Add trainium, inferentia, and efa parameters to @kubernetes decorator

[emattia]

PR Type

• Bug fix
• New feature
• Core Runtime change
• Docs / tooling
• Refactoring

Summary

Mirror @batch's AWS-accelerator surface on @kubernetes:

• @kubernetes(trainium=N) requests N AWS Trainium / Inferentia Neuron
  devices (aws.amazon.com/neuron k8s resource).
• @kubernetes(inferentia=N) is an alias for trainium, mirroring
  @batch(inferentia=N) for API consistency.
• @kubernetes(efa=N) requests N AWS Elastic Fabric Adapter network
  interfaces (vpc.amazonaws.com/efa k8s resource).

Plumbed through kubernetes_job, kubernetes_jobsets, kubernetes_cli,
and the argo / airflow runtimes consistently with how the existing
gpu parameter is handled.

Issue

No tracking issue. Supersedes the original PR scope of just trainium.
Brings the @kubernetes path to parity with @batch for AWS Neuron
and EFA workloads, unblocking customers who run their own EKS clusters
and want first-class Neuron/EFA support without writing raw pod specs.

Reproduction

Runtime: kubernetes (EKS with AWS Neuron and EFA device plugins
installed; nodes labeled with the relevant accelerator).

Commands to run:

from metaflow import FlowSpec, step, kubernetes, environment

NEURON_IMG = "public.ecr.aws/neuron/pytorch-training-neuronx:2.9.0-neuronx-py312-sdk2.29.1-ubuntu24.04"

class NeuronEfaSmoke(FlowSpec):

    @kubernetes(trainium=1, image=NEURON_IMG)
    @step
    def neuron_only(self):
        import subprocess
        print(subprocess.check_output(["neuron-ls"]).decode())
        self.next(self.gpu_efa)

    # Equivalent — inferentia is an alias for trainium
    @kubernetes(inferentia=1, image=NEURON_IMG)
    @step
    def inferentia_alias(self):
        ...

    @environment(vars={"FI_PROVIDER": "efa"})
    @kubernetes(gpu=8, efa=32, image="<aws-dlc-pytorch-cuda>")
    @step
    def gpu_with_efa(self):
        import torch.distributed as dist
        dist.init_process_group(backend="nccl")
        # NCCL debug log will show "Selected provider is efa"

Where evidence shows up: task pod spec (kubectl describe pod) and
NCCL debug log inside the running container.

Before (master)

TypeError: kubernetes() got an unexpected keyword argument 'trainium'

(also for inferentia, efa)

After (this PR)

$ kubectl describe pod ws-...
...
Limits:
  aws.amazon.com/neuron:    1
  vpc.amazonaws.com/efa:    32
  nvidia.com/gpu:           8

# inside the pod
$ NCCL_DEBUG=INFO python train.py
NCCL INFO NET/OFI Initializing aws-ofi-nccl 1.15.0
NCCL INFO NET/OFI Using transport protocol RDMA (platform set)
NCCL INFO NET/OFI Selected provider is efa, fabric is efa-direct (found 32 nics)

Root Cause

Not a bug fix — net-new feature. The underlying Kubernetes resources
(aws.amazon.com/neuron, vpc.amazonaws.com/efa) are advertised by the
respective AWS device plugins; @kubernetes had no decorator-level
surface to request them. @batch already exposed trainium,
inferentia, and efa. This PR brings @kubernetes to parity.

Why This Fix Is Correct

• Mirrors @batch's API surface exactly. inferentia collapses into
  trainium at step_init and is popped before any runtime translation
  — same shape as batch_decorator.py:175-211, only with trainium as
  canonical (since on K8s the underlying resource name is
  aws.amazon.com/neuron and we surface what users running on Trainium
  hardware naturally type first).
• Doesn't disturb the existing GPU path. gpu and trainium are
  enforced as mutually exclusive (matching @batch's convention).
• Argo/Airflow runtimes already had the trainium plumbing pattern from
  earlier in this branch; efa follows the same pattern.

Failure Modes Considered

1. Backward compat: flows using only gpu / gpu_vendor are
   unaffected — new attributes default to None and resource-limit
   emission is gated on non-None values.
2. Mutual exclusion: specifying both inferentia and trainium
   raises a clear error in step_init (mirrors @batch). Specifying
   both gpu and trainium was already enforced.
3. Wire format consistency: inferentia is popped from
   self.attributes after collapsing into trainium, so the runtime
   CLI / argo / airflow translation only ever sees the canonical key.
4. Cross-runtime: changes propagate through kubernetes_job,
   kubernetes_jobsets, argo, and airflow consistently with how
   trainium was already plumbed.
5. Validation: efa value validated as positive integer (mirrors
   trainium and tmpfs_size validation patterns in the same file).

Tests

• Unit tests added/updated — not yet; planned as a follow-up
  mirroring existing kube_utils tests with parametrize cases for
  mutual-exclusion + resource-limit emission. Happy to land tests
  either in this PR (push another commit) or a follow-up — let me
  know reviewer preference.
• Manual reproduction provided above
• Smoke-tested end-to-end on a real EKS cluster with Neuron and
  EFA device plugins. Pod spec contains the right resource limits;
  NCCL via aws-ofi-nccl selects EFA as the network backend.
• CI passes — TBD (CI doesn't have AWS Trainium/EFA hardware to
  truly exercise the runtime path, only static / unit checks).

Non-Goals

• Not touching @batch (already has these parameters).
• Not adding a --inferentia CLI flag — inferentia is purely a
  decorator-time convenience that resolves to trainium before any
  CLI invocation, mirroring @batch's CLI which only exposes the
  canonical name (--inferentia for batch since inferentia is
  canonical there; --trainium for k8s since trainium is canonical
  here).
• Not adding NCCL/libfabric environment-variable defaults
  (FI_PROVIDER, FI_EFA_USE_DEVICE_RDMA). Users set those via
  @environment for now; auto-injection is a separate ergonomics PR.
• Not opining on which Trainium/Inferentia instance type a user
  should target — that's a cluster-side concern (instance allowlist
  • AMI selection on the EKS managed nodegroup side).

AI Tool Usage

• AI tools were used (Anthropic Claude — research on AWS DLC tag
  selection, Karpenter EFA NIC layout prior art, and drafting this
  PR description). All generated code reviewed, understood, and
  tested end-to-end on a live cluster.

[greptile-apps]

Greptile Summary

This PR adds a trainium parameter to @kubernetes, mirroring the existing @batch UX, so users can request AWS Trainium/Inferentia Neuron devices via @kubernetes(trainium=X). The core implementation in kubernetes_job.py and kubernetes_jobsets.py is correct — both the aws.amazon.com/neuron resource limit and the automatic aws.amazon.com/neuron:NoSchedule toleration are injected. However, the same toleration is missing from two execution paths:

• Argo Workflows (non-parallel path): argo_workflows.py adds the Neuron resource limit but calls .tolerations(resources.get(\"tolerations\")) without appending the Neuron toleration, so pods will stay Pending on tainted Neuron nodes.
• Airflow: airflow.py adds the Neuron resource limit to the resources dict but never passes a matching toleration to KubernetesPodOperator, causing the same scheduling failure.

Confidence Score: 4/5

Two execution paths (Argo Workflows non-parallel and Airflow) will silently fail to schedule on Neuron nodes because the required toleration is not injected; the core Kubernetes job/jobset paths work correctly.

Two confirmed P1 bugs mean pods will not schedule on Trainium nodes when using Argo Workflows non-parallel path or Airflow. The core kubernetes_job and kubernetes_jobsets paths are correct. Score is 4 rather than lower because the feature works for the primary direct-Kubernetes execution path.

metaflow/plugins/argo/argo_workflows.py and metaflow/plugins/airflow/airflow.py need the automatic Neuron toleration added to their pod specs.

Vulnerabilities

No security concerns identified. The change adds a resource-limit annotation and a toleration to Kubernetes pod specs; there is no user-controlled input reaching a sensitive API without validation.

Important Files Changed

Filename	Overview
metaflow/plugins/kubernetes/kubernetes_decorator.py	Adds trainium as a new decorator attribute with mutual-exclusion check against gpu, integer validation, and CLI forwarding — correct, though the validator allows trainium=0 which would spuriously add a Neuron toleration.
metaflow/plugins/kubernetes/kubernetes_job.py	Correctly adds aws.amazon.com/neuron resource limit and automatically injects the aws.amazon.com/neuron:NoSchedule toleration when trainium is set.
metaflow/plugins/kubernetes/kubernetes_jobsets.py	Correctly adds aws.amazon.com/neuron resource limit and auto-injects the Neuron toleration for the parallel JobSet path, consistent with kubernetes_job.py.
metaflow/plugins/argo/argo_workflows.py	Adds Neuron resource limit to the non-parallel pod spec and threads trainium through to the JobSet path, but the non-parallel path omits the required aws.amazon.com/neuron:NoSchedule toleration — pods will fail to schedule on Neuron nodes.
metaflow/plugins/airflow/airflow.py	Adds Neuron resource limit to the resources dict but never adds a matching aws.amazon.com/neuron:NoSchedule toleration to the Airflow operator args, so pods will remain pending on tainted Neuron nodes.
metaflow/plugins/kubernetes/kubernetes_cli.py	Adds --trainium CLI option and threads it through to the step command correctly.
metaflow/plugins/kubernetes/kubernetes.py	Adds trainium parameter to both create_job and create_jobset methods and forwards it to the job/jobset constructors correctly.

Comments Outside Diff (1)

1. metaflow/plugins/argo/argo_workflows.py, line 2796 (link)

   Missing automatic Neuron toleration in non-parallel Argo Workflows path

   The non-parallel (non-JobSet) Argo Workflows pod spec adds the aws.amazon.com/neuron resource limit (line ~2871) but does not inject the corresponding aws.amazon.com/neuron:NoSchedule toleration. Trainium/Inferentia nodes carry that taint by default, so any pod that reaches this code path with trainium=N will remain in Pending state — it will never be scheduled.

   The JobSet path correctly auto-injects the toleration (via kubernetes_jobsets.py), and kubernetes_job.py does the same. The fix is to extend the toleration list here analogously:

   .tolerations(
       (resources.get("tolerations") or [])
       + (
           [{"key": "aws.amazon.com/neuron", "operator": "Exists", "effect": "NoSchedule"}]
           if resources.get("trainium") is not None
           else []
       )
   )

_{Reviews (1): Last reviewed commit: "Add trainium parameter to @kubernetes de..." | Re-trigger Greptile}

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