Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs

Machine learning (ML) projects are complex in nature and require an entirely different type of development environment from normal software applications. When the data is huge, a data scientist may want to use several big data tools for quick wrangling or preprocessing needs, and a deep learning (DL) model might require several GPUs for fast training and experimentation. Additionally, dedicated compute resources are required for hosting models in production, and even more to scale them up to the enterprise level. Acquiring such resources and tools is quite costly, and even if we manage to buy and set things up, it takes a lot of effort and technical knowledge to bring them together into a project pipeline. Even after doing all that, there are risks of downtime and data security.

Nowadays, cloud-based solutions are very popular and take care of all the technical hassle, scaling, and security aspects for us....

What is Jupyter Notebook?

Jupyter Notebook is an open source web-based application for writing and sharing live code, documentation, visualizations, and so on. Jupyter Notebooks are very popular among ML practitioners as they provide the flexibility to run code dynamically and collaborate, provide fast visualizations, and can also be used for presentations. Most data scientists and ML practitioners prefer Jupyter Notebook as their primary tool for exploring, visualizing, and preprocessing data using powerful Python libraries such as pandas and NumPy. Jupyter Notebooks are very useful for exploratory data analysis (EDA) as they let us run small code blocks dynamically and also draw quick plots to understand data statistically. Notebooks can also be used for doing quick ML modeling experiments. Another good thing about Jupyter Notebooks is that they let us write Markdown cells as well. Using Markdown, we can explain each code block inside the notebook and turn it into a tutorial. Jupyter...

Vertex AI Workbench

While working on an ML project, if we are running a Jupyter Notebook in a local environment, or using a web-based Colab- or Kaggle-like kernel, we can perform some quick experiments and get some initial accuracy or results from ML algorithms very fast. But we hit a wall when it comes to performing large-scale experiments, launching long-running jobs, hosting a model, and also in the case of model monitoring. Additionally, if the data related to a project requires some more granular permissions on security and privacy (fine-grained control over who can view/access the data), it’s not feasible in local or Colab-like environments. All these challenges can be solved just by moving to the cloud. Vertex AI Workbench within Google Cloud is a JupyterLab-based environment that can be leveraged for all kinds of development needs of a typical data science project. The JupyterLab environment is very similar to the Jupyter Notebook environment, and thus we will be using...

Custom containers for Vertex AI Workbench

Vertex AI Workbench gives us the flexibility of creating notebook instances based on a custom container as well. The main advantage of a custom container-based notebook is that it lets us customize the notebook environment based on our specific needs. Suppose we want to work with a new TensorFlow version (or any other library) that is currently not available as a predefined kernel. We can create a custom Docker container with the required version and launch a Workbench instance using this container. Custom containers are supported by both managed and user-managed notebooks.

Here is how to launch a user-managed notebook instance using a custom container:

1. The first step is to create a custom container based on the requirements. Most of the time, a derivative container (a container based on an existing DL container image) would be easy to set up. See the following example Dockerfile; here, we are first pulling an existing TensorFlow...

Scheduling notebooks in Vertex AI

Jupyter Notebook environments are great for doing some initial experiments. But when it comes to launching long-running jobs, multiple training trials with different input parameters (such as hyperparameter tuning jobs), or adding accelerators to training jobs, we usually copy our code into a Python file and launch experiments using custom Docker containers or managed pipelines such as Vertex AI pipelines. Considering this situation and to minimize the duplication of efforts, Vertex AI-managed notebook instances provide us with the functionality of scheduling notebooks on an ad hoc or recurring basis. This feature allows us to execute our scheduled notebook cell by cell on Vertex AI. It provides us with the flexibility to seamlessly scale our processing power and choose suitable hardware for the task. Additionally, we can pass different input parameters for experimentation purposes.

Configuring notebook executions

Let’s try to configure...

Summary

In this chapter, we learned about Vertex AI Workbench, a managed platform for launching the Jupyter Notebook application on Google Cloud. We talked about the benefits of having notebooks in a cloud-based environment as compared to a local environment. Having Jupyter Notebook in the cloud makes it perfect for collaboration, scaling, adding security, and launching long-running jobs. We also discussed additional features of Vertex AI Workbench that are pretty useful while working on different aspects of ML project development.

After reading this chapter, we should be able to successfully deploy, manage, and use Jupyter Notebooks on the Vertex AI platform for our ML development needs. As we understand the difference between managed and user-managed notebook instances, we should be in good shape to choose the best solution for our development needs. We should also be able to create custom Docker container-based notebooks if required. Most importantly, we should now be able to...