About this Skill
Apply practical large-model training and operations guidance, and evaluate GPU cluster performance and readiness.
Why we picked it
A single repository groups related tasks; the members below link to their individual upstream entrypoints. Follow the upstream setup and each task’s dependencies.
Skills in this collection
2 skills- View →
Ml Engineering
Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference…
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Evaluate Cluster
Evaluates an ML GPU cluster for a cloud trial or acceptance test: environment dump, isolated newest PyTorch, matmul FLOPS (MAMF/MSMF) on every GPU while the others compute, intra-node all-reduce bandwidth and per-call latency of every collective, the same inter-node on every node you were given (omit those sections if there is only one node), fio on local disk and shared FS, dated markdown report.
Before you use it
- The relevant ML stack and cluster access. Cluster evaluation runs hardware/software checks and needs the upstream test dependencies.
Sources & references
Original sources are linked below. Practical starting points are editorial suggestions from SkillHey.
