MachineLearningNotebooks/how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb

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      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/Synapse_Job_Scala_Support.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get AML workspace which has synapse spark pool attached"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace, Experiment, Dataset, Environment\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Leverage ScriptRunConfig to submit scala job to an attached synapse spark cluster"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Prepare data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.datastore import Datastore\n",
        "# Use the default blob storage\n",
        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
        "\n",
        "# We are uploading a sample file in the local directory to be used as a datasource\n",
        "file_name = \"shakespeare.txt\"\n",
        "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n",
        "\n",
        "# Create file dataset\n",
        "file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.data import HDFSOutputDatasetConfig\n",
        "import uuid\n",
        "\n",
        "run_config = RunConfiguration(framework=\"pyspark\")\n",
        "run_config.target = \"link-pool\"\n",
        "run_config.spark.configuration[\"spark.driver.memory\"] = \"2g\"\n",
        "run_config.spark.configuration[\"spark.driver.cores\"] = 2\n",
        "run_config.spark.configuration[\"spark.executor.memory\"] = \"2g\"\n",
        "run_config.spark.configuration[\"spark.executor.cores\"] = 1\n",
        "run_config.spark.configuration[\"spark.executor.instances\"] = 1\n",
        "# This can be removed if you are using local jars in source folder\n",
        "run_config.spark.configuration[\"spark.yarn.dist.jars\"]=\"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
        "\n",
        "dir_name = \"wordcount-{}\".format(str(uuid.uuid4()))\n",
        "input = file_dataset.as_named_input(\"input\").as_hdfs()\n",
        "output = HDFSOutputDatasetConfig(destination=(ws.get_default_datastore(), \"{}/result\".format(dir_name)))\n",
        "\n",
        "from azureml.core import ScriptRunConfig\n",
        "args = ['--input', input, '--output', output]\n",
        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
        "                                    script= 'start_script.py',\n",
        "                                    arguments= args,\n",
        "                                    run_config = run_config)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Experiment\n",
        "exp = Experiment(workspace=ws, name='synapse-spark')\n",
        "run = exp.submit(config=script_run_config)\n",
        "run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Leverage SynapseSparkStep in an AML pipeline to add dataprep step on synapse spark cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.pipeline.core import Pipeline\n",
        "from azureml.pipeline.steps import SynapseSparkStep\n",
        "\n",
        "configs = {}\n",
        "#configs[\"spark.yarn.dist.jars\"] = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
        "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
        "                          file = 'start_script.py',\n",
        "                          jars = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\",\n",
        "                          source_directory=\".\",\n",
        "                          arguments = args,\n",
        "                          compute_target = 'link-pool',\n",
        "                          driver_memory = \"2g\",\n",
        "                          driver_cores = 2,\n",
        "                          executor_memory = \"2g\",\n",
        "                          executor_cores = 1,\n",
        "                          num_executors = 1,\n",
        "                          conf = configs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline = Pipeline(workspace=ws, steps=[step_1])\n",
        "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "feli1"
      }
    ],
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