Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """Modal executor — skeleton for v0. | |
| This file is a STUB. The full Modal integration requires the `modal` | |
| client library installed (`pip install modal`) and a configured Modal | |
| account (`~/.modal.toml`). The user's environment has both, but the | |
| test suite must run without them, so we keep this file import-safe. | |
| Real implementation lives in v0 polish; the docstring below is the | |
| contract. | |
| """ | |
| from __future__ import annotations | |
| from typing import Any, Callable, Mapping | |
| from composer_replication.diloco.serverless.executor import ( | |
| ReplicaHandle, | |
| ServerlessExecutor, | |
| ) | |
| class ModalExecutor(ServerlessExecutor): | |
| """Run replicas as Modal Functions in parallel. | |
| Reference implementation pattern (per ADR-005): | |
| @app.function(gpu="A100-40GB", timeout=3600) | |
| def run_replica(rank: int, rendezvous_uri: str, **kwargs): | |
| os.environ["REPLICA_RANK"] = str(rank) | |
| from composer_replication.diloco.serverless import ( | |
| MockManager, ObjectStoreAllReduce, | |
| ) | |
| store = ObjectStoreAllReduce(rendezvous_uri, | |
| rank=rank, world_size=N) | |
| manager = MockManager(store) | |
| # ... run the trainer with this manager ... | |
| Then `launch_replicas` does: | |
| calls = [run_replica.spawn(rank=i, ...) for i in range(N)] | |
| return [ReplicaHandle(rank=i, backend_name="modal", | |
| metadata={"call_id": calls[i].object_id}) | |
| for i in range(N)] | |
| Pricing reference (2026-05-26): A100-40GB ≈ $1.95/hr, H100 ≈ $5.50/hr. | |
| Cold start ≈ 30s. Inter-job networking via cluster mode (opt-in, | |
| not used by default). | |
| Status: SKELETON. Real implementation pending v0 polish wave. | |
| """ | |
| backend_name = "modal" | |
| supports_inter_replica_network = False # default; cluster mode = True | |
| def __init__(self, *, app_name: str = "composer-replication-diloco") -> None: | |
| try: | |
| import modal # noqa: F401 | |
| except ImportError as e: | |
| raise RuntimeError( | |
| "ModalExecutor requires the modal client. Install with " | |
| "`pip install modal` and configure with `modal token new`. " | |
| "Got: " + repr(e) | |
| ) | |
| self.app_name = app_name | |
| # Real implementation: build a `modal.App` and register `run_replica` | |
| # here so that subsequent `launch_replicas` can `.spawn()` it. | |
| raise NotImplementedError( | |
| "ModalExecutor is a v0 skeleton; full implementation pending. " | |
| "Use LocalProcessExecutor for testing." | |
| ) | |
| # All Protocol methods raise NotImplementedError via __init__ — the | |
| # class never instantiates successfully in the skeleton. Sketch | |
| # signatures here for documentation: | |
| def launch_replicas( | |
| self, | |
| n_replicas: int, | |
| entrypoint: str | Callable[..., Any], | |
| entrypoint_args: Mapping[str, Any], | |
| *, | |
| gpu: str | None = "A100-40GB", | |
| timeout: int = 3600, | |
| ) -> list[ReplicaHandle]: | |
| raise NotImplementedError | |
| def poll(self, handle: ReplicaHandle) -> str: | |
| raise NotImplementedError | |
| def stream_logs(self, handle: ReplicaHandle, *, n_lines: int = 200) -> str: | |
| raise NotImplementedError | |
| def cancel(self, handle: ReplicaHandle) -> None: | |
| raise NotImplementedError | |
| def collect( | |
| self, | |
| handles: list[ReplicaHandle], | |
| *, | |
| timeout: int | None = None, | |
| ) -> list[dict[str, Any]]: | |
| raise NotImplementedError | |
| __all__ = ["ModalExecutor"] | |