![]() ![]() After authenticating, you'll be able to select the type of notebook you want to use from the launcher.įor this lab, select the TensorFlow 2 kernel. Vertex AI Workbench has a compute compatibility layer that allows you to launch kernels for TensorFlow, PySpark, R, etc, all from a single notebook instance. The first time you use a new instance, you'll be asked to authenticate. ![]() Once the instance has been created, select Open JupyterLab. You can leave all of the other advanced settings as is. This means your notebook will shutdown automatically when not in use so you don't incur unnecessary costs. Under Advanced Settings, enable idle shutdown and set the number of minutes to 60. Give your notebook a name, and then click Advanced Settings. ![]() Step 3: Create a Vertex AI Workbench instanceįrom the Vertex AI section of your Cloud Console, click on Workbench:Įnable the Notebooks API if it isn't already. Navigate to the Vertex AI section of your Cloud Console and click Enable Vertex AI API. Navigate to Compute Engine and select Enable if it isn't already enabled. To create a project, follow the instructions here. You'll need a Google Cloud Platform project with billing enabled to run this codelab. By leveraging the notebook executor via the Vertex AI Workbench UI, you'll launch jobs on Vertex AI Training that use these pre-trained models and retrain the last layer to recognize the classes from the DeepWeeds dataset. You'll use TensorFlow Hub to experiment with feature vectors extracted from different model architectures, such as ResNet50, Inception, and MobileNet, all pre-trained on the ImageNet benchmark dataset. In this lab, you'll use transfer learning to train an image classification model on the DeepWeeds dataset from TensorFlow Datasets. It enables data scientists to connect to GCP data services, analyze datasets, experiment with different modeling techniques, deploy trained models into production, and manage MLOps through the model lifecycle. Vertex AI Workbench helps users quickly build end-to-end notebook-based workflows through deep integration with data services (like Dataproc, Dataflow, BigQuery, and Dataplex) and Vertex AI. This lab will focus on Vertex AI Workbench. Vertex AI includes many different products to support end-to-end ML workflows. If you have any feedback, please see the support page. You can also migrate existing projects to Vertex AI. The new offering combines both into a single API, along with other new products. Previously, models trained with AutoML and custom models were accessible via separate services. Vertex AI integrates the ML offerings across Google Cloud into a seamless development experience. This lab uses the newest AI product offering available on Google Cloud. The total cost to run this lab on Google Cloud is about $2. Configure and launch notebook executions from the Vertex AI Workbench UI.In this lab, you'll learn how to configure and launch notebook executions with Vertex AI Workbench. ![]()
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