Configure distributed training setup operations. Auto-activating skill for ML Training. Triggers on: distributed training setup, distributed training setup Part of the ML Training skill category. Use when working with distributed training setup functionality. Trigger with phrases like "distributed training setup", "distributed setup", "distributed".
4.0
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Machine Learning
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The skill provides a clear organizational structure but lacks specific, actionable content for distributed training setup. The description is too generic—it doesn't clarify what distributed training frameworks are supported (PyTorch DDP, Horovod, TensorFlow MultiWorkerMirroredStrategy, etc.), what configurations it will generate, or concrete setup steps. Task knowledge is minimal with no actual code, scripts, configuration templates, or detailed procedures for initializing distributed environments, handling rank/world size, configuring communication backends, or managing multi-node setups. While the structure is clean and well-organized, the content is mostly placeholder text that could apply to any ML task. The novelty is limited because the skill doesn't demonstrate complex distributed training orchestration that would meaningfully reduce token usage—a CLI agent could provide similar generic guidance. To improve: add concrete code examples for popular frameworks, configuration templates (YAML/JSON), multi-node setup scripts, and specific distributed training patterns.
jeremylongshore
Skill Author
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