> ## Documentation Index
> Fetch the complete documentation index at: https://buildfunctions.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Web Quickstart - Docs

> 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**.

<Steps>
  <Step title="Sign Up / Login">
    Head to [https://www.buildfunctions.com/dashboard](https://www.buildfunctions.com/dashboard) and sign in with GitHub or your email.

    <img src="https://mintcdn.com/buildfunctions/bQ7hppAN1Ity-0di/assets/images/web/dashboard_home.jpeg?fit=max&auto=format&n=bQ7hppAN1Ity-0di&q=85&s=69b784d642eb9fc6730691793e8317b2" alt="Dashboard Home" width="1438" height="1028" data-path="assets/images/web/dashboard_home.jpeg" />
  </Step>

  <Step title="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.

    <img src="https://mintcdn.com/buildfunctions/bQ7hppAN1Ity-0di/assets/images/web/deploy_button.png?fit=max&auto=format&n=bQ7hppAN1Ity-0di&q=85&s=ee71a9a5b7a5c980325bfe1355ae2239" alt="Deploy Function" width="1024" height="1024" data-path="assets/images/web/deploy_button.png" />

    <Accordion title="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
      </Info>

      ### 1. Basic GPU Function

      Verifies GPU access and returns device stats.

      ```python theme={null}
      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 theme={null}
      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"}}
      ```
    </Accordion>
  </Step>

  <Step title="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.

    <img src="https://mintcdn.com/buildfunctions/bQ7hppAN1Ity-0di/assets/images/web/network_tab.png?fit=max&auto=format&n=bQ7hppAN1Ity-0di&q=85&s=ef3971ab9c0a61bcdc5c14e1aa825e77" alt="Network Tab" width="1438" height="1029" data-path="assets/images/web/network_tab.png" />
  </Step>
</Steps>