Author: Amy Schouten
Functionalities of custom components – Pipelines using TensorFlow Extended
Let us understand the functionalities of these components.
Components of TFX – Pipelines using TensorFlow Extended
Pipelines break down the machine learning workflow into a series of components, with each component being responsible for a certain stage in the ML process. Standard components and custom components are both offered by TFX as separate groups. Users can construct pipelines with only a few standard components as well. ML process may be expanded
What is TensorFlow Extended – Pipelines using TensorFlow Extended
Introduction In the previous chapter we worked on the pipelines of GCP using Kubeflow and built the first pipeline for the custom model training. In this chapter, we will start understanding the TFX and its components, and how to use these components for custom model training. Structure In this chapter, we will discuss the following
Pipeline comparison – Pipelines using Kubeflow for Custom Models
Users may run multiple pipelines with different models or with a different sample of data. GCP provides users an option to compare the performance of different pipelines. In this exercise, we had built a pipeline to train random forest classifier model. Change random forest to any other model of your choice and run the pipeline
Pipeline – Pipelines using Kubeflow for Custom Models
Follow these steps to analyze the status of the pipeline job, artifacts, lineage and output: Step 1: Pipeline of custom model Open the link as shown in Figure 7.10 to navigate to the pipelines of Vertex AI. The pipeline will start executing and will take about 5 to 10 mins. All the four tasks in
Pipeline code walk through – Pipelines using Kubeflow for Custom Models
We will be using Python 3 notebook file to type commands, create a pipeline, compile and to run it. Follow the following mentioned steps to create a Python file and type the Python codes given in this section. Step 1: Create Python notebook file Once the workbench is created, open the Jupyterlab and follow the
Additional permissions – Pipelines using Kubeflow for Custom Models
We also need to grant additional permission to the service account associated with the compute engine of GCP since we are fetching data from BigQuery. Follow these steps to grant the required permission. Step 1: Open IAM and admin section Follow the steps mentioned in Figure 7.2 to add roles to the service account associated
Data for model training – Pipelines using Kubeflow for Custom Models
Introduction In the previous chapter, we worked on the pipelines of GCP using Kubeflow. We built the first pipeline using AutoML of the platform for model training. In this chapter, we will see how to build pipelines for custom models and compare the results of different pipelines. We will also understand a few differences between
Execution of Pipeline – Introduction to Pipelines and Kubeflow
Once the job is submitted for the execution, pipeline will be visible under the pipeline section of Vertex AI, status of each components will be displayed (yet to start, success, failure).Follow the below steps to check the status of the submitted pipeline. Step 1: Pipeline of image classification Open the link as shown in Figure
Pipeline code walk through – Introduction to Pipelines and Kubeflow-2
Step 6: Pipeline construction Run the following codes to define the pipeline with the custom components and the other GCP components. Important points about the following code are: DISPLAY_NAME = ‘image_boat_classification’@kfp.dsl.pipeline(name=”image-classification”,pipeline_root=pipeline_folder)def pipeline(gcs_source: str = “gs://pipeline_automl/class_labels.csv”,display_name: str = DISPLAY_NAME,project: str = PROJECT_ID,gcp_region: str = “us-central1”,api_endpoint: str = “us-central1-aiplatform.googleapis.com”,thresholds_dict_str: str = ‘{“auPrc”: 0.60}’,):First componentdataset_create_op = gcc_aip.ImageDatasetCreateOp(project=project, display_name=display_name,