ray-distributed-trainer

# ray-distributed-trainer ## Overview Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management across clusters. ## Capabilities - Ray Train for distributed training - Ray Tune for hyperparameter search at scale - Cluster resource management - Fault tolerance and checkpointing - Actor-based parallelism - Integration with PyTorch and TensorFlow - Elastic training support - Multi-node orchestration ## Target Processes - Distributed Training Orchestration - AutoML Pipeline Orchestration - Model Training Pipeline ## Tools and Libraries - Ray - Ray Train - Ray Tune - Ray Cluster ## Input Schema ```json { "type": "object", "required": ["mode", "config"], "properties": { "mode": { "type": "string", "enum": ["train", "tune", "cluster"], "description": "Ray operation mode" }, "config": { "type": "object", "properties": { "numWorkers": { "type": "integer" }, "useGpu": { "type": "boolean" }, "resourcesPerWorker": { "type": "object", "properties": { "cpu": { "type": "number" }, "gpu": { "type": "number" } } } } }, "trainConfig": { "type": "object", "properties": { "trainerPath": { "type": "string" }, "framework": { "type": "string", "enum": ["pytorch", "tensorflow", "xgboost"] }, "scalingConfig": { "type": "object" } } }, "tuneConfig"...