test_gen1_itp_image_classification.ipynb

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    "kernelspec": {
      "name": "python3",
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ------------------------------------------------------------------------------\n",
        "# This is generated from https://ml.azure.com/pipelineruns/id/9f77d021-13d4-4398-b3b4-a0240a22c32f/0720b2c0-6961-4359-80a9-7a5e76f97f5f?wsid=/subscriptions/f92fab0a-38b9-44ed-b800-2ed55fa1b9d9/resourcegroups/Groot-Shared/workspaces/gjd-test&tid=72f988bf-86f1-41af-91ab-2d7cd011db47\n",
        "# To run this code, please install SDK by this command:\n",
        "# !pip install azure-ml-component[notebooks] --extra-index-url https://azuremlsdktestpypi.azureedge.net/modulesdkpreview  --user --upgrade\n",
        "# More detailed guide to set up your environment: https://github.com/Azure/DesignerPrivatePreviewFeatures/blob/master/azure-ml-components/samples/setup-environment.ipynb\n",
        "# ------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "from azure.ml.component import Pipeline, Component, dsl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [],
      "source": [
        "# configure aml workspace\n",
        "\n",
        "subscription_id = 'f92fab0a-38b9-44ed-b800-2ed55fa1b9d9' \n",
        "workspace_name = 'gjd-test' \n",
        "resource_group = 'Groot-Shared'\n",
        "\n",
        "\n",
        "ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get dataset\n",
        "from azureml.core import Dataset\n",
        "imagenet_train_dataset = Dataset.get_by_name(ws, name='ImageNet_Train', version=1)\n",
        "imagenet_valid_dataset = Dataset.get_by_name(ws, name='ImageNet_Val', version=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get components\n",
        "convert_to_image_directory_func = Component.load(ws, name='azureml://Convert to Image Directory')\n",
        "apply_image_transformation_func = Component.load(ws, name='azureml://Apply Image Transformation')\n",
        "init_image_transformation_func = Component.load(ws, name='azureml://Init Image Transformation')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [],
      "source": [
        "init_image_transformation_func = Component.load(ws, name='microsoft.sample.init-image-transformation')\n",
        "module = init_image_transformation_func()\n",
        "module.outputs.output_image_transformation.configure(datastore='datastore_name')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [],
      "source": [
        "# define pipeline\n",
        "@dsl.pipeline(name='test-cosmos-itp-image-classification', description='itp-cosmos-imageclassification', default_compute_target='eus-p40-5-gjd', default_datastore='searchdmparner')\n",
        "def generated_pipeline():\n",
        "    convert_to_image_directory_0 = convert_to_image_directory_func(\n",
        "        input_dataset=imagenet_train_dataset)\n",
        "    \n",
        "    convert_to_image_directory_1 = convert_to_image_directory_func(\n",
        "        input_dataset=imagenet_valid_dataset)\n",
        " \n",
        "    init_image_transformation_0 = init_image_transformation_func(\n",
        "        resize='False',\n",
        "        size=256,\n",
        "        center_crop='False',\n",
        "        crop_size=224,\n",
        "        pad='False',\n",
        "        padding=0,\n",
        "        color_jitter=False,\n",
        "        grayscale=False,\n",
        "        random_resized_crop='False',\n",
        "        random_resized_crop_size=256,\n",
        "        random_crop='False',\n",
        "        random_crop_size=224,\n",
        "        random_horizontal_flip=True,\n",
        "        random_vertical_flip=False,\n",
        "        random_rotation='False',\n",
        "        random_rotation_degrees=0,\n",
        "        random_affine='False',\n",
        "        random_affine_degrees=0,\n",
        "        random_grayscale=False,\n",
        "        random_perspective=False)\n",
        "    \n",
        "    apply_image_transformation_0 = apply_image_transformation_func(\n",
        "        input_image_transformation=init_image_transformation_0.outputs.output_image_transformation,\n",
        "        input_image_directory=convert_to_image_directory_0.outputs.output_image_directory,\n",
        "        mode='For training')\n",
        "\n",
        "    apply_image_transformation_1 = apply_image_transformation_func(\n",
        "        input_image_transformation=init_image_transformation_0.outputs.output_image_transformation,\n",
        "        input_image_directory=convert_to_image_directory_1.outputs.output_image_directory,\n",
        "        mode='For inference')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create a pipeline\n",
        "pipeline = generated_pipeline()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "{'result': 'validation passed', 'errors': []}"
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          "metadata": {},
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      ],
      "source": [
        "# validate pipeline and visualize the graph\n",
        "pipeline.validate()"
      ]
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    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
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          "text": [
            "Submitted PipelineRun c2bd399e-a713-4d4b-8f3a-4a35c738f2c0\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/cosmos-datastore-test-image-classification/runs/c2bd399e-a713-4d4b-8f3a-4a35c738f2c0?wsid=/subscriptions/f92fab0a-38b9-44ed-b800-2ed55fa1b9d9/resourcegroups/Groot-Shared/workspaces/gjd-test\n"
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          "output_type": "execute_result",
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            "text/plain": [
              "PipelineRun(Experiment: cosmos-datastore-test-image-classification,\n",
              "Id: c2bd399e-a713-4d4b-8f3a-4a35c738f2c0,\n",
              "Type: azureml.PipelineRun,\n",
              "Status: NotStarted)"
            ],
            "text/html": "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>cosmos-datastore-test-image-classification</td><td>c2bd399e-a713-4d4b-8f3a-4a35c738f2c0</td><td>azureml.PipelineRun</td><td>NotStarted</td><td><a href=\"https://ml.azure.com/experiments/cosmos-datastore-test-image-classification/runs/c2bd399e-a713-4d4b-8f3a-4a35c738f2c0?wsid=/subscriptions/f92fab0a-38b9-44ed-b800-2ed55fa1b9d9/resourcegroups/Groot-Shared/workspaces/gjd-test\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run.Run?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# submit a pipeline run\n",
        "from azure.ml.component.dsl._version import VERSION\n",
        "\n",
        "pipeline.submit(experiment_name='cosmos-datastore-test-image-classification', tags={'mode':'SDK/dsl','SDK-version':f'{VERSION}'})\n",
        "#.wait_for_completion()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}