szha commented on issue #13684:
URL:
https://github.com/apache/incubator-mxnet/issues/13684#issuecomment-703170471
@ndeepesh this is caused by the same CUDA fork problem we discussed in
#18734. The way to solve it is to fork first before initializing the GPU
context. In this example, the fork happens in data loader in `load_cifar10` and
the GPU initialization happens with `try_all_gpus`. Reordering them should
solve the problem.
```
def train_with_data_aug(train_augs, test_augs, lr=0.001):
batch_size = 256
train_iter = load_cifar10(True, train_augs, batch_size)
test_iter = load_cifar10(False, test_augs, batch_size)
ctx, net = try_all_gpus(), gb.resnet18(10)
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': lr})
loss = gloss.SoftmaxCrossEntropyLoss()
train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs=8)
```
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