Workshop: Build and deploy PyTorch models with Azure Machine Learning

Transcript

1. Henk Boelman & Amy Boyd Cloud Advocates @ Microsoft Build and deploy PyTorch models with Azure Machine Learning @AmyKateNicho @hboelman Workshop:
2. Agenda Part 1 – Prepare your environment Part 2 – Train your model Part 3 – Deploy your model Build and deploy a PyTorch classification model using Azure Machine Learning.
3. 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
4. Vision Speech Language Knowledge Cognitive Services: Pre-Trained models in the cloud and on the edge
6. Azure Machine Learning A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.
7. Is it Marge or Homer or .. or ..?
8. Simpson Lego dataset https://github.com/hnky/dataset-lego-figures
9. 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
11. What is Azure Machine Learning Service? Set of Azure Cloud Services Python SDK Prepare Data Build Models Train Models Manage Models Track Experiments Deploy Models That enables you to:
12. Prepare your environment Experiment with your model & data Deploy Your model into production
13. Step 1: Prepare your environment
14. Setup your environment VS Code Python SDK Azure Portal
15. Create a workspace using the Azure CLI Create a workspace
17. 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
18. Create Compute Create a workspace Create compute
19. Demo: Setup your workspace Create a workspace Create compute Setup storage
20. Step 1 Hands-on lab 1 Get an Azure Pass https://aka.ms/ep/azure Go to the Labs https://aka.ms/ep/workshop One on tech support https://aka.ms/ep/support
21. Step 2: Experiment with your model & data
22. Create an experiment exp = Experiment(workspace=ws, name=“<ExperimentName>”) Create an Experiment
24. Create a workspace Create compute Setup storage Register a dataset ds.upload(src_dir=data_path, target_path='simpsonslego’, overwrite=True, show_progress=True) datastore_paths = [(ds, 'simpsonslego/**')] simpsons_ds = Dataset.File.from_files(path=datastore_paths) simpsons_ds.register(workspace=ws, name='LegoSimpsons', description='Simpsons dataset with Lego Figures', create_new_version = True)
25. Create a training file Create an Experiment Create a training file
26. Create an Environment Curated enviroment curated_env_name = 'AzureML-PyTorch-1.6-GPU' pytorch_env = Environment.get(workspace=ws, name=curated_env_name) pytorch_env = pytorch_env.clone(new_name='pytorch-1.6-gpu’) Custom environment env = Environment('pytorch-1.6-gpu') cd = CondaDependencies.create( pip_packages=['azureml-dataprep[pandas,fuse]', 'azureml-defaults'], conda_packages=['pytorch','torchvision']) env.python.conda_dependencies = cd env.docker.enabled = True Create an Experiment Create a training file Create an ScriptRunConfig
27. Create an ScriptRunConfig args = ['--data-folder', simpsons_ds.as_named_input('simpsons').as_mount(), '--num-epochs', 10] project_folder = "./trainingscripts“ config = ScriptRunConfig( source_directory = project_folder, script = 'train.py', compute_target=compute_target, environment = pytorch_env, arguments=args, ) Create an Experiment Create a training file Create an ScriptRunConfig
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 ScriptRunConfig
29. Create an Experiment Create a training file Submit to the AI cluster Create an ScriptRunConfig 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 = run.register_model( model_name='SimpsonsAI', model_path='outputs') Create an Experiment Create a training file Submit to the AI cluster Create an ScriptRunConfig Register the model
32. Create an Experiment Create a training file Submit to the AI cluster Create an ScriptRunConfig Register the model Demo: Register and test the model
33. Step 1 Hands-on lab 2 Get an Azure Pass https://aka.ms/ep/azure Go to the Labs https://aka.ms/ep/workshop One on tech support https://aka.ms/ep/support
34. Step 3: Deploy your model into production
35. AMLS to deploy The Model Score.py Environment file Docker Image
36. 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)
37. 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')
38. Inference config inference_config = InferenceConfig( runtime= "python", entry_script="score.py", conda_file="myenv.yml" )
39. Deployment using AMLS
40. Deploy to ACI aciconfig = AciWebservice.deploy_configuration( cpu_cores = 1, memory_gb = 2) service = Model.deploy(workspace=ws, name='simpsons-aci', models=[model], inference_config=inference_config, deployment_config=aciconfig)
41. Deploy to AKS aks_target = AksCompute(ws,"AI-AKS-DEMO") deployment_config = AksWebservice.deploy_configuration( cpu_cores = 1, memory_gb = 1) service = Model.deploy(workspace=ws, name="simpsons-ailive", models=[model], inference_config=inference_config, deployment_config=deployment_config, deployment_target=aks_target) service.wait_for_deployment(show_output = True)
42. Demo: Deploy to ACI
43. Step 1 Hands-on lab 3 Get an Azure Pass https://aka.ms/ep/azure Go to the Labs https://aka.ms/ep/workshop One on tech support https://aka.ms/ep/support
44. Q&A
45. Create an Experiment Trained the model Register the model Setup a workspace Setup Compute Setup storage Recap Prepare your environment Experiment with your model & data Deploy your model Create a deployment Deployed the model
46. https://aka.ms/ep/pytorch PyTorch Fundamentals Learn the fundamentals of deep learning with PyTorch! This beginner friendly learning path will introduce key concepts to building machine learning models in multiple domains include speech, vision, and natural language processing. Learn more
47. @hboelman github.com/hnky henkboelman.com Thank you! Read more on: aka.ms/amls-pytorch