pipelines-single-training-step

Exercise Instructions

Prerequisites

Before starting with the exercises, make sure that you have the following in place:

• An Azure Machine Learning workspace
  • Follow this tutorial, no need to configure the networking section!
• A Compute Instance, running in your workspace (Standard_D2_v2 is sufficient)
  • Goto AML Studio (https://ml.azure.com), sign-in, then select Compute, then Compute instance and click Create
  • Give it any name, select Standard_D2_v2 as size and hit create - done!

Running this on a Compute Instance (recommended)

We recommend to run these exercises on a Compute Instance on Azure Machine Learning. To get started, open Jupyter or JupyterLab on the Compute Instance, select New --> Terminal (upper right corner) and clone this repo:

git clone https://github.com/csiebler/azure-machine-learning-mlops-workshop.git
cd azure-machine-learning-mlops-workshop/

Then navigate to the cloned folder in Jupyter, and open pipeline.ipynb from this exercise. In case you're asked for a kernel, please use the Python 3.6 - AzureML kernel that comes with the Compute Instance.

Running this on your own machine

1. Install the Azure CLI
2. Login to your Azure subscription via az login
3. Install the Azure Machine Learning CLI extensive via az extension add -n azure-cli-ml
4. Clone this repo via git clone https://github.com/csiebler/azure-machine-learning-mlops-workshop.git
5. Navigate into the repo cd azure-machine-learning-mlops-workshop/
6. Attach the whole repo to your workspace via az ml folder attach -w <YOUR WORKSPACE NAME> -g <YOUR RESOURCE GROUP>
7. Fire up your favorite notebook experience and get started!

Knowledge Check

❓ Question: From where does train_step = PythonScriptStep(name="train-step", ...) know which Python dependencies to use?

✅ See solution!

It uses the file runconfig.yml, which further defines the step's configuration. The runconfig points to condaDependenciesFile: conda.yml, which defines the conda enviroment, in which this step is executed in. We could have defined all this in Python, but having the conda enviroment in a separate file, allows us to easier test this locally, e.g., by using:

conda env create -f conda.yml
python train.py --data-path ../data-training

❓ Question: How can we make a compute cluster scale down quicker/slower?

✅ See solution!

We can adapt idle_seconds_before_scaledown=3600, which defines the idle time until the cluster scales down to 0 nodes.

❓ Question: From where does ws = Workspace.from_config() how to which workspace it needs to connect?

✅ See solution!

The call Workspace.from_config() has the following behaviour:

• Inside a Compute Instance, it resolves to the workspace of the current instance
• If a config.json file is present, it loads the workspace reference from there (you can download this file from the Studio UI, by clicking the book icon on the upper right):

{
    "subscription_id": "*****",
    "resource_group": "aml-mlops-workshop",
    "workspace_name": "aml-mlops-workshop"
}

• Use the az CLI to connect to the workspace and use the workspace attached to via az ml folder attach -g <resource group> -w <workspace name>

❓ Question: What is the difference between PublishedPipeline and PipelineEndpoint?

✅ See solution!

• PublishedPipeline allows to publish a pipeline as a RESTful API endpoint, from which it can be invoked. Each PublishedPipeline will have a new URL endpoint.
• PipelineEndpoint allows to "hide" multiple PublishedPipelines behind a single URL and routes the request to a specific default version. This enables to continously update the PipelineEndpoint with new PublishedPipelines while the URL stays the same. Hence, the consumer will not notice that the pipeline got "swapped out", "replaced" or "changed". This is very helpful when we want to test pipelines before we release or hand them over to the pipeline consumer.