pytorch_tutorial1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pytorch_tutorial1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMDf97/1MyqY+RAtVZEa3XZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EastbayML/pytorch_tutorial/blob/master/pytorch_tutorial1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5i_26lOZ3nU",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "East Bay Machine Learning pytorch tutorial series.\n",
        "\n",
        "---\n",
        "\n",
        "We will use this notebook as the launch point for our pytorch tutorial series.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxpEAveycl2y",
        "colab_type": "text"
      },
      "source": [
        "We willl used a google doc as a shared online clipboard. \n",
        "https://docs.google.com/document/d/1N-5Ue0rk7g8CImayet-cgnaCkHJzXky7aeHrwUocXEM/edit?usp=sharing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfKyRKincd7P",
        "colab_type": "text"
      },
      "source": [
        "#ENABLE GPU\n",
        "To enable GPU hardware accelerator, just go to Runtime -> Change runtime type -> Hardware accelerator -> GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fvf8AG2BbTkx",
        "colab_type": "code",
        "outputId": "024787e3-5a87-43b0-9b33-ee16e4453d5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda\")          # a CUDA device object\n",
        "    x = torch.empty(5, 3)\n",
        "    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU\n",
        "    x = x.to(device)                       # or just use strings ``.to(\"cuda\")``\n",
        "    z = x + y\n",
        "    print(z)\n",
        "    print(z.to(\"cpu\", torch.double))\n",
        "else:\n",
        "  print(\"Cuda not available.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.]], device='cuda:0')\n",
            "tensor([[1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.],\n",
            "        [1., 1., 1.]], dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2r98kQuar6Yy",
        "colab_type": "text"
      },
      "source": [
        "# Plotting with matplotlib\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "08RTGn_xE3MP",
        "outputId": "c96c1ab3-5cdb-4368-d4ee-ae2dac6b82d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        " \n",
        "x  = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
        "y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]\n",
        "y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]\n",
        "plt.plot(x, y1, label=\"line L\")\n",
        "plt.plot(x, y2, label=\"line H\")\n",
        "plt.plot()\n",
        "\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"y axis\")\n",
        "plt.title(\"Line Graph Example\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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3PYNv4DSMueF/XRs5bYe0ZyzDmS3LYea/XfJpSUlJFBUVUVJS4t58fJCoqCiSkq58I7o1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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHDsHqwZrj6Z",
        "colab_type": "text"
      },
      "source": [
        "# Graph vizualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMb5DNopqJ0b",
        "colab_type": "code",
        "outputId": "57bc862a-441d-425f-f33c-b9888511e9fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "!pip install torchviz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torchviz\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8f/8e/a9630c7786b846d08b47714dd363a051f5e37b4ea0e534460d8cdfc1644b/torchviz-0.0.1.tar.gz (41kB)\n",
            "\r\u001b[K     |████████                        | 10kB 33.6MB/s eta 0:00:01\r\u001b[K     |████████████████                | 20kB 3.0MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 30kB 4.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 40kB 5.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchviz) (1.3.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from torchviz) (0.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->torchviz) (1.17.5)\n",
            "Building wheels for collected packages: torchviz\n",
            "  Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchviz: filename=torchviz-0.0.1-cp36-none-any.whl size=3520 sha256=8b9b8c1f4098d188de1aa696be42ed382bbfb62ccf34920a301964b2265a6321\n",
            "  Stored in directory: /root/.cache/pip/wheels/2a/c2/c5/b8b4d0f7992c735f6db5bfa3c5f354cf36502037ca2b585667\n",
            "Successfully built torchviz\n",
            "Installing collected packages: torchviz\n",
            "Successfully installed torchviz-0.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cxu3FaMgdSDL",
        "colab_type": "code",
        "outputId": "168f1888-ba08-48e2-d800-1b05aa7634c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "from torchviz import make_dot\n",
        "\n",
        "x = torch.empty(5, 3,requires_grad=True)\n",
        "y = torch.empty(5, 3,requires_grad=True)\n",
        "z = x + y * 10\n",
        "make_dot(z)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7faf5a0b81d0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"172pt\" height=\"171pt\"\n viewBox=\"0.00 0.00 171.50 171.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 167)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-167 167.5,-167 167.5,4 -4,4\"/>\n<!-- 140391106708032 -->\n<g id=\"node1\" class=\"node\">\n<title>140391106708032</title>\n<polygon fill=\"#caff70\" stroke=\"#000000\" points=\"118,-21 26,-21 26,0 118,0 118,-21\"/>\n<text text-anchor=\"middle\" x=\"72\" y=\"-7.4\" font-family=\"Times,serif\" font-size=\"12.00\" fill=\"#000000\">AddBackward0</text>\n</g>\n<!-- 140391106707864 -->\n<g id=\"node2\" class=\"node\">\n<title>140391106707864</title>\n<polygon fill=\"#add8e6\" stroke=\"#000000\" points=\"54,-92 0,-92 0,-57 54,-57 54,-92\"/>\n<text text-anchor=\"middle\" x=\"27\" y=\"-64.4\" font-family=\"Times,serif\" font-size=\"12.00\" fill=\"#000000\"> (5, 3)</text>\n</g>\n<!-- 140391106707864&#45;&gt;140391106708032 -->\n<g id=\"edge1\" class=\"edge\">\n<title>140391106707864&#45;&gt;140391106708032</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M39.535,-56.6724C45.4798,-48.2176 52.5878,-38.1085 58.6352,-29.5078\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"61.5714,-31.4169 64.4601,-21.2234 55.8452,-27.3906 61.5714,-31.4169\"/>\n</g>\n<!-- 140391106707976 -->\n<g id=\"node3\" class=\"node\">\n<title>140391106707976</title>\n<polygon fill=\"#d3d3d3\" stroke=\"#000000\" points=\"163.5,-85 72.5,-85 72.5,-64 163.5,-64 163.5,-85\"/>\n<text text-anchor=\"middle\" x=\"118\" y=\"-71.4\" font-family=\"Times,serif\" font-size=\"12.00\" fill=\"#000000\">MulBackward0</text>\n</g>\n<!-- 140391106707976&#45;&gt;140391106708032 -->\n<g id=\"edge2\" class=\"edge\">\n<title>140391106707976&#45;&gt;140391106708032</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M110.404,-63.9317C103.7191,-54.6309 93.821,-40.8597 85.7479,-29.6276\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"88.4395,-27.3753 79.761,-21.2979 82.7553,-31.4608 88.4395,-27.3753\"/>\n</g>\n<!-- 140392853098168 -->\n<g id=\"node4\" class=\"node\">\n<title>140392853098168</title>\n<polygon fill=\"#add8e6\" stroke=\"#000000\" points=\"145,-163 91,-163 91,-128 145,-128 145,-163\"/>\n<text text-anchor=\"middle\" x=\"118\" y=\"-135.4\" font-family=\"Times,serif\" font-size=\"12.00\" fill=\"#000000\"> (5, 3)</text>\n</g>\n<!-- 140392853098168&#45;&gt;140391106707976 -->\n<g id=\"edge3\" class=\"edge\">\n<title>140392853098168&#45;&gt;140391106707976</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M118,-127.9494C118,-118.058 118,-105.6435 118,-95.2693\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"121.5001,-95.0288 118,-85.0288 114.5001,-95.0289 121.5001,-95.0288\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eNsWg54qIx0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a28c9546-9256-4fd7-e984-698cf85bc547"
      },
      "source": [
        "import timeit\n",
        "def func(a):\n",
        "   a=1+2\n",
        "\n",
        "timeit.timeit(\"func(1)\", setup=\"from __main__ import func\",number=10000)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002130821000037031"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    }
  ]
}