Train your own model using Azure Machine Learning

== Workshop AI - Live360 Orlando AI Live ==

Transcript

1. Henk Boelman Cloud Advocate @ Microsoft Train your own model using Azure Machine Learning HenkBoelman.com @hboelman
2. Machine Learning Ability to learn without being explicitly programmed.
3. Programming Algorithm Data Answers
4. Machine Learning Algorithm Data Answers
5. Machine Learning Model Data Answers
6. Machine Learning Model Data Answers
7. Machine Learning Predictions Data Model Data Answers
8. Ask a sharp question Collect the data Prepare the data Select the algorithm Train the model Use the answer The data science process
9. Azure Machine Learning A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.
10. What number is it?
12. Sophisticated pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlow Keras Pytorch Onnx Azure Machine Learning Language Speech … Azure Search Vision On-premises Cloud Edge Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Flexible deployment To deploy and manage models on intelligent cloud and edge Machine Learning on Azure Cognitive Services
13. Prepare your environment Experiment with your model & data Deploy Your model into production
14. Step 1: Prepare your environment
15. Setup your environment VS Code Azure Notebooks Azure Portal
16. Azure Notebook / Jupyter Notebook
17. Create a workspace ws = Workspace.create( name='<NAME>', subscription_id='<SUBSCRIPTION ID>', resource_group='<RESOURCE GROUP>', location='westeurope') ws.write_config() ws = Workspace.from_config() Create a workspace
19. Datasets – registered, known data sets Experiments – Training runs Models – Registered, versioned models Endpoints: Real-time Endpoints – Deployed model endpoints Pipeline Endpoints – Training workflows Compute – Managed compute Datastores – Connections to data Azure Machine Learning Service
20. Create Compute cfg = AmlCompute.provisioning_configuration( vm_size='STANDARD_NC6', min_nodes=1, max_nodes=6) cc = ComputeTarget.create(ws, '<NAME>', cfg) Create a workspace Create compute
22. Demo: Setup your workspace Create a workspace Create compute Setup storage
23. Step 1 Prepare your environment Create a workspace Create compute Setup storage
24. Step 2: Experiment with your model & data
25. Create an experiment exp = Experiment(workspace=ws, name=“<ExperimentName>”) Create an Experiment
26. Create a training file Create an Experiment Create a training file
27. Create an estimator params = {'--data-folder': ws.get_default_datastore().as_mount()} estimator = TensorFlow( source_directory = script_folder, script_params = params, compute_target = computeCluster, entry_script = 'train.py’, use_gpu = True, conda_packages = ['scikit-learn','keras','opencv’], framework_version='1.10') Create an Experiment Create a training file Create an estimator
28. Submit the experiment to the cluster run = exp.submit(estimator) RunDetails(run).show() Create an Experiment Create a training file Submit to the AI cluster Create an estimator
29. Create an Experiment Create a training file Submit to the AI cluster Create an estimator Demo: Creating and run an experiment
30. Azure Notebook Compute Target Experiment Docker Image Data store 1. Snapshot folder and send to experiment 2. create docker image 3. Deploy docker and snapshot to compute 4. Mount datastore to compute 6. Stream stdout, logs, metrics 5. Launch the script 7. Copy over outputs
31. Register the model model = Model.register( ws, model_name=‘My Model Name', model_path='./savedmodel', description='My cool Model') Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model
32. Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model Demo: Register and test the model
33. Step 3: Deploy your model into production
34. AMLS to deploy The Model Score.py Environment file Docker Image
35. Score.py %%writefile score.py from azureml.core.model import Model def init(): model_root = Model.get_model_path('MyModel’) loaded_model = model_from_json(loaded_model_json) loaded_model.load_weights(model_file_h5) def run(raw_data): url = json.loads(raw_data)['url’] image_data = cv2.resize(image_data,(96,96)) predicted_labels = loaded_model.predict(data1) return json.dumps(predicted_labels)
36. Environment File from azureml.core.runconfig import CondaDependencies cd = CondaDependencies.create() cd.add_conda_package('keras==2.2.2') cd.add_conda_package('opencv') cd.add_tensorflow_conda_package() cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')
37. Create the image image_config = ContainerImage.image_configuration( runtime= "python", execution_script="score.py", conda_file="myenv.yml") image = Image.create(name = “my-image", models = [model], image_config = image_config, workspace = ws)
38. Deployment using AMLS
39. Deploy to ACI aciconfig = AciWebservice.deploy_configuration( cpu_cores = 1, memory_gb = 2) aci_service = Webservice.deploy_from_image( deployment_config = aciconfig, image = image, name = ‘<ImageName>', workspace = ws)
40. Demo: Deploy to ACI
41. Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy
42. @hboelman github.com/hnky henkboelman.com Thank you! Read more on: henkboelman.com Aka.ms/AI4DEV02-amls-basics