CIFAR10 Example with Ignite

In this example, we show how to use Ignite to train a neural network:

• 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] multi GPUs on multiple nodes
• [x] TPUs on Colab

Requirements:

• pytorch-ignite: pip install pytorch-ignite
• torchvision: pip install torchvision
• 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

For details on accepted arguments:

python main.py run -- --help

If user would like to provide already downloaded dataset, the path can be setup in parameters as

--data_path="/path/to/cifar10/"

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, on 8 TPUs

Same code can be run on TPUs:

Multiple nodes, multiple GPUs

Let’s start training on two nodes with 2 gpus each. We assuming that master node can be connected as master, e.g. ping master.

1. Execute on master node

python -u -m torch.distributed.launch 
    --nnodes=2 
    --nproc_per_node=2 
    --node_rank=0 
    --master_addr=master --master_port=2222 --use_env 
    main.py run --backend="nccl"

2. Execute on worker node

python -u -m torch.distributed.launch 
    --nnodes=2 
    --nproc_per_node=2 
    --node_rank=1 
    --master_addr=master --master_port=2222 
    main.py run --backend="nccl"

Check resume training

Single GPU

Initial training with a stop on 1000 iteration (~11 epochs)

python main.py run --stop_iteration=1000

Resume from the latest checkpoint

python main.py run --resume-from=/tmp/output-cifar10/resnet18_backend-None-1_stop-on-1000/training_checkpoint_1000.pt

Distributed training

Single node, multiple GPUs

Initial training on a single node with 2 gpus with a stop on 1000 iteration (~11 epochs):

# using torch.distributed.launch
python -u -m torch.distributed.launch --nproc_per_node=2 --use_env main.py run --backend="nccl" --stop_iteration=1000

Resume from the latest checkpoint

python -u -m torch.distributed.launch --nproc_per_node=2 --use_env main.py run --backend="nccl" 
    --resume-from=/tmp/output-cifar10/resnet18_backend-nccl-2_stop-on-1000/training_checkpoint_1000.pt

Similar commands can be adapted for other cases.

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

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Imported from gh:Kentucky-Open-Science/ignitevision. Source last updated 2025-10-06. Synced 2026-07-27.