Shift Remote AI: Build and deploy PyTorch Models with Azure Machine Learning - Henk Boelman (Microsoft)
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Take a deep look into Azure Machine Learning, a cloud service that helps you build, train, deploy, and manage models. Walk through the data science process and then have some fun creating a ML recognition model based on the Simpsons cartoon with PyTorch. You'll leave this session with a better grasp of the technological components of Azure Machine Learning services.
![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](https://image.slidesharecdn.com/henkboelmanbuildanddeploypytorchmodelswithazuremachinelearning-200609120707/85/Shift-Remote-AI-Build-and-deploy-PyTorch-Models-with-Azure-Machine-Learning-Henk-Boelman-Microsoft-21-320.jpg)
![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)](https://image.slidesharecdn.com/henkboelmanbuildanddeploypytorchmodelswithazuremachinelearning-200609120707/85/Shift-Remote-AI-Build-and-deploy-PyTorch-Models-with-Azure-Machine-Learning-Henk-Boelman-Microsoft-30-320.jpg)
![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)](https://image.slidesharecdn.com/henkboelmanbuildanddeploypytorchmodelswithazuremachinelearning-200609120707/85/Shift-Remote-AI-Build-and-deploy-PyTorch-Models-with-Azure-Machine-Learning-Henk-Boelman-Microsoft-34-320.jpg)
![[PL] Code Europe 2016 - Python and Microsoft Azure](https://cdn.slidesharecdn.com/ss_thumbnails/codeeurope-161213101547-thumbnail.jpg?width=560&fit=bounds)
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Shift Remote AI: Build and deploy PyTorch Models with Azure Machine Learning - Henk Boelman (Microsoft)
- 1. Henk Boelman Cloud Advocate @ Microsoft Build and Deploy PyTorch Models with Azure Machine Learning HenkBoelman.com @hboelman
- 2. Azure Machine Learning studio A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.
- 3. What is Azure Machine Learning? Set of Azure Cloud Services Python SDK Azure CLI Web interface ✓Prepare Data ✓Build Models ✓Train Models ✓Manage Models ✓Track Experiments ✓Deploy Models That enables you to:
- 4. Is it Marge or Homer or .. or ..?
- 6. Sophisticated pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlow KerasPytorch Onnx Azure Machine Learning LanguageSpeech … 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
- 8. Prepare your environment Experiment with your model & data Deploy Your model into production
- 9. Step 1: Prepare your environment
- 10. Setup your environment VS Code Python SDKAzure Portal
- 11. 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
- 13. 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
- 14. 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
- 16. Demo: Setup your workspace Create a workspace Create compute Setup storage
- 17. Step 1 Prepare your environment Create a workspace Create compute Setup storage
- 18. Step 2: Experiment with your model & data
- 19. Create an experiment exp = Experiment(workspace=ws, name=“<ExperimentName>”) Create an Experiment
- 20. Create a training file Create an Experiment Create a training file
- 21. 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
- 22. 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
- 23. Create an Experiment Create a training file Submit to the AI cluster Create an estimator Demo: Creating and run an experiment
- 24. 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
- 25. 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 estimator Register the model
- 26. Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model Demo: Register and test the model
- 27. Step 2 Experiment with your model & data
- 28. Step 3: Deploy your model into production
- 29. AMLS to deploy The Model Score.py Environment file Docker Image
- 30. 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)
- 31. 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')
- 32. Inference config inference_config = InferenceConfig( runtime= "python", entry_script="score.py", conda_file="myenv.yml" )
- 34. 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)
- 35. Demo: Deploy to ACI
- 37. @hboelman github.com/hnky henkboelman.com Thank you! Read more on: henkboelman.com