# BERT With Zero Overhead

In this tutorial we're going to demonstrate how you can deploy one of the fastest NLP models, BERT, to remote GPUs and maintain decent latency.

## Why BERT?

BERT encoder is extremely fast, running at 1.5ms on local GPU (tested on Nvidia T4). Deploying that model to remote machines and maintaining low latency is hard. 

Everinfer is highly optimized and will allow you to run that model on remote machines while keeping up with its speed. 

## How to deploy BERT on Everinfer

Install Everinfer and HuggingFace transformers library.

Convert the model to ONNX format:

```
!python3 -m transformers.onnx --model=distilbert-base-uncased onnx/
```

Authenticate on Everinfer using your API key, upload the model, and create inference engine:

```python
from everinfer import Client
client = Client('my_api_key')
pipeline = client.register_pipeline('bert', ['onnx/model.onnx'])
runner = client.create_engine(pipeline['uuid'])
```

You are ready to go!

Since HuggingFace tokenizers are fully compatible with Everinfer expected input format, you can feed tokenizer outputs directly to the deployed model:

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
inputs = tokenizer("Everinfer is fast af", return_tensors="np")
```

After applying tokenizer to input text, running the model is as simple as:

```python
preds = runner.predict([inputs]) 
```

## Performance 

Remote GPU access overhead is virtually zero! 

<figure><img src="/files/KrIMERIKbAxsvNx094H9" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
You could deploy that code to AWS Lambda to go fully serverless, or use it as a part of your self-hosted web app.&#x20;
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.everinfer.ai/examples/bert-with-zero-overhead.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.