model-training

Design and run ML model training pipelines with PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, and HuggingFace. Covers hyperparameter tuning (Optuna, Ray Tune, Bayesian optimization), distributed training (DDP, DeepSpeed, FSDP, Horovod), mixed precision training (AMP, bf16), learning rate scheduling, early stopping, checkpointing, cross-validation, GPU memory optimization (gradient accumulation, gradient checkpointing), reproducibility, and config-driven training pipeline design. Use when training models, tuning hyperparameters, setting up distributed training, or optimizing training performance.

Version

1.0

License

Apache-2.0