In this HOL you are going to use Azure Machine Learning Service and Azure Notebooks to create a MNIST model and run in in a container in Azure
- Goto: notebooks.azure.com
- Login with your Microsoft Passport.
- Click on 'My Projects' in the top menu
- Click on 'New Project'
- Give the project a name and a project ID and make the project private. Make it private just to be sure you don't put any secrets online
- Click +New' and select 'Notebook' from the dropdown
- Give the notebook a name and select Python 3.6
- Click 'New' to create the notebook
- From the file explorer click on the newly created notebook
- You should see an empty Jupyter Notebook now like on the screen below
- Past this code in a cell and press run (Shift-enter).
import azureml
import os
import urllib
import shutil
from azureml.core.model import Model
from azureml.core import Workspace, Run, Experiment
from azureml.core.runconfig import RunConfiguration
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
from azureml.train.dnn import TensorFlow
from azureml.core.image import Image, ContainerImage
from azureml.widgets import RunDetails
from azureml.core.webservice import AciWebservice, Webservice
print("Azure ML SDK Version: ", azureml.core.VERSION)
- Past this code in the next cell to create an Azure Machine Learning Workspace.
| Varname | Value |
|---|---|
| NAME | A unique name for your workspace |
| SUBSCRIPTION ID | The GUID of your azure subscription |
| RESOURCE GROUP | A unique name for a Resource Group |
| LOCATION | Region to run the workspace like: westeurope |
ws = Workspace.create(name='<NAME>',
subscription_id='<SUBSCRIPTION ID>',
resource_group='<RESOURCE GROUP>',
location='<LOCATION>',
exist_ok=True)
- The first time you run the code you need to authenticate with your Azure Subscription. Follow the instructions.
- When Authenticated successfully run the cell again.
- This can take a few minutes to create
- To validate if everyting is ok run the code to view the details of your workspace
ws.get_details()
- Past the code below to create your AI Compute Cluster. Replace CLUSTERNAME with your name.
clusterName = 'CLUSTERNAME'
try:
computeCluster = ComputeTarget(workspace=ws, name=clusterName)
print('Found existing compute target "{}"'.format(clusterName))
except ComputeTargetException:
print('Creating new compute target "{}"...'.format(clusterName))
compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NV6', min_nodes=1, max_nodes=2)
computeCluster = ComputeTarget.create(ws, clusterName, compute_config)
computeCluster.wait_for_completion(show_output=True)
- This is a good time to pick some coffee, because it can take around 5 minutes to create and start the VM.
NOTE: This will create a NV6 machine that is always on. Don't forget to turn it off!!
- Paste the code below in a cell and run it to view some details about your cluster. The Cluster is ready if you see that you have a 1 behind 'idleNodeCount'.
status = computeCluster.get_status()
print('Current node count: ',status.current_node_count)
print('VM Size: ',status.vm_size)
print('Name:',computeCluster.name)
computeCluster.status.node_state_counts.serialize()
- Past the code below in a new cell to connect to your default datastore
ds = ws.get_default_datastore()
print(ds.name)
- Run this code below in a new cell to download the training data to the Azure Notebook
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')
- Run this code in a new cell to upload the files from your Azure Notebook to your datastore
ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)
- Run the code below to create an Experiment in your workspace. Replace 'ExperimentName' with your own name.
ExperimentName = 'ExperimentName'
exp = Experiment(workspace=ws, name=ExperimentName)
print(exp.name)
- Download the 'train.py' and upload it to your Azure Notebook
- Download the 'utils.py' and upload it to your Azure Notebook
- Run the code below to copy the scripts to a scriptfolder that will be used later on.
script_folder = './trainingscripts'
os.makedirs(script_folder, exist_ok=True)
shutil.copy('./train.py', script_folder)
shutil.copy('./utils.py', script_folder)
- Run the code below to create the estimator for the experiment.
script_params = {
'--data-folder': ws.get_default_datastore().as_mount(),
'--batch-size': 50,
'--first-layer-neurons': 300,
'--second-layer-neurons': 100,
'--learning-rate': 0.01
}
est = TensorFlow(source_directory=script_folder,
script_params=script_params,
compute_target=computeCluster,
entry_script='train.py',
use_gpu=True,
framework_version="1.10")
- Run the code below in a new cell to run the experiment
run = exp.submit(config=est)
- When the job is submitted you can run the below to see the progress of your model.
RunDetails(run).show()
- Run the command below to see a list of trainings artifacts.
run.get_file_names()
- Run the code below in a new cell to save the model to your Azure Notebook
os.makedirs('model', exist_ok=True)
run.download_file(name='outputs/model/mnist-tf.model.meta', output_file_path='model')
run.download_file(name='outputs/model/mnist-tf.model.index', output_file_path='model')
run.download_file(name='outputs/model/mnist-tf.model.data-00000-of-00001', output_file_path='model')
- Run the code below in a new cell to register the model in your Azure Machine Learning workspace
model = Model.register(ws, model_name='TensorflowMNISTModel', model_path='model', description='MNIST Model')
- To see some details about the model run this line in a new cell
print(model.name ,model.id, model.version, sep = '\t')
- Run the code below in a new cell to create the score.py.
%%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('TensorflowMNISTModel')
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 json.dumps(y_hat.tolist())
- Run the code below in a new cell to create the myenv.yml.
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')
- Run the code below in a new cell to create the Docker Image for your model.
image_config = ContainerImage.image_configuration(runtime= "python",
execution_script="score.py",
conda_file="myenv.yml")
image = Image.create(name = "tf-mnist-image",
models = [model],
image_config = image_config,
workspace = ws)
image.wait_for_creation(show_output=True)
- Replace NAME with your unique name for the service (like: tf-mnist-yourname)
- Coffee time again, this can take a while ...
aci_service_name = 'NAME'
aciconfig = AciWebservice.deploy_configuration(
cpu_cores = 2,
memory_gb = 4)
aci_service = Webservice.deploy_from_image(
deployment_config = aciconfig,
image = image,
name = aci_service_name,
workspace = ws)
aci_service.wait_for_deployment(True)
print(aci_service.state)
- When you see the message: "Healthy" you can type the code below in a new cell to see the scoring URL for your model.
print(aci_service.scoring_uri)
- Download this Windows x64 program Download
- Unzip the file
- Run the programma 'SimpleMNIST.exe'
- Enter the URL from above in the 'Cloud Scoring URL field'
- Draw a digit on the left
- Click on recognize
- Wait for the result
Finally, If you don't expect to need these resources in the future and want to save cost, you can delete them by deleting the resource group you created. To do so go to the Azure Portal, select 'Resource Groups' and select the resource group for this workshop, select Delete, then confirm the name of the resource group to delete.
Looking to continue your learning of Azure Machine Learning SDK check out more tutorials
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