 Hello, my name is Philip Schmidt and I am technical lead at HygneFace. I'm excited to be able to show you and demo you the new Escher HygneFace endpoints. Okay, let's get started. In the demo, we are going to create a new Escher HygneFace endpoint for text classification. HygneFace endpoints are managed Escher application powered by Escher machine learning. We are currently in the UI where we can create our endpoint. First, we need to select our subscription, then our resource group. Next we are going to select our region, in this case we are going to use East US and define an endpoint name. Let's go with distl, distl, bird. As next we are going to select our model from the HygneFace hub for this we can go to filter, text classification and then select one of the available models. We are going to use the distl, bird model which also fits our endpoint name. If you demodel ID and paste it into our model ID input field, then we are going to select our text classification to cast. Okay, as next we are going to define an application name. Let's go with text distl and then we need to provide a managed resource group name. You can go with HygneFace distl. Our application managed resource group holds all resources that are required to create our HygneFace endpoint. After we have done that, we can select our compute. In this case we are going to use a CPU instance but you can also go with a GPU instance. The nice part about HygneFace endpoints is that they are going to deliver auto scaling out of the box. Meaning we can select our min instance card and our max instance card. We are going to do this for max with two. So we are going to be able to scale from one to two instances and then we need to provide our threshold. We are going to use requests per minute as our threshold target and we are going to select 1000. This means that our endpoint will receive requests and if we have an average over 1000 requests over a period for five minutes, the endpoint will automatically scale out. And if the load decreases, the endpoint will also automatically scale in again. Okay, now let's review and create. All our validation has passed. We can scroll down and then see again our provided will use we have our model ID with distal bird, our task, text classification and the resource name we have provided. We can also see our instance type and the auto scaling configuration. Okay, let's create. Our endpoint will now be created. This will take a few minutes. I will be back when the endpoint all the deployment is complete. Our deployment is complete and we can test our endpoint. Therefore we go to the resource to outputs where we can see our inference UI, our API of authentication token and the playground URL we can use to test our endpoint. Here we have a widget where we can immediately use to run inference and code snippet. Let's test it. The new hugging face and points are easy to use. And we can see our model predicted the correct sentiment for it. We can also copy the Python snippet and execute it for example in a Trubiton notebook. Here you can also see that our model has predicted our label correctly. I am Philip and this was a quick look at the new Azure hugging face and points. Thank you. Thank you.
[{"start": 0.0, "end": 10.68, "text": " Hello, my name is Philip Schmidt and I am technical lead at HygneFace."}, {"start": 10.68, "end": 16.8, "text": " I'm excited to be able to show you and demo you the new Escher HygneFace endpoints."}, {"start": 16.8, "end": 19.56, "text": " Okay, let's get started."}, {"start": 19.56, "end": 31.28, "text": " In the demo, we are going to create a new Escher HygneFace endpoint for text classification."}, {"start": 31.28, "end": 36.12, "text": " HygneFace endpoints are managed Escher application powered by Escher machine learning."}, {"start": 36.12, "end": 39.72, "text": " We are currently in the UI where we can create our endpoint."}, {"start": 39.72, "end": 45.519999999999996, "text": " First, we need to select our subscription, then our resource group."}, {"start": 45.52, "end": 50.400000000000006, "text": " Next we are going to select our region, in this case we are going to use East US and"}, {"start": 50.400000000000006, "end": 52.24, "text": " define an endpoint name."}, {"start": 52.24, "end": 57.120000000000005, "text": " Let's go with distl, distl, bird."}, {"start": 57.120000000000005, "end": 63.400000000000006, "text": " As next we are going to select our model from the HygneFace hub for this we can go to filter,"}, {"start": 63.400000000000006, "end": 67.48, "text": " text classification and then select one of the available models."}, {"start": 67.48, "end": 72.48, "text": " We are going to use the distl, bird model which also fits our endpoint name."}, {"start": 72.48, "end": 77.92, "text": " If you demodel ID and paste it into our model ID input field, then we are going to select"}, {"start": 77.92, "end": 81.4, "text": " our text classification to cast."}, {"start": 81.4, "end": 85.68, "text": " Okay, as next we are going to define an application name."}, {"start": 85.68, "end": 91.24000000000001, "text": " Let's go with text distl and then we need to provide a managed resource group name."}, {"start": 91.24000000000001, "end": 95.56, "text": " You can go with HygneFace distl."}, {"start": 95.56, "end": 100.4, "text": " Our application managed resource group holds all resources that are required to create"}, {"start": 100.4, "end": 102.84, "text": " our HygneFace endpoint."}, {"start": 102.84, "end": 106.12, "text": " After we have done that, we can select our compute."}, {"start": 106.12, "end": 111.88000000000001, "text": " In this case we are going to use a CPU instance but you can also go with a GPU instance."}, {"start": 111.88000000000001, "end": 116.24000000000001, "text": " The nice part about HygneFace endpoints is that they are going to deliver auto scaling"}, {"start": 116.24000000000001, "end": 117.32000000000001, "text": " out of the box."}, {"start": 117.32000000000001, "end": 121.80000000000001, "text": " Meaning we can select our min instance card and our max instance card."}, {"start": 121.80000000000001, "end": 124.60000000000001, "text": " We are going to do this for max with two."}, {"start": 124.60000000000001, "end": 129.20000000000002, "text": " So we are going to be able to scale from one to two instances and then we need to provide"}, {"start": 129.2, "end": 130.64, "text": " our threshold."}, {"start": 130.64, "end": 135.11999999999998, "text": " We are going to use requests per minute as our threshold target and we are going to"}, {"start": 135.11999999999998, "end": 136.76, "text": " select 1000."}, {"start": 136.76, "end": 143.11999999999998, "text": " This means that our endpoint will receive requests and if we have an average over 1000 requests"}, {"start": 143.11999999999998, "end": 147.72, "text": " over a period for five minutes, the endpoint will automatically scale out."}, {"start": 147.72, "end": 152.2, "text": " And if the load decreases, the endpoint will also automatically scale in again."}, {"start": 152.2, "end": 157.32, "text": " Okay, now let's review and create."}, {"start": 157.32, "end": 168.28, "text": " All our validation has passed."}, {"start": 168.28, "end": 173.07999999999998, "text": " We can scroll down and then see again our provided will use we have our model ID with"}, {"start": 173.07999999999998, "end": 178.2, "text": " distal bird, our task, text classification and the resource name we have provided."}, {"start": 178.2, "end": 182.56, "text": " We can also see our instance type and the auto scaling configuration."}, {"start": 182.56, "end": 185.12, "text": " Okay, let's create."}, {"start": 185.12, "end": 194.12, "text": " Our endpoint will now be created."}, {"start": 194.12, "end": 195.6, "text": " This will take a few minutes."}, {"start": 195.6, "end": 202.08, "text": " I will be back when the endpoint all the deployment is complete."}, {"start": 202.08, "end": 205.24, "text": " Our deployment is complete and we can test our endpoint."}, {"start": 205.24, "end": 213.04000000000002, "text": " Therefore we go to the resource to outputs where we can see our inference UI, our API of"}, {"start": 213.04, "end": 219.07999999999998, "text": " authentication token and the playground URL we can use to test our endpoint."}, {"start": 219.07999999999998, "end": 225.28, "text": " Here we have a widget where we can immediately use to run inference and code snippet."}, {"start": 225.28, "end": 227.23999999999998, "text": " Let's test it."}, {"start": 227.23999999999998, "end": 235.76, "text": " The new hugging face and points are easy to use."}, {"start": 235.76, "end": 239.79999999999998, "text": " And we can see our model predicted the correct sentiment for it."}, {"start": 239.8, "end": 245.96, "text": " We can also copy the Python snippet and execute it for example in a Trubiton notebook."}, {"start": 245.96, "end": 258.6, "text": " Here you can also see that our model has predicted our label correctly."}, {"start": 258.6, "end": 263.76, "text": " I am Philip and this was a quick look at the new Azure hugging face and points."}, {"start": 263.76, "end": 264.76, "text": " Thank you."}, {"start": 264.76, "end": 271.76, "text": " Thank you."}]