def test_ddp_sharded_plugin_finetune(tmpdir):
"""
Test to ensure that we can save and restart training (simulate fine-tuning)
"""
model = BoringModel()
trainer = Trainer(
gpus=2,
accelerator='ddp_spawn',
plugins=[DDPShardedPlugin()],
fast_dev_run=True,
)
trainer.fit(model)
checkpoint_path = os.path.join(tmpdir, 'model.pt')
trainer.save_checkpoint(checkpoint_path)
saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
trainer = Trainer(fast_dev_run=True, )
trainer.fit(saved_model)
def test_ddp_sharded_plugin_checkpoint_cpu(tmpdir):
"""
Test to ensure that checkpoint is saved correctly
"""
model = BoringModel()
trainer = Trainer(
accelerator='ddp_cpu',
plugins=[DDPShardedPlugin()],
fast_dev_run=True,
)
trainer.fit(model)
checkpoint_path = os.path.join(tmpdir, 'model.pt')
trainer.save_checkpoint(checkpoint_path)
saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
# Assert model parameters are identical after loading
for ddp_param, shard_param in zip(model.parameters(),
saved_model.parameters()):
assert torch.equal(ddp_param, shard_param)
