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.

1. Sign Up / Login

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

   
2. 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.

   

   Advanced: GPU Functions

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

   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.

   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.

   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"}}
   
3. 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.