Category: What is TensorFlow Extended
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
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