> ## Documentation Index
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# Build an app

> Build a Runpod SDK app step by step: run a function on a GPU, add a load-balanced HTTP service, share data between tasks, and deploy it.

This tutorial builds a small Runpod SDK app one resource at a time. You'll create a project, run a function on a GPU, add an HTTP service, stream results, share files between two tasks through a network volume, and deploy the app.

## What you'll learn

* How to scaffold a project with `rp flash init`.
* How to run queue functions on CPUs and GPUs with `@app.queue`.
* How to serve a stateful HTTP class with `@app.api`.
* How to stream results from a generator function.
* How to share data between tasks with a network volume.
* How to deploy the app with `rp flash deploy`.

## Requirements

* A [Runpod account](https://www.runpod.io/console/signup) with credits.
* Python 3.10 to 3.14 on your machine.
* The `runpod` package, version 2.0 or later. To use the release candidate, run `pip install runpod==2.0.0rc1`.
* Credentials saved with `rp login`. See [Authenticate](/sdk/overview#authenticate).

## Step 1: Create a project

Scaffold a new project:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
rp flash init my-app
cd my-app
```

This creates three files:

* `main.py`: An app with one queue function and a local entrypoint.
* `requirements.txt`: Packages your workers need. They're also installed locally for dev sessions.
* `.runpodignore`: Files to leave out of deployments. See [Ignore rules](/sdk/apps/deploy#ignore-rules).

Open `main.py`. It contains a complete app:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
import runpod
from runpod import App

app = App("my-app")


@app.queue(cpu="cpu3c-1-2")
def hello(name: str):
    # this print streams back to your terminal during rp flash dev
    print(f"running in the cloud, greeting {name}")
    return f"hello {name}!"


@runpod.local_entrypoint
def main():
    # runs on your machine; hello() runs on a cloud worker
    print(hello.remote("world"))
```

`@app.queue` turns `hello` into a queue-based Serverless endpoint running on a small CPU worker. `main` is marked with `@runpod.local_entrypoint`, so it runs on your machine and calls `hello` remotely.

## Step 2: Run it in a dev session

Start a dev session:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
rp flash dev main.py
```

The SDK creates a temporary endpoint for `hello`, runs `main`, and streams the worker's `print` output to your terminal. Leave the session running. Change the greeting in `hello`, save the file, and press Enter to re-run. Workers pick up the new code automatically.

Press Ctrl-C when you're done. The session deletes its temporary endpoints. To learn more, see [Dev sessions](/sdk/apps/dev-sessions).

## Step 3: Run a function on a GPU

Add a GPU function to `main.py`:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
@app.queue(gpu="4090", dependencies=["numpy"])
def matmul_benchmark(size: int = 4096):
    import time

    import torch

    device = torch.cuda.get_device_name(0)
    a = torch.randn(size, size, device="cuda")
    b = torch.randn(size, size, device="cuda")

    torch.cuda.synchronize()
    start = time.time()
    for _ in range(10):
        _ = a @ b
    torch.cuda.synchronize()
    elapsed = time.time() - start

    tflops = 10 * 2 * size**3 / elapsed / 1e12
    return {"device": device, "tflops": round(tflops, 1)}
```

Then call it from `main`:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
@runpod.local_entrypoint
def main():
    print(hello.remote("world"))
    print(matmul_benchmark.remote())
```

A few things to notice:

* `gpu="4090"` requests an RTX 4090. See [GPU types](/sdk/configuration/gpu-types) for other options.
* Packages are imported inside the function, so your machine doesn't need them. `torch` is already installed on the built-in GPU images, so it doesn't need to be listed in `dependencies`.

## Step 4: Add an HTTP service

Use `@app.api` to serve a class over HTTP. Mark routes with `@runpod.get` and `@runpod.post`, and setup code with `@runpod.init`:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
@app.api(cpu="cpu3c-1-2")
class Counter:
    @runpod.init
    def setup(self):
        # runs once when a worker starts, before any request
        self.count = 0

    @runpod.post("/bump")
    async def bump(self, body: dict):
        self.count += body.get("by", 1)
        return {"count": self.count}

    @runpod.get("/value")
    async def value(self):
        return {"count": self.count}
```

Call the routes through the class:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
@runpod.local_entrypoint
def main():
    print(Counter.post("/bump", {"by": 3}))  # {'count': 3}
    print(Counter.get("/value"))             # {'count': 3}
```

`Counter` runs on a load-balanced endpoint. Each worker keeps its own state in memory between requests.

## Step 5: Stream results

A generator function streams its output. Call `.stream()` to receive each chunk as the worker yields it:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
@app.queue(cpu="cpu3c-1-2")
def tokens(prompt: str):
    import time

    for word in f"you said: {prompt}".split():
        time.sleep(0.2)
        yield word


@runpod.local_entrypoint
def main():
    for chunk in tokens.stream("hello streaming world"):
        print(chunk, end=" ", flush=True)
    print()
```

Calling `.remote()` on a generator returns every chunk at once. You can also call `.spawn()` and stream the job later with `job.stream()`.

## Step 6: Share data between tasks

Tasks run on a dedicated Pod that terminates when the function returns. To pass files between tasks, mount the same network volume on both:

```python main.py theme={"theme":{"light":"github-light","dark":"github-dark"}}
checkpoints = runpod.NetworkVolume("checkpoints", size=10)


@app.task(gpu="4090", mounts={"/checkpoints": checkpoints})
def train(steps: int = 200):
    import torch
    import torch.nn as nn

    x = torch.randn(2048, 1, device="cuda")
    y = 3 * x + 1 + torch.randn_like(x) * 0.05

    model = nn.Linear(1, 1).to("cuda")
    opt = torch.optim.SGD(model.parameters(), lr=0.05)
    for _ in range(steps):
        loss = ((model(x) - y) ** 2).mean()
        opt.zero_grad()
        loss.backward()
        opt.step()

    run_dir = checkpoints.path / "linear"
    run_dir.mkdir(parents=True, exist_ok=True)
    torch.save(model.state_dict(), run_dir / "model.pt")
    return {"checkpoint": "linear", "final_loss": round(loss.item(), 5)}


@app.task(gpu="4090", mounts={"/checkpoints": checkpoints})
def evaluate(checkpoint: str):
    import torch
    import torch.nn as nn

    model = nn.Linear(1, 1)
    model.load_state_dict(torch.load(checkpoints.path / checkpoint / "model.pt"))
    return {"weight": round(model.weight.item(), 2), "bias": round(model.bias.item(), 2)}


@runpod.local_entrypoint
def main():
    out = train.remote()
    print("train finished:", out)
    print("evaluation:", evaluate.remote(out["checkpoint"]))
```

The volume is created the first time you use it, and both tasks are placed in its data center. `checkpoints.path` returns the mount path on each Pod. See [Storage](/sdk/configuration/storage).

<Warning>
  The network volume keeps incurring storage charges after the session ends. Delete it from the [Storage page](https://console.runpod.io/user/storage) in the console when you no longer need it.
</Warning>

## Step 7: Deploy the app

When the app works in a dev session, deploy it:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
rp flash deploy
```

This builds your code and dependencies into an artifact and creates long-running endpoints in the app's `default` environment. To deploy to another environment, add `--env`, for example `rp flash deploy --env production`. See [Deploy an app](/sdk/apps/deploy).

To remove the deployed endpoints:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
rp flash undeploy --app my-app
```

## Next steps

* [Learn how dev sessions work](/sdk/apps/dev-sessions).
* [Organize deployments with apps and environments](/sdk/apps/apps-and-environments).
* [Browse the examples](https://github.com/runpod/runpod-python/tree/main/examples/apps) in the runpod-python repository.