A crucial aspect of designing and implementing a Data Science solution on Azure is passing data between steps in a pipeline. A firm understanding of this concept is integral to successfully navigating the DP-100 Designing and Implementing a Data Science Solution on Azure exam. This post will serve as a comprehensive guide, including practical examples, to equip you with the necessary knowledge and techniques on how to pass data between steps in a pipeline.
SECTION 1: UNDERSTANDING STEPS IN A PIPELINE
A pipeline in Azure Machine Learning comprises a series of steps. Each step in the pipeline performs a specific task and contributes towards the completion of a data science experiment. Pipelines ensure that data science tasks are structured, modular, repeatable, and maintainable. It’s crucial to understand that data is passed from one step to another in the pipeline – each step’s output becomes an input to the subsequent step.
SECTION 2: PASSING DATA BETWEEN STEPS
Data can be passed between steps in an Azure ML pipeline using DataReference, PipelineData, or Dataset objects.
- DataReference: This represents a data source in an Azure storage service. It’s used when a pipeline step needs to access data stored in an external Azure storage resource.
- PipelineData: This object is used to pass data from the output of one pipeline step to the input of another. It’s a placeholder for a location in an Azure storage account that the step can output data into.
- Dataset: This is a reference to data in a datastore or a local file which can be passed into a pipeline step.
SECTION 3: EXAMPLES OF PASSING DATA BETWEEN STEPS
Let’s use Python code samples to illustrate how data is passed between pipeline steps using these objects.
EXAMPLE 1:
#Defining the PipelineData object
datastore = ws.get_default_datastore()
pipeline_data = PipelineData(“output_data”, datastore=datastore)
#Passing the PipelineData object as an output to ScriptRunConfig object of first pipeline step
src = ScriptRunConfig(source_directory=’scripts’,
script=’step1.py’,
arguments=[‘–output_folder’, pipeline_data],
compute_target=’aml-compute’,
environment=myenv)
step1 = PythonScriptStep(name=”train_step”, runconfig=src, outputs=[pipeline_data], allow_reuse=True)
#Passing the PipelineData object as an input to ScriptRunConfig object of second pipeline step
src = ScriptRunConfig(source_directory=’scripts’,
script=’step2.py’,
arguments=[‘–input_data’, pipeline_data],
compute_target=’aml-compute’,
environment=myenv)
step2 = PythonScriptStep(name=”extract_step”, runconfig=src, inputs=[pipeline_data], allow_reuse=True)
#Adding steps to the pipeline
steps = [step1, step2]
pipeline = Pipeline(workspace=ws, steps=steps)
In the above example, a PipelineData object “pipeline_data” is being used to capture the output from the first pipeline step “step1”. This output is then used as the input to the second pipeline step “step2”.
Passing data between steps is a critical aspect of designing an efficient and effective data science solution in Azure. It allows data scientists to manage complex workflows and maintain the flow of data across different stages. By mastering this concept, aspiring professionals can significantly boost their chances of performing well in the DP-100 Designing and Implementing a Data Science Solution on Azure exam.
Practice Test
True or False: All steps in an Azure ML pipeline share the same persistent data store.
- True
- False
Answer: False.
Explanation: Each step in an Azure ML pipeline can have its own input data and compute target. They do not share the same persistent data store.
True or False: You cannot pass data directly from one step to another in an Azure ML pipeline.
- True
- False
Answer: False.
Explanation: You can pass data directly from one step to another in an Azure ML pipeline using PipelineData or Datasets.
In Azure ML, which object is used to pass data between pipeline steps?
- A) PipelineData
- B) DataFlow
- C) DataPipeline
- D) None of the above
Answer: A) PipelineData
Explanation: PipelineData offers a mechanism to pass data between steps in an Azure ML pipeline.
Multiple choice: Which of the following ways can be used to pass data between steps in an Azure ML pipeline? Select all that apply.
- A) Using PipelineData
- B) Passing data as function arguments
- C) OS environment variables
- D) Using Datasets
Answer: A) Using PipelineData, B) Passing data as function arguments, D) Using Datasets.
Explanation: Data can be passed through PipelineData, as function arguments, or using Datasets. OS environment variables is not a recommended way to pass data between steps.
True or False: You can use a Datastore as a mechanism to pass data between pipeline steps.
- True
- False
Answer: True.
Explanation: Datastore in Azure ML represents a storage abstraction over an Azure storage account. Datastores are used to read and write data in your workspace.
True or False: A Dataset is a read-only way to pass data from one step to another in Azure ML pipeline.
- True
- False
Answer: True.
Explanation: Datasets in Azure ML are read-only references to data, which can be used to pass input data to pipeline steps.
In Azure ML, how would you pass data from a PythonScriptStep to a subsequent step?
- A) Using a Python variable
- B) Writing data to a file in the output directory
- C) Passing data as a function argument
- D) None of the above
Answer: B) Writing data to a file in the output directory
Explanation: To pass data between steps, you write the output data of the current step to a PipelineData object, which can then be read by the subsequent step.
True or False: The output of a pipeline step can be used as an input for another step within the same pipeline.
- True
- False
Answer: True.
Explanation: The output of one step in an Azure ML pipeline can be used as an input for another step. This is one of the core features of a pipeline.
Multiple choice: Which of the following must be defined when creating a PipelineData object?
- A) Name
- B) Datastore
- C) Both A and B
- D) None of the above
Answer: C) Both A and B
Explanation: When creating a PipelineData object, a name for the object and the Datastore it will be associated with must be specified.
True or False: Different steps in an Azure ML pipeline must run on the same compute target.
- True
- False
Answer: False.
Explanation: Different steps in an Azure ML pipeline can run on different compute targets. Each step can have its own input data and compute target.
Interview Questions
What is the primary way to pass data between steps in an Azure ML pipeline?
The primary way to pass data between steps in an Azure ML pipeline is by using Datastores and Datasets.
What are the two types of Datasets in Azure Machine Learning?
The two types of Datasets in Azure ML are Tabular and File based datasets.
How do we pass data from one pipeline step to another?
We can pass data from one pipeline step to another by outputting it to a PipelineData object in the first step and then inputting it from the PipelineData object in the subsequent step.
What will happen if a dataset is not registered within the workspace?
If a dataset is not registered within the workspace, it will be non accessible for other experiments or for sharing with others who have access to the workspace.
What is a PipelineData object in Azure Machine Learning?
A PipelineData object is a special kind of data reference that is used to pass data from the output of one pipeline step to the input of another, creating a dependency between them.
Can we pass the output of a previous step as the input to more than one subsequent step?
Yes, you can pass the output of a previous pipeline step as the input to multiple subsequent steps.
In what format can data be passed from one pipeline step to another?
Data can be passed from one pipeline step to another in whatever format you choose – the datastore concept in Azure is essentially a pointer to a location where data is stored.
What is an OutputFileDatasetConfig?
An OutputFileDatasetConfig is used to reference or contain the configurations of the data sink (location to write the output data).
In what language are pipeline steps written?
Pipeline steps can be written in Python.
How is data shared between steps in a pipeline?
Data is shared between steps in a pipeline through data dependencies. One step will create a data output, and another step will list that data output as an input.
What are the benefits of passing data between steps in a Azure Machine Learning pipeline?
Passing data between steps in a pipeline allows for efficient and organized data movement and transformation. It allows steps to be run in parallel or series and helps ensure that the necessary data is available for each step.
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