{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/15"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/18"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"A PyTorch model trainer framework that handles training loops, optimization, mixed precision, distributed training, and experiment logging with minimal boilerplate.","skillfed_tags":["pytorch-training","distributed-training","experiment-logging"],"use_cases":["Train standard supervised models with auto-optimization and minimal configuration overhead.","Implement GAN or multi-network training with custom per-step optimization logic and gradient accumulation.","Automatically find the largest batch size that fits on your hardware without manual tuning.","Distribute training across multiple GPUs using DDP or Accelerate without rewriting your model code.","Profile training performance and memory usage with integrated PyTorch profiler and TensorBoard visualization.","Log metrics and checkpoints to multiple experiment tracking platforms simultaneously."],"what_it_does":"Coqui-tts-trainer is a PyTorch training framework that abstracts away boilerplate training loop code while preserving flexibility for custom optimization logic. It provides opinionated defaults for common tasks\u2014auto-optimization, mixed precision, gradient accumulation, and distributed training via DDP or Hugging Face Accelerate\u2014but allows you to override the optimization cycle entirely when needed. The framework integrates with multiple experiment loggers (TensorBoard, ClearML, MLflow, Aim, WandB) and includes utilities like batch size finder and profiling support.\n\nYou define your model by subclassing TrainerModel and overloading its methods, then pass it to a Trainer instance with configuration. The trainer handles the training loop, checkpointing, logging, and device management. It supports both simple auto-optimized training (useful for standard supervised learning) and fully custom optimization loops (useful for GANs and other adversarial setups), making it suitable for researchers and practitioners who want structure without sacrificing control.","worth_installing":"Yes. The package is actively maintained, has low install friction, carries a permissive Apache-2.0 license, and solves a real problem\u2014reducing boilerplate in PyTorch training loops while preserving flexibility. It is suitable for both simple supervised learning and advanced custom training scenarios. No known security vulnerabilities. Best for teams already committed to PyTorch who want a structured but not rigid training abstraction."},"id":"coqui-tts-trainer","links":{"html":"https://skillfed.io/packages/coqui-tts-trainer","md":"https://skillfed.io/packages/coqui-tts-trainer.md","pypi":"https://pypi.org/project/coqui-tts-trainer/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-10","license_spdx":null,"license_treatment":"permissive","name":"coqui-tts-trainer","python_support":"supports_current","summary":"General purpose model trainer for PyTorch that is more flexible than it should be, by \ud83d\udc38Coqui."},"popularity":{"monthly_downloads":146934,"position":11087,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.0"}