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Fix GPU

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This commit is contained in:
Jactus
2020-12-02 13:25:29 +08:00
parent d109d3d44e
commit 7f385345bb
5 changed files with 37 additions and 112 deletions
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33
qlib/contrib/model/pytorch_nn.py
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@@ -79,7 +79,7 @@ class DNNModelPytorch(Model):
self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower()
self.loss_type = loss
self.visible_GPU = GPU
self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_GPU = torch.cuda.is_available()
self.seed = seed
@@ -147,11 +147,7 @@ class DNNModelPytorch(Model):
)
self._fitted = False
if self.use_GPU:
self.dnn_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.visible_GPU)
self.dnn_model.to(self.device)
def fit(
self,
@@ -188,13 +184,9 @@ class DNNModelPytorch(Model):
w_train_values = torch.from_numpy(w_train.values).float()
train_num = y_train_values.shape[0]
# prepare validation data
x_val_auto = torch.from_numpy(x_valid.values).float()
y_val_auto = torch.from_numpy(y_valid.values).float()
w_val_auto = torch.from_numpy(w_valid.values).float()
if self.use_GPU:
x_val_auto = x_val_auto.cuda()
y_val_auto = y_val_auto.cuda()
w_val_auto = w_val_auto.cuda()
x_val_auto = torch.from_numpy(x_valid.values).float().to(self.device)
y_val_auto = torch.from_numpy(y_valid.values).float().to(self.device)
w_val_auto = torch.from_numpy(w_valid.values).float().to(self.device)
for step in range(self.max_steps):
if stop_steps >= self.early_stop_rounds:
@@ -205,14 +197,9 @@ class DNNModelPytorch(Model):
self.dnn_model.train()
self.train_optimizer.zero_grad()
choice = np.random.choice(train_num, self.batch_size)
x_batch_auto = x_train_values[choice]
y_batch_auto = y_train_values[choice]
w_batch_auto = w_train_values[choice]
if self.use_GPU:
x_batch_auto = x_batch_auto.cuda()
y_batch_auto = y_batch_auto.cuda()
w_batch_auto = w_batch_auto.cuda()
x_batch_auto = x_train_values[choice].to(self.device)
y_batch_auto = y_train_values[choice].to(self.device)
w_batch_auto = w_train_values[choice].to(self.device)
# forward
preds = self.dnn_model(x_batch_auto)
@@ -277,9 +264,7 @@ class DNNModelPytorch(Model):
if not self._fitted:
raise ValueError("model is not fitted yet!")
x_test_pd = dataset.prepare("test", col_set="feature")
x_test = torch.from_numpy(x_test_pd.values).float()
if self.use_GPU:
x_test = x_test.cuda()
x_test = torch.from_numpy(x_test_pd.values).float().to(self.device)
self.dnn_model.eval()
with torch.no_grad():