MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.ipynb

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        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/secrets.png)"
      ]
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      "source": [
        "# Providing Secrets\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
        "Licensed under the MIT License."
      ]
    },
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      "source": [
        "Currently, secrets are only persisted for the lifetime of the engine process. Even if the dataflow is saved to a file, the secrets are not persisted in the dprep file. If you started a new session (i.e. start a new engine process), loaded a dataflow and wanted to run it, you will need to call `use_secrets` to register the required secrets to use during execution, otherwise the execution will fail as the required secrets are not available.\n",
        "\n",
        "In this notebook, we will:\n",
        "1. Loading a previously saved dataflow\n",
        "2. Call `get_missing_secrets` to determine the missing secrets\n",
        "3. Call `use_secrets` and pass in the missing secrets to register it with the engine for this session\n",
        "4. Call `head` to see the a preview of the data"
      ]
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    {
      "cell_type": "code",
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      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.dataprep as dprep\n",
        "\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's load the previously saved dataflow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow = dprep.Dataflow.open(file_path='../data/secrets.dprep')\n",
        "dflow"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "You can call `get_missing_secrets` to see which required secrets are missing in the engine."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow.get_missing_secrets()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can now read the secrets from an environment variable, put it in a secret dictionary, and call `use_secrets` with the secrets. This will register the secrets in the engine so you don't need to provide them again in this session.\n",
        "\n",
        "_Note: It is a bad practice to have secrets in files that will be checked into source control._"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "sas = os.environ['SCENARIOS_SECRETS']\n",
        "secrets = {\n",
        "    'https://dpreptestfiles.blob.core.windows.net/testfiles/read_csv_duplicate_headers.csv': sas\n",
        "}\n",
        "dflow.use_secrets(secrets=secrets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can now call `head` without passing in `secrets` and the engine will successfully execute. Here is a preview of the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow.head(5)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "sihhu"
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