In the last blog post, we discussed on developing a machine learning classifier with Azure machine learning service. As mentioned there, we going to utilize the familiar development tools and frameworks for model development.
Key areas of the SDK include:
- Explore, prepare and manage the lifecycle of your datasets used in machine learning experiments.
- Manage cloud resources for monitoring, logging, and organizing your machine learning experiments.
- Train models either locally or by using cloud resources, including GPU-accelerated model training.
- Use automated machine learning, which accepts configuration parameters and training data. It automatically iterates through algorithms and hyperparameter settings to find the best model for running predictions.
- Deploy web services to convert your trained models into RESTful services that can be consumed in any application.
AzureML python SDK acts as the connector between all the resources on the cloud and the dev environment.
Installing Python SDK –
AzureML SDK can be easily installed for your local computer through pip. Refer this guide for the installation process. I’d suggest to go with default installation since it’s enough for the most of the operations we used in the experiment. It’s a good idea to upgrade the SDK before running an experiment since the package is rapidly updating.
Download config file –
For connecting the AzureML workspace, we may need the Azure subscription ID, resource group which the workspace has been created and the workspace name. The easiest way to grab these details is downloading the config.json file from the Azure portal. Place this file inside the experiment directory.
Connect AzureML Workspace –
Connecting the AzureML workspace and and listing the resources can be done by using easy python syntaxes of AzureML SDK (A sample code is provided below). Refer Python SDK documentation to do modifications for the resources of the AML service.
#!pip install --upgrade azureml-sdk[notebooks] import azureml.core from azureml.core import Workspace from azureml.core import ComputeTarget, Datastore, Dataset print("Ready to use Azure ML", azureml.core.VERSION) ws = Workspace.from_config() print(ws.name, "loaded") #View resources in the workspace print("Compute Targets:") for compute_name in ws.compute_targets: compute = ws.compute_targets[compute_name] print("\t", compute.name, ":", compute.type) print("Datastores:") for datastore in ws.datastores: print("\t", datastore) print("Datasets:") for dataset in ws.datasets: print("\t", dataset) print("Web Services:") for webservice_name in ws.webservices: webservice = ws.webservices[webservice_name] print("\t", webservice.name)
In next blog article, will discuss the data loading and experiment creation.