So to begin with, just a quick introduction of what ONIX is. So ONIX stands for Open Neural Network Exchange. And this was essentially built as a standard. And it's an open source format of representing neural networks. So the benefit of having ONIX runtime, sorry, ONIX, is that one of its core principle is to enable interoperability. So what this essentially means is ONIX acts as a bridge across different frameworks. So this allows you to train a model in one particular framework and deploy it in a different framework. So there are different frameworks which are supported by ONIX, some of the very popular ones like PyTorch and TensorFlow and so on. And over here, you can see what it essentially means. So you can train a model in something like TensorFlow or PyTorch and convert that model into ONIX and have that converted ONIX model deploy on your desired framework. So that could be on TensorFlow, TensorFlow RT or TensorFlow Lite. So in essence, ONIX is kind of being used as an intermediate representation of sorts here. So there's another key principle that ONIX is kind of built on. It's portability across different platforms. What this means is you could use the same ONIX file and deploy it across different devices without having to make any modifications. So you could take a model which was run on GPU and take the same model and run it on a CPU without making any significant modification at all. So this allows you as a developer to work with different devices without having to worry about where your application is being deployed at, right? So if you look at the ONIX ecosystem, ONIX is growing, it's always been growing and it has a very diverse community which keeps adding new operators and support for some of the latest advances in deep learning. At a high level, ONIX defines something called special interest groups. Basically, these are called SIGs and everything inside the umbrella of ONIX, whether it's a tool or a library, falls under one or the other SIG, right? So at current, there's SIGs, ONIX SIGs, that's the architecture and infrastructure, compilers, converters, models and tutorials and optimization and operators. So just to talk a little bit more about ONIX, we have Krishna who is the co-chair of the models and tutorials SIG. Thank you, Krishna. Hey guys, this is Krishna. I'm an ML software engineer at AMD and I'm super excited to be here to talk about turnkey ML which is the newest repository under ONIX and ONIX model zoo. So before I jump into what turnkey ML is and talk about the architecture, let me just start with a quick demo. So what you see here is, on the right is the hugging face page for a bird and you have an example and let's say I wanna run the example on a hardware and see how well it performs. So with the turnkey tool, I can just copy an example code, just throw it in my code editor and call turnkey and ask it to run it on device x86 and you will see that the whole flow goes through. The PyTorch model gets exported to ONIX and gets benchmarked on the device that I asked for and all of this happened with no changes to the code. I just had an example file which I can just throw it into turnkey ML and ask it to do a bunch of things. So why do we do this? What's the background here? So in the AI compute stack, the compute landscape is really three dimensional. You have rapidly evolving model architectures, we have transformers, LSTMs, CNNs, GNNs and a lot more and we have diverse software ecosystems today. We have ONIX runtime, TensorFlow, TensorRT, PyTorch and a lot more and there is also diversity in hardware. CPUs and GPUs have always been there and now we have a lot of accelerators showing up, FPGA based accelerators, NPUs and all of these three different dimensions continue to grow. And let's say as a developer or as a user, we live in any part of this stack, how do we know how good this particular component is? Let's say you have, you're developing a runtime, you're developing a software stack for AI, how do you know how good that is? What's the efficacy of the stack? How broad is the support? Or let's say you're developing a hardware, how good is that hardware? Like what are all the different domains of models that it can run and how good is it in each domain or where is it not good enough that it should be improved? Like how do you find that out? What's the efficacy of your compute stack? That's kind of the problem we're trying to address here. So going into TonkML, so TonkML is a suite of tools that seamlessly connect with each other and some of the highlights here are, there is a PyTorch model analysis tool that lets you find out and recognize models just from an example Python file. Like I don't have to take the model out and show that this is the model but the tool can automatically recognize where the model is and what the inputs are and what it looks like. And there is a build tool for transforming models from one model to another. Like any process like quantizing a model is very common, distilling a model, compiling a model, all of those are model to model transformations. And in the end, there is an execution tool. You've built your model and now you're running that model on any specific hardware that you want. So these tools work very well together and they provide a standardized execution and reporting framework. So double clicking into the stack, getting into the architecture of the tool itself, we have models at the top which from all the repositories we know and love, touch hub, touch vision, hugging face, all of these open source repositories with a lot, lots of models. And then we come into the tool itself. The bigger box that you see is the TonkML framework and the access to that is through a CLI or an API. So the first layer of that is called sequences which performs model to model transformations like as I mentioned. So you have a PyTorch model, you want to converge that on X, that's a sequence. You have to quantize a model with your own quantizer, that is a sequence. So you can build your own set of sequences together to go from an input model to the model that you want. And then there are run times which you can pick from a lot of available run times or any, which is a software stack that's used to execute the model. When you want to run on the GPU, you could use TensorRT. If you're running on CPUs, you could use Onyx run time and there's a number of choices for run times. And any time we are running a model, we would like to profile it, like how long is it taking, what's happening in the hardware? Like how do you analyze what's happening underneath the hood? And some examples are Intel VTO, AMD, Uprof, NVDI, SMI. You can choose the right profiler that you want for the job that you're doing. And yeah, in the end of the day there's device which is CPUs, GPUs, or any other accelerator that you want to test on. And all of this has a common reporting infrastructure that measures a lot of metrics throughout the tool, latency throughput of the model, or even power consumption of the model, or whatever output your profiler gave you. So this, now I'm going a little bit deeper into the architecture. So I gave you an example of a layer, sequences run times, profilers, devices. And what we have here is a really extensible framework through this concept called turn KML plugins. So let's say I have a specific hardware and a software stack and I want to build my own plugin, it's as easy as just plugging these components in. And what we already support is ONIX x86 CPU, so you have a model in Torch, you just call a sequence that does Torch to ONIX conversion using the standardized Torch to ONIX exporter and I can run that model on the CPU through the ONIX runtime. And we have support for Torch using, running the same model on the CPU through Torch, or if you want to use the GPU, you can use TensorRT or you can use ONIX runtime DML. And at the end, you see a box in orange because we have the capability to also make certain plugins private, where we don't have to open it up to the public. Internally, we have a Ryzen AI plugin that we run on AMD's own NPU. So what that looks like is you have a Torch model that comes from all of these repositories at the top. You grow from Torch to ONIX FP32 and then we quantize that model with our own quantizer. And we use the ONIX runtime, Y to CP runtime, use the AMD graphics manager as the profiler and run it on Ryzen AI. So let's say you have a new hardware and a runtime, you can build your own plugin like this, install it into the run KML and benchmark away on all of the models or run all of these models that are available in the repositories on your own compute stack. So yeah, we saw a demo of BERT, but ResNet and BERT demos have gotten old. So let's try something new. The resolution is not that great, but what you have on the right is the Lama model from Meta that's available on Huggingface. And what I've highlighted is just the example, just like what we had on BERT before. And the system on the left is my VS code window that's actually running on a 64 gigabyte RAM Azure VM. So all I'm doing here again is just copy pasting the code, getting some example inputs for Lama, importing Torch, and I just ask turn KML to run this model. So at this point, it's going to go through the same flow, it's going to convert this model to ONIX because that's the sequence that I've chosen. And it converts it to ONIX, it optimizes the graph based on the optimizer that I have chosen, in this case the default optimizer, and then it saves the model to disk. And as this process is complete, all of the results and files are saved in the cache folder. So right here, I'm going into the cache folder and you can see that all of the files, including the ONIX files and the weights for these modules are saved to my cache folder. And there is also a separate stats file that gives me in-depth information on everything that happened during this execution. So it tells me how long each stage in this process took and what are all the different operations in this model? What is the diversity of ops? So we have so many macmals, so many sigmoids, like how does this model look? And it also tells me how many parameters are there and what are all the packages that were used when this particular execution happened. And that helps me recreate this whole setup if needed. So given the ability that we have to process thousands of models from so many open-source repositories in a very automated way, we were able to convert a lot of the open-source PyTorch models into ONIX and revamp the entire ONIX model zoo with 2,000 new models. That nearly 10X is the number of models and that's publicly available today. This whole infrastructure that I spoke about has been open-sourced under the ONIX umbrella and that repository is also publicly available today. Really appreciate anybody here taking a look and while you guys are there, give us a star as well. So that was about turn KML and moving on about talking about AMD's investments in open-source. AMD is really committed to open-source. We are contributors to major open-source repositories like Huggingface, PyTorch and ONIX. And we see ONIX as a very key piece of the puzzle in terms of improving developer acceleration and developer experience. So at AMD, we have different execution providers for all of the accelerators that support AI. We use the MLAAS EP on the default CPU for x86 CPUs and we have AMD Rock-M and McGrath X for the AMD GPUs and we also have a separate EP for the Ryzen AI and NPU from AMD. So all of these ONIX models that you have that we saw can be accelerated on these accelerators directly through these ONIX runtime execution providers. These execution providers are a part of a rich ecosystem of EPs that ONIX provides. And here you see that this comes from almost all of the vendors in the AI space. All of them contribute execution providers to ONIX runtime. And this also applies across different device classes like CPUs, GPUs, even mobile and edge all the way to the cloud. That's all I had. I'll hand it over to Kishen to continue on the developer flow. So to move on to the developer flow, what does ONIX do for a developer? How can a developer take advantage of ONIX on their applications? Just to revisit what we talked about before, this at a high level is the core principles of ONIX. It is interoperable. As you can see, it can move between any two supported frameworks that it has. And it's also portable. That means you can generate an ONIX file and run it on any required device, whether it's CPU, GPU, FPGA, or an NPU, right? So one thing that ONIX probably does is given that it's interoperable, let's say as a developer you are planning to use some of the latest models for your application. What is ONIX is able to do is it gives you quick access to some of the latest research models that has come out, right? So it acts as a bridge so that you can take any model deployed, developed in any framework and deploy it in your required framework, right? So essentially ONIX removes the barriers across different frameworks and it accelerates the production from model training to model deployment, right? And what this does is it speeds up the innovation across the AI community and will be able to deliver more models quickly to the customers and using that we can develop much more optimized models. Another thing that ONIX has is the ONIX model zoo that Krishna talked about. Let's say if you wanna use some of the most prevalent machine learning models that have already been developed and well studied for your application, you don't have to do any additional work. There are a lot of models that are present in ONIX model zoo which you can just take and plug in into your application as is and this reduces the time for your application development and depending on your use cases, the models that are trained for general purpose, there are also models that are fine tuned for different applications that you can take and plug in. So another portability principle that ONIX goes on is it's able to deploy across different devices irrespective of whether it's CPU, GPU, or NPU using the same model file. So as a developer, you don't need to worry about where your application is gonna be deployed on, whether it's running on CPU or NPU. So this allows the developer to kind of work on application and make it quickly accessible to the customer without having to consider the environment restrictions for the application. So apart from this, ONIX also gives you easy access to a lot of optimization tools which can lead to better model performance and some of these optimization tools would be like quantization or sparsity and these are something that can be applied on a lot of industry-wide prevalent models that's already available in either the ONIX model zoo or the converted ONIX models. So you can just take those converted models and run these optimization tools on top of them to get better performance for your models. So moving on, if we look at the deployment scenarios for ONIX, and ONIX is very versatile. We talked about it being interoperable as well as portable. So because of how flexible it is, you have deployment scenarios for ONIX on both the cloud as well as the edge scenarios. Each deployment comes with their own challenges where there are particular kind of conditions that you look at when you're deploying on cloud, versus when you're deploying on edge. So Intel's commented to both scenarios. We want to accelerate ONIX wherever it's being deployed. And if you look at on the cloud side, when you're looking at cloud deployment, there are a few conditions that you focus more on. This would be something like the throughput and latency. So in a typical use case, this is what your deployment is gonna look like.  You can either use that directly to deploy your model across different applications on your required hardware device, or you could optimize it further. You could do some kind of a model compression to get like an intake model and get better performance and use that in your deployment. Now factors influencing deployment. We talked about throughput and latency. So why are they important? On the cloud scenarios, you have a large number of incoming requests that your model has to handle and it has to handle in a very efficient manner as well as make sure that it's not taking too much time. So that's where the throughput constraint comes in. You wanna have your model deployed in such a way that it's able to get as much throughput as possible so that you're able to answer more queries in a given span of time. And that gives you better TCO savings as well. And we also need to look at latency because in some real world or real time applications, you also want your application to be responsive. You can't have your user weight too much for the given input to be, for them to get an output for the given input. So these are a few constraints that you kind of look at when you're looking on the cloud scenario. So what Intel helps ONIX do is we've optimized ONIX runtime to kind of be flexible to deploy wherever the requirements are. We have developed some accelerated libraries, provided support for some of the latest hardware instructions and made sure that whenever you're running something, you get the best performance on Intel hardware. So as mentioned earlier, ONIX has access to a bunch of tools for model compression. They're tools like Intel Neural Compressor that you can use to get better performance from models. And you have better cost saving because you have reduced requirements, resource requirements, when you're doing certain optimization, right? Some of these model compressions are techniques like quantization and sparsity. Quantization is a technique where you kind of lower the precision in which your model is being executed. So you can have an FP32 compute be reduced to an intake compute. And what this also does is it helps the model take advantage of different hardware accelerators that are present across diverse devices, right? And sparsity is another technique where you randomly zero out weights across the model. And this helps reduce the memory pressure that some of these models put on the devices, right? There's a few other optimizations as well, something called structured sparsity where you kind of, instead of randomly zeroing out weights, you do a targeted pattern in which you zero out weights. And that also helps you take advantage of some of the hardware accelerators that are present on the devices. Now, another thing that INC does, or the Intel Neural Compressor does, is it makes sure that whatever models that we've optimized, we've quantized, or we sparsified are readily available in the ONIX model zoo. INC has made an active commitment to make available as many quantized performance models possible in the ONIX model zoo so that any developer can readily take them and use them in their applications and see the performance benefit straight out of box without having to do any additional fine tuning on their end. So what does Intel do for ONIX ecosystem on the cloud side? We have worked extensively with ONIX runtime to make sure that it takes advantage of any of the latest Intel instructions that have come out, right? We've optimized the ONIX runtime to run matrix multiplication 4x faster on Gen 4 compared to the previous generations. And this was done through something called Intel Advanced Matrix Extension or AMX. AMX is essentially a set of instructions and registers which are designed particularly to accelerate matrix multiplication on the latest Intel Xeon servers. We also have a few additional hardware-optimized libraries such as OpenVINO and 1DNN. And these hardware-optimized libraries allow users to use the models that they developed or the models that they converted across various Intel devices and still be able to obtain as high performance as possible without having to do too much fine tuning or tweaking for the model. Now, if you look at the cloud side, the set of problems you kind of face on the cloud is gonna be completely different from what you see on the server side, right? On the server side, we saw that throughput and latency was something that we looked at when we are deploying. On the cloud side, the things that you consider are gonna be slightly different. We are looking at the responsiveness of the application, the memory footprint, the battery life, and the disk size. Responsiveness is pretty straightforward. As users, you want your application to be quick. You want whatever inputs you give to be quickly generated outputs, right? So you don't wanna wait whenever you press something or you give an input. Now, memory footprint. In cases of PCs, a lot of times, you're running multiple applications in parallel, right? You have limited memory across all of these applications. You can't have one application taking up majority of the memory and slowing down the other applications. So you need to make sure that whatever applications you're working on or building are optimal in their memory usage, right? Battery life, again. For some of these edge devices, which are portable like PCs or tablets, you wanna make sure that you don't have too much power demand when running or deploying these models, right? So what Onyx does is, Onyx is able to use certain hardware acceleration features through Onyx runtime. And some of these are like the DirectML from Microsoft. Basically what DirectML does is it uses DirectX 12 to run some of the kernels on GPU so that you're able to get better performance and you don't have to wait too long or you don't have to worry about your CPU taking too much time to run or make an inference on the model, right? So this is how it looks at a high level, right? Your app calls into Onyx runtime. Onyx runtime calls into DirectML. Onyx runtime can either go directly to the CPU or to the GPU, right? So that's something that the application can use at runtime, depending on the user needs, whether you wanna use CPU for lower power or whether you wanna use something like GPU so that you can get higher throughput. So over here, given that we don't want our models or our applications to put too much pressure on the resources that are available on the PC, we can also take advantage of Intel Neural Compressor as a model compression technique to reduce the model size. The reason for this is compared to an FP32 model, Intel model is gonna use lesser memory resources and other resources in like a resource constraint edge device, right? So you get better user experience because of these reduced pressure on the device resources. And that's a link of the Intel Neural Compressor. If you wanna check it out, please do. They do some amazing optimizations, sparsity, distillation, quantization and a lot of other fun stuff. On the cloud side, what does Intel do on the cloud side? Again, Intel is extremely committed to making sure that Onyx deployment on cloud is optimal. Why do we do this? Because we've been seeing an increasing trend where we want the inference to be done on the cloud, right? Now, the advantages of doing this is when you're doing inference on cloud, you can circumvent the overhead of sending either your data or any of your inputs to the cloud, right? So by doing your inference on the edge, instead of doing it on the cloud, you're able to avoid the memory transfer time, right? Another advantage is you don't have to be constantly connected to the network in order to make your application work, right? So if you can run your inference on the edge, it's more responsive. You don't have to depend on your memory, sorry, your network bandwidth and other things, right? So to that extent, we at Intel have made sure that the Onyx runtime has been optimized to make use of Intel DL boost so that you can run any of these intate models or the compressed models more optimally, right? We take advantage of VNNI and other such fun accelerators, right? We also have tuned Onyx runtime to be good on hybrid core architecture. Hybrid core architecture is an interesting concept, where Intel came up with a heterogeneous processor design where you have a performant core as well as an efficient core. So performant core, as the name suggests, is good for performance, but it's not so great for, it takes more power than an efficient core. An efficient core is less in performance, but it's optimal when you have to run some applications which are not of higher priority. You can just put them in background, run it on low power and save your battery life as well. So Intel has optimized Onyx runtime to take advantage of this hybrid core architecture as well so that whenever you deploy the workload, there's a balance between how you run it on performant core and efficient core so that you're not using too much power, but also you're making the application be responsive and be quick to your user experience. On the GPU side, Intel has fine-tuned the DirectML so that you get higher throughput when you're running your models through DirectML. And the interesting part, our latest work actually, is on the Intel NPU, the neural processing unit. So in the upcoming Intel Core Ultra, we are able to run DirectML through NPU. So what this does is NPU is a high-performance, low-power accelerator. So you could essentially run some of your models using lesser power than what you would have potentially done if you're running it on GPU, but at the same time see the performance benefits that you can get through an Intel NPU. So as a summary, Onyx is very flexible. It's something that is an open-source format. It's something the community has a great interest in. There's a lot of incoming operators, a lot of optimizations. As you've seen, Krishna talked about a lot of new models that have been pushed into Onyx Model Zoo, which makes it easier for developers to just pick and plug into their application and just see the performance benefits. It's something that's still growing. It's always growing. There are always new optimizations and new things that come in. There are new work that's been done every day by different SIGs. And yeah, do check out Onyx. It's very interesting. There's lots of documentation. There are lots of material out there available. And if you have any questions, anything you wanna talk about, anything you wanna follow up on, feel free to reach out to me or Krishna. We'd be happy to check. Awesome, thank you everyone.


Welcome to Toffy TV and it's the match preview Everton vs Tottenham Hotspur. I'm joined by Mike. We are in the middle of just chaotic transfer deadline day, how we found any time whatsoever and of course you know when we finished this by the end of the day who knows what our squad will look like but obviously Tottenham Hotspur never an easy game for Everton. Yeah, we tend to struggle with this one. Yeah, our past performances against the man great are they. It's going to be another difficult game and at the minute with the squad being as stretched as it is I think most games are aren't it at the moment. Good news with Ghana coming back from the African couple nations so yeah I think it's just going to be another tough game. It's going to look to keep the shape that he's been doing, keep us nice and solid and see if we can pick him off on the break I think would be the idea and the plan. Yeah well we played just over a month ago, you know we think 23rd of December. It was one of those defeats that actually made you feel alright. You know we did really well down there, we went too near down quite early in the game and we went after them obviously on to Gomez, come on and he got a goal and we were really unlucky down due with it in the barn the last minute. Again goal line technology just showing us how far away we were from getting a point and that gave us a little bit of a spring in our step. Since then it's not been as easy obviously playing Manchester City, Wolves was a real low point. Obviously we've beaten Palace but then obviously gone out the cup. But we have had a couple of draws in the Premier League and I think again on Saturday that's going to be news mentioning it there. The backbone is trying to get a clean sheet isn't it? The backbone of the side, the defence trying to get a clean sheet. Cos I'd call it like a bit of a holding pattern at the moment where we have got issues, we know that. You know obviously again Fulham nil nil on Tuesday look at the team, I can't attend to my field. We're in that position where the manager will look at it and he'll just be trying to grind out the clean sheet first and foremost and if we can make it then great. But against tough teams with a squad that's really, really decimated you are just trying to get the basics out aren't you this time just to get us through this period. Yeah I think you've done that on here. I think we're in a situation whereby we've got to look to just like you say keep 10 points taken over. We've had two clean sheets in the Premier League on this month now in January and that'll be the bedrock of it. The irony is for home games I fancies more at home against a better opposition and to do against a team who would try and look to frustrate us because we've got nothing to break them down with. So it sounds strange but I feel a little tiny a little bit more optimistic about this just with off the back of the game away at the, I was going to say White Heart Lane, the Tottenham Hotspur stadium because obviously they play a really, really high line don't they? It might give us a few opportunities in behind. So look yeah we'll set up, we'll look to frustrate them and hopefully we can hit them on a break, we can win some corners, free kicks and that sort of build a bit of pressure from there. So but look it'll be a tough game there, they're banging for them at the moment as well aren't you? Yeah I mean listen my worry for this game is that Spares give you chances but we don't take chances.  I mean Bremford had a few opportunities, sloppy play by Spares leading to opportunities but we're in a run of like not being able to take chances. Dominic Carvelin obviously really struggling better, missing sitters, midfield not really providing much at the moment. Obviously Jack Allison scored last week against Luton but the keeper should be doing well better. So you know you'd be looking at this thinking we'll get opportunities here. If we do get opportunities obviously we have to take them and that's what we haven't been doing. Manager will talk about exiles, about paying your back and all that kind of thing. It's going to have to pay us back if we want something in this game because Spares as you said they do play that high line, they do play with a bravery about them. Also it's not a counter-attack on football, you know I was looking at the possession, 68% possession here against Bremford and a lot of the time it looked like they were trying to play a counter-attack but they do keep the ball and if we get chances we've got to take them because I think they will and they've got the likes of Madison back now, obviously Richard Allison's on fire, they obviously haven't got Sun yet but we have got to, I know that's a stupid thing to say isn't it but we have got to take some chances. The good thing is and this is where my optimism comes a little bit more for this game than like I say than if we were playing a Luton for instance is that they will give you opportunities and obviously we know we need a lot of them to take any. And we might get a few more than we would against other teams so from that perspective I feel like we could get their goal and then we're relying on what we have been all season. We're relying on the back four to be nice and solid and for Pickford to have a good day and realistically that's what we're going to need because player for player they've got a better team than us at the moment. Dice is working with a paper thin squad so yeah look we've just got to hope that we can make it difficult for them and like you say when them chances come is that we look to take one or two with them. You know the positive was down at the ground you know after super but after what 10-15 minutes where we went 2-0 down and possibly could have been 4-0 down. After that I thought we looked a better team and I thought we looked to be in control and they looked like frustrated and they sort of ran out of ideas so if we can be solid and not give anything up stupid I don't know so obviously Madison come back he's the type that can unlock those situations but. He is he is makes them tick and was doing brilliantly before the injured. Let's just have a little look at the space team that beat Brentford. So as you can see there obviously Culesescu on one side Madison, Seymol Verna, with Charlottes and Ben Sirkerys, I think he's a great player in the midfield and van de Ven has been a brilliant sign for them. Vicario and goals being a great sign for them. They do get hide, the fullbacks do get hide, they push you right to the bar and that's how we basically lost both goals. Certainly the second one wasn't the first one was more of a mistake from a set piece but we're going to have to be concerned with that and I understand what you're saying. We are better when we don't have the ball so when you're playing better teams you do have the ball and you're waiting for that moment. The problem with us is it's such a lack of pace on the break as well, obviously it costs us and we are struggling at the moment with players. The manager obviously experimented last week by having Dom and Beto, it didn't really work because they're occupying the same spaces. And Dom I think, I think Dom was a lot better on Tuesday night against Fulham, he won a lot more of the aerial duels, he won 10 out of 14, that brought other people into play. I think what was a positive from the other night was Jack Harrison playing behind him, that's what the manager wants, that probably means Ashley Young is going to play right midfield. Obviously we don't know whether he'll continue with Ben Goffrey at right back, he clearly does not trust Nathan Patterson, certainly defensively and that was the job he was trying to stop them obviously on the left hand side. William has always had joy every time he's played Patterson, Robinson with his pace and maybe you'll look to do the same thing in the next couple of games with Spades and then Manchester City playing. Both of them like to get the full backs over, whatever Manchester City call their full backs. So we might continue with that and Ashley Young, I have no issue with Ashley Young playing right midfield, I think he offers such hard work that he fits the profile and that allows Jack Harrison and I do think that is the manager's back-up plan to having no decor. Because he can go left and right, he can use either foot, that's the difference, Dwight McNeill, we saw him in the centre of my field but that's the difference, I think Dwight McNeill can't play in that position because you know which way he's going to go every single time. I think for me that would be what we'll be looking to do because obviously the corey's not going to be back. I guess a guy, as you mentioned, should be back, he retains Friday and should be fit enough to slot straight in and he's professional to slot straight. Oh no, I can't see him. No, make me get a spot on the bench, mightly. Maybe, but I think that's what we'll go with and I think having a disagree on a game back just gives us that little bit of experience, knowing when to press, not when to press. And Garn, I think, shows you the night that without being flashy and doing unbelievable things, he can get round the pitch and he can put fires out and he gears mobile off to get all round the pitch as well. Yeah, no, I'd like to see that. I'd like to see Ashley Young, James Garner, Jesse Garner, McNeill on the left, Harrison behind, Calvert Loom, but I think could be something to look into obviously as the game goes on. I think Beto could actually benefit playing against a team like this more than because you'll have that space of it to run in behind and as we've seen against Newcastle, when you create that little bit of space, he's got that power of running.  So almost if Calvert Loom can occupy them. So would you start Beto over Dom? No, no, I don't care. No, no, what I'm saying is I'd have Dom start and for the last 20 minutes I'd have Beto. No, I fully get what you're saying. Let's just have a look at the team, obviously, that started just to be fresh anyone's mind against Fulham. Obviously Dan Duma looks like he did pick up an injury, so I think it'll be Dwight McNeill back onto the left hand side. So there won't be too many changes. It will be interesting what we do with Ben Goffy, whether we keep Ben Goffy. I think what we showed you the night, and I think it's pace, I think if the manager's got, the manager I think wants Ashley Young playing right midfield and I think with the other injuries we've got. If he thinks Ben Goffy, Ben Goffy, it's like you give him a job to do and that's it. You let him get on with it kind of thing. And then what we've seen the other night, he was given a job to do and in tandem with Ashley Young, the nullified, the left hand side. I think that could be again the job he does. Teemo Wainer is pacey, very, very pacey. And Patterson doesn't always, he doesn't judge, he doesn't, his timing's typically off, he doesn't always cover the space well. And I think he might be thinking well Ben Goffy at least has got that recovery pace. So I could see him going for it again. The only change I'd like to see there would be a dress of Garnagay coming in for Dan Duhmer and putting Neil back on the left. That's what I would go with. I think going back to what you said about Beto, I think the only problem with Beto is that he's raw. Incredibly raw. He's a player you can tell, a started football late. He doesn't have the fundamentals in his game and you can see that. That sometimes is a good thing by the way. But I don't think we know how to use him. He knows what he wants to do. And you're right, the slip in the ball round, the back, and I think we did try against Luton, but he got caught up too many times jostling with the centre back. Maybe he hasn't got that base couple of yards of pace that get you away from the centre back. Sometimes I think he's running with the fridge on his back. It's like he can't get going on the cartoon with the old ones of the T-Shain, he's like giving it all that. It's the first couple of yards, but I think if we can get him into better areas by releasing him earlier, but it is those fundamentals for me that I think you learn when you, if you get into football younger. As I said, that's not always the deal. And then though kids who come in and don't get those fundamentals sometimes, you can play off the cuff a little bit. You can play off the cuff when we see that. We're seeing that a lot more. Excuse me. But you can see that he comes to football a little bit late. So yeah, I agree with you there. I think if you can stay in the game, you know, and Carvelin can occupy them, get the defence, obviously a little bit tired there. Just sort of a bit like the Newcastle situation where better then comes on and they're still pushing to try and win the game. The spacing behind for them to run into where he's not necessarily like you say trying to pull away from the defender, but he's starting from a position of space already to run into. He's got that running power then once he gets going. He's fast, if you know what I mean. But yeah, it's the first couple of yards, but the first couple of yards are normally the most important because they get you away from the player. If you, once you pick up the ball, you can't get away from the player because obviously they're running without it. Unless you drive mechnir, you can run faster with the ball than you can without the ball, oddly, which is just weird. It's just science. It is somewhat logistics as well. Let's have a little look at the stat pack for the game on Saturday. This Saturday, 12.30pm, kick-off at Goodersham Park. Everton take on Tottenham Hotspur in the Premier League. Everton have played Tottenham a total of 63 times in the Premier League, but only a total of 9 wins go into the Toffees, 24 draws and 30 wins to Tottenham. Everton haven't beaten Tottenham in the Premier League since the 13th of September 2020, a 1-0 win at the Tottenham Hotspur Stadium. Over five this season, Everton have won eight games, drawn four and lost ten. Spurs have won 13, drawn four and lost five. Everton set 18th in the league after a 10 point introduction, while Spurs sit fourth. Everton's biggest win this season was a win over Newcastle 3-0. Tottenham's biggest win this season was a way at Burnley 5-2. Tottenham's top scorer this season is Huming Son, although we won't be playing this Saturday as he's at the Asian Cup. The former Everton player of Charleston is currently on eight goals. Everton's top scorer is still Abdelai Decorate, who is on six goals. Pedro Porro tops the assist chart with seven assists, followed by James Madison with five assists this season. Jordan Pickford is currently the goalkeeper with the most clean sheets in the Premier League with eight clean sheets. Tottenham have five clean sheets this season. There you go. In the stat pack there with Charleston doing really well. Let's have a look at his numbers. Nineteen games, eight goals, XG 6.92, three assists and an XA of 1.06. As you can see his heat map there predominantly on the left hand side but all over the pitch. This is a player that I think for us obviously as Evertonians we've secretly hoped he failed at Spare so we could get him back. But I also think that some people are a bit like that now going see if he told you. We told you he was a good player. We were the Evertonians aren't we? Some of our ex-players we want them to do well to prove that they were good at our club. I think Rich Charleston is one of them for all the grief that people around Jim Carragher gave him while he was at Everton. I think a few of us are quite happy he's doing well because he proved that he is a good player and he did do well for us. He's putting the ball in the back of the net and of course he scored against us at Tottenham hot players. But we don't want him to do well on Saturday because we're seeing that he is a good player and he does work hard. I also think that when he went to Spares I think he was injured and I think he's played for a year and a half with an injury. But he didn't want to stop because that's the kind of person he is. He went to the World Cup. He's the kind of fella that just wants to play, wants to run through big walls for you. And that injury getting that operation that he got I think now you're seeing the real Rich Charleston that we had play without any issues. And he's obviously got out of the shadow of Harry Kane and he's scoring goals and he's given them exactly what he wants. And we're going to have to work hard to stop him because he picks up the score, seeing the goal he scored against Brimford. Keeper saves it and he's there and he puts it in the back of the net and we're used to that. He's good in the air as well and he's going to be difficult to stop. It's just frustrating isn't it? It's frustrating. He's one of the few players in recent history where the fans have had a real connection with us. So it's a difficult one but you hope that obviously on Saturday he doesn't have his best game. And then when we're clapping him at the end they're clapping him because we've beaten them. But the interesting battle between him and Tarkovsky because they've had a lot of history over the years. Even when Tarkovsky was obviously a bit in there as well and they seem to always have a few run-ins and stuff. So look he's going to be obviously the man in form for them. He's going to be the one to watch. But again defensively we're looking solid but we need to just stick to our principles with that and make sure we keep them quiet as possible. Stopping the source isn't it? Yeah that's it isn't it? That's where we suffer from down there and that's maybe why Ben Godfrey might stay in it right back to prevent that. Michael Encoe is doing really well on the other side and it will be hard work. We'll be trying to stop them with the pace they've got in Madison drops into those areas. Makes it difficult and that's why I haven't gone a gaze. Good coming back because he's always one of his biggest strengths. He's always knowing just where to be at the right time. People can talk about his passing or giving it away, whatever they want. It's always that just being there in the right place and of course as he's got older that's become more difficult but he's still a really important play for the squad. Even more so when you don't have any midfielders. Obviously Jordan Pickford who's doing brilliantly that save the other night was unbelievable wasn't he? And we'll need him at his best again. Obviously didn't play a lot in January because it didn't feel like he played a lot. Played a couple of Premier League games but that save again on Tuesday night illustrated how important he is to us and why he's number one. What I would say is prior to January and obviously getting left out for the Cup games I think he needed it. I would say for the best part of two years he's been absolutely excellent. Probably haven't won. I'll give you evidence best player. But I saw the last few weeks before January at the end of December and he started to show tendencies to what he'd done the couple of years before. He looked a little bit erratic. I actually thought the manager had done well taking him out of the team and he looks refreshed again. He's got his head back in the game. And like you said I thought he was excellent the other night. But we all know how valuable and how important he is to this team. For evidence to get out of the position we need Jordan Pickford far on and on. It's not wrong with a bit of competition isn't it? You know what I mean? Jalfa James has come in. He's had three really good games and a stake decline. Maybe not to be the number one but to just keep Pickford on his toes and to say I'm here if you do slip up. And the manager himself is saying well normally I would never rest here but now I'm prepared to rest here. So you better stay on it and I think that's huge. I think for him it's all about mental exhaustion for him.  So I just thought like taking him out was perfect and if we can see the best of him from the running between now and the end of the season he'll be a massive player. December was a hugely mentally draining time for the players and the fans. So many games coming off the back of the 10 point deduction and you're on it every three days. You know how bad it is having to prepare yourself as a fan. But having to prepare yourself as a player and take on as soon as the games finish you're like right well I've got to need to get on it for the next game and I need to get myself up to speed on who's playing, who does what. And it must be hard. I know Janu has been a little bit lighter but it is difficult.  It's always physical fatigue but mental fatigue as a footballer. In that position as well. Yeah and certainly for someone who's illustrated in the past that he didn't always deal with it. He's been brilliant. He's worked on it. He's been brilliant, worked on it. And yeah just so and again you know most clean sheets we need to keep that going and make sure. Cos I think the most important thing is we stopped the rot in terms of the Premier League. Okay it hasn't been pretty two nil nil draws but we stopped the rot if we could get something out of this game. You know ahead of the Manchester City game we're mentally you know what are you thinking? We're not winning that. Only yet he had with the Manchester City team that is fully clicked with Harland and the Bronwyn. Beautiful flown locks. So you're mentally thinking we've already lost that you know. We all will. So if you can make sure that's a one off rod and oh no we've lost X amount of games in a row. So yeah big games that. I think we can get something out of it. I do yeah. I think we can get something out of it. Get in there and just make sure we get a clean sheet and listen Spares never done well in Spares recently. Got a Michael Keane turned up last year. We were down to 10 men. Michael Keane smashes one in. Still the club's best finisher. He smashes one in and you know it got us a really important point. So let's do that again on Saturday. Let us know your thoughts in the comments on this one. Are you confident? Are you worried? Are you worried about Charleston? Or just all that other good players? Let us know your thoughts in the comments. Make sure to give this video a like. Subscribe if you haven't already. And join us over on Toffy TV Premier where we do daily live exclusives. Also no adverts on any of the videos or the podcasts. So if you get annoyed by all the adverts that pop up on YouTube and on your podcast feed. There's none over there so that's well worth it. Links in the description, QR codes coming up on screen now. See you later.