{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Trainer is a PyTorch model training framework that handles the training loop, optimization, and experiment logging for machine learning models with support for mixed precision, distributed training, and callback customization.","skillfed_tags":["pytorch-training","distributed-training","experiment-logging"],"use_cases":["Training standard supervised models with automatic optimization and mixed precision support without writing custom training loops.","Implementing adversarial training (GANs) with separate optimizer steps and gradient accumulation for each component.","Running multi-GPU distributed training across multiple GPUs or nodes using DDP or Accelerate without managing process spawning.","Profiling model training with PyTorch profiler and visualizing results in Tensorboard to identify bottlenecks.","Automatically finding the largest batch size that fits on available hardware to maximize training efficiency.","Logging training metrics and checkpoints to multiple experiment tracking platforms simultaneously."],"what_it_does":"Trainer is a PyTorch training framework designed to reduce boilerplate in model training loops while maintaining flexibility for advanced use cases. It wraps the training cycle\u2014forward pass, loss computation, backpropagation, and optimization\u2014into a configurable abstraction that handles mixed precision training, gradient accumulation, and multi-GPU distributed training via DDP or Hugging Face Accelerate.\n\nThe package is built around subclassing a TrainerModel base class and defining an optimize() method, which can range from a simple auto-optimized loop to a fully custom training procedure (the documentation shows a GAN example with separate discriminator and generator optimization steps). It integrates experiment logging via Tensorboard, ClearML, MLFlow, Aim, and WandB, supports callbacks for custom hooks at training milestones, and includes utilities like a batch size finder to maximize GPU utilization. The codebase is intentionally kept simple and opinionated to avoid over-abstraction.","worth_installing":"Yes, if you are actively training PyTorch models and want to reduce training loop boilerplate without heavyweight abstractions. The package is dormant (last release December 2023) but has no known vulnerabilities and low install friction. It is suitable for research and production use, though you should verify that its dependencies remain compatible with your PyTorch and Python versions. Not recommended if you require active maintenance or support for the latest PyTorch features."},"id":"trainer","links":{"html":"https://skillfed.io/packages/trainer","md":"https://skillfed.io/packages/trainer.md","pypi":"https://pypi.org/project/trainer/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-12-13","license_spdx":null,"license_treatment":"permissive","name":"trainer","python_support":"capped_below_current","summary":"General purpose model trainer for PyTorch that is more flexible than it should be, by \ud83d\udc38Coqui."},"popularity":{"monthly_downloads":85910,"position":13894,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.36"}