trainer

What it is and 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—forward pass, loss computation, backpropagation, and optimization—into a configurable abstraction that handles mixed precision training, gradient accumulation, and multi-GPU distributed training via DDP or Hugging Face Accelerate.

The 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.

Use it for

• 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.

Worth the install?

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Install

trainer on PyPI

Before you install

Low install friction with a pure Python wheel. The package is dormant (last commit 2024-03-07, 975 days since release), but remains functional for its stated purpose. Six runtime dependencies—torch, coqpit, psutil, fsspec, tensorboard, soundfile—are standard in the ML ecosystem.

Requires PyTorch (torch) to be installed; supports Python 3.8–3.11 only (capped below current Python versions).

License in practice

Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most projects without legal friction.

Quickstart

pip install trainer

from trainer import Trainer
from trainer.model import TrainerModel

class MyModel(TrainerModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # define model layers

trainer = Trainer(model=MyModel(), ...)
trainer.fit()

Package facts

Evidence: trainer-0.0.36-py3-none-any.whl

Tags

More Artificial Intelligence packages

See also coqui-tts-trainer · torchtnt · pytorch-ignite · pytorch-lightning · nv-one-logger-pytorch-lightning-integration · lightning · mmengine · pytorch-metric-learning · torch-tb-profiler · accelerate

Further reading