Automated tutorials push

Author: pytorchbot

_downloads/0184bded18578d24a48fdfad2c701b09/polynomial_autograd.ipynb

@@ -42,8 +42,12 @@
     "import torch\n",
     "import math\n",
     "\n",
+    "# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__\n",
+    "# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.\n",
+    "\n",
     "dtype = torch.float\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else \"cpu\"\n",
+    "print(f\"Using {device} device\")\n",
     "torch.set_default_device(device)\n",
     "\n",
     "# Create Tensors to hold input and outputs.\n",

_downloads/377bf4a7b1761e5f081e057385870d8e/fgsm_tutorial.py

@@ -125,14 +125,9 @@
 #    `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__.
 #    For simplicity, download the pretrained model `here <https://drive.google.com/file/d/1HJV2nUHJqclXQ8flKvcWmjZ-OU5DGatl/view?usp=drive_link>`__.
 #
-# -  ``use_cuda`` - boolean flag to use CUDA if desired and available.
-#    Note, a GPU with CUDA is not critical for this tutorial as a CPU will
-#    not take much time.
-#
 
 epsilons = [0, .05, .1, .15, .2, .25, .3]
 pretrained_model = "data/lenet_mnist_model.pth"
-use_cuda=True
 # Set random seed for reproducibility
 torch.manual_seed(42)
 
@@ -184,9 +179,10 @@ def forward(self, x):
             ])),
         batch_size=1, shuffle=True)
 
-# Define what device we are using
-print("CUDA Available: ",torch.cuda.is_available())
-device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")
+# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
+# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
+device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
+print(f"Using {device} device")
 
 # Initialize the network
 model = Net().to(device)

_downloads/56c122e1c18e5e07666673e900acaed5/fgsm_tutorial.ipynb

@@ -149,10 +149,7 @@
     "    trained with\n",
     "    [pytorch/examples/mnist](https://github.com/pytorch/examples/tree/master/mnist).\n",
     "    For simplicity, download the pretrained model\n",
-    "    [here](https://drive.google.com/file/d/1HJV2nUHJqclXQ8flKvcWmjZ-OU5DGatl/view?usp=drive_link).\n",
-    "-   `use_cuda` - boolean flag to use CUDA if desired and available.\n",
-    "    Note, a GPU with CUDA is not critical for this tutorial as a CPU\n",
-    "    will not take much time.\n"
+    "    [here](https://drive.google.com/file/d/1HJV2nUHJqclXQ8flKvcWmjZ-OU5DGatl/view?usp=drive_link).\n"
    ]
   },
   {
@@ -165,7 +162,6 @@
    "source": [
     "epsilons = [0, .05, .1, .15, .2, .25, .3]\n",
     "pretrained_model = \"data/lenet_mnist_model.pth\"\n",
-    "use_cuda=True\n",
     "# Set random seed for reproducibility\n",
     "torch.manual_seed(42)"
    ]
@@ -228,9 +224,10 @@
     "            ])),\n",
     "        batch_size=1, shuffle=True)\n",
     "\n",
-    "# Define what device we are using\n",
-    "print(\"CUDA Available: \",torch.cuda.is_available())\n",
-    "device = torch.device(\"cuda\" if use_cuda and torch.cuda.is_available() else \"cpu\")\n",
+    "# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__\n",
+    "# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.\n",
+    "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else \"cpu\"\n",
+    "print(f\"Using {device} device\")\n",
     "\n",
     "# Initialize the network\n",
     "model = Net().to(device)\n",

_downloads/74249e7f9f1f398f57ccd094a4f3021b/transfer_learning_tutorial.ipynb

@@ -133,7 +133,11 @@
     "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
     "class_names = image_datasets['train'].classes\n",
     "\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+    "# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__\n",
+    "# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.\n",
+    "\n",
+    "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else \"cpu\"\n",
+    "print(f\"Using {device} device\")"
    ]
   },
   {

_downloads/a74022a3afc7f1a190f572b6b9e883e4/polynomial_autograd.py

@@ -17,8 +17,12 @@
 import torch
 import math
 
+# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
+# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
+
 dtype = torch.float
-device = "cuda" if torch.cuda.is_available() else "cpu"
+device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
+print(f"Using {device} device")
 torch.set_default_device(device)
 
 # Create Tensors to hold input and outputs.