Modal — serverless Python & GPU as decorators

Modal runs your Python on remote containers without you ever writing a Dockerfile or a YAML file. The mental model: infrastructure is declared inline as Python decorators. A modal.App is the deployable unit; each @app.function runs in its own container built from a modal.Image you describe in code; you attach a GPU, a Volume, or a Secret as keyword arguments and the platform provisions, scales to zero, and tears down for you. There is no control plane to babysit — the source file is the infra.

Pinned stack: modal 1.4.3 (released 2026-05-18), Python 3.10–3.14 (>=3.10,<3.15). Install with pip install modal then modal setup to authenticate. Everything below uses the Modal 1.0+ API; several pre-1.0 forms were removed and are called out as Bad→Good.

Not this skill

Modal owns the serverless-container-as-decorators surface and its CLI lifecycle; the contents of your function belong elsewhere.

The job	Goes to
Calling a managed prediction API with no container of your own	replicate / together-fireworks / fal
Renting a persistent, SSH-able GPU box by the hour/week	runpod
FastAPI design (routing, Pydantic, deps) independent of host	fastapi
Writing a Dockerfile for a registry / k8s / Compose	docker
General Python language/runtime questions	python
RAG / LLM pipeline orchestration logic itself	llm-pipeline

Decision: which entrypoint?

You want…	Use	Persists after exit?
Run a function once and exit (script, batch)	modal run app.py + @app.local_entrypoint()	No (ephemeral)
Hot-reload dev loop for a web endpoint	modal serve app.py	No (dies on Ctrl-C)
A persistent named deployment (prod, schedules, endpoints)	modal deploy app.py	Yes
Fan out work across many containers	.map() / .starmap() / .spawn() inside an entrypoint	n/a

Rule: schedules and live web endpoints require modal deploy. modal run exits when the entrypoint returns, so a Cron defined under modal run never fires. modal serve is for the dev loop only — it watches your files and redeploys on save, but the app vanishes when you stop it.

The minimal app skeleton

import modal

app = modal.App("hello-modal")

# The image is the container spec. Build it once, reuse across functions.
image = modal.Image.debian_slim(python_version="3.12").uv_pip_install("requests")


@app.function(image=image)
def fetch(url: str) -> int:
    import requests  # imported INSIDE the function: it lives in the remote image, not locally

    return len(requests.get(url).content)


@app.local_entrypoint()
def main() -> None:
    # Runs on your laptop; .remote() ships the call to a Modal container.
    print(fetch.remote("https://modal.com"))

Run it: modal run app.py. Bad = wiring infra with argparse + a bash launcher + a hand-rolled Dockerfile. Good = the decorators above; the app, image, and scaling are all declared in the one file. Note the in-function import: dependencies you uv_pip_install exist in the remote image, so import them inside the function (or guard top-level imports), not at module top where your laptop would need them too.

Images — pin, layer, cache

Build images by chaining methods on modal.Image. Rules, each with its why:

1. Prefer .uv_pip_install(...) over .pip_install(...) — it resolves and installs with uv, materially faster image builds.
2. Pin versions — .uv_pip_install("torch==2.5.1", "transformers==4.46.0"). Unpinned deps make builds non-reproducible and silently drift on rebuild.
3. Order layers stable→volatile — system packages and big wheels first, your fast-changing code last. Modal caches each layer; a change busts that layer and everything after it.
4. Add your own code with .add_local_dir(...) / .add_local_python_source(...), not by pip-installing your repo. These are applied last so editing your source doesn't rebuild torch.
5. .from_registry("...") when you need a specific base image; .apt_install("ffmpeg") for system binaries; .run_commands(...) for arbitrary build steps.

image = (
    modal.Image.debian_slim(python_version="3.12")
    .apt_install("ffmpeg")                                   # stable: rarely changes
    .uv_pip_install("torch==2.5.1", "transformers==4.46.0")  # heavy wheels, pinned
    .add_local_python_source("my_pkg")                       # volatile: your code, applied last
)

→ references/images-gpu-cookbook.md for vLLM / torch+CUDA / diffusers recipes and the download-once weight-cache pattern.

GPU — it's a string now

In Modal 1.0+ the GPU is a string on the decorator. The old modal.gpu.H100() objects were removed.

• Single GPU: gpu="H100".
• Count via colon: gpu="A100:2" (two A100s in one container).
• Memory variant: gpu="A100-80GB" (also A100-40GB).
• Fallback list (first available wins): gpu=["H100", "A100", "any"].
• Supported types: T4, L4, A10, L40S, A100(-40GB/-80GB), RTX-PRO-6000, H100, H200, B200.

# Bad — removed API, raises at import.
# @app.function(gpu=modal.gpu.A100())

# Good — string form.
@app.function(image=image, gpu="A100-80GB", timeout=600)
def embed(texts: list[str]) -> list[list[float]]: ...

Pick the smallest GPU that fits: T4/L4 for cheap inference and small models, A10/L40S mid-range, A100/H100 for training and large-model serving, H200/B200 for frontier-scale. GPU time is billed per second a container is alive — never attach a GPU to a CPU-only job, and keep scaledown_window tight so idle GPU containers don't burn money.

Scaling & lifecycle

Tune these keyword args on @app.function, each with its why:

Param	Effect	Why
min_containers=N	Keep N warm instances always running	Kills cold starts for latency-sensitive endpoints (costs idle compute)
buffer_containers=N	Pre-warm N extra beyond current load	Smooths bursty traffic
scaledown_window=300	Seconds an idle container lingers before shutdown	Reuse hot containers across nearby calls; lower = cheaper, higher = warmer
timeout=600	Max seconds a single call may run	Caps runaway jobs
retries=3	Auto-retry failed inputs	Survives transient failures in .map() fan-outs

Migration note: keep_warm → min_containers and container_idle_timeout → scaledown_window in the 1.0 migration. The old names are gone.

Concurrency within a container is now its own decorator: @modal.concurrent(max_inputs=N) stacked under @app.function (it replaces the old allow_concurrent_inputs= argument). Use it so one container handles N simultaneous requests instead of one-per-container.

Volumes & Secrets

A Volume is a distributed filesystem you mount into containers to persist data across runs — the canonical use is caching downloaded model weights so cold starts skip the re-download.

weights = modal.Volume.from_name("hf-cache", create_if_missing=True)


@app.function(image=image, gpu="H100", volumes={"/cache": weights})
def serve_model():
    # Reader: refresh the view so you see writes from other containers.
    weights.reload()
    # ... load model from /cache ...


@app.function(image=image, volumes={"/cache": weights})
def download_weights():
    # ... write files into /cache ...
    weights.commit()  # WITHOUT this, writes are NOT durable across containers

Gotcha: writers must call vol.commit() to persist; readers call vol.reload() to see another container's committed writes. Forgetting commit() is the #1 "my cache is empty" bug — the files existed in that container and vanished with it.

Secrets land as environment variables in the container:

@app.function(image=image, secrets=[modal.Secret.from_name("hf-token")])
def pull():
    import os

    token = os.environ["HF_TOKEN"]  # value injected from the named Modal Secret

Never bake a token into the image (.run_commands("export TOKEN=...")) — it's recorded in layer history. Use a Secret. The cookbook above also carries the HF/OpenAI secret patterns.

Web endpoints

Stack a web decorator under @app.function. Pick by surface:

Decorator	Use for	Needs
@modal.fastapi_endpoint()	A single GET/POST function-as-URL	fastapi[standard] in image
@modal.asgi_app()	A full FastAPI/Starlette app you return	fastapi[standard]
@modal.wsgi_app()	A Flask/Django WSGI app	the framework
@modal.web_server(port=8000)	Your own server process (e.g. vLLM) on a port	the server

Decorator stack order matters: @app.function is outermost (top), then optional @modal.concurrent, then the web decorator innermost (bottom, closest to def).

@app.function(image=image, gpu="H100", min_containers=1, scaledown_window=300)
@modal.concurrent(max_inputs=10)   # middle
@modal.asgi_app()                  # innermost
def web():
    from fastapi import FastAPI

    api = FastAPI()

    @api.get("/health")
    def health():
        return {"ok": True}

    return api

Develop with modal serve app.py (hot-reload); ship with modal deploy app.py (stable URL). For custom domains, proxy-auth tokens, batching (@modal.batched), and concurrency tuning → references/web-and-scaling.md. For the FastAPI app's own design (routes, Pydantic, deps), that's fastapi — this skill only mounts it.

Scheduled jobs

# Fixed wall-clock time, with timezone — survives redeploys at the same clock time.
@app.function(schedule=modal.Cron("0 6 * * *", timezone="America/New_York"))
def nightly_report(): ...


# Interval relative to deploy time.
@app.function(schedule=modal.Period(hours=5))
def every_five_hours(): ...

Gotcha: Period is measured from deploy time and resets on every redeploy — redeploy at 4:59 and your "every 5 hours" clock restarts. Cron is wall-clock stable; prefer it for "run at 6am" semantics. Either way you must modal deploy (not modal run) for the schedule to live on the platform.

Parallelism

Fan a function out across containers without managing a pool:

@app.local_entrypoint()
def main():
    urls = ["https://a.com", "https://b.com", "https://c.com"]
    # .map: one arg per call, results in input order.
    sizes = list(fetch.map(urls))
    # .starmap: each item is an argument tuple. .spawn: fire-and-forget -> handle.get() later.
    handle = fetch.spawn("https://slow.com")
    print(sizes, handle.get())

.map(iterable) returns results in order by default; pass order_outputs=False to yield as they complete (faster when latencies vary). Combine with retries= on the function so a single bad input doesn't sink the batch.

Anti-patterns

Anti-pattern	Do instead
"I'll use gpu=modal.gpu.A100() like the old docs"	Removed in 1.0. Use the string gpu="A100-80GB".
"Attach a GPU, it might speed up this CPU job"	GPU is billed per second alive. CPU-only job → no gpu=.
"My files are written, the Volume will keep them"	Not without vol.commit() (writer) / vol.reload() (reader).
"Pin later — uv_pip_install('torch') is fine for now"	Unpinned deps drift; builds aren't reproducible. Pin every version.
"modal run it, the endpoint/schedule will stay up"	run is ephemeral; it exits. Use modal deploy for anything persistent.
"Order the decorators however — Modal figures it out"	@app.function outermost, web decorator innermost. Wrong order errors.
"Bake the HF token into the image with run_commands"	Leaks into layer history. Use modal.Secret.from_name(...).
"Just call the model via a managed API through Modal"	If you write no container, that's a managed-API job → replicate.
"I need a box to SSH into for a week"	That's a persistent rental → runpod, not Modal's scale-to-zero.
"Set min_containers high so it's always fast"	Idle warm containers cost money 24/7. Tune scaledown_window first.

Verify

scripts/verify.sh [TARGET] statically lints the nearest emitted Modal *.py: it requires a modal.App(, fails if the removed modal.gpu. object form appears, checks that any web decorator sits under an @app.function, and that any Volume uses from_name(..., create_if_missing=...). It runs python -c "import modal" only if modal is installed (skip-pass otherwise), needs no Modal credentials, and exits 0 on an empty target.

Project grounding (02-DOCS)

In a project with a 02-DOCS/ layer (the harness wiki), read 02-DOCS/wiki/stack/modal.md first, then record this app's real Modal choices there — GPU types, image base, Volume names, schedule, endpoint shape — and index it in 02-DOCS/wiki/index.md. No 02-DOCS/? Skip silently.