Automated tutorials push

Author: pytorchbot

_downloads/0e6615c5a7bc71e01ff3c51217ea00da/tensorqs_tutorial.ipynb

@@ -206,19 +206,20 @@
     "Operations on Tensors\n",
     "=====================\n",
     "\n",
-    "Over 100 tensor operations, including arithmetic, linear algebra, matrix\n",
-    "manipulation (transposing, indexing, slicing), sampling and more are\n",
-    "comprehensively described\n",
+    "Over 1200 tensor operations, including arithmetic, linear algebra,\n",
+    "matrix manipulation (transposing, indexing, slicing), sampling and more\n",
+    "are comprehensively described\n",
     "[here](https://pytorch.org/docs/stable/torch.html).\n",
     "\n",
-    "Each of these operations can be run on the GPU (at typically higher\n",
-    "speeds than on a CPU). If you're using Colab, allocate a GPU by going to\n",
-    "Runtime \\> Change runtime type \\> GPU.\n",
+    "Each of these operations can be run on the CPU and\n",
+    "[Accelerator](https://pytorch.org/docs/stable/torch.html#accelerators)\n",
+    "such as CUDA, MPS, MTIA, or XPU. If you're using Colab, allocate an\n",
+    "accelerator by going to Runtime \\> Change runtime type \\> GPU.\n",
     "\n",
     "By default, tensors are created on the CPU. We need to explicitly move\n",
-    "tensors to the GPU using `.to` method (after checking for GPU\n",
-    "availability). Keep in mind that copying large tensors across devices\n",
-    "can be expensive in terms of time and memory!\n"
+    "tensors to the accelerator using `.to` method (after checking for\n",
+    "accelerator availability). Keep in mind that copying large tensors\n",
+    "across devices can be expensive in terms of time and memory!\n"
    ]
   },
   {
@@ -229,9 +230,9 @@
    },
    "outputs": [],
    "source": [
-    "# We move our tensor to the GPU if available\n",
-    "if torch.cuda.is_available():\n",
-    "    tensor = tensor.to(\"cuda\")"
+    "# We move our tensor to the current accelerator if available\n",
+    "if torch.accelerator.is_available():\n",
+    "    tensor = tensor.to(torch.accelerator.current_accelerator())"
    ]
   },
   {

_downloads/3fb82dc8278b08d5e5dee31ec1c16170/tensorqs_tutorial.py

@@ -99,20 +99,20 @@
 # Operations on Tensors
 # ~~~~~~~~~~~~~~~~~~~~~~~
 #
-# Over 100 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing,
+# Over 1200 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing,
 # indexing, slicing), sampling and more are
 # comprehensively described `here <https://pytorch.org/docs/stable/torch.html>`__.
 #
-# Each of these operations can be run on the GPU (at typically higher speeds than on a
-# CPU). If you’re using Colab, allocate a GPU by going to Runtime > Change runtime type > GPU.
+# Each of these operations can be run on the CPU and `Accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
+# such as CUDA, MPS, MTIA, or XPU. If you’re using Colab, allocate an accelerator by going to Runtime > Change runtime type > GPU.
 #
-# By default, tensors are created on the CPU. We need to explicitly move tensors to the GPU using
-# ``.to`` method (after checking for GPU availability). Keep in mind that copying large tensors
+# By default, tensors are created on the CPU. We need to explicitly move tensors to the accelerator using
+# ``.to`` method (after checking for accelerator availability). Keep in mind that copying large tensors
 # across devices can be expensive in terms of time and memory!
 
-# We move our tensor to the GPU if available
-if torch.cuda.is_available():
-    tensor = tensor.to("cuda")
+# We move our tensor to the current accelerator if available
+if torch.accelerator.is_available():
+    tensor = tensor.to(torch.accelerator.current_accelerator())
 
 
 ######################################################################

_downloads/51f1e1167acc0fda8f9d8fd8597ee626/quickstart_tutorial.py

@@ -84,16 +84,10 @@
 # To define a neural network in PyTorch, we create a class that inherits
 # from `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network
 # in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate
-# operations in the neural network, we move it to the GPU or MPS if available.
-
-# Get cpu, gpu or mps device for training.
-device = (
-    "cuda"
-    if torch.cuda.is_available()
-    else "mps"
-    if torch.backends.mps.is_available()
-    else "cpu"
-)
+# operations in the neural network, we move it to the `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")
 
 # Define model

_downloads/af0caf6d7af0dda755f4c9d7af9ccc2c/quickstart_tutorial.ipynb

@@ -159,7 +159,10 @@
     "We define the layers of the network in the `__init__` function and\n",
     "specify how data will pass through the network in the `forward`\n",
     "function. To accelerate operations in the neural network, we move it to\n",
-    "the GPU or MPS if available.\n"
+    "the\n",
+    "[accelerator](https://pytorch.org/docs/stable/torch.html#accelerators)\n",
+    "such as CUDA, MPS, MTIA, or XPU. If the current accelerator is\n",
+    "available, we will use it. Otherwise, we use the CPU.\n"
    ]
   },
   {
@@ -170,14 +173,7 @@
    },
    "outputs": [],
    "source": [
-    "# Get cpu, gpu or mps device for training.\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
-    ")\n",
+    "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else \"cpu\"\n",
     "print(f\"Using {device} device\")\n",
     "\n",
     "# Define model\n",