas card to that address on. Yes! That's Miss Book's house! How's it going, Daddy? How do you like that? He's linting him out! I hope you don't mind having dinner in the kitchen, Mr. Boynton. I'm not at all, Miss Book. Hey, I like this corned beef hash you cook. It looks different. It must be. It's supposed to be a real cutlet. Miss Marfola could be here in a few minutes. You'd better study your part. I need... We're in the kitchen, Mrs. Davis. There's no need to let Mrs. Davis in on our scene, Mr. Boynton. By the time she gets back from the movies, it'll all be over. I wish it were over now. I'm getting awfully nervous. Excuse me, Coach. I'm sure I'll be at the movies for quite a spell, Connie. I'm going to see Frankie Sinatra, and from here to the carnival. Fine. I don't have to wait for the Mickey Mouse shirt subject spell. I read the book. I've got an idea. Don't forget to fill the goldfish bowl for me, Mr. Boynton. I won't. Good night, Mrs. Davis. Good night, Collin. Good evening, Margaret. Oh, of good conscience. Oh, you're startling me. Hello, Harriet. Oh, hi, Mrs. Davis. Were you leaving? Yes, for the movies. But it's so sweet of you to drop over. Why not visit with Miss Book and Mr. Boynton? Get out in the kitchen. Good idea. Splendid. We'll catch up with you later, Margaret. No. Come on, Daddy. She said they were in the kitchen. That's precisely why we're not going to the kitchen. Having Mrs. Davis let us in without announcing our presence is a stroke of luck. Now we can duck behind these hall curtains and ease the drop. Daddy, isn't that an offering? Smooth and trick? Yes, indeed. Quiet now. Take 30, Mr. Boynton. Better wait in the living room. To there. Where? That sofa near the hall curtain. Right under the statue of the gargoyle. The one that always reminds me of Mr. Compton.  Remember, you're playing an underworld part. I'll do my best, but I'm not sure I can go through with it. Oh, that must be Miss Martell. I'll wait in the kitchen for my shoes. Good luck. Thanks. Let's come in. You're leaving, Mr. Boynton. How are you? I said how are you, Mr. Boynton? Something wrong, Mr. Boynton? Sit down, babe. Babe, what's this mean, Mr. Boynton? Miss Martell? No, it's what. What? No, let's get out of business, doll. Doll? I thought we ought to bat the breeze a little tonight about my salary. I need more loot. Loot? Oh, what come over you? What happened to the sweet, clean-cut boy you used to be, son? Now look, sister. I don't have to go into that namby's namby. Act so seven o'clock, which is when I go on duty for you, according to our deal. So you got me a half hour early. So my agent insists I get a raise. And we ain't standin' for no sullen from no dame, no house.  There must be some kind of a joke.  That's right. I teach kids to pick pockets. Now make something out of it. Oh, good heaven, you've gone mad. I wish you two wouldn't talk so loud. You're discerning the bass game in the back room. Dice game? Who are you? She's my agent. Now you better cough up that raise, chubby. We wouldn't put on to put the screws on you, would we, fellas? That's right. I ain't ever put the screws on no dame since I look shy. Shy? That's underworld talk. Oh, that's what I think I'm going to say. Not here. We'll charge your extra. Now let's get with it, Marcell. You either cough up more moolah or I'll put the clamps on him reading Shakespeare to you.  That's who you think you are, but that's who you're that man. She's my wife, that's who. What? His spouse. The little woman. The old ball and chain. A better hat. The old battle axe. The mother of my father. He calls me mom. Come in. Oh, it isn't our son, Walter. Only 16, but big as a horse. How are you, son? Good. My grandson, he was a cop. What is this, Miss Marcell?   It's only 35 fish a week. What? That's bad news, son. You don't seem to want to give the raise. Oh, silly, gee. Well, well, well, well, am I going to get the move out of by a new crooked roulette? We open schools. That's good enough. Goodbye, Mr. Boyden. And of this moment, consider yourself a god. Does it, Mr. Boyden? Just about. Of course, as soon as I can dig up the amount she paid, I'll send it back to her. Every penny of it. Gosh, when she hired me just to escorted her to dinner and read books to her, perfectly harmless things. The poor woman had such faith in me. And now this had to happen. Oh, come on. Cheer up, Mr. Boyden. I thought everything worked out well. So did I, Mr. Boyden. Me, too, son. Mr. Cartman. Gee, I got to be blowing back to shy. No, you don't. Sit down, babe. Mrs. Davis has harrier than me in as she was leaving for the movies, Miss Brooke. And behind that curtain, we heard every word of your dead-end bits. Ah, yes. I better wait for you out in the car, dad. Bye, father. Well, you know, that little group is there to report him. Is Briggs anywhere? Briggs? Get me a tall one. I can leap off. Mr. Boyden. From what I overheard, it's clear to me now that your association with my sister Josephine was of a perfectly harmless nature. Well, why didn't you come to me and explain that fact? Oh, well, sir, I was afraid that if you... Ireland! It's also clear to me that your little gangster scene was perpetrated to go to her into firing you before I could find out you were in her employ. And now that it's all over, all I can say to a lot of you is... Bless you. Huh? Bless us. Bless you. Who sees? I have tried vainly to persuade Josephine from hiring males and pandas, because those she has had in the past proved to be a mercenary lot of lame-brained loafers. In view of her violent reaction to your performance, I doubt that you'll ever hire another. I feel that I owe you a debt of gratitude. You really mean that, sir? From the bottom of my heart. I must be going now. Thank you, well and all, and good-bye. Wait, I think I'm going to the movies with Harriet, sir. I thought I was the one who was right, Mr. Rook. Bye. Say, that gangster scene did serve a pretty good purpose after all, Mr. Rook. We're not fine. You know, even though it was Walter Denton's brainchild, you're the one who convinced me that I should go through with it. I don't know how to thank you, Mr. Rook. You doubt? Think a little. I wish I could think of something, but I just can't. What can I do, Miss Rook, to show my appreciation? Sit down, babe.


How's it? How's it? It's sometimes tricky to discover new photographers to be inspired by. It's kind of like this this catch-22 that you can only really seek out photographers whom you already know. So how do you find photography that you're you're not familiar with new photographers to inspire you? And when you look through these these books of great photographers the same names surface again and again and again And I thought it might be fun every Friday just to give you a few suggestions of photographers whom you probably don't have heard of Whom you can explore over the weekend at the moment. I'm thinking about calling this series Photographers who you can explore over the weekend and hopefully enjoy looking at their images I thought that's gonna have certain snappiness and a zip to it or It could be like photography what is Gif and I like which is joke probably only about ten of you enjoyed Anyway, photography is is fascinating in that it takes us What is there in front of us normal every day and safe and transforms that into Something unreal and and compelling not like a painter who can use Licence when they're translating what they see in that subject onto the canvas in any way they see fit a photographer For the most part must work with only the raw materials that they are supplied with These three photographers today are standing examples of this Franco Fontana is an Italian photographer said that the purpose of art is to make visible The invisible and what better way to eloquently express the magic in in his photography And what draws you into his photographs Springs from that quote. It's a willingness to see the opportunities for photography that are bound and are all around us This is such a basic thing as often we forget, you know rather than looking for the grand Gesture his photography feels feels quiet. It feels pain to Lee It's simple the colors in many of the images echo back to those primary colors of the 1980s Which transport me to my childhood now some of this photography is from the 1980s and that's but that's not necessarily where this Color is is coming from there. They are strong primary colors, but they don't feel strong Now the more that I look at them. They're overt Simplicity also gives way to story. There's there's something and this is the hallmark of a great photograph There's there's the more that you look at it the more it offers up to you Now they also have this kind of odd vibe of despite being very public photographs of being Private like like the person viewing the image is the only one who is privileged to see that scene All of those scenes are familiar and none of those really make us Feel like it's a well We couldn't really be part of but possibly something that's only sort of glimpsed in a reflection or a mirror over our shoulder His portfolio is is absolutely diverse and and certainly worth digging into We're gonna stay in Europe, but hop over to France and here we find John Batho now unlike Fontana a lot of Bathos photography is staged and orchestrated now I don't think this is a bad thing necessarily, you know It's this staged approach which gives his photography a more kind of thoughtful air now This isn't of course to say that Fontana isn't thoughtful But when you look at Bathos's website his his photographs are presented initially as a Collection of images all together so evidently he wants us to consider the work as a whole You know, you know mentioned a previous video does so often these days We only tend to look at images as single images, you know, they are on the screen by themselves So it's great to be given an idea of how the photographer Intended for them to work together because this I think a point so often overlooked His parasol photographs feel like they could have been created a hundred years ago You almost expect ladies and voluminous swimsuits to step out into the frame or gents and linen Linen suits with with straw boaters and lateig if you see what I did there now He would probably feel right at home here. I'm exceptionally taken by John Bathos's cloud Photography, I think they are absolutely beautiful. They're just they're just lovely now I'm not a hundred percent sure but it feels like the on these photographs that the paper has had either the light sensor for Motion painted on it prior to being exposed in the dark room or perhaps The developer has been painted on to the exposed paper now, obviously I love this effect And I know you can do it quite easily with black and white printing because I've done it myself But perhaps somebody who's a bit more familiar with with color printing By hand could tell me if that would work given the complexities of the of the processes involved my initial kind of gut feeling Is that is that it wouldn't? Because the chemicals are so much more sensitive, but there must be something going on there And you certainly see in his earlier work that there's a lot of It's a chemical manipulation So I'm assuming that this is not Photoshop and well, I I certainly hope it's not photoshop If you've got an idea, let me know in the comments below I'd be fascinated to see if somebody's got a bit more insight into that now We're gonna jump over to Japan and one of my newest discoveries, but also one of my my favorite photographers and I owe a debt of gratitude to Ted Forbes for featuring this photographer on his channel a few years ago This is Hiroshi Sugimoto Now way back before me and maths came to an understanding that I would simply not Really be reaching an understanding with maths I Harbiter desired to be an architect and then I thought about being an architectural Photographer, but I saw an article in a magazine about a freelance architectural photographer and his car was chock-full of 4x5s 8x10s has a black super wide panoramic cameras all the lens boards and tripods You could shake a large stick at and that kind of put me off. I mean obviously as a young student I wasn't gonna be able to afford all that kid However, it didn't put off Sugimoto though and his large format images where he pushes our preconceptions about the technical limits of photography are utterly gorgeous His ultra long exposures of cinema screens and drive-in movies are fantastic, but it's his architectural Images that I absolutely adore Now I'm going to confess that Despite having gone to art school and feeling like I'm an artist myself There's some of his Rational and his accompanying texts to his photographs can feel just a tad overblown You know the idea that in his own words Superlative architecture survived the onslaught of blurred photography now It kind of smacks of somebody who is capable of creating Outstandingly beautiful photographs in quite a short time and sort of needed to pad out the process a bit So he looked like he knew was doing something and so he spent most of his time coming up with these phrases like erosion testing architecture for durability, you know, that's That can put some people off now I really hope that it doesn't put you off and that you go look his Photography and all of it all that to be to be fair you can give his wax up portraits probably just a bit of a mess You know, it's a bit like Monty Python. It's not all cheese shop and parrot sketches. Occasionally, there's there's a couple of Okay, that's not so hot, right Now what I do like about Sugimoto in case you think it's sort of odd that on one hand I say he's become one of my favorite photographers and then Jump straight into how much he kind of has some weirdness about him that doesn't quite resonate with me But what I do enjoy is his his willingness to push those technical boundaries like it like I mentioned earlier You know, he's taking his photography to the extreme Where he's looking at this idea that the camera can see the world in a way that we can't and he exploits that for all that it's worth You may not be familiar with but if you've seen the candle photographs where where he has a long exposure with the candle burns down It's entire length and the seascapes and all these other things. They're beautiful in their simplicity But for something that is is there right in front of us We just need the eyes and the willingness to see it Of course, some might say that these architectural photos are just they're just simply out of focus and that it's just it's just poor Lame photography and that's perfectly fine, you know each each to their own However, his photography does tickle your fancy seek out the aforementioned seascapes and the candle photographs Have a look at some of his work. It's absolutely gorgeous Perhaps one of these photographers is going to give you a bit of inspiration or encourage you to look at more of their photography I certainly hope so now remember every Friday I'm going to be dropping some new suggestions for photographers for you to explore and hopefully I'm going to come up with a snappier name for this series Anyway, thanks ever so much for watching and I'll see you again soon


We are here with the most creatives in 254. Let them introduce themselves, maybe from here. Miwa nani taga kama umudhe, ngaoneza watch filmi angu mpi naji tamzuka. Mendeo produce asawa. Mendeo niya kundisa boku na kenye kama mese ma, mpa niya evo. Yondo movie, enyendo hot keki town, niko masya magic movies. Manze saka schedule lui keme, utafraia sana. I love watching you hand over to the magnificent J.J. Manze saka. Yes, mambo na ito J.J. Film director currently working on a film called It's a Free Country Fundraising Stage, pre-production. Niaze, niaze. Mi na ito adenis Milam Jungu. Mi filmi angu badoi jatoka. Dotko post-production. Tkona kina jeri na kuja? Ay, so. Tawa. Ay, so mi na ito Bolton, tiktua content creator, BolTest254. Thank you. Bari zeyinu mi na ito jeri mukuwa saka, mi ni actor, event emcee, graphic designer, and a film student. Saitu na wakona short film, ni na ito God forbid, alafpi akuna documentary, ni na ito breaking the chains. Napia jume se ma unaw mein sis dwacha tuni seme. Sini Netflix beneficiaries. So we are growing to be producers. Alright, just to ask you guys, today you understand the 6th edition of Kalasha International Film and TV Market. So far, what is the experience so far? Just done 2 sessions so far. So first one was about investment. Second one was about Netflix. So I'd say, still have a long way to go. Film industry, baddo, kuna structures we need to put into place. More access, we need more opportunities to access distribution. We need more opportunities to access funding. So I think it's a, to be honest, it's a mixed bag. We are somewhere, but twendele, twendele kazi baddo ni mob. Yeah, that's my experience. I know the exhibitors from different areas. We have international exhibitors. We have producer-distributors at the exhibition today. So, nye wokis. Kuna sessionia distribution ni nendele ahuko. But sida ni watani yambia kenye na taka kuskia. Kenye na kuskia ni nendele na pereka muviangu Netflix ni yuze ahuko. Na mefanya ma diploma siya po banata sija skia ma li neza huza muviangu. Baddo na appreciate kenye wana do lokal ni nendele aku support. Na nendele aku build capacity, case in pointa ama beneficiary. Nze tu wa paivi. Bata kenye nendeza sema ni, utenye nendeza tu ma movie. Kusaba buwa, imuvi tuki approach kama business. Nginia yo yote. Sema niya koko inja nenda kfungu hotelia. Nena nga na tafta capital, na tafta location, na tafta interior designer, na tafta suppliers wacha kula na funguwa biesha rana tu anakam kukula. So the moment you transfer kwa approach, sinema kama any other business. As in, I think it atu wa kiya vipo, design niye always you have conversation build a product. Kusaba buwa say ata kuskiza. So kikam kwa meza na product wambia yo yung doki tu na huza. As in understand a better chance na believe me, kafil maker yo yung doki tu mi interdu. Like village JJ na duwa na fundraise, atengineze muviangu. Aja kuja apaanze kwa mbia watu yo ati sijuifanya ni. Yeh ame take initiative na atengineza muviangu. Watekin yo initiative. Ataka uto wambama besteza kwa familiar wende kwa bank chuku elonju. Most business men even do wana do. Na the moment you transfer kwa approach, sinema kama business.  We take it up to na neza ran losses, but sinema ni kitu eni wneza huza in perpetuity. So what did you ataka wneza tengeneza pesa ako 10 years from now. Yung no time ilu kwe miandikwe pesa ako ita come through. So ni kwe mi zatu ma producer wengi watengeneza yo moving. Dopiatawasa wana fanya distribution oku ena kazi. Watawe kama meza apwa tu pigi story na sisinda utu nafa kwa partia kazi ako distribute.  So yo tundo kenye na seima. So men time wneza henda filich tv wangali e klik klik bang. Aama u genda pala Instagram ko bayoyangu, kuna bayo apu yu kuna linki apu. Nuna yo apu ni lipu e. Kini nuna ko yo linki angu na lipu a man. So tizaka awewe duyo sama. Ala fuwe. Follow me on social media o plasov niya kundisa boke e SPM buzz to the world. You know? That's what's up. Welcome. We have so many reviews on the world. Of course people in the world will be interested in nasu. Because the echo is actually interesting in the world. Do you feel that this exhibition coming in has a positive impact on the world? At least to me ako doesn't work. True. True, true. Considering mi bado ni mgeni, considering mi ni mgeni ko ige mi ako produce, it's been a couple of years but na land, like na land through the ropes ni me kama pa iwin me mitu asewengi, ni me mitu jon jubi at least yae. Ulimse mi kwa kwa game. Una na, awna wasi wana to shikam kono. Like wana wasi wengia pa iwin je wana to shikam kono. Ion ni kituani nisa se man appreciate sana. Kitambu kido ko ili ko operationa lakini sa iwin weza fanya one, two, three, waku notice, wakam on board, uskumu at least kuwa production moja maingine. Tu na appreciate na pa iwin do play students na network now. Like apa ni wana zawa patawote ni kitaka.  Just to release it in one of our digital project writer Tik Tok creator. Tik Shop. So make the show with us your experience and maybe expertise. Okay, my experience being a content creator I've seen that as we are continuing maybe creating our content the moment we adjust into it, let's work on maybe our I mean content like we work on our own we produce our staff you see. So we can either get people like him maybe helping us growing you see. So that is what I can say. Experienci mi kwa poa fosisi ni zase maa tukona advantage ju kina wali anza na wakunayo platform so adoniza tukulia mo serious ju washa anza, washa tukretia yon zia so see tunapita. Jerry Ya kumadisi ya I've been the game Do you make one day to see an international script being an international site? Ya, I do and from what from what kitunimelan lewa pa ni our say international wana kuja wuku wana taka izi stories waku so e idea e mentality ya tunata kakwenda wudu na taka oli wudu anaka muku so wasewa anze kutawaii contentia tu dutu wii uze hiende wuko tuwacheku struggle kuenda wuko sana So I think that's kitunimelan say alafu wasewa ende kutunin NTV inganya inakuja NTV ma make sure me watching ganya alafu wana ni follow wana ni follow wana ni follow apu IG Jerry Mukuwa apu tu imetosha akuna link akuna link ya minda pata pesa unezantumia tu alright thank you so much guys I wish you all the best see you next time alright good bless