From omer-metin-skills-for-antigravity-2
Helps train large models across multiple GPUs or nodes using DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient checkpointing to optimize throughput.
How this skill is triggered — by the user, by Claude, or both
Slash command
/omer-metin-skills-for-antigravity-2:distributed-trainingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
npx claudepluginhub joshuarweaver/cascade-code-general-misc-2 --plugin omer-metin-skills-for-antigravity-2Guides distributed training setup for ML workflows, covering configuration generation, data preparation, hyperparameter tuning, and experiment tracking.
Organizes PyTorch code into LightningModules, configures Trainers for multi-GPU/TPU, builds data pipelines and callbacks, and runs distributed training (DDP, FSDP, DeepSpeed). Use when structuring training loops or scaling neural-network training.
Provides PyTorch patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.