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Workshop: Build and deploy PyTorch models with ...

Workshop: Build and deploy PyTorch models with Azure Machine Learning

Henk Boelman

July 26, 2021
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  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. Azure Machine Learning A fully-managed cloud service that enables you

    to easily build, deploy, and share predictive analytics solutions.
  5. 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
  6. 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:
  7. 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
  8. 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
  9. 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)
  10. 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
  11. 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
  12. 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
  13. Create an Experiment Create a training file Submit to the

    AI cluster Create an ScriptRunConfig Demo: Creating and run an experiment
  14. 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
  15. 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
  16. Create an Experiment Create a training file Submit to the

    AI cluster Create an ScriptRunConfig Register the model Demo: Register and test the model
  17. 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
  18. 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)
  19. 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')
  20. 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)
  21. 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)
  22. 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
  23. Q&A

  24. 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
  25. 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