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
Wave 3 cleanup: close deferred-LOW review items R5/R6/R11
Browse files- R5: EKSExecutor.cancel + SageMakerExecutor.cancel now re-raise genuinely
unexpected errors instead of swallowing ALL exceptions; only already-terminated
signals are idempotent no-ops (EKS: 404/409; SM: ResourceNotFound +
already-terminal ValidationException via new _is_already_terminal). + a test
that an AccessDenied-class error propagates.
- R6: EKSExecutor.collect() result dicts include a 'result' key (the S3
rendezvous URI, or None) for cross-backend shape uniformity with
Local/Modal/SageMaker.
- R11: seed torch.manual_seed(0) in spike-006 test_alternating_batches_loss_
decreases so the loss-trend assertion is reproducible and no longer flaky
under CPU contention (it failed only in the contended full suite, passed in
isolation). Root cause was unseeded nondeterminism, not a real regression.
All Wave-3 review findings (R1-R11) now closed.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -592,12 +592,14 @@ class EKSExecutor:
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),
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)
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except ApiException as e:
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#
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def collect(
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self,
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"exit_code": exit_code,
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"error": error,
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"job_name": handle.metadata.get("job_name"),
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}
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),
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)
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except ApiException as e:
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# R5: swallow ONLY already-terminated signals (404 Not Found, 409
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# Conflict on a job mid-deletion). A genuinely unexpected API error
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# (403 forbidden, 500, malformed request) must NOT be reported as a
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# successful cancel — re-raise so a real teardown failure (leaking
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# GPU-burning pods) is visible rather than silently swallowed.
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if getattr(e, "status", None) in (404, 409):
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return # already deleted / mid-deletion — idempotent no-op
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raise
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def collect(
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self,
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"exit_code": exit_code,
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"error": error,
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"job_name": handle.metadata.get("job_name"),
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# R6: cross-backend uniformity with Local/Modal/SageMaker collect()
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# shapes. EKS replicas write their real output to the S3 rendezvous
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# (ObjectStoreAllReduce), not back through the k8s API, so the Job
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# status carries no in-band payload — the value is the rendezvous
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# URI when known (callers read the artifact from S3), else None.
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"result": handle.metadata.get("rendezvous_uri"),
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}
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return
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try:
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self._client.stop_training_job(TrainingJobName=meta["job_name"])
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except Exception:
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#
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#
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def collect(
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self,
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return True
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return False
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__all__ = ["SageMakerExecutor"]
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return
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try:
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self._client.stop_training_job(TrainingJobName=meta["job_name"])
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except Exception as e:
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# R5: swallow ONLY already-terminated signals — a vanished job
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# (ResourceNotFound) or an already-Completed/Stopped job (boto3
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# raises ValidationException for "cannot stop a job in status X").
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# A genuinely unexpected error (AccessDenied, throttling that
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# outlived retries, malformed request) must propagate rather than
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# masquerade as a successful cancel.
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if self._is_resource_not_found(e) or self._is_already_terminal(e):
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return
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raise
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def collect(
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self,
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return True
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return False
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def _is_already_terminal(self, exc: Exception) -> bool:
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"""True if ``exc`` is the boto3 "cannot stop a job in status X" error.
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``stop_training_job`` raises a ``ValidationException`` when the job is
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already Completed/Failed/Stopped — that is an idempotent no-op for
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cancel(), distinct from a genuinely unexpected error. Matched on the
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ClientError code + message text (robust to a mock raising a plain
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Exception whose message carries the phrase).
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"""
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resp = getattr(exc, "response", None)
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if isinstance(resp, Mapping):
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err = resp.get("Error", {})
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if err.get("Code") == "ValidationException":
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return True
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msg = str(exc).lower()
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return (
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"cannot be stopped" in msg
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or "already" in msg and ("stopped" in msg or "complete" in msg or "terminal" in msg)
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)
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__all__ = ["SageMakerExecutor"]
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ex.cancel(h) # must not raise
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# unknown handle must also be a no-op
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ex.cancel(ReplicaHandle(rank=42, backend_name="sagemaker", metadata={}))
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ex.cancel(h) # must not raise
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# unknown handle must also be a no-op
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ex.cancel(ReplicaHandle(rank=42, backend_name="sagemaker", metadata={}))
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def test_cancel_reraises_unexpected_error():
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"""R5: a genuinely unexpected error (not already-terminated) must propagate,
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not be silently swallowed as a successful cancel."""
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class _BoomClient(_MockSMClient):
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def stop_training_job(self, TrainingJobName): # noqa: N803
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raise RuntimeError("AccessDeniedException: not authorized")
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client = _BoomClient()
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ex = _make_executor(client)
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h = ex.launch_replicas(1, entrypoint="x", entrypoint_args=_VALID_ARGS)[0]
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with pytest.raises(RuntimeError, match="AccessDenied"):
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ex.cancel(h)
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| R11 | Flaky test `spikes/006-real-hf-model-smoke/tests/test_strict.py::test_alternating_batches_loss_decreases` — fails under CPU contention (full suite w/ concurrent pytest + Docker), PASSES in isolation (verified 3x). Loss-trend assertion is timing/noise-sensitive. Pin seed / widen tolerance / mark flaky. Pre-existing, not a Wave-2 regression. | LOW | OPEN |
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| R12 | B7-complete ✅ (top-level `__all__` now includes the 3 factories) + B4-complete ✅ (the 4 surviving "115" claims → 266/62). | — | DONE |
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-
**Wave 3 — DONE (Phase-7 reconciliation):** R1 ✅ (HeldOutGuard wired into ComposerReplicationTrainer — optional, OFF by default, soft/hard stop; + integration test), R2 ✅ (HeldoutSplit disjointness enforcer `safety/holdout.py` + 10 tests), R3 ✅ (EKS entrypoint contract bug fixed — `replica_entrypoint.__main__` now resolves from env OR argv; proven end-to-end with a pure-env invocation), R4 ✅ (calibrate_kl_threshold rejects factor<=0/negative-baseline + positive floor), R7 ✅ (API_REFERENCE §15-17: EKS/SageMaker/DockerSandbox/safety), R8 ✅ (ADR-015 authored + indexed), R10 ✅ (path-(c) divergence-rate test). R12 ✅ (B4/B7 complete).
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Sandbox refactor verdict: **clean** (no regression to LocalSubprocessSandbox/FeatureDeletionEnv).
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| R11 | Flaky test `spikes/006-real-hf-model-smoke/tests/test_strict.py::test_alternating_batches_loss_decreases` — fails under CPU contention (full suite w/ concurrent pytest + Docker), PASSES in isolation (verified 3x). Loss-trend assertion is timing/noise-sensitive. Pin seed / widen tolerance / mark flaky. Pre-existing, not a Wave-2 regression. | LOW | OPEN |
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| R12 | B7-complete ✅ (top-level `__all__` now includes the 3 factories) + B4-complete ✅ (the 4 surviving "115" claims → 266/62). | — | DONE |
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**Wave 3 — DONE (Phase-7 reconciliation):** R1 ✅ (HeldOutGuard wired into ComposerReplicationTrainer — optional, OFF by default, soft/hard stop; + integration test), R2 ✅ (HeldoutSplit disjointness enforcer `safety/holdout.py` + 10 tests), R3 ✅ (EKS entrypoint contract bug fixed — `replica_entrypoint.__main__` now resolves from env OR argv; proven end-to-end with a pure-env invocation), R4 ✅ (calibrate_kl_threshold rejects factor<=0/negative-baseline + positive floor), R7 ✅ (API_REFERENCE §15-17: EKS/SageMaker/DockerSandbox/safety), R8 ✅ (ADR-015 authored + indexed), R10 ✅ (path-(c) divergence-rate test). R12 ✅ (B4/B7 complete). R5 ✅ (EKS+SageMaker cancel now re-raise unexpected errors, swallow only 404/409/already-terminal + propagation test), R6 ✅ (EKS collect() result dicts include `result`=rendezvous URI), R11 ✅ (spike-006 test seeded torch.manual_seed(0) → no longer contention-flaky). ALL Wave-3 items (R1-R11) CLOSED.
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Sandbox refactor verdict: **clean** (no regression to LocalSubprocessSandbox/FeatureDeletionEnv).
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averaged loss over the first 2 steps. (Looser than the strict-monotonic
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single-batch test, because alternation makes per-step noise larger.)
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"""
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batch_factorial = build_batch(tokenizer, device="cpu", variant="factorial")
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batch_bsearch = build_batch(tokenizer, device="cpu", variant="binary_search")
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
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averaged loss over the first 2 steps. (Looser than the strict-monotonic
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single-batch test, because alternation makes per-step noise larger.)
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"""
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# Determinism (R11): pin the seed so the loss trajectory is reproducible
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# regardless of host CPU contention. Without this the test was flaky under
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# the full suite (competing pytest workers + Docker containers perturbed
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# torch op scheduling enough to occasionally miss the <50% threshold),
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# while passing in isolation. The seed makes the trend assertion stable.
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torch.manual_seed(0)
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batch_factorial = build_batch(tokenizer, device="cpu", variant="factorial")
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batch_bsearch = build_batch(tokenizer, device="cpu", variant="binary_search")
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
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