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---
license: apache-2.0
---

### DISTILBERT RUNNING ON [DEEPSPARSE](https://github.com/neuralmagic/deepsparse) GOES BRHMMMMMMMM. πŸš€πŸš€πŸš€

This model is πŸ‘‡

        β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—   β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
        β–ˆβ–ˆβ•”β•β•β•β•β• β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•—  β–ˆβ–ˆβ•”β•β•β•β•β• β–ˆβ–ˆβ•”β•β•β•β•β•
        β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β• β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β•  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—  
        β•šβ•β•β•β•β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β•β•  β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β•šβ•β•β•β•β–ˆβ–ˆβ•‘β–ˆ β–ˆβ•”β•β•β•  
        β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘      β–ˆβ–ˆβ•‘  β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘  β–ˆβ–ˆ β•‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
        β•šβ•β•β•β•β•β•β• β•šβ•β•      β•šβ•β•  β•šβ•β• β•šβ•β•  β•šβ• β•β•šβ•β•β•β•β•β•β• β•šβ•β•β•β•β•β•β•
                                                                                                         
![Alt Text](https://media.giphy.com/media/4Hmjz2sqdtASJ2gFMH/giphy.gif)


### LOOKS LIKE THIS πŸ‘‡

![Imgur](https://imgur.com/gWfX811.jpg)

### Inference endpoints, outside of outliers (4ms) is avg. latency on 2 vCPUs:

![Imgur](https://i.imgur.com/qceSdjZ.png)


### Handler for access to inference endpoints

```python
class EndpointHandler:

    def __init__(self, path=""):

        self.pipeline = Pipeline.create(task="text-classification", model_path=path)

    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
        """
        Args:
            data (:obj:): prediction input text
        """
        inputs = data.pop("inputs", data)

        start = perf_counter()
        prediction = self.pipeline(inputs)
        end = perf_counter()
        latency = end - start

        return {
            "labels": prediction.labels, 
            "scores": prediction.scores,
            "latency (secs.)": latency
        }
```
      
̷͈̍
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