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Quick demo
ColossalAI is an integrated large-scale deep learning framework with efficient parallelization techniques. The framework can accelerate model training on distributed systems with multiple GPUs by applying parallelization techniques. The framework can also run on systems with only one GPU. Quick demos showing how to use ColossalAI are given below.
Single GPU
ColossalAI can be used to train deep learning models on systems with only one GPU and achieve baseline performances. Here is an example showing how to train a LeNet model on the CIFAR10 dataset using ColossalAI.
Multiple GPUs
ColossalAI can be used to train deep learning models on distributed systems with multiple GPUs and accelerate the
training process drastically by applying efficient parallelization techiniques, which will be elaborated in
the Parallelization section below. Run the code below on your distributed system with 4 GPUs,
where HOST is the IP address of your system. Note that we use
the Slurm job scheduling system here.
HOST=xxx.xxx.xxx.xxx srun ./scripts/slurm_dist_train.sh ./example/train_vit_2d.py ./configs/vit/vit_2d.py
./configs/vit/vit_2d.py is a config file, which is introduced in the Config file section below. These
config files are used by ColossalAI to define all kinds of training arguments, such as the model, dataset and training
method (optimizer, lr_scheduler, epoch, etc.). Config files are highly customizable and can be modified so as to train
different models.
./example/run_trainer.py contains a standard training script and is presented below, it reads the config file and
realizes the training process.
import colossalai
from colossalai.engine import Engine
from colossalai.trainer import Trainer
from colossalai.core import global_context as gpc
model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = colossalai.initialize()
engine = Engine(
model=model,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
schedule=schedule
)
trainer = Trainer(engine=engine,
hooks_cfg=gpc.config.hooks,
verbose=True)
trainer.fit(
train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
max_epochs=gpc.config.num_epochs,
display_progress=True,
test_interval=5
)
Alternatively, the model variable can be substituted with a self-defined model or a pre-defined model in our Model
Zoo. The detailed substitution process is elaborated here.
Features
ColossalAI provides a collection of parallel training components for you. We aim to support you with your development of distributed deep learning models just like how you write single-GPU deeo learning models. We provide friendly tools to kickstart distributed training in a few lines.