Transformers Example with Ignite

In this example, we show how to use Ignite to finetune a transformer model:

• on 1 or more GPUs or TPUs
• compute training/validation metrics
• log learning rate, metrics etc
• save the best model weights

Configurations:

• [x] single GPU
• [x] multi GPUs on a single node
• [x] TPUs on Colab

Requirements:

• pytorch-ignite: pip install pytorch-ignite
• transformers: pip install transformers
• datasets: pip install datasets
• tqdm: pip install tqdm
• tensorboardx: pip install tensorboardX
• python-fire: pip install fire
• Optional: clearml: pip install clearml

Alternatively, install the all requirements using pip install -r requirements.txt.

Usage:

Run the example on a single GPU:

python main.py run

If needed, please, adjust the batch size to your GPU device with --batch_size argument.

The default model is bert-base-uncased incase you need to change that use the --model argument, for details on which models can be used refer here

Example:

#Using DistilBERT which has 40% less parameters than bert-base-uncased
python main.py run --model="distilbert-base-uncased"

For details on accepted arguments:

python main.py run -- --help

Distributed training

Single node, multiple GPUs

Let’s start training on a single node with 2 gpus:

# using torch.distributed.launch
python -u -m torch.distributed.launch --nproc_per_node=2 --use_env main.py run --backend="nccl"

or

# using function spawn inside the code
python -u main.py run --backend="nccl" --nproc_per_node=2

Using Horovod as distributed backend

Please, make sure to have Horovod installed before running.

Let’s start training on a single node with 2 gpus:

# horovodrun
horovodrun -np=2 python -u main.py run --backend="horovod"

or

# using function spawn inside the code
python -u main.py run --backend="horovod" --nproc_per_node=2

Colab or Kaggle kernels, on 8 TPUs

# setup TPU environment
import os
assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator'

VERSION = "nightly"
!curl -q https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
!python pytorch-xla-env-setup.py --version $VERSION > /dev/null

from main import run
run(backend="xla-tpu", nproc_per_node=8)

ClearML fileserver

If ClearML server is used (i.e. --with_clearml argument), the configuration to upload artifact must be done by
modifying the ClearML configuration file ~/clearml.conf generated by clearml-init. According to the
documentation, the output_uri argument can be
configured in sdk.development.default_output_uri to fileserver uri. If server is self-hosted, ClearML fileserver uri is
http://localhost:8081.

For more details, see https://allegro.ai/clearml/docs/docs/examples/reporting/artifacts.html

Imported from gh:Kentucky-Open-Science/ignitenlp. Source last updated 2022-04-05. Synced 2026-07-27.