cifar.md

Running MYOW on CIFAR10 dataset

This page walks through the steps required to run MYOW on the CIFAR10 dataset.

Running training

Training is parallalised using DistributedDataParallel. The pool of candidate views during mining is shared across all instances.

To start training run:

CUDA_VISIBLE_DEVICES=0,1 python3 scripts/cifar-train.py \
    --lr 2.0 \
    --mm 0.98 \
    --weight_decay 5e-5 \
    --optimizer sgd \
    --lr_warmup_epochs 30 \
    --batch_size 256 \
    --port 12354 \
    --logdir myow-cifar \
    --ckptpath myow-cifar

Running evaluation

Evaluation can be done simultaneously or after training on a separate GPU instance. The eval script will automatically run evaluation each time a new checkpoint is saved to ckptpath. It is also possible to start evaluation only after a certain number of epoch using the resume_eval argument.

CUDA_VISIBLE_DEVICES=2 python3 scripts/cifar-eval.py \
    --lr 0.04 \
    --resume_eval 0 \
    --logdir runs-cifar \
    --ckptpath myow-cifar

Running tensorboard

tensorboard --logdir=runs-cifar