Category: Data for model training
Functionalities of custom components – Pipelines using TensorFlow Extended
Let us understand the functionalities of these components.
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
What is machine learning pipeline – Introduction to Pipelines and Kubeflow
Introduction In the previous chapters, we worked on workbench of Vertex AI to train custom models including hyperparameter tuning using Vizer. In this chapter, we will get started with the pipelines of Vertex AI. We will understand what pipeline is, what is Kubeflow, what are the components of the pipeline, and how to configure and
Completion of custom model training job – Vertex AI Custom Model Hyperparameter and Deployment
Based on the complexity of the model, model training might take anywhere between a few minutes to a few hours. Once the model training is completed, it will be listed under the training section, as shown in the Figure 5.18: Figure 5.18: Custom job listed under training module of Vertex AI Training job will be
Image creation – Vertex AI Custom Model Hyperparameter and Deployment-1
Now that we have completed the creation of the Dockerfile and python code for the model building, we can proceed with image creation. Follow the given steps for the same. Step 1: Image creation using docker build Type the following commands in the terminal, as shown in Figure 5.9, for image creation: PROJECT_ID=vertex-ai-gcp-1 IMAGE_URL=”gcr.io/$PROJECT_ID/eye:v1” docker