Automated tutorials push

Author: pytorchbot

_downloads/13cdb386a4b0dc48c626f32e6cf8681d/amp_recipe.ipynb

@@ -261,7 +261,7 @@
     "# The same ``GradScaler`` instance should be used for the entire convergence run.\n",
     "# If you perform multiple convergence runs in the same script, each run should use\n",
     "# a dedicated fresh ``GradScaler`` instance. ``GradScaler`` instances are lightweight.\n",
-    "scaler = torch.cuda.amp.GradScaler()\n",
+    "scaler = torch.amp.GradScaler(\"cuda\")\n",
     "\n",
     "for epoch in range(0): # 0 epochs, this section is for illustration only\n",
     "    for input, target in zip(data, targets):\n",
@@ -308,7 +308,7 @@
     "\n",
     "net = make_model(in_size, out_size, num_layers)\n",
     "opt = torch.optim.SGD(net.parameters(), lr=0.001)\n",
-    "scaler = torch.cuda.amp.GradScaler(enabled=use_amp)\n",
+    "scaler = torch.amp.GradScaler(\"cuda\" ,enabled=use_amp)\n",
     "\n",
     "start_timer()\n",
     "for epoch in range(epochs):\n",

_downloads/cadb3a57e7a6d7c149b5ae377caf36a8/amp_recipe.py

@@ -150,7 +150,7 @@ def make_model(in_size, out_size, num_layers):
 # The same ``GradScaler`` instance should be used for the entire convergence run.
 # If you perform multiple convergence runs in the same script, each run should use
 # a dedicated fresh ``GradScaler`` instance. ``GradScaler`` instances are lightweight.
-scaler = torch.cuda.amp.GradScaler()
+scaler = torch.amp.GradScaler("cuda")
 
 for epoch in range(0): # 0 epochs, this section is for illustration only
     for input, target in zip(data, targets):
@@ -182,7 +182,7 @@ def make_model(in_size, out_size, num_layers):
 
 net = make_model(in_size, out_size, num_layers)
 opt = torch.optim.SGD(net.parameters(), lr=0.001)
-scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
+scaler = torch.amp.GradScaler("cuda" ,enabled=use_amp)
 
 start_timer()
 for epoch in range(epochs):