Category: Exams of Google
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
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,
Submitting training job with Python SDK – Vertex AI Custom Model Hyperparameter and Deployment
In all the exercises that we have covered in the previous chapters, we have mainly worked on graphical user interface of the platform. Let us now see how to submit the job for training using Python code.Step 1: Create a new python notebook on the Jupyter labFollow the given steps to create a new Python
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