Version: v0.3.0

Backends

A backend provides compute resources for task execution. v0.1 ships with local, slurm, docker, and kubernetes backends.

Default behavior (simplest)

If you omit backends: entirely, sflow creates a default local backend:

backend: local (synthetic allocation: localhost, localhost-1, ...)
default operator: bash

This is why a minimal workflow with just workflow: and tasks: works without any backend/operator config.

Explicit local backend

Explicit local backend example:

version: "0.1"

backends:
  - name: local
    type: local
    default: true

workflow:
  name: wf
  tasks:
    - name: hello
      script:
        - echo hello

The local backend can also simulate multiple nodes on one machine: set nodes: N (default 1) and sflow synthesizes hosts localhost, localhost-1, … so you can rehearse multi-node placement locally. Like the other bare-host backends it honors offload_task_logs (default true) to write each task's <task>.log directly instead of streaming through the driver.

Slurm backend

Slurm backend example:

version: "0.1"

backends:
  - name: slurm_cluster
    type: slurm
    default: true
    account: "your_slurm_account"
    partition: "your_slurm_partition"
    time: "00:10:00"
    nodes: 1
    gpus_per_node: 8        # required for sflow planning; set to 0 for CPU-only partitions

workflow:
  name: wf
  tasks:
    - name: slurm_task
      script:
        - echo hello

CPU-only partitions

Set gpus_per_node: 0 to target a CPU-only Slurm partition. With zero capacity, tasks that declare resources.gpus will be rejected up front with a clear error, preventing silent CUDA failures at runtime.

note

gpus_per_node describes cluster topology for sflow resource planning and GPU index assignment. It does not add --gpus-per-node to salloc. If your cluster requires that Slurm allocation flag, add it explicitly in backend extra_args.

Notes:

If you don't specify task.operator, the backend chooses its default operator:
- local backend → bash
- slurm backend → srun
- docker backend → docker_run
- kubernetes backend → no default; declare an explicit k8s operator (the workload image lives on the operator)
You can run sflow asynchronously via sbatch:
- sbatch returns immediately with a job id; sflow runs inside the batch allocation.
- In this mode, sflow will reuse the current allocation (no extra salloc).
- Make sure your --workspace-dir/--output-dir point to a shared filesystem so you can inspect logs while it runs.
- Controller-provided SLURM_* / SLURMD_* environment variables are preserved for tasks, even if workflow variables use the same names. sflow also exposes backend-agnostic aliases such as SFLOW_BACKEND_JOB_ID and SFLOW_BACKEND_NODELIST.

Example:

sbatch --job-name=sflow --output=sflow-%j.out --wrap "cd $SLURM_SUBMIT_DIR && sflow run --file sflow.yaml"

Besides the common fields shown above (account, partition, time, nodes, gpus_per_node, extra_args, include_nodes/exclude_nodes), the Slurm backend also accepts:

Field	Default	Description
`job_name`	workflow name	`salloc --job-name`. Falls back to the backend name, then is set to the workflow name at resolve time.
`offload_task_logs`	`true`	Have `srun` write each task's `<task>.log` on the compute side (via `--output`) instead of streaming every line through the driver. Auto-falls back to streaming on an interactive TTY / `--tui`. Also toggled by `--offload-task-logs` / `--no-offload-task-logs` or `SFLOW_OFFLOAD_TASK_LOGS`.

time accepts either an "HH:MM:SS" string or an integer number of minutes.

Cluster-specific flags (`extra_args`)

Some Slurm clusters require additional flags for job submission (e.g., GPU resources, network segments, or custom policies). Use the extra_args section to pass these cluster-specific options:

version: "0.1"

backends:
  - name: gpu_cluster
    type: slurm
    default: true
    account: "myproject"
    partition: "gpu"
    time: "01:00:00"
    nodes: 2
    gpus_per_node: 8
    extra_args:
      - "--gpus-per-node=8"
      - "--segment=2"
      - "--exclusive"

workflow:
  name: wf
  tasks:
    - name: gpu_task
      script:
        - nvidia-smi
        - echo "Running on GPU nodes"

Common cluster-specific flags include:

Flag	Description
`--gpus-per-node=N`	Request N GPUs per node
`--segment=<name>`	Target a specific network segment or job class, usually GB200 / GB300
`--exclusive`	Request exclusive node access
`--mem=<size>`	Memory per node (e.g., `128G`)

tip

Check your cluster's documentation or run sinfo / scontrol show partition to discover available partitions, segments, and resource constraints.

note

When using sflow batch mode, you can also pass extra Slurm flags directly via the -e flag without modifying the YAML file:

sflow batch -f workflow.yaml -e "--gpus-per-node=8" -e "--segment=2"

This is useful for quick adjustments or when testing different cluster configurations.

Selecting or excluding nodes (all backends)

Restrict which cluster nodes a run may use with two backend-agnostic controls that apply to every backend:

CLI flags on sflow run and sflow batch: --include-nodes and --exclude-nodes. Both accept comma-separated lists, quoted whitespace-separated lists, and/or repeated flags (--exclude-nodes a,b, --exclude-nodes "a b", --exclude-nodes a --exclude-nodes b).
YAML fields on any backend: include_nodes and exclude_nodes. CLI values are unioned over the recipe's values.

include_nodes restricts the candidate pool to the listed hosts; exclude_nodes removes the listed hosts. A host may not appear in both.

# Keep this run off two flaky nodes, across whatever backend the recipe uses.
sflow run -f workflow.yaml --exclude-nodes gpu-07,gpu-16

# Pin an experiment to specific hosts.
sflow run -f workflow.yaml --include-nodes gpu-01,gpu-02

backends:
  - name: gpu_cluster
    type: slurm
    account: myproject
    partition: gpu
    time: "01:00:00"
    nodes: 2
    gpus_per_node: 8
    exclude_nodes: [gpu-07, gpu-16]

Each backend translates the lists to its native node selection:

Backend	`include_nodes`	`exclude_nodes`
Slurm	`salloc`/`#SBATCH --nodelist=` (reused allocations are filtered in-process)	`salloc`/`#SBATCH --exclude=`
Kubernetes	`kubernetes.io/hostname` `In` nodeAffinity on the reservation pods	`kubernetes.io/hostname` `NotIn` nodeAffinity
Docker (with `hosts:`)	keep only matching hosts in the pool	drop matching hosts from the pool
Local / Docker without `hosts:`	ignored (single machine) with a warning	ignored with a warning

note

Complex Slurm hostlist expressions (e.g. node[01-05]) should be passed through Slurm's own flags via extra_args / -e; --include-nodes / --exclude-nodes take plain hostnames.

Docker backend

The Docker backend uses a synthetic local allocation for planning and launches tasks through the docker_run operator, which invokes docker run:

version: "0.1"

backends:
  - name: docker
    type: docker
    default: true
    image: ubuntu:22.04
    nodes: 1
    gpus_per_node: 0

workflow:
  name: docker_hello_world
  tasks:
    - name: hello
      script:
        - echo "hello from docker"

For GPU tasks, install the NVIDIA container toolkit on the Docker host and set gpus_per_node to the host GPU count. When a task declares resources.gpus, sflow narrows Docker --gpus device=... to the planned GPU slice.

Docker backend fields beyond image/nodes/gpus_per_node:

Field	Default	Description
`mounts`	—	Bind mounts (`host:container[:ro]`) applied to every container this backend runs.
`workdir`	—	Working directory inside every container.
`extra_args`	—	Extra raw `docker run` args for every container.
`offload_task_logs`	`true`	Write each task's `<task>.log` on the container side instead of streaming through the driver (auto-streams on a TTY / `--tui`).

Multi-node Docker hosts

Docker can also use an explicit pool of remote Docker hosts. sflow does not auto-discover these hosts; declare each endpoint in backend config:

backends:
  - name: docker_cluster
    type: docker
    default: true
    image: nvcr.io/example/app:1.0
    hosts:
      - name: dgx-a
        docker_host: ssh://dgx-a
        ip_address: 10.0.0.11
        gpus_per_node: 8
      - name: dgx-b
        context: dgx-b-context
        ip_address: 10.0.0.12
        gpus_per_node: 8

Each host entry accepts name, docker_host (or context), ip_address, gpus_per_node, and — per host — mounts and extra_args (merged with the backend-wide mounts/extra_args).

With resources.nodes.count: 2, one sflow task launches one container on each assigned Docker host and waits for all containers. The task fails if any host container exits non-zero. Environment values are forwarded with -e KEY so secrets are not embedded in the Docker command line.

Local daemon vs. remote host — implicit mounts differ:

Target	Implicit workspace/output mounts	Node filtering (`include_nodes`/`exclude_nodes`)
Local daemon (no `docker_host`/`context`)	Auto-mounted at the same paths (`auto_mount_runtime_dirs`, on by default)	Ignored with a warning (single machine)
Remote (`docker_host` / `context`)	Not added — a controller-local path may not exist remotely	Applied against the declared `hosts:` pool

If a task needs workspace or output files on remote Docker hosts, provide explicit backend, host, or operator mounts that point at a shared filesystem available at the same path on every Docker host.

Kubernetes backend

The Kubernetes backend mirrors the Slurm "allocate first" model. At allocation time it reserves real nodes with placeholder pods (one per node), discovers their node names and InternalIPs, and exposes them via backends.<name>.nodes[*].ip_address. Each task then runs as its own scheduler-placed pod(s): a single-node task is one pod; a multi-node task (resources.nodes) is one pod per reserved node (leader = index 0). Pods are pinned to the discovered nodes via a kubernetes.io/hostname nodeSelector.

What the backend does (recipe → pod)

You write the same portable recipe you'd run on Slurm — tasks with a script:, resources, probes, and artifacts, plus logical operators — and the Kubernetes backend translates those semantics into a fully-plumbed pod, so you never hand-write a Pod or Job manifest or wire up the cluster plugins yourself.

For each task it:

Reserves and pins nodes — holds real nodes with placeholder pods, discovers their names/InternalIPs, and pins the task pod(s) to them (kubernetes.io/hostname nodeSelector), with a GPU handoff (default reservation.handoff: auto) so a queued task pod takes the freed GPUs with no gap — see Node reservation.
Shapes the pod topology from resources — one pod for a single-node task, one pod per reserved node (leader = index 0) for a multi-node task, and (by default) merges co-located GPU tasks into a single pod so same-node workers can share NVLink.
Requests GPUs — turns resources.gpus.count into an nvidia.com/gpu device-plugin limit (default) or a DRA ResourceClaimTemplate (scheduling: dra).
Mounts your script and env — the task script: is mounted from a ConfigMap and run as the pod entrypoint; resolved variables/artifacts env is injected via a generated Secret (envFrom) so values never land in the kubectl argv; file:// artifact content is mounted from a ConfigMap; and SFLOW_* output dirs resolve to a writable emptyDir inside the pod.
Wires up the fabric — auto-detects RDMA/InfiniBand and grants the pod verbs access (CAP_IPC_LOCK, NIC resources, control-interface pinning), and joins/creates an IMEX ComputeDomain channel for cross-node NVLink (MNNVL) — so NIXL/UCX KV transfer and NCCL run over the fastest available interconnect instead of falling back to TCP.
Attaches storage — mounts the backend's volumes: (existing PVCs / emptyDir) into every task pod.
Runs the probes in-cluster — tcp_port/http_get/http_post readiness/failure probes run from an in-cluster probe pod (via kubectl exec) so the driver needs no route to the pod network; log_watch reads the task log.
Manages execution off the driver — streams pod logs into <task>.log, determines completion from authoritative pod status, syncs node-local output files back to the driver (kubectl cp), and deletes the pods/reservations on teardown.

The rest of this section is the detail behind each of these steps.

Kubernetes support status (v0.3.0)

Kubernetes support is new in v0.3.0. Current scope and limitations:

Interactive sflow run only. Every task runs as a plain pod managed by the live sflow run driver process (it reserves nodes, streams logs, and collects outputs via kubectl), so the driver must stay attached for the whole run. A detached sflow batch mode for Kubernetes is planned for a later release — for now, run Kubernetes workflows interactively with sflow run.
Built-in monitor: not fully supported yet. The declarative hardware monitor: is not fully supported on the Kubernetes backend yet, so monitor: blocks are skipped on k8s targets (no DCGM/DaemonSet collector).
Tested environments. Kubernetes support has been validated on vanilla bare-metal Kubernetes and Google GKE. Other environments are untested, so expect some tuning — please open an issue if it does not work on your cluster.

GPUs are requested two ways, selected by the backend scheduling field:

device_plugin (default) — the nvidia.com/gpu device-plugin limit (gpu-operator); the widely-available mechanism sflow uses unless you opt into DRA. Override the resource name with gpu_resource_name for clusters that expose GPUs under a different name (a MIG profile like nvidia.com/mig-1g.5gb, or another vendor/plugin).
dra — a resource.k8s.io/v1 ResourceClaimTemplate from a DeviceClass (default gpu.nvidia.com). Requires Kubernetes 1.34+ with nvidia-dra-driver-gpu. DRA GPU allocation is a work in progress; opt in explicitly with scheduling: dra.

On device_plugin clusters sflow checks gpus_per_node against the nodes' real GPU capacity at preflight: leave it unset and it's derived from the candidate nodes' allocatable; set it higher than a node provides and you get a clear warning up front (instead of a placeholder stuck Pending). A value below node capacity is a valid partial-node reservation and is left as-is.

Placeholder pods hold the node's GPUs as a hard reservation; when a GPU task is pinned to a node, sflow hands the GPUs over using reservation.handoff (default auto): normally it applies the (Pending) task pod first and then deletes the placeholder (create-before-destroy) so the freed GPUs bind to the already-queued pod with no gap, but it switches to destroy-before-create when a GPU ResourceQuota is enforced (holding both at once would exceed quota) — see Node reservation. CPU-only tasks (e.g. etcd/nats/frontend) coexist on the node without consuming GPUs.

Connecting to the cluster

sflow runs kubectl on the machine where you invoke sflow run, so that machine must have working kube access. The recipe stays cluster-agnostic — cluster selection and credentials are CLI flags on sflow run, not YAML:

--kubeconfig PATH — kubeconfig file (also exported as KUBECONFIG). Default: $KUBECONFIG or ~/.kube/config.
--kube-context NAME — context within the kubeconfig. Default: its current-context.
--kube-namespace NAME — override the namespace for all kubernetes backends.
--kube-node-selector KEY=VALUE (repeatable / comma-separated) — node-selector label(s) merged into (and overriding) every kubernetes backend's node_selector, keeping node-pool identity (e.g. a tenant label) out of the recipe.
--kube-compute-domain-channel NAME|auto|disable — override compute_domain.channel (Multi-Node NVLink / IMEX) for all kubernetes backends: a channel template name (join it), auto (join the sole existing ComputeDomain), or disable (legacy off; no MNNVL). Lets you tune MNNVL per run without editing the recipe.
--kube-compute-domain-create / --no-kube-compute-domain-create — override compute_domain.create. When on and no channel is joined, sflow stands up its own ComputeDomain CR (named sflow-cd-<backend>-<alloc-id>, keyed to the run) and injects the generated channel into every GPU pod; it deletes only the domain it created on cleanup. Combine with --kube-compute-domain-channel disable to force sflow-owned creation over a recipe's named channel.
--extra-kubectl-args TEXT (repeatable) — any other global kubectl flag, e.g. --extra-kubectl-args=--insecure-skip-tls-verify or --extra-kubectl-args=--request-timeout=30s. The generic --extra-args is also forwarded here, so --extra-args=--request-timeout=30s works too; --extra-kubectl-args is just the kubectl-only form and wins on a conflicting option.

They are applied to every kubectl call sflow makes (allocation, node discovery, the per-task apply/logs/delete, and cleanup). Credentials always live in the kubeconfig, never in the recipe.

sflow run -f recipe.yaml \
  --kubeconfig ~/.kube/prod.config --kube-context prod-east --kube-namespace ml-team

On a real run (not --dry-run), sflow runs a fast pre-flight before allocating anything to confirm the access is usable: it verifies the cluster is reachable and authenticated, that the namespace exists, and — via kubectl auth can-i (non-mutating) — that the credentials hold the RBAC needed for the operations sflow performs (create/delete pods, configmaps and secrets; get pods, pod logs and nodes; plus the DRA resourceclaimtemplates/deviceclasses, and computedomains when compute_domain.create is on). It fails fast with an actionable message (missing permission, wrong namespace, or unreachable cluster) instead of leaving pods stuck. Set SFLOW_SKIP_K8S_PREFLIGHT=1 to bypass the check.

Host namespaces (`host_network`, `host_ipc`) — privileged

Two backend toggles place task pods in the node's namespaces. Both are privileged (the pod steps outside its own sandbox), so they require a permissive PodSecurity level — pods that set them are rejected by the PodSecurity baseline/restricted profiles. On a dedicated GPU cluster they are safe and frequently necessary; on a locked-down or multi-tenant cluster, keep them off (per-option guidance below).

host_network (pod hostNetwork, default true) — the pod shares the node's network namespace, so pod IP == node IP. sflow defaults it on because the allocate-first model hands peers the node InternalIPs returned by reservation: etcd/NATS/frontend registration and the NIXL/UCX KV side-channel host are advertised at those IPs (mirroring the Slurm path), while readiness probes run from an in-cluster probe pod. Trade-offs: pods on one node share the host's ports (two pods can't bind the same hostPort) and the pod sees the node's interfaces. Turn it off (host_network: false) on a CNI-routable cluster where you want pod-network isolation and the driver/probe pod can reach pod IPs — peers are then addressed by pod IP instead of node IP.
host_ipc (pod hostIPC, default false) — the pod shares the node's IPC namespace and mounts a shared hostPath /dev/shm (instead of a private, RAM-backed emptyDir sized by shm_size). This provides the IPC + shared-memory channel that cross-pod CUDA IPC needs, so same-node NIXL/UCX KV transfer between separate prefill/decode pods can use NVLink (cuda_ipc) instead of falling back to TCP. Trade-offs: the pod can read the node's System V IPC + POSIX shared memory (a compromised container can snoop other workloads or exhaust node IPC//dev/shm), the shared /dev/shm can collide across unrelated same-node jobs and leaves stale shm files on the node, and you lose the per-pod shm_size cap. Leave it off unless you run prefill/decode as distinct same-node pods that must cuda_ipc: the default merge_colocated_gpu_pods: auto already co-locates GPU tasks in one pod (shared IPC and shared GPUs), which is the preferred path for intra-node NVLink — cross-pod GPU isolation means separate pods can't cuda_ipc to each other's GPUs on host_ipc alone (see merge_colocated_gpu_pods under RDMA / InfiniBand networking). Recipes that run prefill/decode as distinct same-node pods opt in with host_ipc: true (e.g. examples/self_contained/kubernetes/dynamo_trtllm_disagg.yaml).

Readiness probes run in-cluster (probe pod)

Network readiness/failure probes (tcp_port, http_get, http_post) target the workload's pod/node IPs. The machine running sflow run often cannot route to the cluster pod network, which would make those probes fail even though the service is healthy. To avoid this, the Kubernetes backend runs TCP/HTTP probes from inside the cluster: it creates one small per-allocation probe pod in the backend namespace, and the driver runs each check by kubectl exec-ing curl in that pod. kubectl exec tunnels through the API server, so the driver only needs the kube access it already has — no direct pod-network route.

This is on by default and requires no configuration. Notes:

Lifecycle. The probe pod is created lazily on the first TCP/HTTP check and then lives until the allocation is released (deleted with the rest of the allocation, since it is labeled with it). A workflow with no network probes — e.g. log_watch-only or probe-less batch jobs — never creates one. Individual readiness probes still stop once they trigger (the shared pod just serves any remaining probes, including failure probes on READY services, until the run ends).
The probe pod only needs curl. Its image defaults to curlimages/curl:latest and is configurable per backend via probe_pod_image (point it at a mirror for air-gapped registries). It inherits the backend namespace, image_pull_policy, node_selector, and tolerations; it requests no GPUs.
log_watch probes are unaffected (they read the task's local <task>.log).
To disable in-cluster probing and probe directly from the sflow run host instead, set SFLOW_K8S_PROBE_VIA_POD=0.

backends:
  - name: k8s_cluster
    type: kubernetes
    # Optional: override only for air-gapped/mirror registries.
    probe_pod_image: my-registry.example.com/curlimages/curl:8.11.1

Kubernetes tasks do not automatically receive hostPath mounts for SFLOW_WORKSPACE_DIR, SFLOW_OUTPUT_DIR, SFLOW_WORKFLOW_OUTPUT_DIR, or SFLOW_TASK_OUTPUT_DIR. If environment forwarding is enabled, those variables refer to controller-side paths and may not exist inside the pod. Use Kubernetes storage primitives, explicit kubectl arguments, or image-contained assets for files that must be visible in the pod.

Because tasks run off the controller, fs:// artifact paths (e.g. a model directory) are treated as remote paths on the cluster/image: sflow does not validate or create them locally during pre-flight (a missing path warns instead of failing the run). Ensure they exist inside the pod (baked into the image, or mounted via a PVC/hostPath you configure).

The task script is mounted into the pod from a ConfigMap and run as the entrypoint. Environment variables are passed through a temporary Kubernetes Secret generated from an env-file (mounted via envFrom). This avoids leaking values into the kubectl argv, but the env-file format is line-oriented, so values with embedded newlines are not supported.

Volumes: PVCs and emptyDir

Backend-wide volumes: are mounted into every task pod of the backend (each becomes a pod volume + a container volumeMount at mount_path). Set exactly one source per entry:

claim: — an existing PersistentVolumeClaim (the PVC and its data must already exist in the namespace; sflow only references it). Use for shared data such as a model on RWX/ROX storage. Defaults to read-only, which is also required to share one PVC across pods on multiple nodes.
empty_dir: — an ephemeral, per-pod scratch volume (Kubernetes emptyDir): writable by any container user (no PVC/NFS ownership or root-squash issues), created empty per pod, and deleted with the pod. Defaults to writable. Options: medium: Memory (tmpfs/RAM instead of node disk — avoid for large caches) and size_limit (e.g. 50Gi).

A writable PVC entry (read_only: false) can also set ensure_writable: true. A subPath mount is created root-owned by the kubelet, so a non-root container can't write it; ensure_writable injects a small root initContainer that mkdir -p + chmod 0777s the mounted path (the subPath dir, or the mount root) before the workload runs. It's best-effort — on a root-squashed / read-only backing volume it can't help — but for a normal writable RWX PVC it makes a persistent cache "just work" without any manual chmod.

volumes:
  # Shared model store (read-only PVC).
  - name: model-store
    claim: my-model-pvc
    mount_path: /models
    read_only: true
  # Writable scratch for a JIT/kernel cache (ephemeral; recompiled each run).
  - name: kernel-cache
    empty_dir: {}
    mount_path: /cache
  # ...or PERSIST the cache on a writable RWX PVC (kernels reused across runs):
  # - name: kernel-cache
  #   claim: my-rwx-cache-pvc
  #   mount_path: /cache
  #   sub_path: sflow-kernel-cache
  #   read_only: false
  #   ensure_writable: true   # fix the root-owned subPath so non-root can write

Why this matters: a subPath mount of a PVC is created root-owned, so a non-root container (or a root-squashed NFS export) often can't write it, and pod fsGroup is both ineffective on many NFS drivers and dangerous on a large shared model PVC (recursive chown). Use empty_dir for scratch that needn't persist, or a writable RWX PVC with ensure_writable: true for cross-run persistence.

Skipping PVCs for debugging (--kube-skip-pvc)

On a cluster that lacks the recipe's PVCs, sflow run --kube-skip-pvc drops every PVC-backed volume (a volumes: entry with a claim) from all kubernetes backends for that run, keeping empty_dir volumes. Pods then schedule without editing the recipe volume-by-volume. The PVC data (e.g. a model cache) is not mounted, so real workloads that need it will fail — use it for quick scheduling/plumbing checks, not for functional runs.

Operator: k8s

A Kubernetes task must declare a k8s operator that carries the workload image (the backend has no image of its own). The same operator handles single- and multi-node tasks — the number of pods is decided by the planner-assigned node count, not the operator type.

backends:
  - name: k8s
    type: kubernetes
    namespace: my-namespace   # one namespace per backend; injected into operators
    nodes: 2
    gpus_per_node: 8
    scheduling: device_plugin   # default (nvidia.com/gpu); DRA is a WIP — opt in with `scheduling: dra`
    # gpu_resource_name: nvidia.com/gpu   # device-plugin GPU resource name (override for MIG/other plugins)
    # dra:                      # only used with `scheduling: dra` (Kubernetes 1.34+, nvidia-dra-driver-gpu)
    #   gpu_device_class: gpu.nvidia.com
    # Advanced per-cluster overrides (rarely needed; default to NVIDIA/GKE conventions):
    #   gpu_product_label_key: nvidia.com/gpu.product   # node label read for NVLink-scope detection
    #   gib_installer_namespace: kube-system            # where the GKE gIB (nccl-rdma-installer) DaemonSet lives
    #   collect_grace_seconds: 120                      # grace window (s) for copying node-local outputs back
    # Multi-Node NVLink (IMEX ComputeDomain) is a top-level block, independent of
    # `scheduling` (works with device_plugin or dra). Join an existing domain for
    # cross-node NVLink (MNNVL): name its channel, or `auto` to claim the sole
    # existing one; sflow does NOT create one by default (set `create: true` for
    # that admin op).
    compute_domain:
      channel: auto   # or a channel name, or off (default)

operators:
  - {name: server_op, type: k8s, image: my/server:1.0}

workflow:
  tasks:
    - {name: prefill, operator: server_op, resources: {gpus: {count: 2}}, script: ["..."]}

Where config lives: the backend carries cluster/access info (namespace, nodes, gpus_per_node, scheduling, dra, tolerations); the operator is the executor (image, required, plus image_pull_policy, restart, host_network, node_selector, and optional DRA device_class / device_selectors). namespace is backend-only and injected into operators (setting it on an operator is rejected — use separate backends for separate namespaces).

Live progress bars (tty): set tty: true on a k8s operator to allocate a pod TTY (container stdin+tty) so tools that redraw a line with \r — aiperf, pip, docker pull — stream live to <task>.log via kubectl logs. Without a TTY the container runtime batches newline-less output, so a long-running progress bar only surfaces in large chunks (or all at once at the end). Trade-off: a TTY merges stderr into stdout and keeps raw \r/ANSI control bytes in the log. Applies to single-pod container tasks; ignored for merge/co-located pods and the k8s_mpi route.

Additional pod/container fields. Beyond the knobs above, a k8s operator maps common v1.Container / v1.PodSpec fields, plus two raw escape hatches for anything else:

Field	Maps to	Notes
`run_as_root`	`securityContext.runAsUser/Group=0`	Default `false`. Force root even if the image's default `USER` is non-root — needed to write a root-owned NFS/ceph PVC or bootstrap MPI over SSH (sshd host keys, `/run/sshd`, `~/.ssh`).
`pass_envs`	env Secret	Default `true`. When `false`, the per-task env Secret is not created/mounted.
`shm_size`	`/dev/shm` size cap	Default `null` → a tmpfs bounded by node RAM. Set e.g. `64Gi`; the K8s 64Mi default is too small for MPI/NCCL and segfaults multi-GPU jobs.
`cpu` / `memory`	container `resources.requests`	Optional per-pod CPU/memory requests (ints = cores, or quantity strings like `500m`/`16Gi`).
`cpu_limit` / `memory_limit`	container `resources.limits`	Optional hard caps. Unset → requests-only (CPU shared, never CFS-throttled).
`env`	container `env`	Raw `EnvVar` list — the only way to use `valueFrom` (fieldRef / secretKeyRef / configMapKeyRef). Merged after backend env; on a name clash the user entry wins. Plain values are better set via workflow `variables`.
`working_dir`	container `workingDir`
`security_context`	container `securityContext`	Deep-merged over sflow's managed root / `IPC_LOCK`; user keys win. `capabilities.add`/`drop` are union-merged with sflow's caps (so `IPC_LOCK` is always kept).
`ports`	container `ports`	Informational only.
`lifecycle`	container `lifecycle`	`postStart` / `preStop` handlers.
`image_pull_secrets`	`imagePullSecrets`	List of secret names (private registries).
`service_account`	`serviceAccountName`	RBAC / cloud workload identity.
`runtime_class`	`runtimeClassName`
`priority_class`	`priorityClassName`	Scheduling priority / preemption.
`termination_grace_period`	`terminationGracePeriodSeconds`
`labels` / `annotations`	pod `metadata`	sflow's own task/allocation labels always win.
`pod_security_context`	pod `securityContext`	`fsGroup` / `supplementalGroups` / `sysctls`; deep-merged.
`container_overrides`	container (raw dict)	Deep-merged last — wins over everything.
`pod_overrides`	`pod.spec` (raw dict)	Deep-merged last — wins over everything.

Precedence: sflow-managed manifest → curated fields → *_overrides (win). Avoid using the overrides to change sflow-managed keys (container command, resources.claims, artifact volumeMounts, the env-secret envFrom, the hostname nodeSelector pin) — that can break task execution. For k8s_mpi, these apply on the pods route (base render); the MPIJob CR route does not consume them.

Merge semantics for lists. When a user value and an sflow value collide on the same key, dicts merge key-by-key and lists are replaced by the user's (a warning is logged naming the overridden key). The exception is capabilities.add/drop, which are union-merged + de-duped so sflow's required caps (e.g. IPC_LOCK) are never dropped. The curated env field is also merged by variable name (not replaced). So to extend an sflow-populated list elsewhere, re-include sflow's entries in your value.

Operator: k8s_mpi (MPI workloads)

For any MPI workload (launched with mpirun — e.g. TRT-LLM trtllm-llmapi-launch, multi-GPU tensor/pipeline-parallel serving), use type: k8s_mpi instead of k8s and write the mpirun -np N ... <workload> command explicitly in the task script. sflow reads that command to stand up the correct MPI world for both single-node and multi-node — a lone launcher pod when the job fits one node, launcher + worker pods when it spans nodes — so the same recipe scales from -np 8 (1 node) to -np 16 (2 nodes) unchanged. Your recipe writes a plain mpirun with no SSH glue — sflow injects the SSH keypair, hostfile, and sshd / launcher wiring. It inherits every k8s operator field (image, resources, security, overrides, ...) and adds an mpi: block. Reserve plain k8s for non-MPI, single-pod workloads.

sflow bootstraps the job one of two ways, chosen by mpi.route:

operators:
  - name: trtllm_mpi
    type: k8s_mpi
    image: my/trtllm:1.0
    run_as_root: true          # sshd host keys / ~/.ssh need root
    shm_size: 64Gi
    mpi:
      route: auto              # auto | operator | pods
      slots_per_worker: 8      # default: per-node GPU count
      ssh_port: 2222
      omp_num_threads: 8       # injected OMP_NUM_THREADS per rank (null/0 = image default)

workflow:
  tasks:
    - name: train
      operator: trtllm_mpi
      resources: { nodes: { count: 2 }, gpus: { count: 16 } }
      script:
        - mpirun -np 16 python train.py

mpi: block fields:

Field	Default	Description
`route`	`auto`	`auto` (detect the CRD) / `operator` (MPIJob CR) / `pods` (plain-pods bootstrap).
`slots_per_worker`	per-node GPU count	Slots advertised per worker in the hostfile.
`run_launcher_as_worker`	`true`	The launcher pod also runs a rank.
`launcher_creation_policy`	`WaitForWorkersReady`	`AtStartup` or `WaitForWorkersReady`.
`ssh_port`	`2222`	Port for the injected `sshd`.
`mpi_implementation`	`OpenMPI`	`OpenMPI` / `Intel` / `MPICH` — selects hostfile/env conventions.
`ensure_sshd`	`true`	Install `openssh-server` at startup if the image lacks `sshd`.
`forward_env_prefixes`	`[]`	Extra env-namespace prefixes forwarded to remote ranks (additive to sflow's defaults).
`omp_num_threads`	`8`	Injected `OMP_NUM_THREADS` per rank; `null`/`0` keeps the image default.

The pods route consumes the curated/override operator fields (base render); the MPIJob CR route (operator) does not.

Node reservation (`reservation`)

Before running GPU tasks, the Kubernetes backend reserves and pins nodes with placeholder pods so the whole workflow lands on a stable, co-scheduled set of nodes (and so multi-node MPI ranks are actually adjacent). Tune it with a backend-level reservation: block:

backends:
  - name: k8s
    type: kubernetes
    nodes: 2
    gpus_per_node: 8
    reservation:
      timeout: 600          # max seconds to wait for every placeholder to schedule
      handoff: auto         # auto | create_before_destroy | destroy_before_create
      # placeholder_image: my-registry.example.com/bash:5   # air-gapped/mirror override

Field	Default	Description
`timeout`	`600`	Max seconds to wait for all placeholder pods to reach `Running` before giving up.
`handoff`	`auto`	How GPUs pass from the placeholder to the real task pod (see below).
`placeholder_image`	`bash:5`	Image for the reservation/placeholder pods (a lightweight sleeper that holds the node + its GPUs until the task pod swaps in; it never runs the workload). Override for air-gapped clusters / private-registry mirrors that can't pull from Docker Hub.

GPU handoff — how a node's GPUs transfer from the reservation placeholder to the task pod depends on whether the namespace enforces a GPU ResourceQuota:

`handoff`	Order	Use when
`create_before_destroy`	Apply task pod (Pending) → delete placeholder → GPUs bind to the task	No GPU quota — safest against losing the node to another scheduler.
`destroy_before_create`	Delete placeholder first → then apply the task pod	A GPU `ResourceQuota` is enforced (holding both at once would exceed quota).
`auto`	Picks `destroy_before_create` iff a `requests.nvidia.com/gpu` quota is detected in the namespace, else `create_before_destroy`	Default — let sflow detect the quota.

The heartbeat log while waiting for placeholders is throttled by SFLOW_K8S_WAIT_HEARTBEAT_SECS (advanced).

Output directories inside pods (`SFLOW_*`)

SFLOW_OUTPUT_DIR / SFLOW_WORKFLOW_OUTPUT_DIR / SFLOW_TASK_OUTPUT_DIR point at the driver host output dir — which a remote K8s pod can't see, so a task script that cds or writes there would error. Instead of rewriting those variables, sflow mounts a writable emptyDir at the resolved SFLOW_OUTPUT_DIR path inside the pod (the WORKFLOW/TASK dirs live under it) and mkdir -ps the per-task subdir before your script runs. So $SFLOW_TASK_OUTPUT_DIR etc. keep the same values everywhere (portable recipes) yet resolve to a real, writable location in the pod. This covers k8s, k8s_mpi (the unchanged env is forwarded to worker ranks via mpirun -x, which get the same mount), and merged/co-located pods (one shared mount covers every member). The emptyDir is pod-local node scratch (auto-discarded when the pod ends) — it is not the driver's disk, so files are brought back via output collection (below).

Output collection. A single-pod k8s task's node-local output is synced back to the driver host via kubectl cp: sflow arms a shell EXIT trap before your script, so however the script ends (normal completion, a failure, or an explicit exit in the recipe), if the pod created any files it scans the whole $SFLOW_WORKFLOW_OUTPUT_DIR subtree (the per-task $SFLOW_TASK_OUTPUT_DIR plus any workflow-level dirs a task writes, e.g. aiperf's aiperf_concurrency_*), stages them (up to the size cap) into one tar.gz on the pod, keeps the container alive, and the driver copies that archive out and unpacks the files missing on the host into the real workflow output folder — so result.json, CSVs, plots, aiperf artifacts, etc. land locally and file-based result: works. File bytes never transit the log (this avoids Kubernetes' 16 KiB log-line splitting, which corrupts large payloads, and keeps the console clean). An empty output dir is skipped. Collected files never overwrite files already on the driver — each task's own <task>.log, the sflow logs, and injected artifacts are kept, not clobbered. The task log records a N output file(s) staged … awaiting driver copy line and a driver-side collected N file(s) … kept K existing driver file(s) (not overwritten) summary. Files larger than collect_max_file_size are skipped with a warning — sync those via uploads: (S3) or a mounted PVC. collect_max_file_size (default 10 MiB; bytes or a size string like "50Mi"; 0 disables) can be set per operator or as a backend default (collect_max_file_size: on the kubernetes backend applies to every k8s/k8s_mpi task unless the operator overrides it). The container waits up to ~120 s for the driver's copy before exiting on its own (so a slow/dead driver can never hang the pod), and the pod image must have tar (used by kubectl cp). Automatic output collection runs for single-pod k8s only: k8s_mpi launchers exec mpirun (no post-run hook) and merged members are typically long-running servers — for those, persist outputs with uploads: / a PVC.

operators:
  - name: server_op
    type: k8s
    image: my/server:1.0
    image_pull_secrets: [my-regcred]
    service_account: my-service-account
    labels: { team: my-team }
    env:
      - name: POD_IP
        valueFrom: { fieldRef: { fieldPath: status.podIP } }
    security_context:
      capabilities: { add: [SYS_PTRACE] }
    pod_overrides:                 # escape hatch for any un-mapped field
      hostAliases:
        - ip: "10.0.0.10"
          hostnames: [head.local]

Multi-node tasks receive SFLOW_TASK_NODE_INDEX and SFLOW_LEADER_ADDRESS per pod (plus the shared SFLOW_TASK_ASSIGNED_NODE_IPS) so peers can rendezvous; the leader pod (index 0) determines the task exit code.

GPU requests

When a task declares resources.gpus.count, the Kubernetes backend renders it per the backend scheduling mode rather than planning a client-side CUDA_VISIBLE_DEVICES slice:

dra — a per-pod ResourceClaimTemplate requesting count devices from the DeviceClass.
device_plugin — a pod resources.limits: { "nvidia.com/gpu": "<count>" }.

For a multi-node task the count is split evenly across the assigned nodes (each pod gets count / nodes GPUs), so resources.gpus.count must be a multiple of the node count.

GPU resources.gpus.release_after: task_ready cannot be honored on Kubernetes (a running pod's GPUs cannot be shared); sflow coerces it to task_completion (with a warning) so a GPU is only reused after the owning pod terminates.

version: "0.1"

backends:
  - name: k8s
    type: kubernetes
    default: true
    namespace: default
    nodes: 1
    gpus_per_node: 8
    scheduling: device_plugin   # default (nvidia.com/gpu); use `dra` for DRA (WIP)

operators:
  - {name: gpu_op, type: k8s, image: nvcr.io/nvidia/pytorch:24.12-py3}

workflow:
  name: kubernetes_gpu
  tasks:
    - name: train
      operator: gpu_op
      resources: {gpus: {count: 2}}
      script:
        - nvidia-smi -L

RDMA / InfiniBand networking

At allocation time the backend probes a reservation pod for the node's RDMA HCAs (e.g. mlx5_0) and routable interface, then runs an RDMA provider chain to decide how task GPU pods get verbs access. The matching provider grants the pods CAP_IPC_LOCK and pins the control interface (NCCL_SOCKET_IFNAME / GLOO_SOCKET_IFNAME on the routable NIC), so NIXL/UCX KV transfer and NCCL run over RDMA. NIC device selection is always left to the libraries — sflow sets neither UCX_NET_DEVICES nor NCCL_IB_HCA. NCCL/gIB and UCX pick each GPU's PCIe-local NIC by topology; sflow's job is only to make sure the pod owns those NICs. That ownership is guaranteed by a pod holding the whole node (a merged/full-node pod). A partial-node pod (one sharing its node) cannot guarantee it — the GPU device plugin picks the physical GPUs independently of the rdma-N grant, so they can land on the opposite PCIe side — which is why co-located GPU tasks are merged into one full-node pod (the default). Auto priority order:

GKE — one networking.gke.io.networks/rdma-N extended resource per NIC. GKE gIB (GPUDirect-RDMA NCCL) requires the nccl-rdma-installer DaemonSet deployed on the cluster — it installs /home/kubernetes/bin/gib and drops the gIB NCCL plugins (net + tuner) into /home/kubernetes/bin/nvidia/lib64; it is not a default GKE path. sflow probes for the installer during reservation and treats gIB as workload-agnostic cluster infra: because the device plugin injects that driver dir into /usr/local/nvidia/lib64 on every GPU pod, NCCL auto-loads the gIB plugins everywhere — and they abort NCCL init unless NCCL_CONF_FILE is set (via set_nccl_env.sh). So whenever the installer is detected, sflow mounts the gIB libs and sources set_nccl_env.sh on every GPU pod (single-node, merged, and multi-node) — not gated on node count. The mounts include /home/kubernetes/bin/nvidia → /usr/local/nvidia (the driver path), so bind-mounting a missing host dir there would mask libcuda.so.1; they therefore use hostPath type: Directory (require existence). If the installer is absent, sflow logs a one-line hint, emits no lib mounts / gIB config, and NCCL falls back to its built-in IB transport over RoCE.

sflow never pins NCCL_IB_HCA — NIC selection is left to NCCL/gIB (which pairs each GPU with its PCIe-local NIC). Correct pairing comes from the pod owning the whole node (merged / full-node); a grant-based pin could otherwise force a pod's GPUs onto far NICs, since the GPU and RDMA device plugins choose independently.
shared device plugin — a single shared rdma/* extended resource (k8s-rdma-shared-dev-plugin / NVIDIA Network Operator) grants access to the node's HCAs.
host device — generic bare-metal fallback with no device plugin: hostPath mount /dev/infiniband + CAP_IPC_LOCK (requires host_network: true).

When none applies, sflow falls back to pinning UCX/NCCL/gloo to the routable TCP interface (sockets, not RDMA) and only exposes the detected HCAs via SFLOW_RDMA_HCAS.

GKE (Google Cloud) GPUDirect-RDMA (gIB) — per Google's own docs

On A3 Ultra / A4 nodes, GKE exposes RDMA through Google's GPUDirect-RDMA (gIB) stack. sflow's GKE provider deliberately mirrors Google's documented setup — the prerequisites, paths, and constraints below are Google-documented, and they are why sflow behaves the way it does:

Prerequisites you provision (sflow assumes them). Google requires a supported GKE version (A4: 1.32.2-gke.1475000+; A3 Ultra: 1.31.4-gke.1183000+), Container-Optimized OS nodes (Ubuntu/Windows node images are unsupported), and the nccl-rdma-installer DaemonSet deployed on the cluster. That installer places the RDMA binaries in /home/kubernetes/bin/gib and the NCCL library in /home/kubernetes/bin/nvidia/lib64 on each VM. sflow probes for the installer at reservation and, when present, wires gIB in; when absent it emits no gIB mounts and NCCL falls back to its built-in IB transport over RoCE. See Create a custom A4/A3 Ultra GKE cluster.
The mounts + NCCL tuning sflow injects are exactly Google's. Google's reference JobSet manifests hostPath-mount /home/kubernetes/bin/gib → /usr/local/gib and /home/kubernetes/bin/nvidia → /usr/local/nvidia, and source /usr/local/gib/scripts/set_nccl_env.sh before the workload (it sets NCCL_CONF_FILE and the gIB NCCL tuning; the auto-loaded gIB plugin aborts NCCL init without it). sflow injects the same mounts and sources the same script on every GPU pod when the installer is detected. See Run NCCL on custom A4/A3 Ultra GKE clusters.
One RDMA-NIC resource per interface. An A3 Ultra / A4 node advertises its 8 RDMA NICs as networking.gke.io.networks/rdma-0 … rdma-7 (one per eth2…eth9); sflow requests one extended resource per NIC, sized to the pod's GPU count.
Why sflow merges co-located GPU tasks into one whole-node pod. Google's GPUDirect-RDMA Requirements state, verbatim: "Your GKE workload must use all available GPUs and your Pod must use all available secondary network interface cards (NICs) on a single GKE node. Multiple Pods can't share RDMA on a single GKE node." It follows that a partial-node pod (one not claiming every GPU + NIC) cannot use RDMA on GKE — so sflow packs co-located GPU tasks into a single full-node pod that owns every GPU and every rdma-N NIC. (This is also what keeps GPU↔NIC affinity correct — see below.) Source: the Requirements section of Create a custom A4/A3 Ultra GKE cluster.

These are Google Cloud requirements, quoted from Google's documentation; verify the exact version/OS thresholds against the linked pages for your machine family, as GKE revs them.

GPU ↔ NIC affinity

Because the GPU is requested by count (nvidia.com/gpu or a DRA claim), the device plugin / DRA driver — not sflow — chooses the physical GPU. Pinning a NIC at manifest-build time therefore risks pairing a GPU with a NIC on a different PCIe root (slow GPUDirect-RDMA), or worse, pinning an RDMA device that is not actually usable in the pod (UCX then aborts NIXL with NIXL_ERR_BACKEND instead of degrading to TCP). sflow addresses this two ways:

Runtime selection (device-plugin / host-device / shared plugin). For providers where the pod sees every node HCA, task pods run a small preamble before the workload, controlled by the SFLOW_RDMA_AFFINITY pod env:
- auto (default) — expose every NIC and let the libraries choose. Leaves NCCL_IB_HCA unset (NCCL needs every rank to see every NIC to compute a consistent topology-aware solution) and leaves UCX_NET_DEVICES unset (sflow never pins UCX devices), setting only UCX_MAX_RNDV_RAILS=1 so UCX/NIXL keep each GPU transfer on its single closest NIC (implicit GPUDirect RDMA). This is NVIDIA's recommended setup and needs no per-GPU mapping from sflow.
- explicit — pin each GPU to the NIC on its PCIe root (nvidia-smi bus id → sysfs pcieRoot). Use when auto-detection mispairs because sysfs distance isn't representative (e.g. GB300 Data-Direct sub-interfaces, SR-IOV VFs).
- off — inject nothing; the recipe controls device selection.
In all modes the preamble first verifies RDMA is usable in the pod (rdma_cm + a verbs node + an ACTIVE port). If not, sflow does not force a transport fallback — doing so (NCCL_IB_DISABLE=1 / NCCL_NET_PLUGIN=none / NCCL_IBEXT_DISABLE=1) would also suppress the rack-scale NVLink (MNNVL) path that NCCL/UCX auto-detect on GB200/GB300, and "no ACTIVE IB port" is the normal state on a pure-MNNVL rack. Instead it prints a hint and leaves transport selection to the libraries (they pick cuda_ipc/NVLink, MNNVL, RDMA, or TCP from topology). If your cluster genuinely has no NVLink fabric and an external IB plugin aborts on dead HCAs, set those envs yourself (recipe env / -s) or use rdma: disable (below) to disable RDMA cleanly in one knob — you own that decision because only you know the cluster's real fabric.
DRA topology co-allocation (scheduling: dra, opt-in). Set dra.rdma_device_class to co-request a NIC in the same ResourceClaim as the GPU with a matchAttribute constraint (default resource.kubernetes.io/pcieRoot), so the scheduler places the GPU and NIC on the same PCIe root complex. Requires a NIC DRA driver that publishes the match attribute (e.g. NVIDIA rdma.nvidia.com, DRANET dra.net). Note: on GB300/Vera Rubin/Fractal the NIC's matching root is its Data-Direct sub-interface root, which some NIC DRA drivers do not yet expose — override dra.rdma_match_attribute per cluster if co-allocation finds no candidates.

Config (all optional — the common case needs none of these):

rdma — auto (default) runs the provider chain then exposes all NICs for the libs to auto-select; disable cleanly disables RDMA by forcing NCCL onto its socket net (NCCL_IB_DISABLE=1, NCCL_IBEXT_DISABLE=1, NCCL_NET_PLUGIN=none) so it never probes IB/RoCE HCAs and the external IB plugin can't abort on a dead HCA — the explicit one-knob kill switch (auto never force-disables, it only hints). It is named disable rather than off so an unquoted YAML value isn't coerced to the boolean false. Or force one provider with gke, shared_device_plugin, or host_device (e.g. to opt out of the privileged-ish host-device path on PodSecurity-restricted namespaces).
dra.rdma_device_class — NIC DeviceClass to co-allocate with each GPU (DRA topology alignment; unset = off).
dra.rdma_match_attribute — attribute the GPU + NIC must share (default resource.kubernetes.io/pcieRoot).
dra.device_selectors — optional list of CEL expressions that narrow which GPU devices are eligible (advanced; unset = any device in the class). A k8s operator can override the backend device_class / device_selectors per task.
host_ipc — share the node IPC namespace (pod hostIPC) + a shared hostPath /dev/shm across task pods for cross-pod CUDA IPC (default false, privileged — see Host namespaces above for the trade-offs and when to enable it). On GB200/GB300 the NVLink KV path is Multi-Node NVLink (MNNVL) fabric handles, which additionally need UCX_CUDA_IPC_ENABLE_MNNVL=y (task env), vLLM VMM allocation (--enable-cumem-allocator), and an IMEX domain (nvidia-imex, or compute_domain.create: true, or joining an existing one via compute_domain.channel).
merge_colocated_gpu_pods — merge GPU tasks the planner assigns to the same physical node into one pod / one container requesting the union of their GPUs. Tri-state (auto/on/off or a bool), default auto (enabled): merging is sflow-owned pod topology — it enables intra-node NVLink between co-located workers and guarantees one IMEX-channel-claiming pod per node. Set off to opt out. Cross-pod GPU isolation means separate pods (even with host_ipc) can't cuda_ipc to each other's GPUs; putting the co-located tasks in one container that holds every node GPU is what makes intra-node NVLink work between them. Each task keeps its own <task>.log, probes, readiness, and dependents: the tasks run as concurrent background processes in the shared container. Every task sees all the container's GPUs (its own listed first in CUDA_VISIBLE_DEVICES so it uses that as cuda:0) — exposing the peers' GPUs is what lets cross-task cuda_ipc/NVLink P2P work; each task still gets its own env, and the driver demuxes the single container log stream back into per-task logs. Only single-node GPU tasks co-located on a node merge (CPU-only infra and multi-node tasks keep their own pods); merged tasks must be concurrent (a completion-before-start dependency between two members is rejected). This is the node-local counterpart to IB/RDMA: on a real multi-node IB cluster UCX already auto-selects RDMA, but for same-node disaggregation NVLink needs the tasks in one pod. Privileged-adjacent (pairs naturally with host_ipc), so opt-in.
cpu_per_gpu / cpu_request — opt-in CPU requests for task pods. Both are unset by default, so sflow injects no CPU request and pods run BestEffort (no limit, no floor) — matching a stock pod manifest. Set cpu_per_gpu to give GPU task pods cpu_per_gpu x per-pod GPUs cores and/or cpu_request to give pods without GPUs that many cores. This is a requests-only policy (sflow never sets a CPU limit): the request is a cgroup-weight floor so a CPU-hungry pod (e.g. an aiperf client driving thousands of streams) isn't starved as BestEffort (cpu.shares ~2) under contention, while any pod still bursts into idle CPU and nothing is CFS-throttled. A value of 0 also injects no request. Override per task with the operator cpu field (see below).

Per-pod NIC selection is further tunable at runtime via the SFLOW_RDMA_AFFINITY pod env (auto | explicit | off); see GPU ↔ NIC affinity above.

Per-task CPU/memory override — the k8s / k8s_mpi operator takes flat cpu/memory/cpu_limit/memory_limit fields (still requests-only unless you set a limit). cpu wins over the backend cpu_per_gpu/cpu_request policy. These are the pod's container resources, distinct from task.resources (the planner's node/GPU request):

operators:
  - name: benchmark
    type: k8s
    image: ...
    cpu: 16            # cpu request (cores, or "500m"); default: none unless the
                       #   backend sets cpu_per_gpu / cpu_request
    memory: 32Gi       # optional memory request (default: unset)
    cpu_limit: 24      # optional hard CPU cap (default: unset -> no throttle)
    memory_limit: 48Gi

backends:
  - name: k8s
    type: kubernetes
    default: true
    nodes: 2
    gpus_per_node: 8
    host_network: true               # privileged (default true): pod IP == node IP; see "Host namespaces"
    scheduling: device_plugin
    rdma: auto                       # auto-detect + expose all NICs (or: disable / a provider)
    # cpu_per_gpu: 8                 # opt-in CPU cores/GPU request (default: none -> BestEffort)
    # cpu_request: 4                 # opt-in CPU cores request for pods without GPUs (default: none)
    # collect_max_file_size: 10Mi    # default cap for syncing pod output files back to the driver (0 disables)
    # host_ipc: true                 # privileged: cross-pod CUDA IPC (same-node NVLink KV); see "Host namespaces"
    # merge_colocated_gpu_pods: disable  # opt OUT of merging same-node GPU tasks (default auto)

  # DRA topology co-allocation: schedule each GPU with a PCIe-root-aligned NIC.
  - name: k8s-dra
    type: kubernetes
    nodes: 2
    gpus_per_node: 8
    scheduling: dra
    dra:
      gpu_device_class: gpu.nvidia.com
      rdma_device_class: rdma.nvidia.com          # NIC DRA driver's DeviceClass
      # rdma_match_attribute: resource.kubernetes.io/pcieRoot

Interconnect priority (NVLink → IB/RDMA → TCP)

sflow picks the highest reachable interconnect tier for GPU↔GPU / KV transfer and claims only the resources it owns for it; it stays transport-neutral (it never pins UCX_TLS, so UCX/NCCL still auto-select). The tier depends on the cluster's NVLink domain scope:

node-scope (e.g. B200, H100/H200): NVLink/NVSwitch reaches GPUs within one node only. Cross-node needs IB/RDMA, else TCP.
rack-scope (GB200/GB300 NVL72): NVLink also reaches across nodes via MNNVL — but only with an IMEX ComputeDomain channel (and fabric/VMM KV memory in the app).

sflow detects the scope at preflight/allocate from the GPU product label (nvidia.com/gpu.product) + the ComputeDomain CRD presence.

nvlink_domain is advisory only

Leave nvlink_domain at its default auto in almost all recipes. It is not the Multi-Node NVLink switch — cross-node NVLink (MNNVL) is enabled by compute_domain.channel, not by this field, and setting it does not pin NCCL/UCX transport, inject env, or change GPU scheduling. Its only effects are advisory and warn-only: the resolved scope selects the cross-node interconnect-priority hint above and, together with dra.nvlink_domain_label_key, the multi-domain pod co-location below. Set nvlink_domain: node | rack | disable only to correct a wrong auto-detection (e.g. the ComputeDomain-CRD probe lacks RBAC).

Ownership — who is responsible for each layer of the fast-fabric path:

Owner	Responsible for
sflow (auto)	Pod layout (`merge_colocated_gpu_pods`, default `auto`), interconnect detection, and claiming what it controls — the ComputeDomain channel claim on every GPU pod (when a channel is configured/detected) and RDMA device grants (when IB is up).
recipe / app	The framework's KV-memory mode (VMM / vLLM `--enable-sleep-mode`) and transport env like `UCX_CUDA_IPC_ENABLE_MNNVL=y`. sflow only hints (app-agnostically).
cluster admin	ComputeDomain creation, the GPU/RDMA/DRA drivers, and quotas. sflow detects an existing domain/channel and hints; it does not create one unless `compute_domain.create: true`.

ComputeDomain / MNNVL config (top-level compute_domain:, independent of GPU scheduling):

The IMEX ComputeDomain is a capability of the NVIDIA DRA driver that is independent of DRA GPU allocation, so it is a top-level compute_domain: block (not under dra:) and works with scheduling: device_plugin or dra.

compute_domain.channel — the channel ResourceClaimTemplate every GPU pod claims to join an existing IMEX ComputeDomain. A name, or auto (claim the sole existing domain — skips + hints on zero/many), or disable/empty (default).
compute_domain.create — create a ComputeDomain CR (admin op; needs computedomains RBAC). Default false — the default path is detect + hint.
dra.nvlink_domain_label_key — (stays under dra:) node label key (e.g. nvidia.com/gpu.clique) used for placement + validation only on clusters with multiple NVLink domains (redundant on a single NVL72 rack): reservation pods get a podAffinity on this key so all reserved nodes share one domain, and a post-schedule check warns if they straddle domains.

KV-memory rule (name the vLLM knob only as an example):

Same-node KV + regular memory: classic cuda_ipc over intra-node NVLink — no IMEX, no VMM (cheapest). Enable it with merge_colocated_gpu_pods (default) and/or host_ipc.
Cross-node KV on rack-scope: needs an IMEX ComputeDomain channel (compute_domain.channel) and the app's fabric/VMM KV memory (vLLM --enable-sleep-mode) and UCX_CUDA_IPC_ENABLE_MNNVL=y. On node-scope, cross-node KV must use IB, else TCP.
Enabling VMM (--enable-sleep-mode) without an IMEX channel forces the KV transfer onto slow TCP even intra-node — so keep it off unless you have the channel.

backends:
  - name: k8s
    type: kubernetes
    nodes: 2
    gpus_per_node: 8
    # nvlink_domain: auto              # auto|node|rack|disable (default auto = detect)
    # Multi-Node NVLink (IMEX ComputeDomain) -- top-level, independent of scheduling:
    # compute_domain:
    #   channel: auto                  # join sole existing IMEX domain (or a name)
    dra:
      # nvlink_domain_label_key: nvidia.com/gpu.clique  # multi-domain clusters only

Default behavior (simplest)​

Explicit local backend​

Slurm backend​

Cluster-specific flags (extra_args)​

Selecting or excluding nodes (all backends)​

Docker backend​

Multi-node Docker hosts​

Kubernetes backend​

What the backend does (recipe → pod)​

Connecting to the cluster​

Host namespaces (host_network, host_ipc) — privileged​

Readiness probes run in-cluster (probe pod)​

Volumes: PVCs and emptyDir​

Operator: k8s​

Operator: k8s_mpi (MPI workloads)​

Node reservation (reservation)​

Output directories inside pods (SFLOW_*)​

GPU requests​

RDMA / InfiniBand networking​

GKE (Google Cloud) GPUDirect-RDMA (gIB) — per Google's own docs​

GPU ↔ NIC affinity​

Interconnect priority (NVLink → IB/RDMA → TCP)​

Default behavior (simplest)

Explicit local backend

Slurm backend

Cluster-specific flags (`extra_args`)

Selecting or excluding nodes (all backends)

Docker backend

Multi-node Docker hosts

Kubernetes backend

What the backend does (recipe → pod)

Connecting to the cluster

Host namespaces (`host_network`, `host_ipc`) — privileged

Readiness probes run in-cluster (probe pod)

Volumes: PVCs and emptyDir

Operator: k8s

Operator: k8s_mpi (MPI workloads)

Node reservation (`reservation`)

Output directories inside pods (`SFLOW_*`)

GPU requests

RDMA / InfiniBand networking

GKE (Google Cloud) GPUDirect-RDMA (gIB) — per Google's own docs

GPU ↔ NIC affinity

Interconnect priority (NVLink → IB/RDMA → TCP)