How to train a NetOps team on SONiC

SONiC went from a Microsoft Azure project to the de-facto open network OS of hyperscale data centers, and it's now showing up in enterprise RFPs. If your team is picking up SONiC switches — or you're hiring for AI-datacenter buildouts where SONiC + FRR is the default stack — the training question lands on your desk: how do you get a team of IOS/Junos-shaped engineers productive on SONiC?

Here's the structure we use, distilled from running SONiC-based labs for hundreds of learners.

Why SONiC training is different

SONiC isn't "another CLI dialect." Three shifts trip up experienced engineers:

It's Linux underneath, visibly. SONiC runs as containerized services on Debian. The kernel network stack is load-bearing: VXLAN tunnels are kernel netdevs, bridges are Linux bridges, and ip/bridge commands are first-class diagnostic tools, not last resorts.
Routing is FRR. The BGP brain of SONiC is FRR's vtysh — closer to classic IOS syntax than most expect, but with its own address-family model, and it coexists with SONiC's own config CLI. Knowing which layer owns which config is half the skill.
The design patterns are hyperscale patterns. SONiC deployments are overwhelmingly BGP-to-the-leaf CLOS fabrics with EVPN-VXLAN overlays — not three-tier campus designs. Training that ignores the architecture teaches the CLI but not the job.

A curriculum that works (4 phases)

Phase 1 — The underlay, by hand (1 day)

Have every engineer bring up a small CLOS from scratch: loopbacks, point-to-point /31s, eBGP on every link, ECMP. The goal isn't BGP trivia — it's fluency moving between SONiC's config/show world and FRR's vtysh, and reading show bgp summary on a fabric they built themselves.

This is exactly our first lab; the full walkthrough is free: Build a BGP CLOS underlay for an AI datacenter fabric.

Phase 2 — EVPN-VXLAN overlay (1–2 days)

The centerpiece. Engineers configure VTEPs, map VLANs to VNIs, enable the EVPN address family, and watch Type-3 then Type-2 routes appear as hosts attach. Insist on the kernel view too (bridge fdb show, ip -d link show): when overlay problems hit production, the answer is as often in the Linux dataplane as in BGP.

Guide: Build an EVPN-VXLAN overlay on SONiC/FRR.

Phase 3 — Real traffic and telemetry (1–2 days)

Configs that carry no traffic teach nothing about operations. Put real workloads on the overlay — in our labs, actual GPU-style collective traffic (AllReduce) between workers — then wire up streaming telemetry (gNMI → Prometheus → Grafana) and watch link counters during a training job. Engineers see per-flow ECMP spread across spines, and learn which dashboards matter when a fabric is "slow."

Guides: GPU AllReduce over the overlay, streaming telemetry with gNMI + Grafana.

Phase 4 — The frontier topics (optional, 1–2 days)

For teams heading into AI-datacenter work: SRv6 transport (dual-stack underlay, uSID transport) and multi-pod super-spine designs. For enterprise-pod teams: the campus EVPN-VXLAN track.

The three rules that make it stick

Every engineer needs their own fabric. Shared labs create spectators. Virtual SONiC (sonic-vs) makes per-learner CLOS topologies cheap — no hardware rack required, and the FRR control plane behaves like the real thing.

Verify state, not attendance. Define pass criteria as device state: "4 eBGP sessions established," "remote VTEPs learned on every leaf," "ping mesh 56/56." Our platform machine-grades these as per-step checkpoints; if you build in-house, script the checks. Unverified training is a slideshow.

Sequence labs so each builds on the last. Underlay → overlay → traffic → telemetry mirrors how you'd actually stand up a fabric. Standalone disconnected labs teach commands; a sequence teaches the job.

Build vs buy

Everything above is buildable in-house with containerlab + sonic-vs + FRR and a few weeks of a senior engineer's time for guides and grading scripts. That's a real option — the tools are open source.

Or run it on us: BookMyFabric delivers this exact curriculum with a private fabric per learner, browser consoles, machine-graded checkpoints, and zero infrastructure on your side. Read the guides free, try the AI datacenter course yourself on a free booked slot, and talk to us about team training when you're ready to scale it.