Tutorial 2: Keep compatibility with torch 1.9

docs/tutorial_notebooks/tutorial2/Introduction_to_PyTorch.ipynb

@@ -90,7 +90,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At the time of writing this tutorial (mid of August 2021), the current stable version is 1.9. You should therefore see the output `Using torch 1.9.0`, eventually with some extension for the CUDA version on Colab. In case you use the `dl2020` environment, you should see `Using torch 1.6.0` since the environment was provided in October 2020. It is recommended to update the PyTorch version to the newest one. If you see a lower version number than 1.6, make sure you have installed the correct the environment, or ask one of your TAs. In case PyTorch 1.10 or newer will be published during the time of the course, don't worry. The interface between PyTorch versions doesn't change too much, and hence all code should also be runnable with newer versions.\n",
+    "At the time of writing this tutorial (mid of October 2021), the current stable version is 1.10. You should therefore see the output `Using torch 1.10.0` or `Using torch 1.9.0`, eventually with some extension for the CUDA version on Colab. In case you use the `dl2020` environment, you should see `Using torch 1.6.0` since the environment was provided in October 2020. It is recommended to update the PyTorch version to the newest one. If you see a lower version number than 1.6, make sure you have installed the correct the environment, or ask one of your TAs. In case PyTorch 1.11 or newer will be published during the time of the course, don't worry. The interface between PyTorch versions doesn't change too much, and hence all code should also be runnable with newer versions.\n",
     "\n",
     "As in every machine learning framework, PyTorch provides functions that are stochastic like generating random numbers. However, a very good practice is to setup your code to be reproducible with the exact same random numbers. This is why we set a seed below. "
    ]
@@ -934,10 +934,6 @@
     "\n",
     "## GPU version\n",
     "x = x.to(device)\n",
-    "# The first operation on a CUDA device can be slow as it has to establish a CPU-GPU communication first. \n",
-    "# Hence, we run an arbitrary command first without timing it for a fair comparison.\n",
-    "if torch.cuda.is_available():\n",
-    "    _ = torch.matmul(x*0.0, x)\n",
     "# CUDA is asynchronous, so we need to use different timing functions\n",
     "start = torch.cuda.Event(enable_timing=True)\n",
     "end = torch.cuda.Event(enable_timing=True)\n",
@@ -3648,12 +3644,12 @@
     "    c1 = torch.Tensor(to_rgba(\"C1\")).to(device)\n",
     "    x1 = torch.arange(-0.5, 1.5, step=0.01, device=device)\n",
     "    x2 = torch.arange(-0.5, 1.5, step=0.01, device=device)\n",
-    "    xx1, xx2 = torch.meshgrid(x1, x2, indexing='ij') # Meshgrid function as in numpy\n",
+    "    xx1, xx2 = torch.meshgrid(x1, x2)  # Meshgrid function as in numpy\n",
     "    model_inputs = torch.stack([xx1, xx2], dim=-1)\n",
     "    preds = model(model_inputs)\n",
     "    preds = torch.sigmoid(preds)\n",
-    "    output_image = (1 - preds) * c0[None,None] + preds * c1[None,None] # Specifying \"None\" in a dimension creates a new one\n",
-    "    output_image = output_image.cpu().numpy() # Convert to numpy array. This only works for tensors on CPU, hence first push to CPU\n",
+    "    output_image = (1 - preds) * c0[None,None] + preds * c1[None,None]  # Specifying \"None\" in a dimension creates a new one\n",
+    "    output_image = output_image.cpu().numpy()  # Convert to numpy array. This only works for tensors on CPU, hence first push to CPU\n",
     "    plt.imshow(output_image, origin='lower', extent=(-0.5, 1.5, -0.5, 1.5))\n",
     "    plt.grid(False)\n",
     "    return fig\n",