In machine learning, integrated development environments (IDEs) and notebooks play crucial roles in the development and execution of machine learning models. IDEs provide developers with comprehensive coding environments for writing, testing, and debugging their machine learning code, while notebooks offer an interactive and exploratory environment for data analysis, experimentation, and collaboration. In this article, we will thoroughly explore these two approaches and examine their applications in machine learning. Additionally, we will discuss how to use Databricks Connect to set up IDEs with Databricks.
IDEs vs. Notebooks for Machine Learning Development
Both IDEs and notebooks have their own advantages and use cases in machine learning.
The benefits of using IDEs
IDEs are traditional development environments with advanced code editing features, debugging tools, and project management capabilities.
- Building projects: When it comes to machine learning, IDEs provide more flexibility in terms of project organization, code structuring, and modularization. You can have multiple code files or modules within a project, making it easier to manage large machine learning projects.
- Debugging: IDEs offer more control over code execution. You can run code blocks or sections selectively, allowing you to test or debug specific parts of your code easily. Hence they also support interactive coding, allowing you to experiment with different code snippets, test hypotheses, and visualize results in real-time. IDEs come with advanced debugging tools that help in identifying and fixing code issues or errors. You can set breakpoints, inspect variables, and step through the code line by line for better code understanding and debugging.
The benefits of using Notebooks
On the other hand, notebooks, such as Databricks Notebooks or Jupyter notebooks are interactive web-based environments that combine code, documentation, and visualizations in a single document.
- Storytelling: Notebooks allow you to document your code, provide explanations, and include visualizations or graphs in the same document. This makes it easier to share and collaborate with others by conveying your thought process and findings.
- Reproducibility: Notebooks provide a reproducible workflow where you can execute code cells sequentially, capturing the entire analysis process. This makes it easy to reproduce and share the exact steps followed for a machine learning task.
- Visualization: Notebooks support in-line visualization, making it convenient to create charts, graphs, or plots to analyze and visualize data during the machine learning process.
- Rapid prototyping: Notebooks are excellent for rapid prototyping and iterative development. You can quickly write and test code chunks or snippets without needing to organize a complete project structure.
- Debugging: You can unlock a bonus feature by using Databricks Notebooks. The Debug Mode allows you to run code step-by-step, enabling you to pause execution at specific breakpoints and examine the state of variables at each step.
When to use IDEs and when to use notebooks?
ML practitioners may have different preferences when it comes to using notebooks or IDEs for their work. Both notebooks and IDEs have their own advantages and considerations. The choice often depends on the individual's workflow, requirements, and personal preferences.
- Notebooks are particularly popular in the data science and machine learning (ML) community due to their interactive nature, and storytelling and documenting capabilities. Data scientists use them to present their work in a narrative format, combining code, visualizations, and text explanations.
- IDEs, on the other hand, are preferred for large-scale ML engineering projects with many files, modules, and dependencies since they offer better organization, project management, and code navigation features compared to notebooks.
Many ML engineers use a combination of notebooks and IDEs, leveraging the strengths of each tool for different stages of their work.
Our recommendation: IDEs and notebooks can complement each other. Use notebooks to develop and prototype ML models in notebooks and migrate to IDEs for more extensive development, testing, and deployment processes.
What is Databricks Connect?
Databricks Connect (also referred to as DBConnect) is a Python library that allows you to connect your local development environment to a Databricks. It enables you to interact with the Databricks cluster and submit code from your local machine to utilise the resources and capabilities of Databricks.
How can DBConnect enhance your ML development experience?
Develop Locally and Run at Scale on Databricks
With Databricks Connect, you can develop and test your code locally (e.g., your laptop) using the tools and libraries of your preferred IDE, and then easily switch to executing your code on a Databricks cluster at scale. It provides a seamless experience for developing and debugging code by allowing you to write and execute code in an interactive manner. It also allows you to take advantage of your local development environment features such as code autocompletion, and version control.
When using Databricks Connect, you can directly connect your local IDE to the Databricks workspace, allowing you to execute cells or run scripts remotely on the Databricks cluster.
You can switch between running code on your local machine and executing it on Databricks clusters without the need for any code modifications. This makes it easy to move from working with small datasets on your local machine to running your machine learning experiments on larger datasets and more powerful computing resources on Databricks. You can take advantage of Apache Spark™'s distributed computing capabilities to handle big data, perform parallel processing, and train models faster.
This flexibility lets you harness the full capabilities of Databricks clusters for large-scale model training and distributed data processing, scale your experiments effortlessly, and adjust the cluster resources to meet the demands of processing larger datasets or complex model training.
How to set up an IDE with Databricks?
There are multiple ways to connect your local IDE to Databricks workspaces.
For VSCode Users
If your IDE is VSCode, our recommendation is to use the VSCode Extension. For Databricks Runtime 13.0 or higher, the VSCode Extension uses DBConnect behind the scenes. The VSCode Extension extends the functionality of DBConnect to a tighter integration with VSCode features.Follow these instructions to set up the VSCode Extension.
For other IDEs
To connect other IDEs to Databricks you can use the combination of DBConnect and Databricks CLI to develop your code locally and execute it remotely. Follow these instructions to set up DBConnect with your IDE.
Summary
In this article, we explored the roles of integrated development environments (IDEs) and notebooks in machine learning development.
The choice between IDEs and notebooks depends on individual preferences, workflow, collaboration requirements, and project needs. Many practitioners use a combination of both, leveraging the strengths of each tool for different stages of their work. IDEs are preferred for ML engineering projects, while notebooks are popular in the data science and ML community.
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Next blog in this series: MLOps Gym - Beginners’ Guide to Monitoring
