Welcome to ZAE-Engine’s Documentation!¶

ZAE-Engine is a modular AI framework designed to streamline and accelerate AI workflows. Currently, it supports PyTorch and provides tools for model training, evaluation, and deployment.

Note

This documentation is for the pre-release version of ZAE-Engine and is subject to change.

Contents¶

Quick Links¶

installation
usage
modules/core
api_reference

Getting Started¶

If you’re new to ZAE-Engine, we recommend starting with the Installation and Usage sections.

Key Features¶

Simplifies repetitive coding tasks with modular utilities.
Supports PyTorch for flexible AI workflows.
Provides easy integration of add-ons for state management, distributed training, and logging.

Command Line Interface¶

After installing ZAE-Engine, you can use the zae command to simplify various tasks:

`zae hello`: Verifies that the installation was successful.
`zae example`: Generates an example script (zae_example.py) for quick reference.
`zae tree`: Displays the available classes and functions in the package.

Quick Start¶

Here’s a quick example of using the Trainer class for model training:

from zae_engine.trainer import Trainer
from torch.optim import Adam
from torch.nn import Linear

model = Linear(10, 2)  # Example model
trainer = Trainer(
    model=model,
    optimizer=Adam(model.parameters(), lr=0.001),
    device='cuda'
)
trainer.run(n_epoch=10, loader=train_loader, valid_loader=valid_loader)

For more detailed examples, check out the Usage section.

Modules¶

Explore the core functionalities of ZAE-Engine through its modules: