---
title: "Web Quickstart - Docs"
label: "Web Quickstart"
description: "Deploy from the Buildfunctions Dashboard"
---

## Get Started

If you prefer a visual interface or want to deploy standard HTTP functions without setting up a local agent environment, use the **Buildfunctions Dashboard**.

### Sign Up / Login

Head to [https://www.buildfunctions.com/dashboard](https://www.buildfunctions.com/dashboard) and sign in with GitHub or your email.

![Dashboard Home](/assets/images/web/dashboard_home.jpeg)

### Create & Deploy

Navigate to the **Functions** section and click the **New** tab.

Simply click **Deploy Function** to immediately deploy a standard "Hello World" handler. You can also write your own code directly in the browser editor and deploy it.

![Deploy Function](/assets/images/web/deploy_button.png)

#### Advanced: GPU Functions

To deploy a GPU function, select the **GPU** option when creating a function and use one of these templates:

> **Info**
>
> **Config**: Runtime: Python, Memory: 10000 MB, vCPUs: 6, Timeout: 120 seconds, and `torch` in requirements.txt

### 1. Basic GPU Function
Verifies GPU access and returns device stats.

```python
import sys
import json
import torch

def handler():
    try:
        cuda_available = torch.cuda.is_available()
        device_count = torch.cuda.device_count() if cuda_available else 0
        device_name = torch.cuda.get_device_name(0) if cuda_available and device_count > 0 else "No GPU"
        print(f"Device set to: {device_name}")

        return {
            "statusCode": 200,
            "headers": { "Content-Type": "application/json" },
            "body": json.dumps({
                "message": "Hello from a Buildfunctions GPU Function!",
                "cuda_available": cuda_available,
                "device_name": device_name
            })
        }
    except Exception as e:
        print(f"Error: {e}", file=sys.stderr)
        return { "statusCode": 500, "body": json.dumps({"error": str(e)}) }
```

### 2. Streaming Response
Simulates real-time AI generation.

```python
import sys
import json
import time
import torch

MOCK_RESPONSE = "The most mysterious phenomenon in the universe is dark energy..."

async def stream_mock_response():
    try:
        yield b"<<START_STREAM>>\n"
        for i, word in enumerate(MOCK_RESPONSE.split()):
            token = f" {word}" if i > 0 else word
            yield f"<<STREAM_CHUNK>>{token}<<END_STREAM_CHUNK>>\n".encode()
            time.sleep(0.05)
        yield b"<<END_STREAM>>\n"
    except Exception as e:
        yield b"<<STREAM_ERROR>>\n"

async def async_stream_wrapper():
    async for chunk in stream_mock_response():
        yield chunk

def handler():
    try:
        cuda_available = torch.cuda.is_available()
        device_name = torch.cuda.get_device_name(0) if cuda_available else "No GPU"
        return {
            "statusCode": 200,
            "headers": {
                "Content-Type": "text/event-stream",
                "Cache-Control": "no-cache",
                "X-Device-Name": device_name
            },
            "body": async_stream_wrapper(),
        }
    except Exception as e:
        return {"statusCode": 500, "body": {"error": "Internal Server Error"}}
```

### Test Your Endpoint

Once deployed, click on your function and navigate to the **Network** tab.

Here you will see your active **Test Endpoints**. You can hit this endpoint with any HTTP client to trigger the function.

![Network Tab](/assets/images/web/network_tab.png)