make dnn model compatible with CPU & update docs for init param

docs/component/estimator.rst

@@ -574,10 +574,24 @@ The `qlib_data` field describes the parameters of qlib initialization.
       region: "cn"
     
 - `provider_uri`
-    The local directory where the data loaded by 'get_data.py' is stored.
+    Type: str. The URI of the Qlib data. For example, it could be the location where the data loaded by ``get_data.py`` are stored.
 - `region`
-    - If region == ``qlib.config.REG_CN``, 'qlib' will be initialized in US-stock mode. 
-    - If region == ``qlib.config.REG_US``, 'qlib' will be initialized in china-stock mode.
+    - If `region` == "us", ``Qlib`` will be initialized in US-stock mode. 
+    - If `region` == "cn", ``Qlib`` will be initialized in china-stock mode.
+- `redis_host`
+    Type: str, optional parameter(default: "127.0.0.1"), host of `redis`
+        The lock and cache mechanism relies on redis.
+- `redis_port`
+    Type: int, optional parameter(default: 6379), port of `redis`
+
+    .. note:: 
+        
+        The value of `region` should be aligned with the data stored in `provider_uri`. Currently, ``scripts/get_data.py`` only provides China stock market data. If users want to use the US stock market data, they should prepare their own US-stock data in `provider_uri` and switch to US-stock mode.
+
+    .. note::
+        
+        If Qlib fails to connect redis via `redis_host` and `redis_port`, cache mechanism will not be used! Please refer to `Cache <data.html#cache>`_ for details.
+
 
 Please refer to `Initialization <../start/initialization.html>`_.
 

docs/start/initialization.rst

@@ -44,7 +44,7 @@ Besides `provider_uri` and `region`, `qlib.init` has other parameters. The follo
         - ``qlib.config.REG_US``: US stock market.
         - ``qlib.config.REG_CN``: China stock market.
 
-        Different modse will result in different trading limitations and costs.
+        Different modes will result in different trading limitations and costs.
 - `redis_host`
     Type: str, optional parameter(default: "127.0.0.1"), host of `redis`
         The lock and cache mechanism relies on redis.

examples/estimator/estimator_config.yaml

@@ -51,3 +51,4 @@ qlib_data:
   # when testing, please modify the following parameters according to the specific environment
   provider_uri: "~/.qlib/qlib_data/cn_data"
   region: "cn"
+  redis_port: 1222

examples/estimator/estimator_config_dnn.yaml

@@ -55,3 +55,4 @@ qlib_data:
   # when testing, please modify the following parameters according to the specific environment
   provider_uri: "~/.qlib/qlib_data/cn_data"
   region: "cn"
+  redis_port: 1222

qlib/contrib/model/pytorch_nn.py

@@ -133,11 +133,14 @@ class DNNModelPytorch(Model):
         )
 
         self._fitted = False
-        self.dnn_model.cuda()
+        self.use_gpu = torch.cuda.is_available()
 
-        # set the visible GPU
-        if self.visible_GPU:
-            os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+        if self.use_gpu:
+            self.dnn_model.cuda()
+
+            # set the visible GPU
+            if self.visible_GPU:
+                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
 
     def fit(
         self,
@@ -175,13 +178,14 @@ class DNNModelPytorch(Model):
         train_num = y_train_values.shape[0]
 
         # prepare validation data
-        x_val_cuda = torch.from_numpy(x_valid.values).float()
-        y_val_cuda = torch.from_numpy(y_valid.values).float()
-        w_val_cuda = torch.from_numpy(w_valid.values).float()
+        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()
 
-        x_val_cuda = x_val_cuda.cuda()
-        y_val_cuda = y_val_cuda.cuda()
-        w_val_cuda = w_val_cuda.cuda()
+        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()
 
         for step in range(self.max_steps):
             if stop_steps >= self.early_stop_rounds:
@@ -193,17 +197,18 @@ class DNNModelPytorch(Model):
             self.train_optimizer.zero_grad()
 
             choice = np.random.choice(train_num, self.batch_size)
-            x_batch = x_train_values[choice]
-            y_batch = y_train_values[choice]
-            w_batch = w_train_values[choice]
+            x_batch_auto = x_train_values[choice]
+            y_batch_auto = y_train_values[choice]
+            w_batch_auto = w_train_values[choice]
 
-            x_batch_cuda = x_batch.float().cuda()
-            y_batch_cuda = y_batch.float().cuda()
-            w_batch_cuda = w_batch.float().cuda()
+            if self.use_gpu:
+                x_batch_auto = x_batch_auto.float().cuda()
+                y_batch_auto = y_batch_auto.float().cuda()
+                w_batch_auto = w_batch_auto.float().cuda()
 
             # forward
-            preds = self.dnn_model(x_batch_cuda)
-            cur_loss = self.get_loss(preds, w_batch_cuda, y_batch_cuda, self.loss_type)
+            preds = self.dnn_model(x_batch_auto)
+            cur_loss = self.get_loss(preds, w_batch_auto, y_batch_auto, self.loss_type)
             cur_loss.backward()
             self.train_optimizer.step()
             loss.update(cur_loss.item())
@@ -220,8 +225,8 @@ class DNNModelPytorch(Model):
                     loss_val = AverageMeter()
 
                     # forward
-                    preds = self.dnn_model(x_val_cuda)
-                    cur_loss_val = self.get_loss(preds, w_val_cuda, y_val_cuda, self.loss_type)
+                    preds = self.dnn_model(x_val_auto)
+                    cur_loss_val = self.get_loss(preds, w_val_auto, y_val_auto, self.loss_type)
                     loss_val.update(cur_loss_val.item())
                 if verbose:
                     self.logger.info(
@@ -245,7 +250,8 @@ class DNNModelPytorch(Model):
 
         # restore the optimal parameters after training ??
         self.dnn_model.load_state_dict(torch.load(save_path))
-        torch.cuda.empty_cache()
+        if self.use_gpu:
+            torch.cuda.empty_cache()
 
     def get_loss(self, pred, w, target, loss_type):
         if loss_type == "mse":
@@ -261,11 +267,16 @@ class DNNModelPytorch(Model):
     def predict(self, x_test):
         if not self._fitted:
             raise ValueError("model is not fitted yet!")
-        x_test = torch.from_numpy(x_test.values).float().cuda()
+        x_test = torch.from_numpy(x_test.values).float()
+        if self.use_gpu:
+            x_test = x_test.cuda()
         self.dnn_model.eval()
 
         with torch.no_grad():
-            preds = self.dnn_model(x_test).detach().cpu().numpy()
+            if self.use_gpu:
+                preds = self.dnn_model(x_test).detach().cpu().numpy()
+            else:
+                preds = self.dnn_model(x_test).detach().numpy()
         return preds
 
     def score(self, x_test, y_test, w_test=None):