The. Default. Runtime. For no GS v8 super, fast it's had tons of resources put into it by come like Google, and. We've seen benchmarks, where the. GS, interpreter, in, node is 10 times faster than the Python. By. Enabling. Tensorflow. With no GS we actually get access to that, high-end hardware so those cloud TPU is the GPU and so on. So. Those are all exciting, things I wanted, to showcase one. Real, simple use case of nodejs and, tensorflow. The. Code, snippet I have up here on the screen is actually a really simple Express app if anyone's used it it's, just a request response, Handler, and. We just handle the endpoint slash, model which. Has a request and a response that, will write out - so. This model right now actually, we. Have a model that we've defined and we're going to do some prediction, on input that's been passed into this endpoint, now. To turn on tensorflow. GS but note it's one line of code it's. Just importing, the binding so, this is a binding we ship over NPM and it gives you the, high end power of tensorflow, C library, all executed. Under the note GS runtime. And. What can you do today under with server-side so all, those stemless we showed of doing. Writing. The model in the browser those actually just run under under, node as well you can use our conversion script. We. Ship. The. Three major platforms, Mac OS Linux in Windows CPU and then, we also have. GPU. And CUDA for Linux and Windows we just launched Windows late last week. And. All. The full, library supports so the layers API in our core API all work to get today right out of the box with no GS. And. To kind of highlight how. We can bring all these components, of NPM and tensorflow. GS and no, GS together, we. Built a little interactive demo. So. I, know. Not everybody is super familiar with baseball, but Major, League Baseball advanced media has this huge data set where, they record using. Sensors at all the stadiums the. Different types of pitches that, players throat games so. There's, there's. Pitches that are really fast to have a high velocity and low movement, and then, there are pitches who are a little slower that have, more movement so we. We, curated this data set and built, a model all in tensorflow GS that, trains against this data and detects I think seven or eight different types of pitches and. It. Renders it through a socket, so don't, get too hung up on the. Intricacies. Of baseball, this is just really solving a bread-and-butter, ml, problem of, taking sensor data and drawing. Up a classification. So. For this. I'll have ping run through the demo. Okay. All. Right so for. Web, developers you know you could really use tens, of loaches to build, a full, stack kind of application. So, on the left side is a browser that I. Started. A client. On, the on, the on the browser inside a browser which, trying to connect to the servers through socket IO on. The right side I have my console, are gonna start my server, no, Jess server, immediately. You see that is binded, to our our, tens, of low CPU, runtime. And. As. It goes the models getting trained and the. Train stats, are feed, back to the client side, as. Training. Progress you can see the accuracy increase, for, all labels. You. Know the curveball has. About 90% accuracy right now, with. You. Know kind of service, I implementation. Is easy to, fini data not like inside a browser is much harder let. Me try, to click this button what this would do is that will load live. MLB. Pitch. Data into. This application, and we will try to run new foreign Sandow. So. Let me click on that. So. Immediately. You can see the orange bar, is the prediction accuracy, for. All of these labels some. Of them we actually. Get better with the live data it's 90 percent for, changer, something. We did a little bit less accurate. Fastball. To seem is only 68, percent, so, all of all I think is, to, demonstrate that the model actually generalize.