À propos de ce Skill
Apply practical large-model training and operations guidance, and evaluate GPU cluster performance and readiness.
Pourquoi ce choix
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 de cette collection
2 skills- Voir →
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…
- Voir →
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.
Avant de l’utiliser
- The relevant ML stack and cluster access. Cluster evaluation runs hardware/software checks and needs the upstream test dependencies.
Sources et références
Les sources originales sont liées ci-dessous. Les pistes pratiques sont des suggestions de la rédaction de SkillHey.
