Choosing the right IDE for your Jupyter Notebook projects
Choosing the right IDE for working with Jupyter Notebook can have a significant impact on a developer’s productivity and effectiveness. With the growing popularity of notebooks, there are now numerous IDE options available for developers, each with its unique set of features and advantages.
In this article, we’ll compare some of the most popular IDEs for working with Jupyter notebooks and highlight their key features and use cases to help you make an informed decision.
PyCharm is a powerful python IDE with decent integration for Jupyter notebooks, designed to help data scientists work effectively with their notebook environment. Jupyter notebook support is exclusive to the PyCharm professional edition. It boasts a range of valuable features that streamline your workflow.
For instance, the per-cell toolbar facilitates quick actions like running individual cells, while the tool window provides you quick access to the Jupyter Notebook server logs. The Jupyter variable tool window simplifies debugging by showing values of all variables in use. PyCharm allows you to configure server settings flexibly, either by using a managed server or connecting to a local or remote kernel through a URL.
Data Vision is a standout feature that previews variable values and offers a convenient way to explore dataframes in a separate, editable, and sortable editor tab.
If you’re already using PyCharm professional edition then you might want to consider using the same IDE for Jupyter notebooks as well.
Visual Studio Code
VS Code by Microsoft is a free, open-source IDE that has grown in popularity in the last few years. There’s a reasonable Jupyter notebook support in VSCode.
The automatic generation of a Table of Contents for notebooks is a key feature, making navigation and organization easier. Another powerful addition is the local notebook diffing functionality, which allows you to show or hide notebook metadata and output diffs, simplifying the comparison of changes between versions.
Pro Tip: While VS Code offers git diffs for local notebook changes, you might need ReviewNB for GitHub PR / Commit diffs.
VS Code also offers connections to remote kernels, providing an advantage when collaborating or working with cloud resources. The notebook search functionality saves time by allowing you to find specific content within your notebooks quickly.
Dataframes can be displayed in a data viewer table that is filterable and sortable, making it ideal for data analysis tasks.
VS Code’s range of data analysis tools, reasonable Jupyter Notebook support, and compatibility with multiple programming languages make it a good choice for notebook users.
Amazon SageMaker is a managed cloud service offering the JupyterLab IDE with custom extensions & preset environment.
SageMaker caters to various needs by offering two distinct notebook options—
- Studio Notebooks, and
- Notebook Instances
Studio Notebooks is a fully managed, multi-user notebook service designed for team collaboration that provides features such as a shared workspace and real-time co-editing of notebooks. In contrast, Notebook Instances delivers a more basic, individualized experience on EC2. Although Studio Notebooks comes with added benefits, it is approximately 30% more expensive than Notebook Instances of the same capacity, so it’s crucial to consider the trade-off between cost and features.
A significant constraint of SageMaker is its cloud-only nature, with no option to connect to a local Jupyter kernel. To address this limitation, consider pairing SageMaker with a local Jupyter IDE like JupyterLab, PyCharm, or VS Code Notebooks. This combination will allow you to benefit from the flexibility of cloud while not racking up unnecessary bills when local compute is sufficient.
SageMaker is an excellent choice for running model-building pipelines, particularly if your company already uses AWS. However, before committing to SageMaker as your primary Jupyter Notebook environment, ensure you explore the platform thoroughly and remain aware of potential challenges & limitations.
Gradient Notebooks by Paperspace presents a cloud-based IDE tailored for Jupyter notebooks, designed to provide a range of robust tools for machine learning workflows. With GPU-accelerated computing resources, collaboration features, version control, and data management, Gradient offers an appealing platform for your projects.
Key features that set Gradient Notebooks apart include:
- One-click deployment of models and APIs, streamlining the process of sharing your work and putting it into production.
- Seamless integration with popular machine learning frameworks like TensorFlow, PyTorch, and Keras.
- A built-in dataset management system that simplifies storing, versioning, and sharing datasets with your team.
Gradient serves as an excellent option for users prioritizing access to high-performance computing resources for machine learning workflows, particularly for deep learning applications. However, its pricing model could be prohibitive for individual users or small teams, and its focus on machine learning may not cater to those who primarily work with non-machine learning tasks. Consider these factors when deciding whether Gradient Notebooks aligns with your specific needs.
JupyterLab represents the next-generation web-based user interface for Project Jupyter, delivering a powerful and flexible environment for Jupyter notebooks. It’s an open source application developed & maintained by diverse set of contributors in the Jupyter community.
JupyterLab supports various document types such as Notebooks, Markdown and code files & caters to diverse workflows. Here are some notable features of JupyterLab:
- A highly customizable interface with many extensions & enhancements, empowering users to tailor their workspace to suit their preferences.
- A built-in extension manager that streamlines discovering and installing new extensions to augment your JupyterLab experience.
- JupyterLab RTC support, enabling real-time collaboration by allowing multiple users to edit the same document simultaneously.
JupyterLab serves as an optimal choice for users seeking a versatile, customizable, and open-source IDE for Jupyter notebooks. Additionally, it appeals to those who wish to remain within the Jupyter ecosystem while benefiting from an improved user interface and supplementary features.
Google Colab is a cloud-based IDE that supports Jupyter notebooks and seamlessly integrates with Google Drive. This integration enables users to store, share, and collaborate on notebook files easily. Moreover, Colab grants access to Google’s robust computing infrastructure, which includes free GPU and TPU resources for accelerated machine learning tasks.
While Google Colab provides free access to GPU, there’s no guarantee on the type & duration of the compute resource you will get, even on their paid plans. This makes Google Colab unsuitable for production workflows.
When evaluating Google Colab, consider these distinct features:
- Smooth integration with other Google services like Google Sheets, Google Cloud Storage, and Google BigQuery, streamlining data import and export tasks.
- Built-in support for TensorFlow, making it an attractive choice for deep learning projects.
- A variety of preinstalled libraries and environments, allowing users to dive into their projects without spending time on configuration.
Google Colab is a good choice for users seeking a free, cloud-based IDE for quick prototyping or exploration. For anything serious, you’ll need to see elsewhere.
Choosing the ideal IDE for Jupyter notebooks involves considering various factors such as your specific requirements, collaboration needs, and available resources. Each of the IDEs discussed, including PyCharm, VS Code, SageMaker, Google Colab, Gradient, and JupyterLab, cater to different use cases and offer unique benefits. Take time to analyze the strengths and limitations of these IDEs so that you can make an informed decision and choose the platform that best complements your workflow, ultimately enabling you to maximize productivity and achieve your goals.
If you found this article useful, check out ReviewNB, a tool that helps you with code reviews and collaboration on Jupyter notebooks.