pytorch-trainer

# pytorch-trainer ## Overview PyTorch model training skill with custom training loops, gradient management, GPU optimization, and integration with experiment tracking systems. ## Capabilities - Custom training loop execution - Learning rate scheduling (StepLR, CosineAnnealing, OneCycleLR, etc.) - Gradient clipping and accumulation - Mixed precision training (AMP) - Checkpoint management and resumption - DataLoader optimization - Multi-GPU training (DataParallel, DistributedDataParallel) - Early stopping with patience ## Target Processes - Model Training Pipeline with Experiment Tracking - Distributed Training Orchestration - AutoML Pipeline Orchestration ## Tools and Libraries - PyTorch - PyTorch Lightning (optional) - torchvision, torchaudio, torchtext - CUDA toolkit ## Input Schema ```json { "type": "object", "required": ["modelPath", "dataConfig", "trainingConfig"], "properties": { "modelPath": { "type": "string", "description": "Path to model definition file" }, "dataConfig": { "type": "object", "properties": { "trainPath": { "type": "string" }, "valPath": { "type": "string" }, "batchSize": { "type": "integer" }, "numWorkers": { "type": "integer" } } }, "trainingConfig": { "type": "object", "properties": { "epochs": { "type": "integer" }, "learningRate": { "type": "number" }, "optimizer": { "type": "string" }, "scheduler": { "type":...