From 0799a5d06a96eb692dab9883bd8d9f789fbc4953 Mon Sep 17 00:00:00 2001 From: Azory YData Bot Date: Thu, 30 Nov 2023 01:20:37 +0000 Subject: [PATCH] fix(linting): code formatting --- docs/get-started/create_pipeline.md | 34 +++++++++++++---------------- 1 file changed, 15 insertions(+), 19 deletions(-) diff --git a/docs/get-started/create_pipeline.md b/docs/get-started/create_pipeline.md index 04ac62a2..8e40971e 100644 --- a/docs/get-started/create_pipeline.md +++ b/docs/get-started/create_pipeline.md @@ -3,23 +3,23 @@ :fontawesome-brands-youtube:{ .youtube } Check this quickstart video on how to create your first Pipeline. -The best way to get started with Pipelines is to use the interactive Pipeline editor available in the Labs with Jupyter Lab set as IDE. +The best way to get started with Pipelines is to use the interactive Pipeline editor available in the Labs with Jupyter Lab set as IDE. If you don't have a **Lab** yet, or you don't know how to create one, check our quickstart guide on how to create your first lab. Open an already existing lab. A Pipeline comprises one or more nodes that are connected (or not!) with each other to define execution dependencies. Each pipeline node is and should be implemented as a component that is expected to manage a single task, such as read the data, profiling the data, training a model, -or even publishing a model to production environments. +or even publishing a model to production environments. -In this tutorial we will build a simple and generic pipeline that use a **Dataset** from Fabric's **Data Catalog** and profile to check it's quality. +In this tutorial we will build a simple and generic pipeline that use a **Dataset** from Fabric's **Data Catalog** and profile to check it's quality. We have the notebooks template already available. For that you need to access the *"Academy"* folder as per the image below.
Academy folder
-Make sure to copy all the files in the folder "3 - Pipelines/quickstart" to the root folder of your lab, as per the image below. +Make sure to copy all the files in the folder "3 - Pipelines/quickstart" to the root folder of your lab, as per the image below.
Select your pipeline editor @@ -38,18 +38,18 @@ The following screen will be shown. Click in copy. Dataset code snippet
-Now that we have copied the code, let's get back to our **"1. Read data.ipynb"** notebook, and replace the first code cell by with the new code. This will allow us to use a -dataset from the Data Catalog in our pipeline. +Now that we have copied the code, let's get back to our **"1. Read data.ipynb"** notebook, and replace the first code cell by with the new code. This will allow us to use a +dataset from the Data Catalog in our pipeline.
Dataset code snippet Dataset code snippet
-With our notebooks ready, we can now configure our **Pipeline**. +With our notebooks ready, we can now configure our **Pipeline**. For this quickstart we will be leveraging an already existing pipeline - double-click the file *my_first_pipeline.pipeline*. You should see a pipeline as depicted in the images below. -To create a new Pipeline, you can open the lab launcher tab and select **"Pipeline Editor"**. +To create a new Pipeline, you can open the lab launcher tab and select **"Pipeline Editor"**.
Open pipeline @@ -57,15 +57,15 @@ To create a new Pipeline, you can open the lab launcher tab and select **"Pipeli
Before running the pipeline, we need to check each component/step properties and configurations. Right-click each one of the steps, select *"Open Properties"*, and a -menu will be depicted in your right side. Make sure that you have *"YData - CPU"* selected as the **Runtime Image** as show below. +menu will be depicted in your right side. Make sure that you have *"YData - CPU"* selected as the **Runtime Image** as show below.
Open pipeline My first pipeline
-We are now ready to create and run our first pipeline. In the top left corner of the pipeline editor, the run button -will be available for you to click. +We are now ready to create and run our first pipeline. In the top left corner of the pipeline editor, the run button +will be available for you to click.
Select your pipeline editor @@ -77,7 +77,7 @@ Accept the default values shown in the run dialog and start the run Pipeline configuration confirm dialog
-If the following message is shown, it means that you have create a run of your first pipeline. +If the following message is shown, it means that you have create a run of your first pipeline.
Select your pipeline editor @@ -95,23 +95,19 @@ Your most recent pipeline will be listed, as shown in below image. My first pipeline listed
-To check the run of your pipeline, jump into the **"Run"** tab. You will be able to see your first pipeline running! +To check the run of your pipeline, jump into the **"Run"** tab. You will be able to see your first pipeline running!
My first pipeline listed
By clicking on top of the record you will be able to see the progress of the run step-by-step, and visualize the outputs of each and every -step by clicking on each step and selecting the **Visualizations** tab. +step by clicking on each step and selecting the **Visualizations** tab.
My first pipeline listed
**Congrats!** 🚀 You have now successfully created your first **Pipeline** a code environment, so you can benefit from Fabric's -orchestration engine to crate scalable, versionable and comparable data workflows. +orchestration engine to crate scalable, versionable and comparable data workflows. Get ready for your journey of improved quality data for AI. - - - -