Evaluate Cluster

stas00/ml-engineering

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.

Contenu mis à jour:

Dans la collectionML Engineering Skills ↗
Voir sur GitHub
19 702Étoiles1 301ForksDernier push du dépôt

À propos de ce Skill

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.

Pourquoi ce choix

A task from ML Engineering Skills. Retain the upstream supporting files and follow the repository’s setup instructions.

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.