My name is Jose Navarro, and this is Briana Galiz, and we both are the machine learning platform team at Cookpad. Today, we are going to talk to you about how NVIDIA Triton inference server can help you optimize three areas of your inference engine. Performance, user experience for your machine learning teams, and cost. But first, let me introduce you to Cookpad a little bit for context. Cookpad is the largest online community for home cooking lovers. We are making everyday cooking fun. But why? Well, the act of eating has a major impact in everyone's physical and mental health. But also, the choices we make when we cook has also a big impact on our planet. And with those two in minds, we believe that there is a big difference between creators and consumers. When you are creating or you are cooking, suddenly your awareness starts to grow. You start to care about where your ingredients come from, or how the taste changes if those ingredients are in season or they are produced in a more controlled environment. And when people start caring, they tend to make better decisions that impact not only their health, but also our environment. We are an online community, a global community that is available in more than 70 countries and support more than 30 languages, which is important to understand some of the challenges that we have as an ML platform team. We have more than 100 million users monthly, and you can find more than 6 million recipes shared globally. In the app, you can browse recipes from ingredients that are in season to get inspired, or you can follow authors like Craig, one of my favorites, who uploads amazing recipes. But you could also search by ingredients, dish, or cooking process, et cetera. Machine learning, it's more available than ever. In the past few years, with the adoption of ML practices and tools, we have removed most of the pain points to deploy ML in production. And that means that more ML teams have moved from working in isolation, creating proof of concept towards being deployed and distributed in product teams, delivery teams, or feature teams, whatever the name your companies give these types of teams. As a result, more ML has moved now from running heavy batch jobs in the background towards running more and more online inference. And with more ML complex models available, that means that the infrastructure requirement to run online inference in GPU comes along, because we need those inference to run smoothly and quick for good user experience. Also, as a machine learning platform team, you probably don't want to lock your ML teams to use a specific ML framework to simplify your inference server. So multiple ML framework support is a requirement, while keeping the user experience for ML engineers easy for them to deploy new models in production. In the next few minutes, I'm going to tell you how Triton inference server can help you improve the performance while keeping the user experience simple for the ML engineers and also reduce the cost. And I know what you're probably thinking, he's going to start talking about cost and you care about performance. But I promise you that this is going to be also a key element to performance. In my personal experience, and also talking to other ML practitioners, running ML in GPUs is not particularly difficult. You add the right nodes to your auto scaling groups. You add the right tolerations to your deployments, and then request a GPU, and you got it. You've got your application running in a GPU. The challenge is that you have to balance the user value that you are adding by deploying a new feature with the business value that you get in return with the cost, which could be quite high. And why is that? In this simplified example, let's say that I want to deploy a model application into a CPU node. I deploy my application. I request an amount of resources. And if I am done my homework correctly, my application will utilize a good portion of that, leading into a well-utilized and healthy cluster. And what happens if I want to deploy a new model? Well, since CPU and memory are resources in which you can request a portion of, your new model will share the same node than your previous one. So as a result, you have added new value to your users by deploying a new feature. You have probably increased some business metrics, and the infrastructure cost has maintained stable. Happy days. But what happens if your model requires GPU for inference? As we have listened today before, GPUs resources, you cannot request a fractional amount of them. So if your model requires GPU, you have to request the full GPU for it. And that means low utilization. But OK, let's say that the feature is worth it.  What happens the next time you want to deploy a new model? Exactly. You have to request another GPU. And remember when I said before that we are a global community in more than 70 countries and supporting more than 30 languages? No matter how simple the feature we want to deploy is, there is very little chance that we can train a model that will perform well in all of the regions or all the languages. So that means that for every feature that we want to deploy, we end up having three, four, five models for it to cover for the most popular regions or languages. A decent GPU card in our cloud provider is around $3 an hour, which will take you to over $2,000 a month. And if you have to deploy four or five every time you want to deploy a new feature, we are talking about considerable money. And if you are thinking that maybe we could scale the cost by using multiple GPUs environments, the cost of a node with four GPUs is exactly or very similar to four times a single GPU. So you can even scale the cost that way. However, if you deploy Triton inference server on a GPU, you can concurrently host multiple models in the same GPU. And if you deploy it in an environment with multiple GPUs, Triton will replicate those models in each GPU so that it can balance the inference compute in each of them to maximize utilization. You're probably wondering what this means. If it's a GPU on a walk in the countryside or you see GPU deploying Windows XP, well, what I'm trying to say here is that Triton has enabled you a happy path to deploy models in GPU in a cost-effective way. And that is the first performance gain that you can get with Triton. Every time you deploy your models in CPU because GPU is too expensive, you could migrate them to GPU. But Triton also comes with a few other options for optimization. If your model allows batching, you could easily configure dynamic batching for your model so that Triton accumulates a number of individual requests and then build a larger batch that will compute more efficiently than doing it individually. Dynamic batching can also be configured by you could select a maximum amount of batch. You could add a maximum delay so that Triton will run the inference of the batch as it is if it reaches that delay. But more like you could preserve the order so that Triton responds in the same order that the requests arrived. And moving on from dynamic batching, another interesting optimization is model instances. You can easily configure Triton to replicate an amount of times each model in a given GPU. That allows Triton to overlap the transportation of data from and to CPU and GPU and overlap with the inference compute. Finally, you could also combine the both into your model so you could configure dynamic batching and model instances. Before, I also said that Triton will help you to improve the user experience. In the worst case scenario, very simplified, our ML engineers, if they want to deploy a new model, they will have to create several Kubernetes resources for it, a deployment, a service, a config map, service account to give some permissions, HPA to make sure that we scale dynamically under certain circumstances and a pod disruption budget to make sure that we always run a minimal amount of replicas. But allow me to repeat myself when I said that we have a challenge with the amount of languages and regions that we deploy, that we support, so that for every feature, we end up replicating those resources. Well, it's not that bad, really, because using an open source tool called Customize, we are able to template the base of the application and then deploying new models will result in just patching a config map, et cetera. But that doesn't resolve all the issues because if you also need to modify the resource allocation, depending on the region, or more and more, you end up patching more and more files, and it's a bit complex. Now, with Triton, our machine learning engineers don't have to worry about what ML framework they used to train their model because Triton supports all the ML backends that we desire, like PyTorch or TensorFlow, TensorFlow RTE, ONX. They only need to package the model following a layout so that Triton understands what type of backend it needs to use, and that process is documented in Triton. Also, once the model is available in the given packet, they only need to create a PR to modify the right config file, and our cluster automation will do the rest. Is this working? Here we are. So to summarize, big thumbs up for NVIDIA for creating Triton inference error, who has enabled us to improve the performance of our models, sharing resources in GPU, and improving our GPU utilization while keeping the user experience easy for our machine learning engineers. And with that, I'll leave you with Pry who is going to demo some of these features. Thank you very much. Thank you. Thank you. This will not be much of a demo, more of showing you how we deploy our models on GPU and how it performs versus our previous deployment, which is on CPU. We will be using Apache Bench for a simple benchmarking for this section. We'll first show you how easy it is to deploy a model on Triton. And even before that, a little bit of context for our session today. We are going to measure a PyTorch-based image transformation model. So it accepts an image data, and then it returns that image representation in embedding space. So the input will be a 244 by 244 by 3 multidimensional array, and the output will be 300 floating point values. And for those of you who've deployed a machine learning model before, in some cases, GPU is not even necessary for real-time inferencing. But the one that we are going to show you is the kind of model that get the most benefit when deployed on a GPU. And this is actually one of the models that we actively use at Cookpad. So the setup is that for the CPU best deployment, it will be a simple Python service. It will load the model using PyTorch. We are going to deploy it on a compute optimized EC2 instance. So we got a BFIR CPU because model deployment is mostly a CPU-bound task. And for the GPU-based one, we will drop the model in Triton. But as you can see, we are adding a front-end Python service in front of it just to make it easier for Apache Bench to hammer it. This, though, adds another network overhead between Apache Bench and Triton. But you can see next that it's not significant, and also, in real world deployment, you usually need a place to pre- and post-processing the input and output of your model. And in fact, this is what we end up deploying all our services at Cookpad. You're probably familiar with how to load a model and deploy it using Python. So we're moving next to how to deploy it on Triton. And a few slides ago, Jose showed you this. In Cookpad, our teams have to provide their own manifest when they want to deploy a service. And there are some tools available to abstract that for you, but we're not currently using that. The reason for that is a whole new story. And for Triton, because we as a platform team provide and manage the deployment, we've taken those manifest out of the equation, and then what's left for every team want to deploy a model is that they just need to package the models, put it somewhere accessible. In this case, it's our private S3 packet, and put that S3 URI to that package in our shared config. And one thing to note is that we need to package each model with a structure that Triton can work with. And it looks like this. So it's very simple. Those who've worked with TensorFlow surfing probably familiar with the structure of TensorFlow. This is what came out if you do a safe model using TensorFlow. At the root is the name of the model you want to expose it as. And the next is the version number of the model. And then the model file itself. You can put any model you train with multiple kinds of platform here. And this is the minimum that you need. Excluding that config.pbtxt, that's optional. Triton will try to make sense of your model and then generate that file for you. But it's there if you want to make an explicit configuration of your model. For example, like this. So as mentioned before, you can deploy a model from multiple platform, multiple framework. Triton supports TensorFlow, PyTorch, Scalar model. And one of the models we use is that TensorFlow RT, which is an optimized compile version of a model. So you feed to provide this at the model level and Triton will load it. After you specify the platform, you specify the input and output signature. And this is also where you're going to put your model's inference tuning config, which we'll see later in the presentation. OK, so compress the whole directory and then upload it to S3. And what they need to do next is just add this line, specifying where the model is, and boost the changes. And then let your CI CD process that file, which is actually, in our case, it's a repository full of Kubernetes manifest. We use flux in our case to synchronize those and wait for it to roll out. This is usually when I go to procrastinate and browse stuff on the internet, but you don't want to see me doing that today. Now we're going to see how they perform. So a simple test at the top is the CPU deployment, and at the bottom is GPU deployment. It takes quite a bit of while, so let's just skip. So this is the baseline number. We start with no concurrent requests at all. This is single-threaded. These numbers are as fast as we can get out of these services. It's already fun looking at this when we first deploy Triton. We'll try next. And remember, this is including the network overhead between our front-end service and Triton. Now let's try with two concurrent requests. It'll take a little bit longer than the previous one, so we'll skip again. And this is a result. Not much changes from Triton, which is good. But as you can see, the one running on CPU already doubles the latency. So then a little bit more work. Now we are going to look at how the resource utilization with these two services. So we're going to leave the single-threaded benchmark running for a bit longer in the background. And this is what we get. Let's practice down. At the top, you see the CPU first GPU and memory usage. As you can see, on the CPU, it uses one, which is 100% of a single CPU available for that deployment. We don't limit the CPU resource for this one, so that's the most they can use. The reason being, if you do an inference on CPU, that's they only use one CPU at all at a time. And for GPU, we are only using 50% of a single GPU. So it's a little bit of room there. On the memory, we see the reverse of that. So the CPU only uses around 200 megabyte, but Triton uses almost twice that number. But that's FI RAM, FI memory, and not RAM on the server itself. So it's not Apple to Apple, but you get a sense of how you should plan your capacity with this. And the fun part is, at the bottom, we get almost 10 times throughput and then 10 times faster latency out of GPU. So this is fun. Double the resource from memory, half the processing time. Sorry, processing capacity. But you get 10 times the throughput. Also, of course, it's not all magic. At some point, you exhaust the results. As you can see here, we are starting at two concurrent requests. GPU is at 100%. Request rate already maxed at 100 requests per second. And each time you double, which you can see at the two arrows there, latency will only increase because requests are waiting in queue. Well, to be honest, at this point, this is already good for us. Most people can just wrap it up and then move on to the next model. But I do understand that we need to scale. From this point, you have multiple options. So the easiest one is horizontal scaling. That's self-explanatory. The other one is four-decker scaling. This is using single GPU on a node. If you use multiple GPU, Triton will spread the models on whatever GPU available. So you get parallel processing. The other two is the one that Jose mentioned previously. You have two options. You can specify an instance group. So you deploy multiple instances of your model in one GPU. That is, if you have a room in the GPU capacity. So Triton will try to provide multiple instances of the model, and then you get parallel processing. The other one, if your models allows dynamic batching, that's the next thing you can use to squeeze more throughput out of it. So you can specify the conflict on the right to tell Triton to pull all the incoming requests and then execute it in one pass. That's benefit in memory data transfer between the server and the GPU. And that's it. You can also combine these techniques to get more out of your deployment. And that's it for me. That's it from us. Thank you for your time. Next slide. Thank you. So it's time for Q&A. We are hiring. And also, you want to mention about? Yes, so we will be having some drinks later next to the city of arts and science. So if anyone wants to join us to talk about ML, cooking, or anything, just come and see us so we can send you an invite. We've got some budget from the company to pay the first round, so. That's good to take questions. Yeah, yeah, yeah, of course. It's time. Hi. Which backends are currently supported in Triton? You've got PyTorch, TensorFlow, TensorFlow RT, Onyx, SQLer. You can use that as well at SQLen. But in order to run PyTorch on Triton, you need to convert the model to Torch script or Torch serve, something like that. You know, did you did something like that? You need to convert your model? So for this demo, OK, sorry. For PyTorch, for this demo, we tried two stuff, actually, the Triton itself and then the TensorFlow RT. Yes, you're correct. You need to, for TensorFlow RT, you need Torch model, PyTorch model that supports Torch script. So if everything works on TensorFlow, basically you can use that. Is it working straightforward? Do you have any problem with that? Identifying which part in the graph that use TensorFlow or not, that's the hard part. But once you isolate them, you can take it out for pre-processing and then move the pure TensorFlow operation into this. Thank you very much. Any other questions? So Triton inference error is actually one of the few servings that support mixed precision on TensorFlow. Have you done any benchmarking on the mixed precision versus the TensorFlow RT in the float point 16? Let me repeat that. So benchmark? The difference between the floating point 16 operation on TensorFlow RT and mixed precision on TensorFlow, because they are kind of comparable but not entirely. Have you done any benchmarks between those models? Yeah, yeah. So what we check for floating points, the different floating points, 16 versus 32, for example, what we do is that we check the difference between the output and then we compare it. We find the difference between the two and then we kind of compare the performance of the output and then come to a value that we accept. So there are differences. So I believe for this particular model, it was 10 to the power of minus 9. And that's if you don't reduce the precision of the floating point. So just by floating point 32 to 32 from PyTorch to TensorFlow RT, you get that much difference. And what about not like the accuracy differences between the floating point 32 and floating point 16?  It's not much difference. It's not very significant, but I'm not exactly remembered number. OK, thank you. Any other questions? Hi. Thank you for the talk. So I'm wondering, there are more inference surfing frameworks out there. What made you guys pick the Triton inference server? Yes. So we found with Python inference the ability to host multiple models in a single GPU, being the killer feature for us because that allows us to share the cost between several different applications so that the decision between deploying a feature or not comes back to just understanding the value that you add into the users and the value that you get back. Whereas the cost element of the decision has been reduced. And because now you can share the cost with multiple applications as we do with the CPU workloads. And also that it supports multiple frameworks. That's why we choose Triton because you cannot force your machine learning team to just use one framework that have preferences. So just in this case, we initially have PyTorch and TensorFlow deployed on our cluster, just combining that into one instead of having to manage two deployment stack is beneficial to us. Thank you. All right. Thank you very much. Thank you.


Good afternoon, Howard Wigg, Code Green, Sink Tech, Hawaii. Welcome one and all. Adaptive reuse, what in the world is adaptive reuse? In this case, it refers to buildings that have expired their original intent. And very often they're just sitting abandoned and neglected. And instead of tearing that building down and building a new one, why not breathe new life into that old building? That's what our guest of the day is all about. But what am I, a white energy person doing, talking about adaptive reuse? Well, think about it, when you build a building, you had to mine all that steel, glass, cement, and then more energy to erect the building, tremendous amounts of energy. And then if you tear it down, there's more energy, you dispose of the rubble, there's more energy, and then you build a new building, that's even more energy. Why not practice adaptive reuse? Where you ask, what good use would this building be now in this neighborhood? And you adjust accordingly and make it into a commercial, commercially viable, brand spanking new use with brand spanking new equipment in it. So Linda Mickey, principal of G71 of the largest architectural firms in all of Honolulu is a specialty specialist in this field. And she thinks not just of adaptive reuse, but of redemption. The building has committed the sin of getting old and useless, and she redeems it to an entirely new life. So welcome, Linda Mickey. So glad to have you on board. Thank you, Howard. I love the way you describe redemptive. So you have four projects to describe and each one of them is entirely a different ballgame. This is gonna be so fascinating. And we've got a lot of material to cover. So why don't you get into Kupu at first, Linda? Take it away. Okay, terrific. So if we can show the first slide, I wanted to share a few projects that really show the breadth of adaptive reuse. And so the first slide is looking at Kupu and it was originally designed as a net and canoe shed. And unfortunately over time, it got used into basically a miscellaneous storage unit. And what Kupu did is they had a vision and they wanted to create a community center and a training center for some of their students. And so really what we're talking about is how do you open your eyes to see things so that you can actually have visions of how we can reuse buildings versus tearing them down and building new ones. Because I really think that that is going to be the way that we're going to be a sustainable community. Where is Kupu? Where is this building located? It's near Alamoana on the basin. So it has beautiful, I mean, it's near the ocean. So it has beautiful ocean sunsets. It's just a great community place. We've had our office Christmas parties there and the students there cook fabulous food. I mean, so it all around, they have a great program. It's a great venue and it's an open-air place to really bring community in. And we love just the mission that they have and the vision they have, but also that it's taking something that was really unused and bringing life back to it. And that's the exciting thing about adaptive reuse. It's taking something and bringing new life by bringing people to it. Maybe you could show this slide again and point to what examples of what's going on in there. Well, a couple of the, you know, the little sketch is just showing how it was a net shed. It became a storage. And then now it's a community center with sort of a kind of open-air plaza. And that's just a couple of pictures of before and after and really at their opening. And it has an artwork done by Kaili Chan, one of our in-house artists. And it's a beautiful place to be. It's naturally ventilated and it's a great venue. And it's a small project, but that's why I'm trying to share that, you know, if you go to the next slide, you can have adaptive reuse in all kinds of sizes, the projects. For example, this picture is actually of our new office. We've moved in a couple of years ago and it used to be the old Bank of Hawaii main branch. And they had a huge branch that took up the whole first floor. And this space actually wasn't even on the, it wasn't even available for leasing. But one of my partners and I were in, we had just toured some office space and it was kind of in the tower. And we went into the main branch and there's this beautiful two-story, two-story spaces. There wasn't a connection to the mezzanine at the time. And we were just kind of ideating and I was telling her, you know, I really would love to have the office in this space. So we came up with a crazy proposal. We presented it to Peter Ho and he liked it. And he made it happen because really it was taking into account the new way people are banking. So they don't need as many teller spaces. It's really like kind of bringing it up to the current, you know, electronic means of how a lot of people do their banking. And so it worked out perfectly timing-wise because they actually used the half of the branch bank that our office is in, while they renovated the other half of the building for their new branch. And then the last thing they did is they had a filming of Hawaii Five Bowl as the bank of Oahu. And that was the last use of that sort of bank branch on that side. But, you know, there's a lot of things that we reused in the bank that people don't really notice either. You know, one of the things is they had these beautiful four by four ceiling tiles and they were fabulous. And you can't find that type of ceiling tiles any longer. And they were original. And so we reuse them, which was great. And they had another beautiful, really large filing storage. And so the contractor figured out how to almost like Flintstone, Egyptian roller on rollers to roll this large cabinet to our storage area. So we actually reused a lot of things that were beautiful and still viable. But, you know, one of the new things we added was a big stair going up to the mezzanine. And at first the mezzanine looked very low. So they had kind of long, long low lights. And by removing them and changing the light fixtures to the new upgraded light fixtures, it really kind of opened the space. And we actually opened the ceiling. So we have a part of our second floor ceiling that's open to the mechanical ducting. And it just really feels a lot more open and inviting. And so it's really just trying to take the time to look at things a little differently on how we can reuse, which is very sustainable. You're saving a heck of a lot of energy along the way, Linda. The way you stayed in the office and the governor, thanks you so much for that. You're welcome. So the next project I wanted to share was the Aloha Tower Marketplace. Could we show that? So HPU, Hawaii Pacific University had this great vision in their master plan to have an anchor to their downtown campus. And the Aloha Tower Marketplace had been in, you know, somewhat low occupancy, especially on the second floor. Second floor retail is very challenging. And so what we partnered with HPU is to create dormitory lofts. So we were able to fit in two-story dormitories on the second floor of the Aloha Tower Marketplace. And the nice thing about it is that we really got a chance to show some really interesting spaces. If you could go to the next slide. So we had some very interesting and unique dorm rooms. So they had living areas. And we even reutilized some of the, they had some roll-up doors. And so if you notice on the right bottom picture, that's an existing, those are all existing storefronts. So we did a really kind of an interesting thing in reutilizing the storefronts by putting in decorative patterning on our window film for privacy and reutilizing some of the really kind of neat storefront areas that we could in order to give it sort of that industrial and also urban feel for the downtown campus. So that was actually a slightly larger project in the downstairs actually had some of the multi-purpose areas for the university. And so that they actually then had a gathering place for them to come together and use. So the next project I wanted to share a little bit more detail about is an old iconic building. You can go to the next slide. And I don't know if you remember this it's the Waikiki Trade Center. It was built in 1980. So it has some very iconic circular windows. So I think in Waikiki, if you're driving by on Kohio, you'll see a old iconic tower that was built with the intent to be offices. So they had offices, they had parking and they had retail. And one of the interesting things about this project was it was a really good adaptive reuse for basically the needs because there weren't enough at that time in 2014. There really weren't enough hotel rooms. And so this was also to me the need. But the other thing is that offices in Waikiki were not in high demand. So the building itself had come into some disrepair because it was pretty much almost 50% vacant. So highly underutilized building. So I'll go into a little bit more details about how we went about doing this. But one of the first things we did you can go to the next slide is we actually tried to make the hotel have its own identity and entrance off of seaside where the existing port kosher was. So the hotel was not necessarily related to the retail space that we modified. One of the most important things for a hotel use was to check on the ventilation because it's a change in use from office to hotel. There are several requirements that change ventilation. We had to make sure that we could still reuse the existing curtain wall and that we also were able to get enough of the sewer connection for the added bathrooms and water needed for the restaurant and other areas. So you can go to the next slide, please. This is one of the areas that are while it was under construction. And so remember I was telling you about kind of having new eyes to think about things and to see how we could really transform the space. So they had a really large roof deck. And so we had envisioned actually creating a kind of like a semi-sky lobby. So you'd go up to the eighth floor and you would enter into an expanse of lobby and bar and it would open up to the pool deck which we put on the existing roof that was underutilized. You can go to the next slide. So this is a picture of the new lobby and it really was a transformation because we took a portion of the existing window glazing and we actually put in open doors so that it flowed out and opened into the new pool area. So if you go to the next slide, you can see sort of the unusual configuration of the tower itself as well. So I think we had, I don't know, something like 20 something different unit types because of the unusual configuration of the building. We had to be creative on how we got guest rooms in there. And so because of the deep space, we ended up getting some very gracious, large guest rooms on areas where maybe the view wasn't as nice but you had a really large kind of a guest room for them to enjoy. So part of the challenges that occurred as well, you can go to the next slide, is when we were looking at the existing pool deck, one structurally we had to figure out how we were gonna reinforce the structure to be able to carry the pool water. Also there was another wind factor involved. And really if you look at it before, it was really a very large underutilized space and we didn't even use the whole roof. We only used a portion of it that was sort of protected by the tower from the wind. And so it was a very fun project in trying to create a really nice environment and space for people to come and have a little oasis for them to enjoy when they came back from their days ventures. You can go to the next slide. So these are just some examples of the different types of guest rooms. And one of the beautiful things about this adaptive reuse is that the office building already had fabulous views of the ocean. You could see the Royal Hawaiian Hotel, the ocean beyond. And we really enjoyed sort of celebrating the circular windows because there's something special about looking through any view through a circular window. I don't know what it is Howard, but it just makes it feel special. It like frames it and it makes it feel very special. So we had a lot of fun with that as well. Go to the next slide. So this shows you some of the rooms and some of the very kind of interesting circular windows that we had as well there. So on the ground floor, that is where we had some very interesting retail adventures. And so the next slide shows the before and after. On the top, you'll see the floor plan before. And it had a very large atrium that ran through the retail space. So in a way you had retail that was internal to the atrium area. Now at the time when it was built, it was very classic 1980s. It had sort of metallic finishes on the round columns. And they had a lot of nightclubs and discos during that time. And so unfortunately the metal on the columns and the cladding took its toll. And there were a lot of fist images and people who had punched it. So it was quite beaten up by the youth. And I think this transformation for the community was that it changed the environment. And this was happening all along Kuhio Avenue. It was like, this was the time when there was a real revitalization going on on Kuhio Avenue with international marketplace, just the whole street. And I believe it really changed the environment and the community.  So if we could go back to that slide, what we had looked at doing was to separate the hotel entrance, which is that little orange small piece, and the retail along Kuhio to really help to celebrate what was going on. This also had a large second floor tenant. And so Charter's Larching and Coleswood Capital Group, they really did a great job on really looking for tenants that were fabulous tenants. And so they basically leased out pretty much all of their tenant space. So the interesting thing is that Nordstrom Rack, which is there, and they take up the whole second floor, they had an interesting concept because we needed to provide a two-story entry to get up to Nordstrom Rack and to create a facade that would sort of open up and give that feel of that two-story space. So one of the things that was also, you can go to the next slide. So one of the things that we had to do was if you look at the upper left-hand picture, it shows part of the demolition of some of the large concrete beams. And they had three large circles that ran across the front facade. And that is actually the structure behind the Nordstrom facade that you see currently. And Nordstrom had actually opened before the hotel. So part of the other strategic planning we had to do was to actually run all of the plumbing lines, everything that would go through the tenant space. We had to have that actually planned and installed before the tenant took over the space. So there was a lot of logistics and it was actually quite a challenge to keep the project going as well as they kept all of the parking available and open during construction. So during the year and a half of construction, everyone still was able to access the parking garage because I guess parking is very valuable in Waikiki. So they ended up keeping it open and available for everyone to use. And so what we ended up doing is to really get the best out of this property. We cleaned up the front facade and there were several reasons for doing that. One is when you're doing a renovation, it's really important to upgrade to what's needed. And so if you go back to the slide that we were looking at earlier, so one of the pictures in the middle is actually showing the floor infill. And our structural engineers had to be very savvy on how they figured out how to do this because one of the really important things that the intent was we were trying to minimize any new foundation or footings. Because in Waikiki, there's a lot of archeological survey and inventory that we have to do. And so even digging for the elevator hit was something that we had to take care of very early on with the cultural surveys to make sure that we didn't find anything, even though it was previously disturbed, there was nothing that was found and even when we planted the coconut trees outside. So we were very careful on how we did the infill of the atrium and we were able to actually offset some of the retail space with the unused floor area underneath the new pool. And so we really took actually square footage that was underutilized under the pool and moved it to the retail space so that they could really maximize the retail square footage that they had on Cajillo Avenue. So in that other photo, one of the other things I wanted to show was that we actually had hidden in the corners in front of the Nordstrom building, little corner, let me see, one of the lower left corner picture, it shows one of the corners of the stone and it's actually to receive a special type of flood barrier gate. And so we actually had installed in all of the entries flood barriers so that you would have these flood barriers that they could put in front of the doors and flood barriers almost work opposite from fire exiting. So in fire exiting, you want people to be able to run out of the building quickly, but when you put up flood barriers, they want to keep the building protected from the floodwaters from getting in. So the elevation is carefully designed so that the doors can still open, but should there be a flood, you can keep the floodwaters out. And it comes up to maybe almost three feet high on the flood barriers. And so it's just really interesting because there's a lot of interesting challenges that you also have to do when you do an adaptive reuse in bringing up the building to current code and systems. Okay, next slide. We've got about two minutes Linda. Okay, thank you. So that's just the facade with the hidden flood barriers that hopefully we'll never have to see. And the last thing in enclosing the last slide is really a lot of adaptive reuse is how we rethink about things and bringing people back into the space and bringing life to the new buildings that we already have. So it's like bringing new life into every building. So I hope everyone will keep their eyes open for some creative thoughts and ideas. Wow, thank you so much Linda. If you notice, I didn't butt in at any time because you're such a great storyteller. You just really, you flow so smoothly there. And just to me from what I saw the best example of adaptive reuse is the Aloha Tower space. I used to go up to that second bottom floor. Aside from, there was the brewery there that was active but everything else was pretty well dead. And then the heart of downtown, that's almost a sin. Now you've got all these colleges living there and all that space, the meeting space on the ground floor. Well, and then they still have the restaurants, which was great. I mean, that was the other thing is renovated around the restaurants that were already in place. And so a lot of the restaurants remained. And Aloha Tower also has some of the boats and other things that, so they still remain and in pretty much for most of these renovations, you have tenants who are there, you need to schedule and plan around. But really in the end, as I was telling you, sometimes it's very hairy when we're walking through and when my shoes get stuck to the floor, it kind of grosses you out. But when you see the transformation in the end and people enjoying the space, that's really what it's all about. And yeah, and the concept of underutilized space in the heart of Waikiki, what a contradiction and can you imagine the value of a square foot in there? And here you go revitalizing it, bringing the people back in. Wow, I am so impressed and that we have run out of time. So I thank you very, very, very much. And come back again when you have, I don't think any new projects can be this exciting but I know you're working on new things. Oh, thank you Howard for having me appreciate it. This has been a whole lot of fun and bidding fun farewell. This is Howard Wiig, Cold Green, Sink Tech, Hawaii. See you next time and bless you for your work Linda. Oh.


Unlike many people today, I'm an optimist. This strange condition began when I was 15 years old. That was 1996. I discovered that the future of human knowledge is on the web, and my textbooks were all out of date. So I told my teachers, I want to quit school and start my education on the web. Surprisingly, the teachers all agreed with it. A year later, I founded a startup, working on web technologies, and I get to join this fabulous internet community that runs with this crazy idea, an open, multi-stakeholder political system that runs the internet. Still, today. Today, as Taiwan's first digital minister, I'm putting into practice the ideas that I learned when I was 15 years old. Rough consensus, civic participation, and radical transparency. Surprisingly, it's working, and it's transforming our society. Two years ago, our president, Cai Yingwen, said an inspiring statement in her inauguration speech. She said, Before, democracy was a clash between two opposing values. But now, democracy must become a conversation between many different values. Indeed, in conventional thinking, social benefits and business profits, for example, are opposing forces and often contracted each other, forcing the government to make trade-offs in this paradox. However, the idea of social innovation brings a brand new way of thinking. For people working on social innovation, the core objective may be achieved by, for example, developing business models to address social issues or environmental issues, and the government's roles has changed. Instead of being the arbiter torn between different sides, we're now asking a different set of questions. We ask, What are our common values, despite different positions? And we ask, Given the common values, can we find solutions that works for everyone? And this is the spirit of co-creation, a spirit for subversion of paradoxes. Civic technology, the branch of technology that enables millions of people to listen to each other instead of one person speaking to millions of people, is a core ingredient to co-creation. Indeed, in the past couple of years, Taiwan has been consistently ranked the top country internationally on open data, on internet participation, on women's digital access, digital inclusivity, etc. And all this was because we adopted open data and crowdsourcing as national direction since 2014. It was capitalized and epitomized by an Occupy movement in March 2014. There was a live demo of mass participation we occupied the parliament for 22 days. At the time, the MPs in Taiwan were refusing to deliberate a trade service agreement with Beijing, and so the occupiers got into the parliament at night and stayed there. For 22 days, we demonstrated how to deliberate a trade service agreement with the host society. There were over 20 NGOs participating, the Greens, the Labour, the Independents, everybody. And we supported this whole deliberation with a radically transparent broadcasting, live streaming logistics system which we exported to Hong Kong for the umbrella movement in the same year was powered by this community called GovZero. GovZero is a civic tech community with a call to fork the government. We take the government websites which all ends in GOVTW and make better open alternatives that ends in G0V.TW. For example, the annual national budget is hundreds page long in a PDF file and it's very hard to read. But the GovZero community's very first project was Budget GovZero TW which shows the national budget in a way that everybody understands and you can drill down to each and every budget details. Today, the system is adopted by seven city governments powers a participatory budget platform for Taipei City at Budget.Taipei so that anyone can look at this map, find a part of city budget they care and type in any question they want to ask and the career public servants actually comes forward and answer for that part of the question. And starting next month, we're adopting this for the national government also. So this becomes a direct dialogue platform not through the city council or the legislative but for the career public servants to communicate directly with citizens. So why are there so many civic hackers in Taiwan like me who spoke to my clients during the Occupy movement saying okay I have to take a three-week leave because democracy needs me? I think it's because our generation and 37 years ago now were the first generation that enjoyed the freedom of speech after three decades of martial law and dictatorship. The freedom arrived in 1989 the year of personal computers. So for us personal computer revolution and freedom of speech is the same thing. Our first presidential election by popular vote 1996 is also the year that Royal Web got popular. So internet and democracy they're not two things, they're one and the same thing in Taiwan. So for the past 30 years when we see free software we always think freedom of assembly freedom of speech freedom from surveillance freedom from coercion and never free of cost because we know that freedom is never free of cost. Our parents generation our grandparents generation paid dearly for the freedom and we need to use the software freedoms to keep it free as we did during the Occupy movement. And the movement caused a revolution out of a peaceful revolution. There was a radical transformation of social expectations at the end of that year and we just found ourselves elected mayors when we did not expect it. And because of this the prime ministers resigned and a new prime minister and engineer said okay so from now on crowdsourcing open data is just going to be the national direction. And Occupyers and the civic tech people who supported them were then invited as mentors as advisors to the public service to solve issues international issues like Uber. And Uber is very interesting because it was a MIM a virus of the mind. A MIM was called Sharing Economy and it said that code dispatch cars better than laws and so we have to obey code instead of laws. The MIM spreads through apps from driver to passenger to drivers and you can't really argue with a MIM just like you can't argue with the flu it's not in the same category. So there's the protest the taxi drivers surrounded the Ministry of Transport demanding negotiation but how do we negotiate with a virus of the mind. For us the solution is through a deliberation that involves thousands of stakeholders. It is scaling down of the deliberation we just did with half a million people so we think we can do it. And deliberation thinking deeply about something together is an effective vaccine against a virus of the mind when everyone passengers and drivers, academics and public servants they listen to each other and form a consensus we become immune to divisive PR campaigns in the future. A proper deliberation with a focused conversation method involves four stages. The first stage is FACTS where we collect evidences, firsthand experiences, objective data and then after that is confirmed by everyone we move to collect everybody's feelings about us in FACTS you may feel angry, I may feel happy and it's all okay and after people converge on their feelings that resonate with everybody we then talk about ideas and the best ideas are the ones that address the most people's feelings. Then we translate them into legal deeds and slide them into decisions. However if the decision making process is not transparent then the people on the street would speak a different language than people in the government so they're not even agreeing on basic facts like let alone others feelings. And so in that situation ideas become ideologies virus of the mind so potent that they can blind people to new facts and to each other's feelings. So our first step is always open data that is making all the facts available and as the private sector and civil society to share what they have. And next we create an interactive survey on Polis to ask about how people feel. Four groups of people emerged taxi drivers, Uber drivers, Uber passengers and other passengers. And the Polis system shows each group how their share sentiments are received by other groups. And the interesting thing is it lowers people's antagonism because you see all these people on different sides are your Facebook and Twitter friends didn't talk about this over dinner. And so at the beginning the people were on all the corners but because we say we only give binding power of anything that people propose that convince a majority of all the different people so participants converge on feelings that resonate not only with like-minded people but across the aisle. So instead of distracting people with divisive statements we attract people to focus on consensus statements that we can then use as the agenda to discuss in the face-to-face meetings. After we get such a set of feelings that resonates with practically everybody it's now much easier for the government to engage the stakeholders and check with them one by one. Here's the consensus of the people so do you agree if you do agree how do we translate that into law? Because it's live streamed and there's a full transcript they're bound to the words that they said during this live stream consultation and the stakeholders eventually agreed. When we ratified their agreements in August 2016 everybody knew it was coming and everybody anticipated it. So today Uber operates legally under the new framework but so do the taxi companies who are now adopting the same model that Uber is using for dispatching its cars. So this method works. The next question is can we scale the process of listening? So right after the ratification I joined the cabinet as the digital minister to explore the possibility through PDIS the public digital innovation space. It's like a policy lab in a national level. We have designers, we have programmers and we're automating away a lot of this chores the public servants are doing in order to make participation possible. More interesting perhaps than the technical contributions we have a culture that we're bringing to the government. For example I'm a radically transparent minister by that I mean all the journalists or the lobbyists everybody get to ask me questions but I only answer publicly. So if I get a question from a private email I will reply asking if it's okay to give my answer publicly if they're not okay with it I'll just link to my previous statements. It's not just to the lobbyists and journalists but also for internal meetings for the hundreds of internal meetings that are held as I was the digital minister everything was transcribed. The written record is checked for 10 working days for all the participants and we publish it to the internet. The effect is very surprising. The civil service actually become very innovative and restating. They propose some very good ideas under this condition. That's because previously before the political transparency the minister would get a credit if things go right and the public service often gets a blame if things go wrong but now with this accountable record if things go right they get a credit because their name is not a transcript but because this experimental method if things go wrong is all my fault. So under this new condition they become very innovative and open to a lot of interesting ideas. One of the idea is adopting this thoroughly free software program called Sandstorm. It's a platform of our public service internal collaboration. We use the same tools but open source tools is like EtherPAT, like Weekend which is like Trello, Rocket Chat which is like Slack and how the free software community is organizing themselves these days we're also using it in a public service and previously the roadblock was on the cyber security issue but we were able to find a community platform and for people to publicly audit this open source platform to solve the cyber security worries it gets audited by our top-notch white hat hackers so that we're now reasonably sure that the free software running on top of it doesn't suffer from cyber security attacks and issues so we were able to do a lot of free software working methods just by adopting this like there's interesting system proposed and written by young public servants like an app for ordering lunchbox together or plan traveling together or whatever it's really good to have this choice also we established an ePetition platform as a way for people to participate at the beginning it did not receive much attention because while for single ministry issues people were able to get satisfactory answers for cross ministry issues people would get this very blank very bureaucratic answer that doesn't really solve their problem this explains why each individual ministry can't do much about it so after I become the digital minister we asked each ministry to send a team at least one person to serve as participation officer or PO's we assembled this virtual team of about 50 people online using rocket chat and auto send storm tools for engagement so now in Taiwan when people started petition they know that instead of just a beautiful response they would actually get to meet with all the relevant ministries in Taipei or we would travel to those rural areas and islands if they're about petition for local development we solve a lot of very interesting problems like this without exposing any public servants to risk so we relieve their fear uncertainty and doubt around civic participation for example a petitioner last May said we have an explosively difficult to use income tax filing system that doesn't work on Mac and Linux we invited all the people who complained the loudest and eventually we co-created this year's tax filing system and so by collaborating with the civic sector we're building a robust environment suitable for the social innovators to grow where the power of civil society could be brought into full play and the venue we hold those collaboration meetings today the social innovation lab was also co-created by 5 workshops of more than 100 civil innovators were invited to communicate their conceptions and expectations for the lab as such we achieve a blended consensus fulfilling all the purposes for example it has a resident chef a resident kitchen and cafe is the open until 11pm and people ask for me to personally stay here as a mentor like office hour from 10am to 10pm and I do that provided that my visitors agree to have our conversation posted online anyone can have a discussion with me in the social innovation lab and it's not just in Taipei city the different regional city social enterprises social innovators they also gather around me on my bi-weekly Taiwan tours it just me that travels but everybody else there's like 12 different ministries about 20 people remains in Taipei we still have a good video conferencing and transcription that makes it very easy for the public service in Taipei to see the local issues being surfaced and being resolved in a very quick fashion because all the related ministries are there and once the people solve it and publish online all the other unrelated ministries now also understand okay so this problem is to be solved this way and so it is a virtual learning team that's recurrently solved the social issues so we tour around Taiwan we see a lot of civil society initiated grassroots civic tech projects like the Gov0 air pollution observation network by combining the diverse talents in network communities this project utilize the simple air quality sensor air box which is becoming very popular and apply IOT technologies so that little by little and bit by bit thousands of contributors accumulate massive database about air quality that is closer to the actual places where people are active and the exceptional advantage in Taiwan is we fully support instead of reject this civil society grassroots initiative as part of the forward looking infrastructure plan we launch a IOT for public good program with a four year budget of about 5 billion Taiwan dollars in the program an enormous amount of environmental data on air products, meteorology water resource earthquake disaster relief are integrated into a high speed computing environment so that we can collaboratively discover the correlation between social activities and environmental phenomenon more quickly we're also working with research institute to assist with the manufacturing of domestic affordable high quality PM2.5 detectors so communities can yield data of a higher accuracy so why do we encourage such innovations currently there's many misunderstandings between government and people due to lack of transparency and insufficient data and using the air quality as an example establishing effective dialogue is difficult until the source of daily air pollution are disclosed from various sources to the whole society on the same platform including what pollution came from outside from fixed sources from mobile sources etc and we're very proud that the air box related projects and applications have been introduced over the world so to speak by uniting the strengths of the government and the public Taiwan proved to be capable of not only solving our own problems but providing such solution to other countries in similar situations as a reference moreover we have a sandbox act in Taiwan so if you want to experiment in fintech in self-driving vehicles you can apply for experimentation for 12 months up to 3 years to break some laws during that period do you need to explain why these laws need to be broken to achieve the common good for society and during that experiment we assemble a multi stakeholder panel that collectively decide whether the society think this is a good idea moving forward or if it's a bad idea if it's a good idea regulations and laws get changed because of social innovation if it's not a good idea well at least the risk is limited and everybody learns from it so we can try a different model next time through this way Taiwan contributes our experience to the planetary civic society focusing not just on one or two sustainable development goals but especially on SDG 17 which is cross sectoral, international and cross discipline collaboration in conclusion I would like to share a prayer with you about this version of paradoxes when we see internet of things let's make it a internet of beings when we see virtual reality let's make it a shared reality when we see machine learning let's make it collaborative learning and when we see user experience let's make it about human experience when we hear the singularity is near let us remember the plurality is here


Hay-o-silver! A fiery horse with a speed of light, a cloud of dust and a hearty hay-o-silver! The Lone Ranger! With his faithful Indian companion, Tonto, the masked rider of the plains led the fight for law and order in the early western United States. The stories of his strength and courage, his daring and resourcefulness have come down to us through the generations and nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeats of a great horse, Silver! The Lone Ranger rides again! Come on, Silver! We've got to meet Tonto at Tomahawk Basin! Hay-o-silver! The Lone Ranger and Tonto, seeking to get real proof that Benjamin Steele, king of the cattle country, is actually a criminal worse than any outlaw, were up against a tough foe. The vast Tomahawk Basin was the center of Steele's activities and the masked man and Tonto made their camp on the rim of the Great Basin. One morning, as they stood by the campfire, there came a faint whisper of sound from the surrounding woods. Then, a knife whistled through the air. Kimosabe, somebody throw a knife. Teddy Tonto, the knife was meant to miss. Just a warning. Step out into the clearing. I've got you covered. Right into the clearing or I'll shoot. I'll give you a quick advice, man. You don't scare his ear. Kimosabe, horse wear Tomahawk brand. Rick, you don't know who I am. Tomahawk, brand of Benjamin Steele ranch. You work for him. I don't think this man worked for Steele, Tonto. You've never met me by his command. But I don't mind telling you who I am. I know who you are. Oh, you do, eh? I can guess. Your name is Benjamin Steele. Uh-huh. Mr. What they say about you is sure a fact. You're the coolest ombre I ever met outside of myself. If I'm Steele. Steele, you're a member of the Black Arrow. I was a member of the Black Arrow. But the Black Arrow's gone. You smashed it. Just as you'll be smashed someday. Well, you're holding a six-shooter on me. Why don't you take me to jail?  What the matter, Kimosabe? You've got prisoner. We take him to jail, get him to sheriff. We do. He'll be free inside of an hour. Sure. Because you gotta have proof to put a man behind boys' engine. There's nothing on earth to prove that I've been a member of the Black Arrow. Yes, there's nothing on earth to prove I don't run things fair and square in accordance with the law. Steele, this gun is aimed at your heart. I pull the trigger. There would be no need of a judge or a jury. No need for jail. You know, mister. But you won't pull the trigger. No?  A lone ranger believes in law and order. You do everything according to what's honest. That's why I came here, bold as brass. I knew you wouldn't shoot. All right, Steele. Got to the point. Mister, the point is as sharp as that knife sticking to the tree. Clear out. You and the engine both. Clear out. Because you say so? I run things in Tomhawk Basin. If you try to stop me, you'll end up six feet underground in your grave. I don't hire staying here until you land in jail, Steele. So it's that way, huh? War is it? Call it anything you like. But you'll end up in jail. What can you do against me? A man's smarter than an engine. He's stubborn, that's what. So be it then. It's your funeral. I hope you give me a good fight. Because I like fights, savvy. I like them and always win them. Tomhawk Basin belongs to me. I don't have to cattle to roam the grasslands. Soon I'll own all of them. I'll use any methods I like and nobody's going to stop me.  And that goes for you, mister. Steele, clear out before I change my mind. I hope you live to regret that. Adios, guinevere. Im plenty bad. He's clever, Tonto. He knows we haven't a shred of proof against him. That's why he could meet us face to face. We've got plenty men and plenty guns. We stay in Tomhawk Basin. Maybe any time we ride into ambush, get shot. We've got a job to do, Tonto, and nothing will stop us. We're riding into the Basin. Young is the waterhole, father. Shall we water the horses before we go into the ranch house? Yeah, Abbey. I'm on my dry ride from Leadville. Say, who's that? It looks like Ted Bailey. A girl in love sure has sharp eyes. Don't deny it, Abbey. You're flushing clean up your ears. But Ted Bailey's a nice young fella. I wouldn't mind having him for a son-in-law. He hasn't even asked me. Sure, sure. Now, don't tell him I said so. Boo, boo, there, boy. Boo, boo. Well, hottie, Miss Abbey. It's nice to see you. I'm here, too, Bailey. Of course, Mr. Kerry. Hottie, do you too? You didn't think I overlooked you. I wasn't sure, son. Been waiting long? Waiting? Well, I haven't been waiting. Oh, just a coincidence, huh? Yeah. Then how come your horses trample down the ground so much? Well, I... what that is, I... Now, Father, you stop that. Ted Bailey wanted to wait around to pass the time of day with this. There's no call for you to think it's funny. Well, I reckon you must be right thirsty, Miss Abbey. Let me help you off the wagon. It's mighty nice seeing you again. It's been quite a while. Well, I ride herd for Mr. Steele. I had to make a trip to Chisholm. Oh, the water's nice and clear, isn't it? Yeah. Haven't watered my horse yet. I didn't want him to muddy up the water before you came. There you are. Take a sip. Don't drink that water. Hey, what in thunder? Ted, it's a mask, Miss Abbey. Stand back. Let me get my gun. Now, get that gun.  Hey, hey, what's the meaning of this? Lady, are you hurt? Oh, no, Mr. Carey. If it isn't hurt, I will have hit the gun.  Is this a whore? Well, what else could it be, Miss Abbey? These ombres riding in spur for leather. And besides, my boss, Mr. Steele, warned all us riders it was an outlaw roaming the basin. An outlaw riding a white horse and wearing a mask an engine part with him. We're not outlaws. Mr. Steele's my boss. I work for him and I believe what he tells me. If you'd taken a drink of that water, you wouldn't be working for Steele much longer. The water's poisoned. What? What's that? The water's poisoned. That's why I fired those shots as a warning. I don't believe it. You were up on the ridge. How could you tell if the water was poisoned? Yeah, did you taste it? I don't have to taste it. Look across the waterhole. Huh? Get it, Tunnel.  Why is holding up a jackrabbit? Here, Kim, Miss Abbey. Thanks, Tunnel. This jackrabbit was lying on the far side of the waterhole. Take a look at it, Kerry. Gee, hussafat, mister, you're right. It was drinking from this waterhole and now it's dead. Bailey, would you know anything about this? Me? How come you didn't drink before we arrived? How come you... Are you saying I poisoned the water? It looks mighty suspicious. Father! Keep out of this, Abbey. Kerry, if you wasn't Miss Abbey's father... Don't hide behind a woman's skirts, Bailey. I asked you what you... That does it. I got another gun. Drop that! Keep your hands away from that holster. Let him draw, mister. That's enough, Kerry. Bailey, get on your horse and travel. If you asked me, Mass Man, I'd say it was you poison this water. I said, get on your horse.  I'm leaving for her sake. Goodbye, Miss Abbey. Oh, you shouldn't have said that. Dead Bailey couldn't have poisoned the water. He couldn't. Somebody did, Abbey. Now we're licked for certain. There's nothing left for me to do but to sell our ranch. I'll have to accept Steel's offer. What's that? Steel, Benjamin Steel, owner of the Tomahawk Ranch. He's made me an offer. You're not going to sell? Sure I am. Lock, stock and bell. What else can I do? This is the only water in my range.  Kerry, have you thought that Steel himself might have poisoned this water? What? Why should he do that? They got your cattle? Oh, that's ridiculous. Benjamin Steel's the biggest man in this valley. He wouldn't do a thing like that. You asked me. I think Ted was right. I think you poisoned the water. No, that's not true. You're outlaws. You should do it and then want us away. But I just... We're not poison water. We're friends. That's enough, Tonneau. There's no way to settle the argument now. It gets us no place. I don't know what to think. I'm all mixed up. Kerry, would you wait a little while before making up your mind to sell out? I can't wait long. Tonneau, I saved your life just now. We're asking you not to sell. Mister, I'll think it over. That's all I can say. Good. You may hear from us later. Adios. Get him on the scout. Adios. Come on, Silver. Come on, Abby. I'm going to drive right to the ranch house. Follow those men or outlaws. Yes, Billy says Mr. Steele warned all his men about them. I don't know, Abby. Mr. Steele's a mighty big man, but somehow that mess, Gent, he didn't sound like an outlaw. I don't care what he sounded like. He must be an outlaw. Right straight back here to the ranch house, Mr. Steele. And if I'd been able to, I'd have shot that masked hombre, like you said. He's an outlaw, all right. No honest man would be so fast on the draw. Kino Billy. I'm glad you told me. So Kerry's water hole was poisoned, eh? Well, him and me had a couple of words. Yeah. I thought you and Kerry were pretty friendly. I'm sorry about it now. He accused me of poisoning the water. No. Of course, now that I'm calmed down, I'm going to forget about it. Poor Kerry's had so much tough luck lately. He's all on edge. Yes, poor fellow. I hope he takes my offer and at least he'll give him some cash to sell a lot of new plays. I reckon I'd better go now, huh, Mr. Steele? Yes, Billy. Head back and write a line on that south herd. You better stop in the kitchen and have some grub seen as you missed the name. Well, thanks, Mr. Steele. Gosh, you're a straight ombre to work for. Howdy-oast. Howdy-oast, Billy. Straight ombre to work for. That Billy kid's still wet behind the ears. I sure got him fooled. You've got everybody fooled in your stead. Howdy, Breed. You been here all this time? Yes, sir. I stay in corner very quiet. Here it would be, he said. The master man, he make trouble already, yes. Breed, if him and the engine had one din, that don't carry his ranch. Sure. Caring his daughter would be dead from that water. That is true, yes. It'd be dead. It would no living relatives. They could have taken their place like that. Well, the Bailey, he would be dead, too. Whatever.  It's easy to get his local weed. I want you more careful, Breed. The master man hasn't seen that dead rabbit. They're taking that water. Señor, I put the poison in the water hole. I do not stay around to watch. I have a moose pronto. Well, never mind. You go somewhere, Señor. Send him a hoarse, Breed. I'm riding over to see Kerry. I'm going to pay cash and get his ranch right now. Here you are, Kerry.  Just sign this bill of sale. I'll get you a ranch, and the cash is yours. Well, I sort of promised the master man. Master man? You wouldn't cactus that on Breed's an outlaw. I'm doing your neighborly turn, offering to buy you a place. I've got a good mind to take back my office. Father, Mr. Steele is right. Sign the bill of sale. You know, if I could only locate the skunk who's poisoned my water hole and burnt my winter hay... I don't reckon I'm wasting my time. Give me my cash and I'll use to you, Kerry. No. No, wait, Mr. Steele. He know I. I'll sign. Hey. I knew you'd have sense, Kerry. There's a pen dipped right in the ink well. What's the... The math, man. I put a smashed ink well. There's ink all over the bill of sale. Kerry isn't signing, Steele. Clear out. Kerry, are you going to let this ornery outlaw talk you into losing his sale? You heard me, Steele. Clear out and take your money with you. And don't come back. The curtain falls on the first act of our Lone Ranger story. Before the next exciting scenes, please permit us to pause for just a few moments. Now to continue our story. Faced with the two six guns and the steady hands of the Lone Ranger, Steele rode away from Kerry's ranch in a fuming rage, leaving the masked man in tonto. Kerry, I thought you promised me you'd think it over. In another minute, Steele would have had your ranch. Mister, you and the engine get out of here. Kerry, you mustn't, sir. Right now I probably lost my one chance. Steele won't make that offer again. Not after this. Then there must be some other way. There's no other way. Then Steele was big-hearted enough to make me an offer.   I was a fool in the first place to listen to you. Well, you stopped us from drinking that water, so I listened. We're not listening anymore. Right, Abby. We're quits now, Masked Man. I won't start any gunplay because of what you did at the waterhole. And also because you're inside my house.  And the next time I see you, be ready to shoot. Let's go, Tunno. Hey, what's the matter? Come on, Silver. What we do now, Kimus, honey? Kerry doesn't know what he's getting into, Tunno. Steele's as crafty and dangerous as a rattlesnake. Ah, him worse than rattlesnake. Rappler give warn before him strike. We're going to keep on the alert every minute. Day and night. That's right. We may not corner Steele, but at least we can try and save Kerry.  And we'll help him inspire him himself. Steele, him plenty smart ombre. Come to wonder what he'll do next. Oh, there. Oh, boy. Breed. Breed, where are you? I'm here, senor. You get the land from Kerry. Yeah, it doesn't look like it. No, senor.  What happened? A loan ranger. That's what happened. Oh. Kerry was all set to sign. Had the pen in his hand. Then the ranger was signed. Senor. Well, you were away. I think of another way to get cattle from Kerry. What's that? I have a plan, senor. It'll be easy to work, I think. There is a white horse in the corral. A horse that looks something like silver. What do you aim at, Breed? I'll tell you. But first I ask you a question. Well... If I get in trouble, senor, it may be bad for you, yes? What do you mean? To the rest of the people in Tomahawk base, and you are a very honest man. Only me know you have done many crimes, yes? Go on. It would be to your best interest to... to protect me, eh? If I should go to jail, perhaps. You won't. But if I should... Breed, I'll tell you right here now. You won't go to jail. But if you ever did, you can cut on me to get you out. I hope so, senor. I'll have to get you out. If you talk to you, put her over on my neck. See. I make sure you understand that it's all. Let's have it. What's your scheme?  You have a water hole not far from senor Carey's ranch, yes? That water's mine. It's not open water. Oh? Then perhaps you will open it, yes? Senor, for this scheme, I use the white horse. I rest like the long ranger. And to take the message to senor Carey, we can use Ted Bailey.  I got word the boss wanted me. Is he around, Breed? He'd take a little walk for a moment, Ed. Oh. That's what you ought to do. I am foreman for senor Steele. Sure thing, Breed. The boss he offered to buy Carey out. Say, that's right. Nice of him. Mr. Carey must have been mighty pleased. Well, this sale was not completed, Bailey. That mascot law, he stopped it. No. What dog gone that gents hide? I tell you, Breed. Well, senor Steele is very generous man. He's going to let Carey water his hole that the cottonwood water hole. After what happened? See, you are a good friend to Mr. Carey. Well, him and me had a few words. Ah. It does not matter. You are still friends. Tomorrow you'll ride. Tell senor Carey he can water cattle at the cottonwood. You do that, yes? Yes, sir. I sure will. I'll get there around noon tomorrow.  I didn't cook it just to look at. Abby, I can't help feeling it. Howdy. I've been riding all morning. Got some chow for a hungry man? Ted. Ted Bailey. What, Ed? Tarnation. Oh, now, oh there. Hold on, Mr. Carey. My holsters are empty. You left my guns out with a horse. And besides, I'm the bearer of good news. Yeah? Yes, sir.  At first off, I'm apologizing for getting riled the other day. And second and most important, Ben Steele sent me to tell you that you can drive your cattle to the cottonwood water hole and let them drink their fill. The cottonwood? But that belongs to Mr. Steele. I haven't any money to pay him the watering fee. It won't cost you one red cent. What's that?  Say, that is good news. I don't have to get back to my job to the day after tomorrow, Mr. Carey. If you want me, I'll be glad to give you a hand getting the cattle to the water hole. Ted. Youngster, draw up a chair and set down. Abby, dish up some more of that grub. I could eat a wolf. A hide and whoops in there. You're getting good food, Ted Bailey. Pile the plates high, Abby. I got my appetite back. Yep, Billy. We'll round up the cows this afternoon. Start moving them right out. I reckon we ought to get the cottonwood water hole a mite before sundown tomorrow. I've just now come from the water hole, Senor. I lured the heavy sack with a poison into the water. In an hour, all the water will be poisoned. What about Carey? I saw his herd, the cattle from the top of the hill. In another three hours, then they will drink. Good. You better get back there in time to put on your act. Please, Senor. As they approached the water hole, they were caught sight of a masked man riding away at full gallop. Only it will not be the long ranger, eh? He will be someone else. Water! There's a water hole up ahead. Listen to this deer. They can smell it. Don't let them go too fast. They've been thirsty so long, I'll drink too much. Keep them in control. Yes, sir. You be along, horns are going to drink your fill. Nice, fresh, sweet water coming mighty soon. Get along, ma'am. Move along. We'll herd them tandem. Our ride point up front. Good boy. Ted. Yeah, Miss Abby? Thanks for all you've done. Oh, shucks, Miss Abby. This was Mr. Steele's doing. Thanks anyway. You reckon your paw's going to sell to Mr. Steele? Of course he will. All the bad luck we've been having and Ben Steele being so friendly naturally will sell. Oh, too bad the water on your ranch has been poisoned. Usually takes a month for the poison to disappear. You couldn't wait that long. Well, you could now if you keep on using this water hole. Can I pay for it? No, Ted. We can't take any more advantage of Ben Steele. Father will sell, so at least we can do it and stay honorable. Yeah, I reckon so. I guess that means you'll leave Tomahawk Basin, doesn't it? There's no more land to settle here. I sure hate to see you leave. You would? Well, I... Miss Abby, I haven't asked your father yet, but Abby, will you marry me? Will you? Ted. Oh, I know I'm just a plain cow puncher and I shouldn't even ask. I think my father will say it's all right. What's that? So will I. Abby, you mean you'll marry me? I've been waiting for you, Dad. Oh, gosh. Gosh, Abby. Baby. I don't know what this... What's that, Mr. Kerry? Look up ahead. Huh? Dog-garded baby. Stop looking so carefied. Look, they're galloping away from the water hole. That's a white horse. Father, isn't... Isn't that the mask man? I don't see. Did you hear that? It sure is him. I don't know, Father. That horse doesn't look as big somehow. Those words he shouted, they don't... Say, there's something mighty peculiar going on. What was that ombre doing at the water hole? You know, maybe he was waiting in ambush for me. Then when he saw there was two of us, he changed his mind. Well, still, there's no use figuring. These here cows are thirsty. Let's get that water. Get up there.  Wait a minute. It's your horse. It's the mask man again in the engine. No rage for your guns. We've been waiting for you. Oh, God, if I savied this. Why, we just saw you riding north over the ridge and now you're suddenly combusting in from the west. That man was an imposter. What? You mean...  No one in Blazes would do that for her. Because he poisoned that water hole and he wanted to put the blame on me. Poisoned? What's that you're saying? Keep your cattle away from that water till I get back. Where are you going? I'm catching that man. Tonneau, stay here and make sure they don't move. You want your... Come on, Silver. I'll bring him. Well, I'll be dogged. This here's more confusing than chasing a jackrabber through a bramble. I can't keep those cattle from water. You do what mask friends say. How to make sure of that. Father, Ted, I believe that first man wasn't imposter. How are we going to tell, Abby? You're too big a lead for this one to ever catch up with him. That's just what you're wrong. Look. Look at that white stallion race over the ground. Why, he's twice as fast as that other horse. Look. Why, say, so he is. Maybe he will catch that hombre after all. Say, engine, who is that partner of yours anyway? We fixed Lawrence at good, eh, horse? But he soon got his hold of that guy. Getting mask man, do it. Breeds horse had crossed the ridge and loaded an easy gallop with a poisoner sitting easy in a saddle. Breed had no fear of pursuit. Until suddenly...  What is this? A ramble. Breed straightened, turned and looked over his shoulder. Over the ridge behind him flashed a great white stallion. Lawrence, get up.  In sudden panic, breed clapped spurred to his mount. Flattened out in a saddle as his horse lengthened his stride. There was no use. Closer and closer pounded silver's great strides. Closer and closer. No use. Stop where I'll make you stop. You'll never catch me alive. You'll never catch me alive. You'll never catch me alive. You'll never catch me alive. You'll never catch me alive. The gun dropped from Breed's nervous fingers. Then a rope snaked through the air, whipped tight around his body. Who are you struggling for? You haven't got a chance. They're going back. Understand? Oh, silver, whoa. Whoa, boy. You got him. You brought him back. Quiet. Breed. Here's a man who tried to poison this water hole, Kerry. Just as he poisoned yours. You can't prove nothing. No, there's a lie. You cannot prove I do such a thing. No? Very well, then. I suppose you'd take a drink from that water hole. Huh? Otto, pull him off his horse. No, no, no. The water is poison.  What did I say? Never mind, Ancient. He said enough already. He admitted his guilt. Ted, you reckon we can turn this orderly coyote over to the sheriff? Oh, we sure can, Mr. Kerry. Here's some more evidence for you, Kerry.  It was at the bottom of the water hole. I got it. Who else helped you do this awful thing, Breed? Thank me to jailer. I say no more. Not one word. All right, Kerry. You can water your cattle now. Go ahead. Water my cattle? Yes. We'll do it again in a few days. Their cattle won't suffer anymore. Well, Mr., what do you mean? Breed poisoned the water. He was afraid to drink it himself. Otto and I were watching him several hours ago. He put the sack in the water hole and then left. Before he came back, we took the sack out. That water was fit to drink all the time, but Breed didn't know it. That's how he convicted himself. Well, if that, don't beat everything. Um, bad man, make heap big mistake. Come, Teddo. Our work here is done. Breed, I reckon you're more than met your match this time. Me and Ted are going to take you to the jailhouse just as soon as we get the cattle watered. And thanks to this match, man, you'll be... Say, he's gone. Him and the engine net out already. Yes, father. There they go. He didn't wait for thanks, but we'll be eternally grateful whoever he is. Oh, sir. You have just heard as a copyrighted feature of the Lone Ranger Incorporated.