From jeremylongshore-claude-code-plugins-plus-skills
Guides distributed training setup for ML workflows, covering configuration generation, data preparation, hyperparameter tuning, and experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:distributed-training-setupThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for distributed training setup tasks within the ML Training domain.
This skill provides automated assistance for distributed training setup tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with distributed training setup" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin j-rigGuides TensorFlow model training tasks including data preparation, hyperparameter tuning, and experiment tracking with production-ready code.
Helps train large models across multiple GPUs or nodes using DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient checkpointing to optimize throughput.
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.