tinyray

A minimal, actor-only Ray for ML experiments. Rust core, Python API.

import tinyray as tr

@tr.remote(num_gpus=1, max_restarts=3)
class Rollout:
    def __init__(self, cfg):
        self.env = make_env(cfg)
    def step(self):
        return self.env.rollout()          # ~10 MB

rollouts = tr.create_actors(Rollout, cfg, count=32)   # atomic, all or nothing
refs = [r.step.remote() for r in rollouts]            # returns immediately
ready, pending = tr.wait(refs, num_returns=24)        # 24 finished; 8 still running
learner.update.remote(ready)                          # data goes rollout -> learner

wait returns two lists of ObjectRef, never values: ready are the ones that have settled, pending the ones still running. Both are just names -- a task id and the address of the actor holding the result -- so the driver moves a few dozen bytes per reference and never the payload. Passing ready to the learner hands over those names, and the learner fetches each 10 MB batch straight from the rollout that produced it.

Dropping the eight slow rollouts drops their results, not their work: they are still running, and their outputs still occupy the producers' stores until the watermark or TTL reclaims them. And if the group runs NCCL collectives, those same eight actors must still attend the next barrier -- see tinyray.collective.

What it is, and is not

tinyray is an HTTP control plane. The data plane belongs to the framework.

SGLang, vLLM, Megatron and torchrun already do distributed compute well. What they want from a cluster manager is placement, rank assignment, supervision and restart -- and then to be left alone.

  • Actors only. No stateless tasks. ML workers hold models and CUDA contexts.
  • HTTP for control. At 200 ms per call a ~200 us round trip is 0.1% overhead, and every message is inspectable with ordinary tools.
  • It never claims a process-global resource. A process has one default torch.distributed group, one CUDA context, one set of signal handlers. Those belong to your framework. tinyray assigns ranks and injects the torchrun environment; you call init_process_group yourself.
  • It supervises processes it did not write. An SGLang server is an ordinary process: give it GPUs, wait until it actually serves, label its logs, restart it when it dies.
  • No object store. For pure-Python rollouts a result stays in the actor that produced it and consumers fetch it directly. When a framework owns the data, tinyray moves references and nothing else.
@tr.remote(num_gpus=1)
class Trainer:
    def __init__(self):
        import torch.distributed as dist
        dist.init_process_group(backend="nccl")   # yours, not tinyray's

group = tr.create_worker_group(Trainer, size=8, name="trainer")
group.run("train_step", batch)          # dispatched to all ranks, then awaited

server = tr.launch_process(              # not a tinyray actor at all
    ["python", "-m", "sglang.launch_server", "--port", "{port}"],
    name="rollout", num_gpus=4, ready_when="http:/health",
)

Two details that are load-bearing rather than cosmetic:

  • Constructors run concurrently. A framework that rendezvous in __init__ blocks rank 0 until the last rank arrives, so constructing a group serially deadlocks on the first worker.
  • Readiness is observed, not assumed. An inference server binds its port minutes before it can answer; ready_when="http:/health" waits for the second event, not the first.

tinyray.collective still exists for pure-tinyray NCCL groups, but it takes the default process group and therefore cannot coexist with Megatron or SGLang. Use create_worker_group unless nothing else in the process wants that group.

Why Rust

An actor running a 200 ms training step holds the GIL. If the data path were Python, it could not serve result fetches during that time, and 32 actors fetching from each other would degrade to serial. The Rust core removes that coupling entirely:

10 MB decode idle 4 GIL-bound threads slowdown
native thread (the real serving path) 0.37 ms 0.38 ms 1.04x
initiated from Python 0.75 ms 36.7 ms 49x

Which also yields an architectural rule: the serving path must be driven by tokio, never by a Python-side loop. Work initiated from Python inherits GIL scheduling latency no matter how little of it runs in the interpreter, so /task/fetch never enters the interpreter at all.

Install

pip install tinyray

Wheels are built per interpreter version (3.9-3.13) for Linux and macOS on both x86_64 and aarch64. There is no abi3 build: the buffer protocol that makes results zero-copy is absent from the limited API on older interpreters, and version-specific wheels are a cheap price for it.

The package ships py.typed and hand-written stubs for the Rust extension, so type checkers see the full API.

From source

Requires a Rust toolchain.

python -m venv .venv
.venv/bin/pip install -e ".[dev]"
.venv/bin/maturin develop --release
pre-commit install

Tests

./scripts/test.sh                    # everything
cargo test --workspace               # 120 Rust tests
pytest tests/ -q                     # 216 Python tests
pytest benchmarks/ -q -s -m bench
python scripts/mutate.py             # 19 mutants: do the tests actually work?

Testing the tests

A green suite proves nothing about the tests. Three mechanisms keep them honest:

  • tests/test_suite_quality.py -- structural checks encoding the six blind spots that let real bugs through: unwired modules, timing constants that only ever run at their production value, options accepted but ignored, collapsed error taxonomies, untested lock branches, and design claims with no test.
  • tests/test_driver_byte_budget.py -- parks a 32 MB result in an actor, exercises every driver-side operation and asserts what crossed the driver's wire. The design's central claim is that payloads move between actors and never through the driver; this is where that claim is enforced for every operation rather than one.
  • scripts/mutate.py -- deliberately breaks 21 invariants and reports which ones the suite catches. It has already found three tests that passed while the behaviour they claimed to check was disabled.

Each check exists because a real bug got through in that exact shape.

Development

pre-commit install wires up the gates that CI also runs:

hook what it guards
ruff check / ruff format Python lint and formatting
cargo fmt / cargo clippy -D warnings Rust formatting and lint
mypy the public API's annotations, checked against the stubs

Releases are cut by pushing a v* tag; .github/workflows/release.yml runs the full suite, builds the wheel matrix and an sdist, and publishes to PyPI through a trusted publisher. workflow_dispatch targets TestPyPI by default so a dry run cannot reach the real index by accident.

Operations

tinyray status 127.0.0.1:41234 127.0.0.1:41235   # what is each actor doing?
tinyray introspect 127.0.0.1:41234               # raw report
tinyray health 127.0.0.1:41234

status calls out stalled callers, evictions, backpressure and stragglers, because "which actor is stuck, and on what?" is the question that dominates distributed ML debugging.

Layout

crates/tinyray-core/      wire protocol, framing, identifiers
crates/tinyray-runtime/   store, ordered queue, transport, cluster, collective, shm
crates/tinyray-py/        PyO3 bindings (all unsafe lives in buffers.rs)
python/tinyray/           API, serde, launcher, head, collective, pool, CLI

Project links

Data verified by PyPI on Aug 14, 2026
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  • Released:
Newer release available (0.13.0)

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Avatar for whxway from gravatar.com whxway

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.9

Provides Extra

dev test

Tags

actor distributed ray machine-learning reinforcement-learning

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The following attestation bundles were made for tinyray-0.1.0-cp39-cp39-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl:

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Release history Release notifications | RSS feed

0.13.0

13 files

0.12.0

13 files

0.11.0

13 files

0.10.0

13 files

0.9.1

13 files

0.9.0

13 files

0.8.1

13 files

0.7.1

13 files

0.6.1

13 files

0.6.0

13 files

0.5.0

13 files

0.4.1

13 files

0.4.0

13 files

0.3.0

13 files

0.2.1

16 files

0.2.0

16 files

This release

0.1.0 This release

16 files

0.0.3

16 files

0.0.2

16 files

0.0.1

16 files

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