<jupyter_start><jupyter_text>Copyright (c) Microsoft Corporation. All rights reserved.  Licensed under the MIT License. ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.png)  Azure Machine Learning Pipeline with HyperDriveStepThis notebook is used to demonstrate the use of HyperDriveStep in AML Pipeline.  Prerequisites and Azure Machine Learning BasicsIf you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc.  Azure Machine Learning and Pipeline SDK-specific imports<jupyter_code>import azureml.core
from azureml.core import Workspace, Experiment
from azureml.core.datastore import Datastore
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.exceptions import ComputeTargetException
from azureml.data.data_reference import DataReference
from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.train.dnn import TensorFlow
# from azureml.train.hyperdrive import *
from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal
from azureml.train.hyperdrive import choice, loguniform

import os
import shutil
import urllib
import numpy as np
import matplotlib.pyplot as plt

# Check core SDK version number
print("SDK version:", azureml.core.VERSION)<jupyter_output><empty_output><jupyter_text>Initialize workspaceInitialize a workspace object from persisted configuration. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure the config file is present at .\config.json<jupyter_code>ws = Workspace.from_config()
print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\n')<jupyter_output><empty_output><jupyter_text>Create an Azure ML experimentLet's create an experiment named "tf-mnist" and a folder to hold the training scripts. > The best practice is to use separate folders for scripts and its dependent files for each step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step. > The script runs will be recorded under the experiment in Azure.<jupyter_code>script_folder = './tf-mnist'
os.makedirs(script_folder, exist_ok=True)

exp = Experiment(workspace=ws, name='Hyperdrive_sample')<jupyter_output><empty_output><jupyter_text>Download MNIST datasetIn order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally.<jupyter_code>os.makedirs('./data/mnist', exist_ok=True)

urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')<jupyter_output><empty_output><jupyter_text>Show some sample imagesLet's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels.<jupyter_code>from utils import load_data

# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.
X_train = load_data('./data/mnist/train-images.gz', False) / 255.0
y_train = load_data('./data/mnist/train-labels.gz', True).reshape(-1)

X_test = load_data('./data/mnist/test-images.gz', False) / 255.0
y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)

count = 0
sample_size = 30
plt.figure(figsize = (16, 6))
for i in np.random.permutation(X_train.shape[0])[:sample_size]:
    count = count + 1
    plt.subplot(1, sample_size, count)
    plt.axhline('')
    plt.axvline('')
    plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)
    plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)
plt.show()<jupyter_output><empty_output><jupyter_text>Upload MNIST dataset to blob datastore A [datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) is a place where data can be stored that is then made accessible to a Run either by means of mounting or copying the data to the compute target. In the next step, we will use Azure Blob Storage and upload the training and test set into the Azure Blob datastore, which we will then later be mount on a Batch AI cluster for training.<jupyter_code>ds = ws.get_default_datastore()
ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)<jupyter_output><empty_output><jupyter_text>Retrieve or create a Azure Machine Learning computeAzure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:1. Create the configuration2. Create the Azure Machine Learning compute**This process will take a few minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**<jupyter_code>cluster_name = "gpu-cluster"

try:
    compute_target = ComputeTarget(workspace=ws, name=cluster_name)
    print('Found existing compute target {}.'.format(cluster_name))
except ComputeTargetException:
    print('Creating a new compute target...')
    compute_config = AmlCompute.provisioning_configuration(vm_size="STANDARD_NC6",
                                                               max_nodes=4)

    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)
    compute_target.wait_for_completion(show_output=True, timeout_in_minutes=20)

print("Azure Machine Learning Compute attached")<jupyter_output><empty_output><jupyter_text>Copy the training files into the script folderThe TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array.<jupyter_code># the training logic is in the tf_mnist.py file.
shutil.copy('./tf_mnist.py', script_folder)

# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.
shutil.copy('./utils.py', script_folder)<jupyter_output><empty_output><jupyter_text>Create TensorFlow estimatorNext, we construct an [TensorFlow](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) estimator object.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.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.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.<jupyter_code>est = TensorFlow(source_directory=script_folder,                 
                 compute_target=compute_target,
                 entry_script='tf_mnist.py', 
                 use_gpu=True,
                 framework_version='1.13')<jupyter_output><empty_output><jupyter_text>Intelligent hyperparameter tuningNow let's try hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling.In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, the best validation accuracy (`validation_acc`).<jupyter_code>ps = RandomParameterSampling(
    {
        '--batch-size': choice(25, 50, 100),
        '--first-layer-neurons': choice(10, 50, 200, 300, 500),
        '--second-layer-neurons': choice(10, 50, 200, 500),
        '--learning-rate': loguniform(-6, -1)
    }
)<jupyter_output><empty_output><jupyter_text>Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric.Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparametersspecify-an-early-termination-policy) for more information on the BanditPolicy and other policies available.<jupyter_code>early_termination_policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)<jupyter_output><empty_output><jupyter_text>Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster.<jupyter_code>hd_config = HyperDriveConfig(estimator=est, 
                             hyperparameter_sampling=ps,
                             policy=early_termination_policy,
                             primary_metric_name='validation_acc', 
                             primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, 
                             max_total_runs=4,
                             max_concurrent_runs=4)<jupyter_output><empty_output><jupyter_text>Add HyperDrive as a step of pipeline Setup an input for the hypderdrive stepLet's setup a data reference for inputs of hyperdrive step.<jupyter_code>data_folder = DataReference(
    datastore=ds,
    data_reference_name="mnist_data")<jupyter_output><empty_output><jupyter_text>HyperDriveStepHyperDriveStep can be used to run HyperDrive job as a step in pipeline.- **name:** Name of the step- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run- **estimator_entry_script_arguments:** List of command-line arguments for estimator entry script- **inputs:** List of input port bindings- **outputs:** List of output port bindings- **metrics_output:** Optional value specifying the location to store HyperDrive run metrics as a JSON file- **allow_reuse:** whether to allow reuse- **version:** version<jupyter_code>metrics_output_name = 'metrics_output'
metirics_data = PipelineData(name='metrics_data',
                             datastore=ds,
                             pipeline_output_name=metrics_output_name)

hd_step_name='hd_step01'
hd_step = HyperDriveStep(
    name=hd_step_name,
    hyperdrive_config=hd_config,
    estimator_entry_script_arguments=['--data-folder', data_folder],
    inputs=[data_folder],
    metrics_output=metirics_data)<jupyter_output><empty_output><jupyter_text>Run the pipeline<jupyter_code>pipeline = Pipeline(workspace=ws, steps=[hd_step])
pipeline_run = exp.submit(pipeline)<jupyter_output><empty_output><jupyter_text>Monitor using widget<jupyter_code>from azureml.widgets import RunDetails
RunDetails(pipeline_run).show()<jupyter_output><empty_output><jupyter_text>Wait for the completion of this Pipeline run<jupyter_code>pipeline_run.wait_for_completion()<jupyter_output><empty_output><jupyter_text>Retrieve the metricsOutputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will show the result metrics.<jupyter_code>metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)
num_file_downloaded = metrics_output.download('.', show_progress=True)
import pandas as pd
import json
with open(metrics_output._path_on_datastore) as f:  
    metrics_output_result = f.read()
    
deserialized_metrics_output = json.loads(metrics_output_result)
df = pd.DataFrame(deserialized_metrics_output)
df<jupyter_output><empty_output><jupyter_text>Find and register best modelWhen all the jobs finish, we can find out the one that has the highest accuracy.<jupyter_code>hd_step_run = HyperDriveStepRun(step_run=pipeline_run.find_step_run(hd_step_name)[0])
best_run = hd_step_run.get_best_run_by_primary_metric()
best_run<jupyter_output><empty_output><jupyter_text>Now let's list the model files uploaded during the run.<jupyter_code>print(best_run.get_file_names())<jupyter_output><empty_output><jupyter_text>We can then register the folder (and all files in it) as a model named `tf-dnn-mnist` under the workspace for deployment.<jupyter_code>model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')<jupyter_output><empty_output><jupyter_text>Deploy the model in ACINow we are ready to deploy the model as a web service running in Azure Container Instance [ACI](https://azure.microsoft.com/en-us/services/container-instances/).  Create score.pyFirst, we will create a scoring script that will be invoked by the web service call. * Note that the scoring script must have two required functions, `init()` and `run(input_data)`.   * In `init()` function, you typically load the model into a global object. This function is executed only once when the Docker container is started.   * In `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but you are not limited to that.<jupyter_code>%%writefile score.py
import json
import numpy as np
import os
import tensorflow as tf

from azureml.core.model import Model

def init():
    global X, output, sess
    tf.reset_default_graph()
    model_root = Model.get_model_path('tf-dnn-mnist')
    saver = tf.train.import_meta_graph(os.path.join(model_root, 'mnist-tf.model.meta'))
    X = tf.get_default_graph().get_tensor_by_name("network/X:0")
    output = tf.get_default_graph().get_tensor_by_name("network/output/MatMul:0")
    
    sess = tf.Session()
    saver.restore(sess, os.path.join(model_root, 'mnist-tf.model'))

def run(raw_data):
    data = np.array(json.loads(raw_data)['data'])
    # make prediction
    out = output.eval(session=sess, feed_dict={X: data})
    y_hat = np.argmax(out, axis=1)
    return y_hat.tolist()<jupyter_output><empty_output><jupyter_text>Create myenv.ymlWe also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify packages `numpy`, `tensorflow`.<jupyter_code>from azureml.core.runconfig import CondaDependencies

cd = CondaDependencies.create()
cd.add_conda_package('numpy')
cd.add_tensorflow_conda_package()
cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')

print(cd.serialize_to_string())<jupyter_output><empty_output><jupyter_text>Deploy to ACINow we can deploy. **This cell will run for about 7-8 minutes**. Behind the scene, AzureML will build a Docker container image with the given configuration, if already not available. This image will be deployed to the ACI infrastructure and the scoring script and model will be mounted on the container. The model will then be available as a web service with an HTTP endpoint to accept REST client calls.<jupyter_code>%%time
from azureml.core.model import InferenceConfig
from azureml.core.webservice import AciWebservice
from azureml.core.webservice import Webservice
from azureml.core.model import Model

inference_config = InferenceConfig(runtime = "python", 
                                   entry_script = "score.py",
                                   conda_file = "myenv.yml")

aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, 
                                               memory_gb=1, 
                                               tags={'name':'mnist', 'framework': 'TensorFlow DNN'},
                                               description='Tensorflow DNN on MNIST')

service = Model.deploy(ws, 'tf-mnist-svc', [model], inference_config, aciconfig)
service.wait_for_deployment(show_output=True)<jupyter_output><empty_output><jupyter_text>**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**<jupyter_code>print(service.get_logs())<jupyter_output><empty_output><jupyter_text>This is the scoring web service endpoint:<jupyter_code>print(service.scoring_uri)<jupyter_output><empty_output><jupyter_text>Test the deployed modelLet's test the deployed model. Pick 30 random samples from the test set, and send it to the web service hosted in ACI. Note here we are using the `run` API in the SDK to invoke the service. You can also make raw HTTP calls using any HTTP tool such as curl.After the invocation, we print the returned predictions and plot them along with the input images. Use red font color and inversed image (white on black) to highlight the misclassified samples. Note since the model accuracy is pretty high, you might have to run the below cell a few times before you can see a misclassified sample.<jupyter_code>import json

# find 30 random samples from test set
n = 30
sample_indices = np.random.permutation(X_test.shape[0])[0:n]

test_samples = json.dumps({"data": X_test[sample_indices].tolist()})
test_samples = bytes(test_samples, encoding='utf8')

# predict using the deployed model
result = service.run(input_data=test_samples)

# compare actual value vs. the predicted values:
i = 0
plt.figure(figsize = (20, 1))

for s in sample_indices:
    plt.subplot(1, n, i + 1)
    plt.axhline('')
    plt.axvline('')
    
    # use different color for misclassified sample
    font_color = 'red' if y_test[s] != result[i] else 'black'
    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys
    
    plt.text(x=10, y=-10, s=y_hat[s], fontsize=18, color=font_color)
    plt.imshow(X_test[s].reshape(28, 28), cmap=clr_map)
    
    i = i + 1
plt.show()<jupyter_output><empty_output><jupyter_text>We can also send raw HTTP request to the service.<jupyter_code>import requests

# send a random row from the test set to score
random_index = np.random.randint(0, len(X_test)-1)
input_data = "{\"data\": [" + str(list(X_test[random_index])) + "]}"

headers = {'Content-Type':'application/json'}

resp = requests.post(service.scoring_uri, input_data, headers=headers)

print("POST to url", service.scoring_uri)
print("input data:", input_data)
print("label:", y_test[random_index])
print("prediction:", resp.text)<jupyter_output><empty_output><jupyter_text>Let's look at the workspace after the web service was deployed. You should see * a registered model named 'model' and with the id 'model:1'* an image called 'tf-mnist' and with a docker image location pointing to your workspace's Azure Container Registry (ACR)  * a webservice called 'tf-mnist' with some scoring URL<jupyter_code>models = ws.models
for name, model in models.items():
    print("Model: {}, ID: {}".format(name, model.id))
    
images = ws.images
for name, image in images.items():
    print("Image: {}, location: {}".format(name, image.image_location))
    
webservices = ws.webservices
for name, webservice in webservices.items():
    print("Webservice: {}, scoring URI: {}".format(name, webservice.scoring_uri))<jupyter_output><empty_output><jupyter_text>Clean upYou can delete the ACI deployment with a simple delete API call.<jupyter_code>service.delete()<jupyter_output><empty_output>
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