def run_catalyst(irunner: dl.IRunner,
idx: int,
device: str = "cuda",
num_epochs: int = 10):
utils.set_global_seed(idx)
loader = irunner.get_loaders()["train"]
model = irunner.get_model().to(device)
criterion = irunner.get_criterion()
optimizer = irunner.get_optimizer(model)
runner = dl.SupervisedRunner()
runner.train(
engine=dl.GPUEngine() if device == "cuda" else dl.CPUEngine(),
model=model,
criterion=criterion,
optimizer=optimizer,
loaders={"train": loader},
num_epochs=num_epochs,
verbose=False,
callbacks=[
dl.AccuracyCallback(
input_key=runner._output_key,
target_key=runner._target_key,
topk=(1, ),
)
],
)
return (
runner.epoch_metrics["train"]["accuracy01"],
runner.epoch_metrics["train"]["loss"],
_get_used_memory(),
)
def test_run_on_cpu():
train_experiment(dl.CPUEngine())
criterion = torch.nn.MSELoss()
optimizer = {
"actor": torch.optim.Adam(actor.parameters(), lr_actor),
"critic": torch.optim.Adam(critic.parameters(), lr=lr_critic),
}
loaders = {
"train_game": DataLoader(
ReplayDataset(replay_buffer, epoch_size=epoch_size), batch_size=batch_size
),
}
runner = CustomRunner(gamma=gamma, tau=tau, tau_period=tau_period)
runner.train(
engine=dl.CPUEngine(), # for simplicity reasons, let's run everything on cpu
model=models,
criterion=criterion,
optimizer=optimizer,
loaders=loaders,
logdir="./logs_ddpg",
num_epochs=10,
verbose=True,
valid_loader="_epoch_",
valid_metric="v_reward",
minimize_valid_metric=False,
load_best_on_end=True,
callbacks=[
GameCallback(
env=env,
replay_buffer=replay_buffer,
