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AML Environments and SDKs

Tomaz Kastrun continues an advent of Azure ML. First up is environments:

We have explored how to create a compute instance and compute target and learned that ML frameworks and scripting packages always come preinstalled.

Choosing the right set of components (CPU, GPU, RAM, Core) and corresponding software (OS, ML Framework, packages) can be time-consuming.

Under Curated environments, you will find predefined environments, with settings for running particular frameworks, like PyTorch or TensorFlow.

Then an overview of the Azure CLI and Python SDK for AML:

What is Azure CLI? It is an Azure Command Line, a great tool for running commands out of CMD. It is a multi-platform and can be run from Azure or from the client’s machine. It is great for scripting and automating repetitive tasks or making the complex task look like lines of code, especially when it comes to infrastructure, managing, provisioning and monitoring. It can also be run from Azure Cloud Shell. It is native to Azure and can be used across all the services and offerings. Usually, the Azure CLI commands start with “az ..”. On top of that, you can also install Azure Machine Learning CLI, as an extension to Azure CLI. The AML CLI will give you additional commands to manage resources for machine learning.

The same functionality (to some extent) in Azure Machine Learning can be achieved with Python SDK. In addition to that, it offers also great ways to create and manage resources you use for training and deployment of models.

And, so that we can catch up a bit to Tomaz, one more post covering the Python SDK:

Looking briefly into Azure CLI and Python SDK, let’s explore the power of SDK and the most important namespaces.