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

Built for one workload: RL rollouts and hyperparameter sweeps, ~32 actors, ~10 MB payloads, ~200 ms per call.

  • Actors only. No stateless tasks. ML workers hold models and CUDA contexts.
  • HTTP for control and results. At 200 ms per call a ~200 µs round trip is 0.1% overhead, and every message is inspectable with ordinary tools.
  • No object store. A result stays in the actor that produced it; consumers fetch it directly. No plasma, no spilling, no distributed refcounting.
  • NCCL for weights. tinyray implements no collective transport at all. It assigns ranks and manages the epoch state machine; torch.distributed moves the bytes.

Design notes, measurements and the decisions the implementation forced us to revise are kept with the source rather than published here.

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. Two 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.
  • scripts/mutate.py -- deliberately breaks 19 invariants and reports which ones the suite catches. It has already found two 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
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (0.13.0)

1 maintainer

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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

Classifiers

Report project as malware

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SHA256 97f328516d9955ab813c46513a8d712d552318eb14d4a1395e5046a8d65f6f9f
MD5 bc14620530c6864843c54a8be9697627
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The following attestation bundles were made for tinyray-0.0.2-cp39-cp39-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl:

Publisher: release.yml on HSPK/tinyray

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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

0.1.0

16 files

0.0.3

16 files

This release

0.0.2 This release

16 files

0.0.1

16 files

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