Colossal-AI Engine & Customize Your Trainer

Colossal-AI engine

To better understand how Engine class works, let’s start from the conception of the process function in common engines. The process function usually controls the behavior over a batch of a dataset, Engine class just controls the process function. Here we give a standard process function in the following code block.

def process_function(dataloader, model, criterion, optim):
    data, label = next(dataloader)
    output = model(data)
    loss = criterion(output, label)

The engine class is a high-level wrapper of these frequently-used functions while preserving the PyTorch-like function signature and integrating with our features.

import torch
import torch.nn as nn
import torchvision.models as models
import colossalai
from colossalai.engine import Engine
from torchvision.datasets import CIFAR10

model = models.resnet18()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

dataset = CIFAR10(...)
dataloader = colossalai.utils.get_dataloader(dataset)

engine, dataloader, _,  _ = colossalai.initialize(model, optimizer, criterion, dataloader)

# exmaple of a training iteratio
for img, label in dataloader:
    output = engine(img)
    loss = engine.criterion(output, label)

More information regarding the class can be found in the API references.

Customize your trainer


To learn how to customize a trainer which meets your needs, let’s first give a look at the Trainer class. We highly recommend that you read Get Started section and Colossal-AI engine first.

The Trainer class enables researchers and engineers to use our system more conveniently. Instead of having to write your own scripts, you can simply construct your own trainer by calling the Trainer class, just like what we did in the following code block.

trainer = Trainer(engine)

After that, you can use the fit method to train or evaluate your model. In order to make our Trainer class even more powerful, we incorporate a set of handy tools to the class. For example, you can monitor or record the running states and metrics which indicate the current performance of the model. These functions are realized by hooks. The BasicHook class allows you to execute your hook functions at specified time. We have already created some practical hooks for you, as listed below. What you need to do is just picking the right ones which suit your needs. Detailed descriptions of the class can be found in the API references.

These hook functions will record metrics, elapsed time and memory usage and write them to log after each epoch. Besides, they print the current loss and accuracy to let users monitor the performance of the model.

import colossalai
from colossalai.trainer import hooks, Trainer
from colossalai.utils import MultiTimer
from colossalai.logging import get_dist_logger

... = colossalai.initialize(...)

timer = MultiTimer()
logger = get_dist_logger()

# if you want to save log to file

trainer = Trainer(

hook_list = [
    hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=False),
    hooks.TensorboardHook(log_dir='./tb_logs', ranks=[0]),
    hooks.LogTimingByEpochHook(timer, logger),


If you have your specific needs, feel free to extend our BaseHook class to add your own functions, or our MetricHook class to write a metric collector. These hook functions can be called at different stage in the trainer’s life cycle. Besides, you can define the priorities of all hooks to arrange the execution order of them. More information can be found in the API references.


You can write your own metrics by extending our Metric class. It should be used with the MetricHook class. When your write your own metric hooks, please set the priority carefully and make sure the hook is called before other hooks which might require the results of the metric hook.

We’ve already provided some metric hooks and we store metric objects in runner.states['metrics']. It is a dictionary and metrics can be accessed by their names.