trainer-tools 0.4.1

Small utilities to simplify trainining of PyTorch models.

Trainer Tools

A lightweight, hook-based training loop for PyTorch. trainer-tools abstracts away the boilerplate of training loops while remaining fully customizable via a powerful flexible hook system.

Full Documentation

📘 Read the complete documentation here!

There you will find in-depth guides and API references on:

• Customizing train_step and eval_step validation runs
• Powerful robust metric tracking
• Writing your own custom hooks
• All built-in hooks (AMP, Checkpointing, EMA, Gradient Accumulation, HF Accelerate, etc.)

Features

• Hook System: Customize every step of the training lifecycle (before/after batch, step, epoch, fit).
• Built-in Integrations: Comes with hooks for wandb or trackio, Progress Bar, and Checkpointing.
• Optimization: Easy Automatic Mixed Precision (AMP), Gradient Accumulation, and Gradient Clipping.
• Metrics: robust metric tracking and logging to JSONL or external trackers.
• Memory Profiling: Built-in tools to debug CUDA memory leaks.

Installation

pip install trainer-tools

# With optional integrations
pip install trainer-tools[wandb]      # Weights & Biases logging
pip install trainer-tools[trackio]    # Trackio logging
pip install trainer-tools[hydra]      # Hydra config management
pip install trainer-tools[all]        # All optional dependencies

Quick Start

Here is a minimal example of training a simple model:

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from trainer_tools.trainer import Trainer
from trainer_tools.hooks import MetricsHook, Accuracy, Loss, ProgressBarHook

# 1. Prepare Data
x = torch.randn(100, 10)
y = torch.randint(0, 2, (100,))
ds = TensorDataset(x, y)
dl = DataLoader(ds, batch_size=32)

# 2. Define Model and Optimizer
model = nn.Sequential(nn.Linear(10, 2))
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# 3. Define the Training Step
def train_step(batch, trainer):
    inputs, labels = batch
    logits = trainer.model(inputs)
    loss = nn.CrossEntropyLoss()(logits, labels)
    
    # Must return a dictionary containing at least the "loss" key!
    return {
        "loss": loss,
        "logits": logits,
        "labels": labels
    }

# 4. Setup Hooks
metrics = MetricsHook(metrics=[Accuracy(pred_key="logits", target_key="labels"), Loss()])
pbar = ProgressBarHook()

# 5. Train
trainer = Trainer(
    model=model,
    train_dl=dl,
    valid_dl=dl,
    optim=optimizer,
    train_step=train_step,
    epochs=5,
    hooks=[metrics, pbar],
    device="cuda" if torch.cuda.is_available() else "cpu"
)

trainer.fit()