runplz 2.0.0

Tiny Modal-shaped job harness — one declaration, multiple backends (local Docker / Brev / Modal)

runplz

Tiny Modal-shaped job harness — one Python decoration, multiple backends.

# jobs/train.py
from runplz import App, BrevConfig, Image

app = App("my-job", brev=BrevConfig())  # auto-creates the Brev box if missing

image = (
    Image.from_registry("pytorch/pytorch:2.4.0-cuda12.1-cudnn9-runtime")
    .apt_install("rsync", "build-essential")
    .pip_install("pandas>=2.0", "scikit-learn")
    .pip_install_local_dir(".", editable=True)
)

@app.function(
    image=image,
    gpu="T4",
    min_cpu=4, min_memory=26, min_gpu_memory=16, min_disk=100,
    timeout=60 * 60,
)
def train():
    import subprocess
    subprocess.run(["bash", "scripts/train.sh"], check=True)

@app.local_entrypoint()
def main():
    train.remote()

Invoke via the CLI:

runplz local  jobs/train.py
runplz brev   --instance my-box jobs/train.py
runplz modal  jobs/train.py

…or from pure Python (notebook, REPL, python jobs/train.py):

# at the bottom of jobs/train.py
if __name__ == "__main__":
    app.bind("brev", instance="my-box")   # or "local" / "modal"
    train.remote()

app.bind(...) is the programmatic equivalent of the CLI — it attaches a backend (plus the same flags: instance=, outputs_dir=, build=) so .remote() knows where to dispatch.

How it's structured

The CLI is the only entry point. runplz <backend> <script> does three things:

1. Imports your script (finds the App instance at module scope).
2. Binds the chosen backend to that App (the reason python script.py won't work — nothing has told the App where to dispatch).
3. Calls whatever you've decorated with @app.local_entrypoint().

Inside that entrypoint you call train.remote(), which serializes a minimal dispatch (env vars + a path to your script) and runs on the selected backend. Args and kwargs must be JSON-serializable.

Decorators you'll use

• @app.function(image=..., gpu=..., ...) — marks a function as running on the backend. Its body never executes locally (unless you call .local(); see below).
• @app.local_entrypoint() — marks the driver that runs inside the CLI process, on your machine. Typical body: build args, call fn.remote(...) once, maybe inspect the result. There can be at most one per script.

Ways to invoke a function

• train.remote(...) → dispatch on the currently-selected backend (what the CLI set). This is the normal case.
• train.local(...) → run the body in this Python process. No container, no remote. Useful for pytest or a quick REPL sanity check where you don't want to shell out to docker/brev/modal.
• train(...) → raises. Always go through .remote() or .local() so the dispatch is explicit.

What the CLI flags do

• --instance <name> — required for brev; the Brev box to attach to. If it doesn't exist and BrevConfig(auto_create_instances=True) (the default), runplz provisions it for you (cheapest match for your resource constraints, or an explicit BrevConfig(instance_type=...) if you pinned one).
• --no-build — local only; reuse the last tagged docker image instead of rebuilding.
• --outputs-dir <path> — where to collect /out back to on the host (default ./out/).

All three have app.bind(...) equivalents (instance=, build=False, outputs_dir=) for the pure-Python invocation path.

Backend config

App(..., brev=BrevConfig(...), modal=ModalConfig(...)). Both default to instances of their respective config class, so you only pass one when you need to override something.

BrevConfig

All fields are validated at construction time — an invalid config raises ValueError immediately, not later during dispatch.

field	default	what it does
auto_create_instances	True	If --instance points at a non-existent box, brev create it. Set False to hard-fail instead of auto-provisioning.
instance_type	None	Pin a specific Brev instance type string (e.g. "n1-standard-4:nvidia-tesla-t4:1"). Skips the constraint-based picker.
mode	"vm"	"vm" = full Brev VM + docker-in-VM. "container" = the box IS the base image; runplz applies Image DSL ops inline over ssh (lighter, no DinD).
use_docker	True	VM-mode only. False skips docker and installs a native venv on the box. Legacy escape hatch for providers where container mode isn't available.

Invalid combinations (rejected at construction):

• mode not in {"vm", "container"}
• mode="container" with use_docker=False (contradictory — the box is the image)
• instance_type="" (must be a non-empty string or None)

ModalConfig

ModalConfig() is a no-op today. Modal reads auth from ~/.modal.toml and schedules resources from @app.function(gpu=..., cpu=..., memory=...); we don't expose Modal-specific knobs. The class exists as a slot in App(modal=...) so the signature doesn't break when fields are added.

Why not one unified config?

Surveyed the fields — there is no genuine overlap today. Brev has real provisioning knobs (mode, instance type, docker-or-native); Modal has nothing we expose. A shared base class would be empty. If/when a genuinely cross-backend concept shows up (e.g. per-App secrets, a shared retry policy), we'll factor it into a BaseConfig then. Until then, the split is the honest API.

Image DSL

Declared once, translated per backend:

Image.from_registry("pytorch/pytorch:2.4.0-cuda12.1-cudnn9-runtime")
    .apt_install("bzip2", "rsync")
    .pip_install("pandas>=2.0", index_url="https://...")
    .pip_install_local_dir(".", editable=True)
    .run_commands("echo hi")

• Modal — rendered as a modal.Image.from_registry(...) chain; layers build on Modal's cluster and cache per-hash.
• local — synthesized into a Dockerfile passed to docker build -f - with the repo as context (so pip_install_local_dir can COPY your source).
• Brev (mode=vm) — same Dockerfile synthesis, shipped over rsync and built on the remote box.
• Brev (mode=container) — the box IS the base image; the layer ops run inline over ssh. Lighter, and sidesteps a historical Brev GPU+docker flakiness (see docs/brev-ssh-bug-report.md).

You can also use Image.from_dockerfile("path/to/Dockerfile") to point at an existing Dockerfile you maintain; runplz just runs it.

Resource constraints

All memory/disk fields in GB:

@app.function(
    image=image,
    gpu="T4",            # modal-style label; "A100", "H100", "L4", ...
    min_cpu=4,
    min_memory=26,       # RAM
    min_gpu_memory=16,   # VRAM
    min_disk=100,
    timeout=60 * 60,
)

How they're honored per backend:

constraint	local	brev	modal
gpu	—	brev search --gpu-name	@app.function(gpu=...)
min_cpu	—	--min-vcpu	cpu=
min_memory	—	--min-ram	memory= (converted to MB)
min_gpu_memory	—	--min-vram	baked into gpu string: A100-80GB
min_disk	—	--min-disk (filter + provision)	warned, dropped (no modal kwarg)

local ignores these — it uses whatever your machine has and auto-detects NVIDIA runtime via docker info.

On brev, the constraints drive brev search --sort price and runplz picks the cheapest match. Override with BrevConfig(instance_type="...") when you need a specific shape.

Install

pip install runplz                 # core (local + brev)
pip install 'runplz[modal]'        # add Modal support

The core dependency set is empty. Backends shell out to system CLIs:

• local → docker
• brev → brev, docker (or skipped in mode="container"), ssh, rsync
• modal → modal>=1.1,<2 Python package

Outputs

Write to $RUNPLZ_OUT inside your function. runplz collects that directory back to ./out/ on the host (rsync on brev, tar-return on modal, bind-mount on local). On modal, returns are capped at ~256 MB — if you're writing more, switch to modal.Volume for now (a runplz-native volume abstraction is TODO).

Caveats

• .remote() args must be JSON-serializable. No closures, no custom objects. Deliberate: the remote dispatch is env vars + a path.
• Your job script is imported by path at runtime (not installed as a package), so it can live anywhere in the repo.
• One App per script. Multiple Apps in one file is ambiguous for the CLI loader and errors.

Tests

pytest tests/

~120 offline tests — DSL rendering, BrevConfig validation, Modal GPU-label translation, instance picker with mocked subprocess, CLI guards.

License

Apache 2.0 — see LICENSE.