Jupyter Is Now a Full-fledged IDE: Annual Review | by Dimitris Poulopoulos | Sep, 2022
Omnipresence, tools to keep you in the zone, and education were the main themes for Project Jupyter in 2022
Jupyter notebooks are great for software development and documentation. They are widely used in the world of data science and Machine Learning (ML), and it’s an ideal tool to use if you want to experiment with new algorithms, analyze and get familiar with your datasets, and create quick sketches of new approaches.
Almost two years ago, JupyterLab introduced a visual debugger, and Jeremy Howard announced nbdev, a python library to write, test, document, and distribute software packages and technical articles, all in one place, your notebook. Jupyter began a transition to look more like an IDE, although it still kept its own way of doing this, and it still is nothing like any other conventional IDE.
Last year was also a great year for Project Jupyter. A new major release (JupyterLab 3.0), extensions that permit you to transform code-cells into ML pipelines, a new widget engine, and the integration of the visual debugger were the announcements that paved the way. Also, nbdev continues to move forward and is now trusted by big players like Netflix and Lyft.
This year, the sign is again positive, and the progress is likewise outstanding. The team focused on bringing JupyterLab everywhere, creating tools to keep you in the zone while coding, while one of the project’s most underrated usages is education. Let’s see what happened this year in the magical world of Project Jupyter!
Learning Rate is a newsletter for those who are curious about the world of AI and MLOps. You’ll hear from me on the first Saturday of every month with updates and thoughts on the latest AI news and articles. Subscribe here!
Seems that everyone is attempting to host your favorite IDE on a cloud VM nowadays. If you’re working with Visual Studio Code, you’ve probably heard about GitHub codespaces. Then, Coder develops code-server, allowing you to build your own VSCode server and host it anywhere. AWS also acquired Cloud9, a cloud IDE for writing, running, and debugging code.
Jupyter takes a radical approach; JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser languages. That means it’s cheaper, easier to deploy, it is highly customizable, and you can embed it on your website. As a matter of fact, NumPy already does this.
You can play with a public instance of JupyterLite here. There are some limitations, you may find that your favorite extension does not work as expected, but I can’t wait to see the direction it will go.
JupyterLab has great code completion tools, it provides code highlighting, code completion, a debugger with rich variable rendering, and many more.
The JupyterLab inspector is another tool that aims to keep you in the zone. It offers a UI panel that provides contextual help while you are typing. You can bring it up by pressing Ctrl + I on Windows or on ⌘ + I on Mac.
One of the cool things you can do with this tool is that you can render docstring into HTML using Sphinx and create the feeling like you always have a manual on your bedside table.
Documenting your work using Jupyter Notebooks is a great way of communicating what you want to achieve to the public. This text + ready-to-run code feature that Jupyter offers makes it a great tool for education as well.
Jeremy Howard is a pioneer in this area. He and his colleagues have created one of the most popular ML courses using only Notebooks. He and Sylvain Gugger have written the book that accompanies the course using Notebooks, and even the entire documentation of nbdev is written in Notebooks. That means you can open a page from the documentation in JupyterLab and play with the examples.
Another great resource is the labs by the Full Stack Deep Learning team. Packed with insightful explanations of various concepts, embedded views of tools like Weights & Biases, TensorBoard, and Gradio, and even exercises at the end of the Notebooks, the FSDL team has provided an amazing resource for those who want to play with tools that help bring your models to production.
And since we talked about exercises in Jupyter Notebooks, Nbgrader released a new version a couple of weeks back. Nbgrader is a tool that allows educators create and automatically grade assignments in the Jupyter notebook. If you are working as an educator and haven’t seen this yet, look at the video below.
Last but not least, an awesome extension called Blockly aims to make coding easier and more accessible to kids through a block-based visual programming interface. Blockly is an open-source library designed by Google that uses interlocking graphical blocks, much like legos, to represent coding concepts like if statements, loops, or functions.
Jupyter notebooks are great for software development and documentation. Almost two years ago, JupyterLab began a transition to look more like an IDE, although it still kept its own way of doing this.
This year, the project continues to move forward. The team focused on bringing JupyterLab everywhere, creating tools to keep you in the zone while coding, while one of the project’s most underrated usages is education.
JupyterLab and VSCode are my go-to tools for ML. I can’t wait to see what will happen in 2023.
My name is Dimitris Poulopoulos, and I’m a machine learning engineer working for Arrikto. I have designed and implemented AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.
If you are interested in reading more posts about Machine Learning, Deep Learning, Data Science, and DataOps, follow me on Medium, LinkedIn, or @james2pl on Twitter.
Opinions expressed are solely my own and do not express the views or opinions of my employer.
Omnipresence, tools to keep you in the zone, and education were the main themes for Project Jupyter in 2022
Jupyter notebooks are great for software development and documentation. They are widely used in the world of data science and Machine Learning (ML), and it’s an ideal tool to use if you want to experiment with new algorithms, analyze and get familiar with your datasets, and create quick sketches of new approaches.
Almost two years ago, JupyterLab introduced a visual debugger, and Jeremy Howard announced nbdev, a python library to write, test, document, and distribute software packages and technical articles, all in one place, your notebook. Jupyter began a transition to look more like an IDE, although it still kept its own way of doing this, and it still is nothing like any other conventional IDE.
Last year was also a great year for Project Jupyter. A new major release (JupyterLab 3.0), extensions that permit you to transform code-cells into ML pipelines, a new widget engine, and the integration of the visual debugger were the announcements that paved the way. Also, nbdev continues to move forward and is now trusted by big players like Netflix and Lyft.
This year, the sign is again positive, and the progress is likewise outstanding. The team focused on bringing JupyterLab everywhere, creating tools to keep you in the zone while coding, while one of the project’s most underrated usages is education. Let’s see what happened this year in the magical world of Project Jupyter!
Learning Rate is a newsletter for those who are curious about the world of AI and MLOps. You’ll hear from me on the first Saturday of every month with updates and thoughts on the latest AI news and articles. Subscribe here!
Seems that everyone is attempting to host your favorite IDE on a cloud VM nowadays. If you’re working with Visual Studio Code, you’ve probably heard about GitHub codespaces. Then, Coder develops code-server, allowing you to build your own VSCode server and host it anywhere. AWS also acquired Cloud9, a cloud IDE for writing, running, and debugging code.
Jupyter takes a radical approach; JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser languages. That means it’s cheaper, easier to deploy, it is highly customizable, and you can embed it on your website. As a matter of fact, NumPy already does this.
You can play with a public instance of JupyterLite here. There are some limitations, you may find that your favorite extension does not work as expected, but I can’t wait to see the direction it will go.
JupyterLab has great code completion tools, it provides code highlighting, code completion, a debugger with rich variable rendering, and many more.
The JupyterLab inspector is another tool that aims to keep you in the zone. It offers a UI panel that provides contextual help while you are typing. You can bring it up by pressing Ctrl + I on Windows or on ⌘ + I on Mac.
One of the cool things you can do with this tool is that you can render docstring into HTML using Sphinx and create the feeling like you always have a manual on your bedside table.
Documenting your work using Jupyter Notebooks is a great way of communicating what you want to achieve to the public. This text + ready-to-run code feature that Jupyter offers makes it a great tool for education as well.
Jeremy Howard is a pioneer in this area. He and his colleagues have created one of the most popular ML courses using only Notebooks. He and Sylvain Gugger have written the book that accompanies the course using Notebooks, and even the entire documentation of nbdev is written in Notebooks. That means you can open a page from the documentation in JupyterLab and play with the examples.
Another great resource is the labs by the Full Stack Deep Learning team. Packed with insightful explanations of various concepts, embedded views of tools like Weights & Biases, TensorBoard, and Gradio, and even exercises at the end of the Notebooks, the FSDL team has provided an amazing resource for those who want to play with tools that help bring your models to production.
And since we talked about exercises in Jupyter Notebooks, Nbgrader released a new version a couple of weeks back. Nbgrader is a tool that allows educators create and automatically grade assignments in the Jupyter notebook. If you are working as an educator and haven’t seen this yet, look at the video below.
Last but not least, an awesome extension called Blockly aims to make coding easier and more accessible to kids through a block-based visual programming interface. Blockly is an open-source library designed by Google that uses interlocking graphical blocks, much like legos, to represent coding concepts like if statements, loops, or functions.
Jupyter notebooks are great for software development and documentation. Almost two years ago, JupyterLab began a transition to look more like an IDE, although it still kept its own way of doing this.
This year, the project continues to move forward. The team focused on bringing JupyterLab everywhere, creating tools to keep you in the zone while coding, while one of the project’s most underrated usages is education.
JupyterLab and VSCode are my go-to tools for ML. I can’t wait to see what will happen in 2023.
My name is Dimitris Poulopoulos, and I’m a machine learning engineer working for Arrikto. I have designed and implemented AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.
If you are interested in reading more posts about Machine Learning, Deep Learning, Data Science, and DataOps, follow me on Medium, LinkedIn, or @james2pl on Twitter.
Opinions expressed are solely my own and do not express the views or opinions of my employer.
Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to publish your materials, please contact us by email – [email protected]. The content will be deleted within 24 hours.