update samples from Release-94 as a part of SDK release (#1418)

Co-authored-by: amlrelsa-ms <amlrelsa@microsoft.com>
2025-12-19 17:17:04 -05:00 · 2021-04-06 22:06:12 +05:30
parent bda592a236
commit 6f893ff0b4
25 changed files with 134 additions and 81 deletions
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -103,7 +103,7 @@
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -21,8 +21,8 @@ dependencies:

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.25.0
+  - azureml-widgets~=1.26.0
  - pytorch-transformers==1.0.0
  - spacy==2.1.8
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.25.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.26.0/validated_win32_requirements.txt [--no-deps]
--- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
@@ -21,8 +21,8 @@ dependencies:

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.25.0
+  - azureml-widgets~=1.26.0
  - pytorch-transformers==1.0.0
  - spacy==2.1.8
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.25.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.26.0/validated_linux_requirements.txt [--no-deps]
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -22,8 +22,8 @@ dependencies:

 - pip:
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.25.0
+  - azureml-widgets~=1.26.0
  - pytorch-transformers==1.0.0
  - spacy==2.1.8
  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.25.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.26.0/validated_darwin_requirements.txt [--no-deps]
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
@@ -105,7 +105,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -93,7 +93,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
@@ -96,7 +96,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -81,7 +81,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
@@ -91,7 +91,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
@@ -113,7 +113,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -87,7 +87,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -97,7 +97,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
@@ -94,7 +94,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -82,7 +82,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
@@ -96,7 +96,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
@@ -96,7 +96,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -92,7 +92,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
@@ -255,9 +255,6 @@
        "# Set compute target to AmlCompute target created in previous step\n",
        "run_config.target = cpu_cluster.name\n",
        "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
        "azureml_pip_packages = [\n",
        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
        "]\n",
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
@@ -401,7 +401,12 @@
        "\n",
        "# Use configs and models generated above\n",
        "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
+        "try:\n",
+        "    service.wait_for_deployment(show_output=True)\n",
+        "except WebserviceException as e:\n",
+        "    print(e.message)\n",
+        "    print(service.get_logs())\n",
+        "    raise"
      ]
    },
    {
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
@@ -257,9 +257,6 @@
        "# Set compute target to AmlCompute target created in previous step\n",
        "run_config.target = cpu_cluster.name\n",
        "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
        "# Set Docker base image to the default CPU-based image\n",
        "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
        "\n",
@@ -502,7 +499,12 @@
        "\n",
        "# Use configs and models generated above\n",
        "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
+        "try:\n",
+        "    service.wait_for_deployment(show_output=True)\n",
+        "except WebserviceException as e:\n",
+        "    print(e.message)\n",
+        "    print(service.get_logs())\n",
+        "    raise"
      ]
    },
    {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
@@ -42,15 +42,13 @@
      "outputs": [],
      "source": [
        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Datastore, Dataset\n",
+        "from azureml.core import Workspace, Environment, Experiment, Datastore, Dataset, ScriptRunConfig\n",
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun, PythonScriptStep\n",
        "from azureml.pipeline.core import Pipeline, PipelineData, TrainingOutput\n",
-        "from azureml.train.dnn import TensorFlow\n",
-        "# from azureml.train.hyperdrive import *\n",
        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
        "from azureml.train.hyperdrive import choice, loguniform\n",
        "\n",
@@ -282,13 +280,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create TensorFlow estimator\n",
-        "Next, we construct an [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) estimator object.\n",
-        "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker.\n",
-        "\n",
-        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release.\n",
-        "\n",
-        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
+        "## Retrieve an Environment\n",
+        "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. Curated environments are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment."
      ]
    },
    {
@@ -297,12 +290,45 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "est = TensorFlow(source_directory=script_folder,                 \n",
+        "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Setup an input for the ScriptRunConfig step\n",
+        "You can mount dataset to remote compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_folder = dataset.as_mount()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure the training job\n",
+        "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "src = ScriptRunConfig(source_directory=script_folder,\n",
+        "                      script='tf_mnist.py',\n",
+        "                      arguments=['--data-folder', data_folder],\n",
        "                      compute_target=compute_target,\n",
-        "                 entry_script='tf_mnist.py', \n",
-        "                 use_gpu=True,\n",
-        "                 framework_version='2.0',\n",
-        "                 pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
+        "                      environment=tf_env)"
      ]
    },
    {
@@ -366,7 +392,7 @@
      },
      "outputs": [],
      "source": [
-        "hd_config = HyperDriveConfig(estimator=est, \n",
+        "hd_config = HyperDriveConfig(run_config=src, \n",
        "                             hyperparameter_sampling=ps,\n",
        "                             policy=early_termination_policy,\n",
        "                             primary_metric_name='validation_acc', \n",
@@ -375,25 +401,6 @@
        "                             max_concurrent_runs=4)"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Add HyperDrive as a step of pipeline\n",
-        "\n",
-        "### Setup an input for the hypderdrive step\n",
-        "You can mount dataset to remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data_folder = dataset.as_mount()"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -402,7 +409,6 @@
        "HyperDriveStep can be used to run HyperDrive job as a step in pipeline.\n",
        "- **name:** Name of the step\n",
        "- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run\n",
-        "- **estimator_entry_script_arguments:** List of command-line arguments for estimator entry script\n",
        "- **inputs:** List of input port bindings\n",
        "- **outputs:** List of output port bindings\n",
        "- **metrics_output:** Optional value specifying the location to store HyperDrive run metrics as a JSON file\n",
@@ -437,7 +443,6 @@
        "hd_step = HyperDriveStep(\n",
        "    name=hd_step_name,\n",
        "    hyperdrive_config=hd_config,\n",
-        "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
        "    inputs=[data_folder],\n",
        "    outputs=[metrics_data, saved_model])"
      ]
--- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
+++ b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb
@@ -45,16 +45,6 @@
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!jupyter nbextension install --py --user azureml.widgets\n",
-        "!jupyter nbextension enable --py --user azureml.widgets"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -278,12 +268,14 @@
      "outputs": [],
      "source": [
        "from azureml.core import Environment\n",
+        "from azureml.core.runconfig import DockerConfiguration\n",
        "\n",
        "chainer_env = Environment.from_conda_specification(name = 'chainer-5.1.0-gpu', file_path = './conda_dependencies.yml')\n",
        "\n",
        "# Specify a GPU base image\n",
-        "chainer_env.docker.enabled = True\n",
-        "chainer_env.docker.base_image = 'mcr.microsoft.com/azureml/intelmpi2018.3-cuda9.0-cudnn7-ubuntu16.04'"
+        "chainer_env.docker.base_image = 'mcr.microsoft.com/azureml/intelmpi2018.3-cuda9.0-cudnn7-ubuntu16.04'\n",
+        "\n",
+        "docker_config = DockerConfiguration(use_docker=True)"
      ]
    },
    {
@@ -307,7 +299,8 @@
        "                      script='chainer_mnist.py',\n",
        "                      arguments=['--epochs', 10, '--batchsize', 128, '--output_dir', './outputs'],\n",
        "                      compute_target=compute_target,\n",
-        "                      environment=chainer_env)"
+        "                      environment=chainer_env,\n",
+        "                      docker_runtime_config=docker_config)"
      ]
    },
    {
--- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
@@ -100,7 +100,7 @@
        "\n",
        "# Check core SDK version number\n",
        "\n",
-        "print(\"This notebook was created using SDK version 1.25.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.26.0, you are currently running version\", azureml.core.VERSION)"
      ]
    },
    {
--- a/setup-environment/configuration.ipynb
+++ b/setup-environment/configuration.ipynb
@@ -102,7 +102,7 @@
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.26.0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/tutorials/quickstart/score.py
+++ b/tutorials/quickstart/score.py
@@ -0,0 +1,51 @@
+import os
+import torch
+import json
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+def init():
+    global net
+    global classes
+
+    model_filename = 'cifar_net.pth'
+    model_path = os.path.join(os.environ['AZUREML_MODEL_DIR'], model_filename)
+    net = Net()
+    net.load_state_dict(torch.load(model_path))
+    classes = ('plane', 'car', 'bird', 'cat',
+               'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+
+def run(data):
+    data = json.loads(data)
+    images = torch.FloatTensor(data['data'])
+    outputs = net(images)
+
+    _, predicted = torch.max(outputs, 1)
+
+    result = [classes[predicted[j]] for j in range(4)]
+    result_json = json.dumps({"predictions": result})
+
+    # You can return any JSON-serializable object.
+    return result_json