Primus

Compare a Primus backend against an upstream repository or reference, verify git state, dependencies, directory changes, and integration coupling, then generate comparison reports, dashboard metadata, and a deployable dashboard index. Also owns the shared Primus engineering dashboard under `tools/backend_gap_report/`, which surfaces both backend-gap reports and weekly engineering reports as first-class sections. Use when comparing TorchTitan, Megatron, or other Primus backends with upstream branches, tags, or releases, or when integrating weekly engineering reports into the shared dashboard.

Inventory and explain the patch (monkey-patch) optimizations Primus layers over upstream training backends such as Megatron-LM, TorchTitan, and MaxText, by reading the current repository code only. Use when the user asks which patches a backend has, wants a customer-facing patch table, asks how a specific patch works (for example deepep or DeepEP), or wants guidance to port a Primus patch into their own upstream framework. Read-only; no training or cluster commands.

Opinionated guide for using Primus Projection to choose parallelism (TP/PP/EP/CP/DP) and pipeline schedules, validate memory fit on target nodes, reason about communication collectives, and explore optimization trade-offs with minimal compute. Use when the user asks how to pick a parallelism strategy or pipeline schedule, whether a model fits in memory, which optimizations such as DeepEP, SyncFree, zero-bubble, FP8, recomputation, or FSDP2 matter most, or how to run primus projection commands. Read-only planning guidance; no large multi-node training runs required.

Check available idle nodes in a SLURM cluster. Use when the user wants to find usable idle nodes, verify node health, check docker status on SLURM nodes, check NIC QoS/DCQCN configuration, check RDMA link status, validate GID table, or troubleshoot cluster node availability.

Validate SLURM cluster nodes by running actual training jobs in groups. Use when the user wants to test which idle nodes can successfully run training, verify node health through real workloads, or identify broken nodes in a SLURM cluster.