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The flash run command starts a local development server that lets you test your Flash application before deploying to production. The development server runs locally and updates automatically as you edit files. When you call a @Endpoint function, Flash sends the latest function code to Serverless workers on Runpod, so your changes are reflected immediately.

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Start the development server

From inside your project directory, run: 
flash run
The server starts at http://localhost:8888 by default. Your endpoints are available immediately for testing, and @Endpoint functions provision Serverless endpoints on first call.

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Using a custom host and port

# Change port
flash run --port 3000

# Make accessible on network
flash run --host 0.0.0.0

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Test your endpoints

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Using curl

# Call a queue-based endpoint (gpu_worker.py)
curl -X POST http://localhost:8888/gpu_worker/runsync \
  -H "Content-Type: application/json" \
  -d '{"input": {"input_data": {"message": "Hello from Flash"}}}'

# Call a load-balanced endpoint (lb_worker.py)
curl -X POST http://localhost:8888/lb_worker/process \
  -H "Content-Type: application/json" \
  -d '{"input_data": {"message": "Hello from Flash"}}'
Queue-based endpoints require the {"input": {...}} wrapper to match the deployed endpoint behavior. The inner payload structure maps to your function’s parameter names—the skeleton template uses input_data: dict, so the payload is {"input_data": {...}}. Load-balanced endpoints accept the payload directly without the input wrapper.

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Using the API explorer

Open http://localhost:8888/docs in your browser to access the interactive Swagger UI. You can test all endpoints directly from the browser. Flash extracts the first line of each function’s docstring and displays it as the endpoint description in the API explorer. Add docstrings to your @Endpoint functions to make your API self-documenting: 
@Endpoint(name="gpu-worker", gpu=GpuGroup.ANY)
def process_data(input_data: dict) -> dict:
    """Process input data and return computed results."""
    # Function implementation
    return {"result": "processed"}
The docstring “Process input data and return computed results” appears in the Swagger UI, making it easier to understand what each endpoint does.

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Using Python

import requests

# Call queue-based endpoint
response = requests.post(
    "http://localhost:8888/gpu_worker/runsync",
    json={"input": {"input_data": {"message": "Hello from Flash"}}}
)
print(response.json())

# Call load-balanced endpoint
response = requests.post(
    "http://localhost:8888/lb_worker/process",
    json={"input_data": {"message": "Hello from Flash"}}
)
print(response.json())

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Reduce cold-start delays

The first call to a @Endpoint function provisions a Serverless endpoint, which takes 30-60 seconds. Use --auto-provision to provision all endpoints at startup: 
flash run --auto-provision
This scans your project for @Endpoint functions and deploys them before the server starts accepting requests. Endpoints are cached in .runpod/resources.pkl and reused across server restarts.

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How it works

With flash run, Flash starts a local development server alongside remote Serverless endpoints: What runs where:
ComponentLocation
Development serverYour machine (localhost:8888)
@Endpoint function codeRunpod Serverless
Endpoint storageRunpod Serverless
Your code updates automatically as you edit files. Endpoints created by flash run are prefixed with live- to distinguish them from production endpoints.

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Clean up after testing

Endpoints created by flash run persist until you delete them. To clean up: 
# List all endpoints
flash undeploy list

# Remove a specific endpoint
flash undeploy ENDPOINT_NAME

# Remove all endpoints
flash undeploy --all

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Troubleshooting

Port already in use 
flash run --port 3000
Slow first request Use --auto-provision to eliminate cold-start delays: 
flash run --auto-provision
Authentication errors Run flash login to authenticate: 
flash login
Alternatively, set RUNPOD_API_KEY as an environment variable or in your .env file: 
export RUNPOD_API_KEY="your_api_key_here"
Values in your .env file are only available locally for CLI commands. They are not passed to deployed endpoints.

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Next steps