dask example

examples/Dask/dask.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "from dask.distributed import Client, LocalCluster, progress\n",
+        "client = Client(LocalCluster(ip='0.0.0.0', scheduler_port=12345, n_workers=0))\n",
+        "client"
+      ],
+      "outputs": [],
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "source_hidden": false,
+          "outputs_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        },
+        "gather": {
+          "logged": 1720186325924
+        }
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import time\n",
+        "import random\n",
+        "\n",
+        "def inc(x):\n",
+        "    time.sleep(random.random())\n",
+        "    return x + 1\n",
+        "\n",
+        "def double(x):\n",
+        "    time.sleep(random.random())\n",
+        "    return 2 * x\n",
+        "\n",
+        "def add(x, y):\n",
+        "    time.sleep(random.random())\n",
+        "    return x + y\n",
+        "    "
+      ],
+      "outputs": [],
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1720186729193
+        }
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "zs = []"
+      ],
+      "outputs": [],
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "source_hidden": false,
+          "outputs_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        },
+        "gather": {
+          "logged": 1720186731185
+        }
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%time\n",
+        "\n",
+        "for i in range(1024):\n",
+        "    x = client.submit(inc, i)     # x = inc(i)\n",
+        "    y = client.submit(double, x)  # y = inc(x)\n",
+        "    z = client.submit(add, x, y)  # z = inc(y)\n",
+        "    zs.append(z)"
+      ],
+      "outputs": [],
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "source_hidden": false,
+          "outputs_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "client.shutdown()"
+      ],
+      "outputs": [],
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "source_hidden": false,
+          "outputs_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        },
+        "gather": {
+          "logged": 1720187187366
+        }
+      }
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "name": "python38-azureml",
+      "language": "python",
+      "display_name": "Python 3.8 - AzureML"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.19",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "microsoft": {
+      "ms_spell_check": {
+        "ms_spell_check_language": "en"
+      },
+      "host": {
+        "AzureML": {
+          "notebookHasBeenCompleted": true
+        }
+      }
+    },
+    "kernel_info": {
+      "name": "python38-azureml"
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

examples/Dask/dask.job

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+#!/bin/bash
+
+conda init
+source activate base
+pip install dask==2023.2.0 distributed==2023.2.0 
+export PATH=$PATH:/home/azureuser/.local/bin
+dask worker tcp://10.0.1.5:12345